Citation: Bernhard Voelkl. Quantitative characterization of animal social organization: Applications for epidemiological modelling[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 5005-5026. doi: 10.3934/mbe.2020271
[1] | Mnvrl Kumar, R. Ramakrishnan, Alnura Omarbekova, Santhosh Kumar. R . Experimental characterization of mechanical properties and microstructure study of polycarbonate (PC) reinforced acrylonitrile-butadiene-styrene (ABS) composite with varying PC loadings. AIMS Materials Science, 2021, 8(1): 18-28. doi: 10.3934/matersci.2021002 |
[2] | Mark A. Atwater, Kris A. Darling, Mark A. Tschopp . Synthesis, characterization and quantitative analysis of porous metal microstructures: Application to microporous copper produced by solid state foaming. AIMS Materials Science, 2016, 3(2): 573-590. doi: 10.3934/matersci.2016.2.573 |
[3] | Cheng Xiong, Yi Xiao, Qing-Hua Qin, Hui Wang, Zhuo-Ran Zeng . Bandgap design of 3D single-phase phononic crystals by geometric-constrained topology optimization. AIMS Materials Science, 2024, 11(3): 415-437. doi: 10.3934/matersci.2024021 |
[4] | Bandar Abdullah Aloyaydi, Subbarayan Sivasankaran, Hany Rizk Ammar . Influence of infill density on microstructure and flexural behavior of 3D printed PLA thermoplastic parts processed by fusion deposition modeling. AIMS Materials Science, 2019, 6(6): 1033-1048. doi: 10.3934/matersci.2019.6.1033 |
[5] | Anthony E. Hughes, Y. Sam Yang, Simon G. Hardin, Andrew Tulloh, Yudan Wang, You He . Diversity of internal structures in inhibited epoxy primers. AIMS Materials Science, 2015, 2(4): 379-391. doi: 10.3934/matersci.2015.4.379 |
[6] | Giovanni Di Girolamo, Alida Brentari, Emanuele Serra . Some recent findings on the use of SEM-EDS in microstructural characterisation of as-sprayed and thermally aged porous coatings: a short review. AIMS Materials Science, 2016, 3(2): 404-424. doi: 10.3934/matersci.2016.2.404 |
[7] | Mohamed Lokman Jalaluddin, Umar Al-Amani Azlan, Mohd Warikh Abd Rashid, Norfauzi Tamin . Effect of sintering temperatures on the physical, structural properties and microstructure of mullite-based ceramics. AIMS Materials Science, 2024, 11(2): 243-255. doi: 10.3934/matersci.2024014 |
[8] | Mica Grujicic, Jennifer Snipes, S. Ramaswami . Meso-scale computational investigation of polyurea microstructure and its role in shockwave attenuation/dispersion. AIMS Materials Science, 2015, 2(3): 163-188. doi: 10.3934/matersci.2015.3.163 |
[9] | Mica Grujicic, S. Ramaswami, Jennifer S. Snipes . Use of the Materials Genome Initiative (MGI) approach in the design of improved-performance fiber-reinforced SiC/SiC ceramic-matrix composites (CMCs). AIMS Materials Science, 2016, 3(3): 989-1021. doi: 10.3934/matersci.2016.3.989 |
[10] | Anita Haeussler, Stéphane Abanades, Julien Jouannaux, Martin Drobek, André Ayral, Anne Julbe . Recent progress on ceria doping and shaping strategies for solar thermochemical water and CO2 splitting cycles. AIMS Materials Science, 2019, 6(5): 657-684. doi: 10.3934/matersci.2019.5.657 |
In 2009, buildings accounted for 32% of total global final energy IEA [1]. The building sector emits 8.1 Gt of CO2 per year [2]. Also, the built environment consumes more natural resources than necessary and , therefore, generates a large amount of waste [3]. The high energy consumption, high CO2 emissions, and wasteful resources all have huge negative impacts on the environment. Although the greatest share of emissions has so far been from developed countries, the greatest burden of the impacts is on developing countries. Thus, there is an urgent need for concerted efforts from both the developed and developing countries to minimise or eliminate activities that contribute to climate change. The increasing concern from developing countries has steadily been reflected in their participation in some high profile international conferences such as Conference of the Parties(COP)15-Copenhagen, COP17-Durban, and lastly, COP 18-Doha in 2009, 2011 and 2012 respectively.
The afore-mentioned statistics confirm that the built environment is a major sector to consider in designing strategies for combating the impacts of climate. Some examples of strategies include the improvement of construction and energy efficiency processes/techniques/technologies, adoption of passive design, use of renewable energy and the appropriate selection of building materials. This study will focus on building materials used for housing construction because the share of materials often used in construction is huge and most other factors depend on them. Also, building materials constitute a significant share of house construction cost. Adedeji [4]noted that about 60% of the total house construction cost goes towards the purchase of construction materials. Embodied energy and CO2 are currently two main parameters commonly used in assessing the importance of building materials [5]. The European Union(EU)Construction Products Directive has recommended embodied energy as a key factor in the selection of building materials or construction products [6]. Although CO2 is the least potent of all the Kyoto greenhouse gases, it is by far the most plentiful and largest contributing compound in the greenhouse effect [7]. Because of the emerging nature of embodied energy and CO2, this paper will investigate their shares in the two most common houses in Cameroon. Findings of this paper are important to Cameroon given that housing in that country has recently become too expensive for local residents, especially in urban areas where the cost of imported building materials is reported to be too exorbitant and less environmentally friendly than the locally available building materials [8]. Cerutti et al.(2010)argued in [9]that most of Cameroon’s market for domestic timber, for example, has been on the rise. Thus it is imperative to use parameters(e.g., embodied energy and CO2)to guide the selection of environmentally benign materials from a list of options for alternative uses in buildings. Based on the review of the literature, there is a lack of quantitative studies in Cameroon regarding embodied energy and CO2 of buildings(see section 2.2). To facilitate underst and ing, the assessment of embodied energy and CO2 will be examined in the ensuing section.
Embodied energy describes the amount of energy consumed in all processes associated with the production of a building, from mining and processing of natural resources/materials to manufacturing, transport and then the delivery of the product [10]. For many years, embodied energy content of a building was assumed to be small compared to operational energy. Consequently, most energy-related research efforts have been directed toward reducing operational energy largely by improving energy efficiency of the building envelope. Operational energy of buildings is the energy required to condition(heat, cool, ventilate, and light)the interior spaces and to power equipment and other services. Milne and Reardon [10]observed that according to research by the Australian-based CSIRO(Commonwealth Scientific & Industrial Organisation), an average household contains about 1000 GJ of energy embodied in the materials used in the construction of the house, and this is equivalent to 15 years of normal operational energy. Weight and Rawlinson[11]reported that the construction materials sector alone accounts for 5-6% of total UK emissions, with 70% of emissions being associated with the manufacturing and 15% being associated with the transportation of the materials.
In addition to embodied energy, the production of building materials(e.g., extraction, transportation and manufacturing processes)releases CO2 mainly due to the use of fuel or electricity. This is often called embodied CO2. Thormark [12]reported that embodied energy in traditional buildings can be reduced by approximately 10-15% through proper selection of building materials with low environmental impacts. González and Navarro [13]estimated that the selection of building materials with low impacts can reduce CO2 emissions by up to 30%. Thus embodied energy and CO2 are quite important in environmental building assessment. Before embarking on their assessment methodology, it is important to gain insights into the content of peer-reviewed literature about embodied energy and CO2 in Africa in general and Cameroon in particular.
To gain insights into how similar studies on embodied energy and CO2 might have been conducted in the African continent and Cameroon in particular, a literature review was conducted. A systematic search of key peer-reviewed papers from renowned databases including ScienceDirect(http://www.sciencedirect.com/), EI Compendex(http://www.ei.org/) and EBSCO(http://www.ebsco.com/index.asp)about embodied energy and CO2 analysis was conducted. Key phrases such as “embodied energy and buildings in Africa/or Cameroon”, “embodied CO2 and buildings in Africa/or Cameroon”, “carbon footprint and buildings in Africa/or Cameroon” were used. These searches yielded few results with little relevance. The first overarching outcome was the general agreement among peer-reviewed literature about the importance of embodied energy and CO2 in assessment of building impacts on the environment [14,15]. The second outcome was that despite acknowledgement of the need to consider embodied energy and CO2 in building impact analysis, very few quantitative studies have been conducted in this respect. Hugo et al. [16] computed embodied energy and CO2 of construction materials of three South African Bus Rapid Transit stations. Irurah and Holm [17] demonstrated discrepancy and conflicts of data of basic embodied energy intensities of building construction materials between building systems and building types. What emerges from these findings is that studies on building embodied energy and CO2 assessment with regards to Africa in general and Cameroon in particular are scarce. This outcome underpins the motivation for this study. The assessment methodologies based on life cycle analysis of embodied energy and CO2 are examined in the ensuing section.
The dissipation of embodied energy, and the emission of CO2 are directly associated with each phase of a building’s life cycle and vary by building types [18]. Although there is a lack of consensus as to the different types of phases in a building life cycle, generally, the product phase(raw materials supply, transport and manufacturing), construction phase(transport and construction-installation on-site processes), use phase(maintenance, repair and replacement, refurbishment, operational energy use: heating, cooling, ventilation, hot water and lighting and operational water use) and end-of-life phase(deconstruction, transport, recycling/re-use and disposal)[19]are quite common and encompass most other life cycle classifications. The increased awareness of the importance of environmental protection, and the possible impacts associated with construction processes, have increased interest in the development of methods to better underst and and address these impacts. The most widely used technique is the life cycle assessment(LCA)which considers the building’s life cycle [19]. LCA is the most comprehensive tool in the assessment of inventories and environmental impacts and has been adopted for use in many types of products and processes by reputable institutions such as the International St and ard Organisation(i.e., ISO 14040 environmental management st and ards), US Environmental Protection Agency, the European Union(i.e. European St and ard) and the UK(i.e., British St and ard).
In this study the assessment methodology adopted follows the European St and ard which has been adopted as part of the British St and ard for evaluating environmental impacts of building projects [20]. The st and ard provides the calculation method, based on the LCA to assess and evaluate the environmental performance and thus provides design options and specifications for new and existing buildings. The main guidelines of the st and ard stipulated in European and British st and ardsare: the description of object of the assessment, the system boundary that applies at the building level, the procedure to be used for inventory analysis and the requirements for the data necessary for the calculation. The applications of these guidelines and their rationale will be examined in the ensuing sections.
The object refers to what is being developed or is in the process of being developed and the process of development and /or its existence after development has impacts on the environment. In this study two residential buildings are the objects of assessments. The two buildings represent typical houses common in Cameroon. One is predominantly made up of what is generally referred to as imported materials, i.e., cement-block house; the other is constructed predominantly out of local building materials, i.e. mud-brick house.
According to ISO 14040, the system boundary is a set of criteria specifying which unit processes are part of a product system. It also describes the limits of what is included or not included in the assessment of the whole life cycle for a new building or any remaining cycle stages for the existing building. Figure 1 is used to clarify the boundaries considered in this study.
The first boundary to be defined is around the product phase. Based on Figure 1, this boundary includes the extraction of material from its original sources and the transportation to the production unit where manufacturing into different products is undertaken. The manufactured components are then transported to the construction site where the construction phase begins. Thus, the physical boundary considered is from cradle to gate. This choice has a major advantage in that it provides the possibility to use directly the inventory of carbon and energy for construction materials developed by Bath University in the UK [5]. With respect to the boundary on processes or activities, the material extraction, material transportation, material transformation and transportation of components from gate to construction site have been considered.
The second boundary is about the construction phase. In practice, two major categories of activities that have impacts on the environment occur. The first category consists of activities that are aimed at erecting the building. In this study, four main activities make up this category. These are the site installation, the transportation of plants/equipment, plant/equipment use, and the use of temporary materials. The second category consists of induced activities that occur during the erection of the building. This includes onsite construction waste and the transportation of waste from packaging. In this study, only the first category will be considered. In particular, based on the limited information about site installation, only embodied energy and CO2 of building materials used in erecting the houses will be considered while the transportation of plants/equipment is out of scope.
The use phase of a building consists of the operational energy use, the maintenance of degraded or defective parts, and the replacement of the defected or degraded parts. The impacts from building use are determined from appliances’ characteristics in the building and thus, constitute operation energy or operation CO2. The impacts from the maintenance activities are determined in the same way as in the construction phase. This includes the site installation, the transportation of plants/equipment, plant/equipment use, operation of site office and the use of temporary materials and transportation of waste. If the maintenance work entails repairs or replacement of materials originally installed, then the frequency of repairs will be required. However, because of limited data and information about the replacement of building components in Cameroon, only onsite construction phase will be considered.
Any building has a life cycle that begins with extraction of building materials and ends with demolition of the building when it becomes obsolete. When a building becomes obsolete, it is demolished or deconstructed and the materials are re-cycled for use, dumped in a l and fill, taken to the incinerator, or a combination of the three. Similar to the construction phase, the impacts from the demolition phase can be determined. However, data and information about building demolition waste in Cameroon is not available; hence, it will not be considered in this study. After the establishment of the physical and process boundaries, the assessment methods considered in this study will be examined in the ensuing section.
In this section, the different embodied energy and embodied CO2 assessment methods are examined. The aim is to establish which method(s)to use. In the literature, three methods of assessment are quite common: the input-output analysis, the process analysis, and the hybrid analysis.
Input-output LCA is a top-down method for analyzing the environmental interventions of a product using a combination of national sector-by-sector economic interdependent data which quantifies the dependencies between sectors, with sector level environmental effects and resource use data [21,22]. Using matrix operations, a change in economic dem and from a sector can be quantified in environmental effects or resource use. For example, the purchase of a construction crane would directly impacts steel, aluminium, and plastic. Other examples are the indirect impacts from the production of steel as well as the entire supply chain of the plant through the economy.
In process LCA, known environmental inputs and outputs are systematically modelled through the utilisation of a process flow diagram. The process LCA is often called a bottom-up approach. This is because the subjects of analysis in process LCA are individual processing units and the flow rate and composition of streams entering and exiting such units. For example, a steel mill requires iron ore, coal and electricity and this will often be considered in process CA. However, indirect supplies such as office equipment, food, and vehicles are generally excluded to keep the analysis simple and manageable.
The above two life cycle methods of assessment have advantages and disadvantages which have been extensively discussed in [22,23,24,25]. In order to justify the choice of the methods used in this study, a summary of the advantages and disadvantages of the preferred choice is examined. Input-output analysis suffers from lack of representativeness being used due to over-aggregation of data. Also, national sector-by-sector economic interdependent data or sectoral matrix is often too old and out of date in developed countries and worse in developing countries. Process-based LCA allows for a detailed analysis of a specific process at a point in time and space. Nonetheless, it is often criticized for its subjectivity in the definition of the processes that should be considered and the data sources to be used. Also, process LCA can be complex if the building has so many different types of building materials. Furthermore, the emerging BIM can be used to model a building that can systematically simplify the complexity of building materials and hence facilitating the task of embodied energy and CO2 assessment. In this study, BIM was employed to simplify and validate results obtained from manual implementation of the process-based me thod.
Both the British St and ard and PAS-2050 recommend that the data sources and key assumptions are to be explicitly stated in order to facilitate the verification of the environmental emissions quantified at the end of the assessment. To measure embodied energy and CO2, building processes need to be identified first. Then, the activities involved and materials used in the processes are determined. Advanced drafting and modelling software applicable to the design of buildings, e.g., Revit, allow users to generate building quantities automatically from the 3D models. The generated quantities represented by the physical dimensions need to be converted to masses using relevant densities and then multiplied by suitable CO2 emission factors from embodied energy and CO2 inventories. While embodied energy and CO2 inventories for developing countries are lacking, they are quite common in the developed countries and often used in environmental impact assessment studies. These inventories developed in developed countries are now also being used in developing countries [16] partly because most construction materials are imported from developed countries.
The computation of environmental emissions depends largely on the accuracy, relevance and completeness of inventory data. However, in most cases, complete data is often impossible to obtain and the computation of emissions is often found on the “best evidence” as a compromise. As individual data inventories do not contain all the emission factors for the estimation of embodied CO2 for all building processes, a combination of various inventories are often used to carry out the estimation. The common embodied energy and CO2 emission inventories used include:
x The Bath Inventory of Carbon and Energy(ICE)which contains emission factors for construction materials. This is the most popular and most widely used emission factors dataset developed by the Sustainable Energy Research Team at the University of Bath [5]. The current version ICE V2.0 was developed in 2011;
· Eco-inventory database developed by the Swiss Centre for Life Cycle Inventories;
· Bilan Carbon 6 developed by the French Environment and Energy Management Agency;
· Emission factors for road vehicles by UK Department of Transport [26];
· Emission factors for off-road equipment by DEFRA [27];
·Intergovernmental Panel on Climate Change(IPCC)Emission Factor Database(EFDB): This is a web-based tool developed by IPCC that contains greenhouse emission factors for use by the community.
On investigating the different impact factors’ inventories afore-mentioned, the Bath ICE is more specific to buildings than all the others. Furthermore, it is widely used in Europe and is already being used in developing countries [16]. Consequently, Bath ICE will be used in this study. To maintain the applied objectivity of this study, the embodied energy and CO2 results obtained from using the Bath ICE should be used in a comparative sense. To facilitate underst and ing of computation variables, mathematical models relating the different impact factors to the building material quantities will be examined in section 2.8.
The main reason for using emission or impact factors is to facilitate computation of emissions. By using emission factors, tedious tasks that would have involved chemical equations are avoided. This is because emission factors are expressed as quantity of embodied energy or CO2 per functional unit. For example, according to Bath ICE, the emission factor of virgin aluminium is 11.46 KgCO2/Kg. The functional unit is the “Kg” in the denominator as it denotes quantity of virgin aluminium in 1 Kg. Therefore, to compute the emission from a given quantity of virgin aluminium, a simple multiplication of the total quantity and the emission factor is conducted. If there are several construction materials considered, then the products of the emission from different materials are added. This is modelled mathematically as in equations(1) and (2).
EEk=n∑k=1(1+ξk)⋅Qk⋅Ik |
(1) |
ECk=n∑k=1(1+ξk)⋅Qk⋅Ik |
(2) |
Where:
· EEk and ECk are embodied energy and embodied CO2 of material type k with units MJ and KgCO2 respectively;
·ξk
· Qk is the total functional quantity of material;
· Ik is the embodied energy factor or embodied CO2 factor with units MJ/functional unit and KgCO2/functional unit of material respectively.
Because of lack of information about waste data in Cameroon, the waste factor was considered to be zero.
There are available calculation tools in the market for the computation of emissions. For example, the Greenhouse Gas Protocol Initiative toolset based on Excel Spread sheet can be used to calculate various greenhouse gases of different products. There are also emerging BIM authoring tools that can be used to automate the computational process of embodied energy and CO2. Some of these tools(e.g., Revit, Bentley Systems, Tekla)have been reviewed in [28], hence their work will not be duplicated here. Given that some of these tools are not affordable, especially from developing countries perspectives, a manual computation process was adopted(see section 3.2) and then Revit, a BIM tool, was used to validate the manual computational results.
Aggregation is a straight forward task. First, the emissions from a category are added independently. In other words, emissions from all the different construction materials, equipment/plants, and personnel transport types used are independently computed. Then, the emissions from the three different categories are summed up to obtain a total. Sections 2.1-2.10 have all been about embodied energy and CO2 assessment. These steps will now be implemented in assessing embodied energy and CO2 of the two case studies considered.
Due to the complexity and variety of housing types in the construction sector, the Ministry of Housing and Urban Development(MINHUD)of Cameroon has categorised houses according to sizes and content. MINHUD categorises domestic dwellings according to the following minimum requirements: 1)Gross Floor Area(GFA)usually denoted(T1: GFA ≥ 20m2, T2: GFA ≥ 32m2, T3: GFA ≥ 62 m2, T4: GFA ≥ 89 m2, T5: GFA ≥ 106 m2, T6: GFA ≥ 130 m2). 2)All the dwellings except T1 must contain a kitchen, corridors, lounge and dining room. 3)T1 and T2 should contain 1 bedroom each while T3, T4, T5, and T6 should contain 2, 3, 4 and 5 bedrooms respectively. 4)T1, T2, and T3 should contain 1 toilet each, T4 and T5 should contain 2 toilets while T6 should contain 3 toilets. For purposes of this study T3 and T4 are employed as case studies for mud-brick and cement-block houses respectively. The choice is based on their popularity of use in Cameroon. The 2D drawings of T3 and T4 are presented in Figure 2 and Figure 3.
In this section, only the foundation components of the buildings have been chosen to illustrate the computation process of embodied energy and CO2. The foundations of both T3 and T4 are geometrically the same in form except differences in dimensions. Consequently, for illustrative purposes detailed steps in computation of embodied energy and CO2 performed on T3 will be presented. The results for the computation of the complete houses will be presented in summarized tabular forms.
The emission intensities used in the computation of the emissions consider the product phase excluding the manufacturing of components in the fabrication shop. Although construction waste has recently been noted to be significant in Cameroon [29,30], no studies have actually determined the share or fraction of construction waste in relation to the various construction materials used. Consequently, in this study the value for the construction waste factor is assumed to be zero. The quantity of the various parts of the foundation were measured and multiplied by the density of the respective materials to deduce their weight for use in the calculation of emissions.
According to the architect’s specifications, the foundations have been used in bearing the concrete slab floor. The foundation dimensions are shown in Figure 4. The different foundation material components are:
· Lean concrete: Its role is to provide the uniform surface to the foundation concrete and to prevent the direct contact of foundation concrete from the soil. Its thickness is 5 cm. It is mixed at 150 Kg/m3;
· Concrete for ground beam, column footings: This is mixed at 350 Kg/m3;
· Ground floor slab: This is concrete with a cement finish. The concrete is mixed at 300 Kg/m3;
· Foundation wall: This wall is made up of cement blocks of dimension 20 × 20 × 40 cm completely filled with concrete;
· Damp proof course of thickness 0.05 cm;
· Substrate of gravel;
· Sand .
In general, the mass, Q, of any substance is related to the Volume V through the formula: Q = ρV, where ρ is the material density; Volume(V)= Length × Width ×Thickness.
Volume of lean concrete: Vl = 3 m3. Therefore, the total mass of lean concrete(Ql)is given by:
Ql =(22 kN/m3)×3 m3; Ql = 6600 Kg.
The total embodied energy is defined by EEtp = Qtp × Ip(ee), where Ip(ee) is the embodied energy factor for concrete. In Bath ICE, the embodied energy and CO2 intensities of concrete dosed at 150 Kg/m3 have not been provided. However; values for concrete dosed at 120 Kg/m3 and 200 Kg/m3 have been provided. From Bath ICE the embodied energy intensities for concrete dosed at 120 Kg/m3 and at 200 Kg/m3 are 0.49 MJ and 0.67 MJ; while embodied CO2 intensities are 0.06 KgCO2 and 0.091 KgCO2 respectively. While using the lower or upper values would be an underestimation or overestimation, an average of both values is most probable, especially as these intensities are increasing functions.
Based on this assumption, the computed embodied energy and CO2 intensities for lean concrete dosed at 150 Kg/m3 are:
0.58 MJ { =(0.58 + 0.67)/2} and 0.0755 KgCO2 { =(0.06 + 0.091)/2}.
EEl = 6600 Kg ×(0.58 MJ/Kg); EEl = 3828 MJ.
Total embodied CO2:
ECl = Ql x Il(CO2), where Il(CO2) is the embodied CO2 factor for concrete.
ECl = 6600 Kg ×(0.0755 KgCO2/Kg).
ECl = 498.3 KgCO2.
The plastic used for damp proof is the general type and has thickness 5 × 10-4 m. The floor covered by the plastic is slightly higher than the gross floor internal. The value is 100m2. Therefore the volume is 100 m2 × 5 × 10-4 m = 0.05 m3. The density of general plastic is 960 Kg/m3.
Therefore mass Qdp = 0.05 m3 x 960 Kg/m3 = 48 Kg.
From the Bath ICE, the embodied energy and CO2 intensities for general plastic are 80.5 MJ/Kg and 2.73 KgCO2/Kg respectively. Therefore the embodied energy and CO2 are:
EEdp = 48 Kg × 80.5 MJ/Kg = 3864 MJ.
ECdp = 48 Kg × 2.73 KgCO2/Kg = 131.04 KgCO2.
The solid foundation wall is made up of cement-blocks of dimensions 20cm × 20cm × 40cm. It is arrayed in two columns along the perimeter of T3 floor plan as indicated in Figure 2. The total volume is 11.65 m3. From the Bath ICE, its density, embodied energy and CO2 are 1900 Kg/m3, 1.33 MJ/Kg and 0.208KgCO2/Kg respectively. Therefore the embodied energy and CO2 emissions are:
EEsw = 11.65 m3 ×1900 Kg/m3 x 1.33 MJ/Kg = 29439.6 MJ.
ECsw = 11.65 m3 ×1900 Kg/m3 x 0.208 KgCO2/Kg = 4604.1 KgCO2.
The volume of the mortar for the foundation wall is estimated at 1.93 m3. Based on the Bath ICE, the density, embodied energy and CO2 intensities are 1650 Kg/m3, 1.11 MJ/Kg and 0.171 KgCO2/Kg. Therefore the embodied energy and CO2 emissions are:
EEmf = 1.93 m3 ×1650 Kg/m3 x 1.11 MJ/Kg = 3535 MJ.
ECmf = 1.93 m3 ×1650 Kg/m3 x 0.171KgCO2/Kg = 544.55 KgCO2.
The total mass of concrete(Qg)is given by:
Qg =(24 kN/m3)×4 m3
Qg = 9600 Kg
Based on this assumption the computed embodied energy and CO2 intensities for concrete dosed at 350 Kg/m3 are 1.025 MJ { =(0.91 + 1.14)/2} and 0.1505 KgCO2 { =(0.06 + 0.091)/2}
EEg = 9600 Kg ×(1.025 MJ/Kg).
EEg = 9840 MJ.
Total embodied CO2 ECg = Qg x Ig(CO2), where Ig(CO2) is the embodied CO2 factor for concrete;
ECg = 9600 Kg × 0.1505 KgCO2/Kg.
ECg = 1445 KgCO2.
The volume of the timber for the foundation wall formwork is estimated at 0.7 m3. Based on the Bath ICE, the density, embodied energy and CO2 intensities are 90 Kg/m3, 10 MJ/Kg and 0.71 KgCO2/Kg. Therefore, the embodied energy and CO2 emissions are:
EEmf = 0.7 m3 ×90 Kg/m3 ×10 MJ/Kg = 630 MJ
ECmf = 0.7 m3 ×90 Kg/m3 ×0.71 KgCO2/Kg = 45 KgCO2
The total mass of concrete(Qs)is given by:Qs =(24 kN/m3)×8.69 m3; Qs = 20860 Kg.
Based on the Bath ICE embodied energy and CO2 intensities for concrete dosed at 300 Kg/m3 that were available and directly used, the values are 0.91 MJ and 0.131 KgCO2 respectively.
EEs= 20860 Kg× 0.91 MJ/Kg.
EEs = 18979 MJ.
Total embodied CO2 ECs = Qs x Is(CO2), where Is(CO2) is the embodied CO2 factor for concrete;
ECs = 20860 Kg × 0.131 Kg CO2/Kg.
ECs = 2733 KgCO2.
The substrate used is made up of crushed rocks of average thickness 2cm. The volume of substrate is estimated at 0.17 m3. Based on the Bath ICE, the density, embodied energy and CO2 intensities are 2240 Kg/m3, 0.083 MJ/Kg and 0.0048 KgCO2/Kg. Therefore, the embodied energy and CO2 emissions are:
EEsg = 0.17 m3 ×2240 Kg/m3 x 0.083 MJ/Kg = 32 MJ
ECsg = 0.17 m3 ×2240 Kg/m3 x 0.0048 KgCO2/Kg = 1.83 KgCO2
The volume of s and is estimated at 0.04 m3. Based on the Bath ICE, the density, embodied energy and CO2 intensities are 0.2240 Kg/m3, 0.081 MJ/Kg and 0.0048 KgCO2/Kg. Therefore, the embodied energy and CO2 emissions are: EEsa = 0.04 m3 × 2240 Kg/m3 ×0.081 MJ/Kg = 7.30 MJ;
ECsa = 0.04 m3 ×2240 Kg/m3 x 0.0048 KgCO2/Kg = 0.43 KgCO2.
Thus, the total embodied energy and CO2 values for the foundation are 70154.9 MJ and 10003.25 KgCO2 respectively. Similarly, the embodied energy and CO2 for the other components are computed and the summation takes to obtain the embodied energy and CO2 for the whole T3 house as 137934.91 MJ and 15665.56 KgCO2 respectively. Similarly, the embodied energy and CO2 for the whole T4 house are 292326.81 MJ and 37829 .19 KgCO2 respectively.
Based on the computation of embodied energy and CO2 of the foundation the challenges encountered in doing the same for the whole building cannot be underestimated. This is a key weakness of manual computation, where mundane computational tasks are repeated for each identified building material. Furthermore, the manual process is very susceptible to errors and the chances of identifying the errors are slim. As discussed earlier in section 2.5, emerging BIM can be used to enhance the accuracy of process-based methods in the computation of embodied energy and CO2. BIM also serves as an alternative to validate the computational results manually obtained. Based on literature review(e.g. [28]), there are many BIM software that have emerged and are currently being applied to model buildings. Revit is quite popular in the BIM software market. A major advantage of Revit is that its building models can readily be converted into interoperable or communicable formats that can be processed by other software [31]. A comma-separated value(CSV)is a common, relatively simple file format that is easily supported by Revit and Microsoft Excel. Data stored in CSV format can be read by Excel. Outputting or representing building model information in CSV can be read by Revit or MS Excel. The computational power of MS Excel lends it a great choice in modelling equations 1 and 2 which subsequently are used in computing embodied energy and CO2. Also, MS Excel can easily be used to present computational results according to st and ard formats such as New Rules of Measurements in the UK and the Cahier des Prescriptions Techniques in France. These are rules of construction quantity measurements and output presentation. The latter was adopted for this study as it is more commonly used in Francophone countries including Cameroon. Based on the 2D drawings and Architects specification T3 and T4 were modelled in Revit. The 3D equivalents are presented in Figures 5 and 6.
Schedules and quantities are then generated from these models using the “Modify Schedules/Quantities” function under the “View” tab in Revit 2014. The output is converted to CSV format using the “Export” function in Revit 2014. The CSV format is stored in any preferred location on the computer and read with MS Excel. Equations 1 and 2 are modelled in Excel and computations are easily conducted in this environment. The initial results obtained differ slightly from those obtained through manual computations in section 3.2.2. The manual process is rechecked to identify and correct errors. Also, the BIM model is rechecked to identify missing components. These activities were conducted several times until common results were obtained. The results are presented in Tables 1 and 2.
N° | DESCRIPTION | Volume (m3) | Density(Kg/m3) | Qty(Kg) | Embodied Energy(EE)Intensity(MJ/Kg) | Embodied Carbon (EC)Intensity KgCO2/Kg | EE Emissions(MJ) | EC Emissions(KgCO2) |
O | SITE INSTALLATION | No data | ||||||
Sub-total | ||||||||
I | FOUNDATION | |||||||
1.1 | Lean concrete mix at 150 Kg/m3 of thickness = 5 cm | 3.000 | 2200 | 6600 | 0.58 | 0.0755 | 3828 | 498.3 |
1.2 | Damp proof course/membrane of thickness=0.05 cm(plastic-general) | 0.050 | 960 | 48 | 80.5000 | 2.7300 | 3864 | 131.04 |
1.3 | Solid foundation wall of dimension 20 cm × 20 cm × 40 cm | 11.650 | 1900 | 22135 | 1.3300 | 0.2080 | 29439.55 | 4604.08 |
1.4 | Mortar for wall joints | 1.930 | 1650 | 3184.5 | 1.1100 | 0.1710 | 3534.795 | 544.5495 |
1.5 | Concrete mix at 350 Kg/m3 for footing, ground beam, substructure column | 4.000 | 2400 | 9600 | 1.0250 | 0.1505 | 9840 | 1444.8 |
1.6 | Timber for formwork(hardwood unspecified) | 0.700 | 90 | 63 | 10.0000 | 0.7100 | 630 | 44.73 |
1.7 | Concrete slab mix at 300 Kg/m3 of thickness = 10 cm | 8.690 | 2400 | 20856 | 0.91 | 0.131 | 18978.96 | 2732.136 |
1.8 | Substrate made of gravel and crushed rocks of thickness = 20 cm | 0.170 | 2240 | 380.8 | 0.0830 | 0.0048 | 31.6064 | 1.82784 |
1.9 | S and of thickness 5 cm | 0.040 | 2240 | 89.6 | 0.0810 | 0.0048 | 7.2576 | 0.43008 |
Sub-total | 70, 154.17 | 10, 001.89 | ||||||
II | ELEVATIONS | |||||||
2.1 | Brick walls(mud) | 29.400 | 1730 | 50862 | 0.0000 | 0.0000 | 0 | 0 |
2.2 | Mortar for wall joints | 5.250 | 1650 | 8662.5 | 1.1100 | 0.1710 | 9615.375 | 1481.2875 |
2.3 | Concrete mix at 350 Kg/m3 for super-structural beams and columns | 3.000 | 2400 | 7200 | 1.0250 | 0.1505 | 7380 | 1083.6 |
2.4 | Timber for formwork(hardwood unspecified) | 1.400 | 90 | 126 | 10.0000 | 0.7100 | 1260 | 89.46 |
Sub-total | 18, 255.38 | 2, 654.35 | ||||||
III | CARPENTRY AND ROOFWORK | |||||||
3.1 | Timber joist of dimension 3 cm x 15 cm(sawn hard wood) | 0.850 | 90 | 76.5 | 10.4000 | 0.8900 | 795.6 | 68.085 |
3.2 | Roof battens of dimension 8 cm x 8 cm(sawn hardwood) | 0.250 | 90 | 22.5 | 10.4000 | 0.8900 | 234 | 20.025 |
3.3 | Aluminium roof covering | 0.060 | 2700 | 162.00 | 155.0000 | 8.2400 | 25110 | 1334.88 |
3.4 | Ceiling plywood | 0.320 | 540 | 172.8 | 15.0000 | 1.0700 | 2592 | 184.896 |
3.5 | Aluminium ridge board | 0.002 | 2700 | 5.4 | 155.0000 | 8.2400 | 837 | 44.496 |
3.6 | Wooden fascia(sawn hardwood) | 0.030 | 700 | 21 | 10.4 | 0.89 | 218.4 | 18.69 |
3.7 | Aluminium on fascia | 0.006 | 2700 | 16.2 | 155.0000 | 8.2400 | 2511 | 133.488 |
Sub-total | 32, 298.00 | 1, 804.56 | ||||||
IV | ELECTRICITY | No data | ||||||
Sub-total | ||||||||
V | PLUMBING | No data | ||||||
Sub-total | ||||||||
VI | TILES AND PAINTINGS | |||||||
6.1 | Bathroom wall ceramic tiles of dimension 30 cm × 30 cm | 0.040 | 2000 | 80 | 10.0000 | 0.66 | 800 | 52.8 |
6.2 | Bathroom tiles 15 cm × 15 cm | 0.008 | 1700 | 13.6 | 29.0000 | 1.5100 | 394.4 | 20.536 |
6.3 | Mortar for posing of tiles | 0.009 | 1900 | 17.1 | 1.3300 | 0.2080 | 22.743 | 3.5568 |
Sub-total | 1, 217.14 | 76.89 | ||||||
VII | WOOD AND STEEL WORKS | |||||||
7.1 | Wooden door panel of thickness 4 cm including frames | 0.220 | 700 | 154 | 10.4 | 0.89 | 1601.6 | 137.06 |
7.2 | Aluminium locks | 3 | 155.0000 | 8.2400 | 465 | 24.72 | ||
7.3 | Timber window including frames | 0.200 | 700 | 140 | 10.4 | 0.89 | 1456 | 124.6 |
7.4 | Glass louvers | 0.090 | 25 | 2.25 | 15.0000 | 0.86 | 33.75 | 1.935 |
7.5 | Aluminium glass louvers' holders | 4 | 155.0000 | 8.24 | 620 | 32.96 | ||
7.6 | Steel window protectors | 0.075 | 7850 | 588.75 | 20.1 | 1.37 | 11833.875 | 806.5875 |
Sub-total | 16, 010.23 | 1, 127.86 | ||||||
Gr and -total | 137, 934.91 | 15, 665.56 |
N° | DESCRIPTION | Volume(m3) | Density(Kg/m3) | Qty(Kg) | Embodied Energy(EE)Intensity(MJ/Kg) | Embodied Carbon(EC)Intensity KgCO2/Kg | EE Emissions(MJ) | EC Emissions(KgCO2) |
O | SITE INSTALLATION | No data | ||||||
Sub-total | ||||||||
I | FOUNDATION | |||||||
1.1 | Lean concrete mix at 150 Kg/m3 of thickness = 5 cm | 3.850 | 2200 | 8470 | 0.58 | 0.0755 | 4912.60 | 639.49 |
1.2 | Damp proof course/membrane of thickness = 0.05 cm(plastic-general) | 0.070 | 960 | 67.20 | 80.5000 | 2.7300 | 5409.60 | 183.46 |
1.3 | Solid foundation wall of dimension 20 cm × 20 cm × 40 cm | 16.640 | 1900 | 31616 | 1.3300 | 0.2080 | 42049.28 | 6576.13 |
1.4 | Mortar for wall joints | 2.400 | 1650 | 3960 | 1.1100 | 0.1710 | 4395.60 | 677.16 |
1.5 | Concrete mix at 350 Kg/m3 for footing, ground beam, substructure column | 5.840 | 2400 | 14016 | 1.0250 | 0.1505 | 14366.40 | 2109.41 |
1.6 | Timber for formwork(hardwood unspecified) | 0.900 | 90 | 81.00 | 10.0000 | 0.7100 | 810.00 | 57.51 |
1.7 | Concrete slab mix at 300 Kg/m3 of thickness = 10 cm | 11.500 | 2400 | 27600 | 0.91 | 0.1310 | 25116.00 | 3615.60 |
1.8 | Substrate made of gravel and crushed rocks of thickness = 20 cm | 0.240 | 2240 | 537.60 | 0.0830 | 0.0048 | 44.62 | 2.58 |
1.9 | S and of thickness 5 cm | 0.060 | 2240 | 134.40 | 0.0810 | 0.0048 | 10.89 | 0.65 |
Sub-total | 97, 114.99 | 13, 861.97 | ||||||
II | ELEVATIONS | |||||||
2.1 | Cement blocks for walls(Cement-s and mix ratio 1:3) | 38.000 | 1900 | 72200 | 1.3300 | 0.2080 | 96026.00 | 15017.60 |
2.2 | Wall joint mortar(Cement-s and ration 1:4) | 6.100 | 1650 | 10065 | 1.1100 | 0.1710 | 11172.15 | 1721.12 |
2.3 | Concrete mix at 350 Kg/m3 for super-structural beams and columns | 4.000 | 2400 | 9600 | 1.0250 | 0.1505 | 9840.00 | 1444.80 |
2.4 | Timber for formwork(hardwood unspecified) | 1.700 | 90 | 153 | 10.0000 | 0.7100 | 1530.00 | 108.63 |
2.5 | Mortar for wall plastering | 4.300 | 1900 | 8170 | 1.3300 | 0.2080 | 10866.10 | 1699.36 |
Sub-total | 129, 434.25 | 19, 991.51 | ||||||
III | CARPENTRY AND ROOFWORK | |||||||
3.1 | Timber joist of dimension 3 cm x 15 cm(sawn hardwood) | 1.050 | 90 | 94.50 | 10.4000 | 0.8900 | 982.80 | 84.11 |
3.2 | Roof battens of dimension 8 cm x 8 cm(sawn hardwood) | 0.350 | 90 | 31.50 | 10.4000 | 0.8900 | 327.60 | 28.04 |
3.3 | Aluminium roof covering | 0.080 | 2700 | 216 | 155.0000 | 8.2400 | 33480.00 | 1779.84 |
3.4 | Ceiling plywood | 0.480 | 540 | 259.20 | 15.0000 | 1.0700 | 3888.00 | 277.34 |
3.5 | Aluminium ridge board | 0.003 | 2700 | 8.10 | 155.0000 | 8.2400 | 1255.50 | 66.74 |
3.6 | Wooden fascia(sawn hardwood) | 0.026 | 700 | 18.20 | 10.4 | 0.89 | 189.28 | 16.20 |
3.7 | Aluminium on fascia | 0.007 | 2700 | 18.90 | 155.0000 | 8.2400 | 2929.50 | 155.74 |
Sub-total | 43, 052.68 | 2, 408.00 | ||||||
IV | ELECTRICITY | No data | ||||||
Sub-total | ||||||||
V | PLUMBING | No data | ||||||
Sub-total | ||||||||
VI | TILES AND PAINTINGS | |||||||
6.1 | Bathroom wall ceramic tiles of dimension 30 cm × 30 cm | 0.075 | 2000 | 150.00 | 10.0000 | 0.66 | 1500.00 | 99.00 |
6.2 | Bathroom tiles 15 cm × 15 cm | 0.015 | 1700 | 25.50 | 29.0000 | 1.5100 | 739.50 | 38.51 |
6.3 | Mortar for posing of tiles | 0.020 | 1900 | 38.00 | 1.3300 | 0.2080 | 50.54 | 7.90 |
Sub-total | 2, 290.04 | 145.41 | ||||||
VII | WOOD AND STEEL WORKS | |||||||
7.1 | Wooden door panels of thickness 4cm including frames | 0.220 | 700 | 154.00 | 10.4 | 0.89 | 1601.60 | 137.06 |
7.2 | Aluminium locks | 4.00 | 155.0000 | 8.2400 | 620.00 | 32.96 | ||
7.3 | Timber window including frames | 0.200 | 700 | 140 | 10.4 | 0.89 | 1456.00 | 124.60 |
7.4 | Glass louvers | 0.130 | 25 | 3.25 | 15.0000 | 0.8600 | 48.75 | 2.80 |
7.5 | Aluminium glass louvers' holders | 6.00 | 155.0000 | 8.24 | 930.00 | 49.44 | ||
7.6 | Steel window protectors | 0.100 | 7850 | 785 | 20.1 | 1.37 | 15778.50 | 1075.45 |
Sub-total | 20, 434.85 | 1, 422.31 | ||||||
Gr and -total | 292, 326.81 | 37, 829.19 |
From the Spreadsheet, the total embodied energy and total embodied CO2 for the construction materials of a cement-block house used in the sub-structure, superstructure, floor and wall finishes are 292326.81 MJ and 37829.19 KgCO2 respectively. When converted to energy and carbon footprint(using Gross Internal Area(GIA)= 95.36 m2), the values are 3065.51 MJ/m2 and 396.7 KgCO2/m2 respectively. Other elements such as ceiling finishes, fittings and building services are not included in the result due to the lack of design specification data. Also, the embodied energy and CO2 for the mud-brick house are 137934.91 MJ and 15665.56 KgCO2 respectively. By dividing by the GFA(68.7 m2), the following values are obtained: 2007.8 MJ/m2 and 228.03 KgCO2/m2 respectively.
Current available data or computation results about embodied energy and CO2 for houses are scarce, and when they exist, they are very diverse and lack consistency. Hence, it is often too difficult to compare results from different research and draw generalizations. These disparities in results are often caused by differences in computational methods and boundary systems and differences in construction materials, technologies and techniques used and discrepancies in the various database inventories used. However, to appreciate the findings of this study, results from other studies will be discussed. Pullen [32] has reported an embodied energy value of 3.6 GJ/m2 for a residential building. Hammond and Jones [5] reported a mean of 5.3 GJ/m2 and 403 KgCO2/m2 emb odied energy and CO2 respectively for 14 residential case studies. Twelve of the 14 case studies are in the UK while the other two are in the US. Dixit et al. [25] also reported a mean of 5.506 GJ/m2 of embodied energy for residential buildings. In India, Reddy and Jagadish [33] reported embodied energy values of 4.21 GJ/m2, 2.92 GJ/m2 and 1.61 GJ/m2 for a clay brick masonry walls building with reinforced concrete structure, load bearing brickwork and a soil-cement block house respectively. Also, another study in India revealed the embodied energy for reinforced cement concrete and mud houses are 3702.3 MJ/m2 and 2298.8 MJ/m2respectively [34].
What emerges from these studies is the fact that the values obtained for embodied energy and CO2 for two typical houses in Cameroon are in the same range to those from other countries, especially India. The results reveal that a cement block house(T4)expends at least 1.5 times more embodied energy than earth or mud brick houses(T3). Furthermore, a cement block house emits at least 1.7 times more embodied CO2 than a mud brick house.
While embodied energy and CO2 are important factors, it is also important to consider the effects of material choice on the energy requirements for cooling and heating over the life time of the building [35,36]. Some studies have revealed embodied energy to be equivalent to a few years of operating energy [37], although cases in which embodied energy can be much higher have also been reported [10,38]. In particular, in most developing countries, embodied energy of most traditional buildings can be largely compared to operating energy [37]. What these discrepancies suggest is that a holistic approach should be undertaken where embodied energy and operational energy should be considered in assessing the energy use and environmental impacts of a building.
In this study, the process-based approach supported by some mathematical models was used to compute embodied energy and CO2 for two typical houses in Cameroon. The process-based approach was manual and because of susceptibility of such an approach to errors, BIM software was used to validate the computational results. Because of lack of data, embodied energy and CO2 for site installation, electricity and plumbing were not computed. Also because of data scarcity, emissions from transport of construction materials and personnel and onsite equipment such as concrete mixer and vibrator were not assessed. It is important to note that this is an emerging field and knowledge in this field is gradually being explored. Hence, only emissions from construction materials were assessed. The results obtained were converted to per unit m2 to facilitate comparison. Furthermore, when compared to other studies, the computational results were in the same range, although significantly lower than values obtained in the developed countries(e.g. UK). The comparison revealed cement-block houses consumed more embodied energy and CO2 than mud-brick houses.
The authors declare that there are no conflicts of interest related to this study.
[1] | E. O. Wilson, Sociobiology: The New Synthesis, Belknap Press, 1975. |
[2] | R. A. Hinde, Ethology: Its Nature and Relation with Other Sciences, Oxford University Press, 1982. |
[3] | H. Whitehead, Analyzing Animal Societies, University of Chicago Press, 2008. |
[4] | A. F. Fraser, D. M. Broom, Farm Animal Behaviour and Welfare, CAB International, 1997. |
[5] | E. O. Price, Animal Domestication and Behaviour, CABI Publishing, 2002. |
[6] | R. A. Hinde, Primate Social Relationships, Blackwell Scientific Publications, 1983. |
[7] | T. T. Strusaker, Correlates of ecology and social organization among African cercopithecines, Folia Primatol., 11 (1969), 80-118. |
[8] | S. R. Sundaresan, I. R. Fishhoff, J. Dushoff, D. I. Rubenstein, Network metrics reveal differences in social organization between two fission-fusion species, Grevy's zebra and onager, Oecologia, 151 (2007), 140-149. |
[9] | D. P. Croft, R. James, J. Krause, Exploring Animal Social Networks, Princeton University Press, 2008. |
[10] | T. Wey, D. T. Blumstein, W. Shen, F. Jordan, Social network analysis of animal behaviour: A promising tool for the study of sociality, Anim. Behav., 75 (2008), 333-344. |
[11] | C. Kasper, B. Voelkl, A social network analysis of primate groups, Primates, 50 (2009), 343-356. |
[12] | J. Moreno, Who Shall Survive?, Beacon, 1934. |
[13] | J. Scott, P. J. Carrington, The SAGE Handbook of Social Network Analysis, SAGE Publications, 2011. |
[14] | G. A. Lundberg, M. Steel, Social attraction-patterns in a village, Sociometry, 1 (1938), 375-419. |
[15] | J. A. Barnes, Class and committee in a Norwegian island parish, Hum. Relat., 7 (1954), 39-58. |
[16] | J. H. Levine, The sphere of influence, Am. Sociol. Rev., 37 (1972), 14-27. |
[17] | M. Granowetter, The strength of weak ties, Am. J. Sociol., 78 (1973), 1360-1380. |
[18] | B. Ryan, N. C. Gross, The diffusion of hybrid seed corn in two Iowa communities, Rural Sociol., 8 (1943), 15-24. |
[19] | E. Katz, H. Levine, M. L. Hamilton, Traditions of research on the diffusion of innovation, Am. Sociol. Rev., 28 (1963), 237-253. |
[20] | S. Milgram, The small-world problem, Psychol. Today, 2 (1964), 60-67. |
[21] | E. M. Rogers, Diffusion of Innovations, Free Press, 2003. |
[22] | M. Bond, Social influences on corporate political donations in Britain, Brit. J. Sociol., 55 (2004), 55-77. |
[23] | D. Knoke, Policy Networks, in The SAGE Handbook of Social Network Analysis (eds. J. Scott, P. J. Carrington), SAGE Publications, 2011, 210-222. |
[24] | M. Diani, Social Movements and collective actions, in The SAGE Handbook of Social Network Analysis (eds. J. Scott, P. J. Carrington), SAGE Publications, 2011, 223-235. |
[25] | M. O. Jackson, Social and Economic Networks, Princeton University Press, 2008. |
[26] | A. L. Barabâsi, J. Hawoong, N. Zoltan, R. Erzsebet, A. Schubert, T. Vicsek, Evolution of the social network of scientific collaborations, Phys. A, 311 (2002), 590-614. |
[27] | M. E. Newman, Coauthorship networks and patterns of scientific collaboration, Proc. Natl. Acad. Sci. USA, 101 (2004), 5200-5205. |
[28] | D. J. Watts, S. H. Strogatz, Collective dynamics of "small-world" networks, Nature, 393 (1998), 440-442. |
[29] | M. E. Newman, Networks: An Introduction, Oxford University Press, 2010. |
[30] | H. Kummer, Soziales Verhalten einer Mantelpavianen-Gruppe, Schweizerische Zeitschr. Psychol., 33 (1957), 1-91. |
[31] | D. S. Sade, Some aspects of parent-off spring and sibling relations in a group of rhesus monkeys, with a discussion of grooming, Am. J. Phys. Anthropol., 23 (1965), 1-18. |
[32] | R. A. Hinde, Interactions, relationships and social structure, Man, 11 (1976), 1-17. |
[33] | R. W. Byrne, A. Whiten, S. P. Henzi, Social relationships of mountain baboons: Leadership and affiliation in a non-female-bonded monkey, Am. J. Primatol., 207 (1989), 191-207. |
[34] | B. D. Chepko-sade, K. P. Reitz, D. S. Sade, Sociometrics of Macaca mulatta IV: Network analysis of social structure of a pre-fission group, Soc. Netw., 11 (1989), 293-314. |
[35] | C. A. Chapman, Association patterns of spider monkeys: The influence of ecology and sex on social organization, Behav. Ecol. Sociobiol., 26 (1990), 409-414. |
[36] | C. P. Yeager, Proboscis monkey (Nasalis larvatus) social organization: Group structure, Am. J. Primatol., 106 (1990), 95-106. |
[37] | D. S. Sade, Sociometrics of Macaca mulatta I. Linkage, cliques in grooming matrices, Fol. Primatol., 18 (1972), 196-223. |
[38] | D. S. Sade, Sociometrics of Macaca mulatta Ⅲ. n-path centrality in grooming networks, Soc. Netw., 11 (1989), 273-292. |
[39] | D. S. Sade, M. Altmann, J. Loy, G. Hausfater, J. A. Breuggeman, Sociometrics of Macaca mulatta: Ⅱ. Decoupling centrality and dominance in rhesus monkey social networks, Am. J. Phys. Anthropol., 77 (1988), 409-425. |
[40] | S. Wasserman, K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, 1994. |
[41] | M. Barthelemy, B. Gondran, E. Guichard, Spatial structure of the internet traffic, Phys. A, 319 (2003), 633-642. |
[42] | P. Bonacich, Factoring and weighting approaches to status scores and clique identification, J. Math. Sociol., 2 (1972), 113-120. |
[43] | M. E. J. Newman, A measure of betweenness centrality based on random walks, Soc. Netw., 27 (2005), 39-54. |
[44] | M. E. J. Newman, M. Girvan, Finding and evaluating community structure in networks, Phys. Rev. E, 69 (2004), 026113. |
[45] | A. Clauset, Finding local community structure in networks, Phys. Rev. E, 72 (2005), 026132. |
[46] | M. E. J. Newman, Modularity and community structure in networks, Proc. Natl. Acad. Sci. USA, 103 (2006), 8577-8582. |
[47] | D. Knoke, S. Yang, Social network analysis, Sage Publications, 2019. |
[48] | D. Lusseau, The emergent properties of a dolphin social network, Biol. Lett., 270 (2003), 186-188. |
[49] | D. P. Croft, J. Krause, R. James, Social networks in the guppy (Poecilia reticulata), Biol. Lett., 271 (2004), 516-519. |
[50] | B. Voelkl, Does group structure influence the social transmission of information?, Fol. Primatol., 75 (2004), 423. |
[51] | D. P. Croft, R. James, A. J. W. Ward, M. S. Botham, D. Mawdsley, J. Krause, Assortative interactions and social networks in fish, Oecologia, 143 (2005), 211-219. |
[52] | J. C. Flack, M. Girvan, F. B. M. de Waal, D. C. Krakauer, Policing stabilizes construction of social niches in primates, Nature, 439 (2006), 426-429. |
[53] | C. Sueur, O. Petit, Organization of group members at departure Is driven by social structure in Macaca, Int. J. Primatol., 29 (2008), 1085-1089. |
[54] | S. P. Henzi, D. Lusseau, T. Weingrill, Cyclicity in the structure of female baboon social networks, Behav. Ecol. Sociobiol., 63 (2009), 1015-1021. |
[55] |
J. Lehmann, C. Boesch, Sociality of the dispersing sex: The nature of social bonds in West African female chimpanzees, Pan troglodytes, Anim. Behav., 77 (2009), 377-387. doi: 10.1016/j.anbehav.2008.09.038
![]() |
[56] | J. Lehmann, R. I. M. Dunbar, Network cohesion, group size and neocortex size in female-bonded Old World primates, Proc. R. Soc. B, 276 (2009), 4417-4422. |
[57] | G. Ramos-Fernández, D. Boyer, F. Aureli, L. G. Vick, Association networks in spider monkeys (Ateles geoffroyi), Behav. Ecol. Sociobiol., 63 (2009), 999-1013. |
[58] | N. J. B. Boogert, S. M. Reader, W. Hoppitt, K. N. Laland, The origin and spread of innovations in starlings, Anim. Behav., 75 (2008), 1509-1518. |
[59] | B. Voelkl, R. Noë, The influence of social structure on the propagation of social information in artificial primate groups: A graph-based simulation approach, J. Theoret. Biol., 252 (2008), 77-86. |
[60] | M. Franz, C. L. Nunn, Network-based diffusion analysis: A new method for detecting social learning, Proc. R. Soc. B, 276 (2009), 1829-1836. |
[61] | C. Vital, P. Martins, Using graph theory metrics to infer information flow through animal social groups: A computer simulation analysis, Ethology, 115 (2009), 347-355. |
[62] | W. Hoppitt, A. Kandler, J. R. Kendal, K. N. Laland, The effect of task structure on diffusion dynamics: Implications for diffusion curve and network-based analyses, Learn. Behav., 38 (2010), 243-251. |
[63] | L. M. Aplin, D. R. Farine, J. Morand-Ferron, A. Cockburn, A. Thornton, B. C. Sheldon, Experimentally induced innovations lead to persistent culture via conformity in wild birds, Nature, 7540 (2015), 538. |
[64] | B. Voelkl, C. Kasper, Social structure of primate interaction networks facilitates the emergence of cooperation, Biol. Lett., 5 (2009), 462-464. |
[65] | B. Voelkl, The "Hawk-Dove" game and the spread of the evolutionary process in small heterogeneous populations, Games, 1 (2010), 103-116. |
[66] | B. Voelkl, The evolution of generalized reciprocity in social interaction networks, Theoret. Popul. Biol., 104 (2015), 17-25. |
[67] | B. Mccowan, K. Anderson, A. Heagarty, A. Cameron, Utility of social network analysis for primate behavioral management and well-being, Appl. Anim. Behav. Sci., 109 (2008), 396-405. |
[68] | B. A. Beisner, M. E. Jackson, A. Cameron, B. Mccowan, Effects of natal male alliances on aggression and power dynamics in rhesus macaques, Am. J. Primatol., 801 (2011), 790-801. |
[69] | V. Dufour, C. Sueur, A. Whiten, The impact of moving to a novel environment on social networks, activity and wellbeing in two new world primates, Am. J. Primatol., 811 (2011), 802-811. |
[70] | M. C. Crofoot, D. I. Rubenstein, A. S. Maiya, T. Y. Berger-wolf, Aggression, grooming and group-level cooperation in white-faced capuchins (Cebus capucinus): Insights from social networks, Am. J. Primatol., 833 (2011), 821-833. |
[71] | B. Tiddi, F. Aureli, G. Schino, B. Voelkl, Social relationships between adult females and the alpha male in wild tufted capuchin monkeys, Am. J. Primatol., 73 (2011), 812-820. |
[72] | A. J. J. MacIntosh, A. Jacobs, C. Garcia, K. Shimizu, K. Mouri, M. A. Huffman, et al., Monkeys in the middle: Parasite transmission through the social network of a wild primate, PLoS One, 7 (2012), e51144. |
[73] | L. J. N. Brent, S. Semple, Social capital and physiological stress levels in free-ranging adult female rhesus macaques, Behaviour, 102 (2011), 76-83. |
[74] | P. C. Lopes, P. Block, B. König, Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks, Sci. Rep., 6 (2016), 31790. |
[75] | M. Dow, F. B. M. de Waal, Assignment methods for the analysis of network subgroup interactions, Soc. Netw., 11 (1989), 237-255. |
[76] | I. Matsuda, P. Zhang, L. Swedell, U. Mori, A. Tuuga, A., H. Bernard, et al., Comparisons of intraunit relationships in nonhuman primates living in multilevel social systems, Int. J. Primatol., 33 (2012), 1038-1053. |
[77] | R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon, Network motifs: Simple building blocks of complex networks, Science, 298 (2002), 824-827. |
[78] | N. Snyder-Mackler, J. C. Beehner, T. J. Bergman, Defining higher levels in the multilevel societies of geladas (Theropithecus gelada), Int. J. Primatol., 33 (2012), 1054-1068. |
[79] | C. Sueur, O. Petit, A. de Marco, A. T. Jacobs, K. Watanabe, B. Thierry, A comparative network analysis of social style in macaques, Anim. Behav., 82 (2011), 845-852. |
[80] | B. Voelkl, R. Noë, Simulation of information propagation in real-life primate networks: Longevity, fecundity, fidelity, Behav. Ecol. Sociobiol., 64 (2010), 1449-1459. |
[81] | D. I. Rubenstein, Networks of terrestrial ungulates: linking form and function, in Animal Social Networks (eds. J. Krause, R. James, D. W. Franks, D. P. Croft), Oxford University Press, 2015, 184-196. |
[82] | E. A. Foster, D. W. Franks, L. J. Morrell, K. C. Balcomb, K. M. Parsons, A. van Ginneken, et al., Social network correlates of food availability in an endangered population of killer whales, Orcinus orca. Anim. Behav., 83 (2012), 731-736. |
[83] | R. Albert, A. L. Barabasi, Statistical mechanics of complex networks, Rev. Mod. Phys., 74 (2002), 47-97. |
[84] | M. E. J Newman, The structure and function of complex networks, SIAM Rev., 45 (2003), 167-256. |
[85] | S. Macdonald, B. Voelkl, Primate social networks, in Animal Social Networks (eds. J. Krause, R. James, D. W. Franks, D. P. Croft), Oxford University Press, 2015,123-136. |
[86] | R. R. Kao, L. Danon, D. M. Green, I. Z. Kiss, Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain, Proc. R. Soc. B, 273 (2007), 1999-2007. |
[87] | R. R. Kao, D. M. Green, J. Johnson, I. Z. Kiss, Disease dynamics over very different time-scales: Foot-and-mouth disease and scrapie on the network of livestock movements in the UK, J. R. Soc. Interface, 4 (2007), 907-916. |
[88] | L. Danon, A. P. Ford, T. House, C. P. Jewell, M. J. Keeling, G. O. Roberts, et al., Networks and the epidemiology of infectious disease, Interdiscipl. Persp. Infect. Dis., 2011 (2011), 284909. |
[89] | R. M. Anderson, R. M. May, Population biology of infectious diseases: Part 1, Nature, 280 (1979), 361-367. |
[90] | M. J. E. Newman, The spread of epidemic disease on networks, Phys. Rev. E, 66 (2003), 016128. |
[91] | R. M. May, Network structure and the biology of populations', Trends Ecol. Evol., 21 (2006), 394-399. |
[92] | L. Hufnagel, D. Brockmann, T. Geisel, Forecast and control of epidemics in a globalized world, Proc. Natl. Acad. Sci. USA, 101 (2004), 7794-7799. |
[93] | L. A. Meyers, Contact network epidemiology: Bond percolation applied to infectious disease prediction and control, Bull. Am. Math. Soc., 44 (2007), 63-86. |
[94] | R. M. May, R. M Anderson, Transmission dynamics of HIV infection, Nature, 326 (1987), 137-142. |
[95] | A. S. Klovdahl, J. J. Potterat, D. E. Woodhouse, J. B. Muth, S. Q. Muth, W. W. Darrow, Social networks and infectious disease: The Colorado Springs study, Soc. Sci. Med., 38 (1994), 79-88. |
[96] | F. Liljeros, C. R. Edling, L. A. Nunes Amaral, E. Stanley, Y. Åberg, The web of human sexual contacts, Nature, 411 (2001), 907-908. |
[97] | M. A. Nowak, Evolutionary Dynamics, Harvard University Press, 2006. |
[98] | M. W. Schein, M. H. Fohrman, Social dominance relationships in a herd of dairy cattle, Brit. J. Anim. Behav., 3 (1955), 45-55. |
[99] | M. Bigras-Poulin, R. A. Thompson, M. Chriel, S. Mortensen, M. Greiner, Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread, Prevent. Vet. Med., 76 (2006), 11-39. |
[100] | L. Fiebig, T. Smieszek, J. Saurina, J. Hattendorf, J. Zinsstag, Contacts between poultry farms, their spatial dimension and their relevance for avian influenza preparedness, Geospat. Health, 4 (2009), 79-95. |
[101] | B. Martinez-Lopez, A. M. Perez, J. M. Sanchez-Vizcaino, Social network analysis. Review of general concepts and use in preventive veterinary medicine, Transb. Emerg. Dis., 56 (2009), 109-120. |
[102] | V. V. Volkova, R. Howey, N. J. Savill, M. E. J. Woolhouse, Sheep movement networks and the transmission of infectious diseases, PLoS One, 5 (2010), e11185. |
[103] | R. P. Smith, A. J. C. Cook, R. M. Christley, Descriptive and social network analysis of pig transport data recorded by quality assured pig farms in the UK, Prevent. Vet. Med., 108 (2013), 167-177. |
[104] | J. Ribeiro-Lima, E. A. Enns, B. Thompson, M. E. Craft, S. J. Wells, From network analysis to risk analysis-An approach to risk-based surveillance for bovine tuberculosis in Minnesota, US, Prevent. Vet. Med., 118 (2015), 328-340. |
[105] | H. H. Lentz, A. Koher, P. Hövel, J. Gethmann, C. Sauter-Louis, T. Selhorst, et al., Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany, PLoS One, 11 (2016), 0155195. |
[106] | P. Bajardi, A. Barrat, L. Savini, V. Colizza, Optimizing surveillance for livestock disease spreading through animal movements, J. R. Soc. Interface, 9 (2012), 2814-2825. |
[107] | M. M. Mweu, G. Fournié, T. Halasa, N. Toft, S. S. Nielsen, Temporal characterisation of the network of Danish cattle movements and its implication for disease control: 2000-2009, Prevent. Vet. Med., 110 (2013), 379-387. |
[108] | S. Nickbakhsh, L. Matthews, J. E. Dent, G. T. Innocent, M. E. Arnold, S. W. Reid, et al., Implications of within-farm transmission for network dynamics: Consequences for the spread of avian influenza, Epidemics, 5 (2013), 67-76. |
[109] | B. Vidondo, B. Voelkl, Dynamic network measures reveal the impact of cattle markets and alpine summering on the risk of epidemic outbreaks in the Swiss cattle population, BMC Vet. Res. 14 (2018), 88. |
[110] | J. Krause, D. Lusseau, R. James, Animal social networks: An introduction, Behav. Ecol. Sociobiol., 63 (2009), 967-973. |
[111] | M. J. Silk, D. P. Croft, R. J. Delahay, D. J. Hodgson, M. Boots, N. Weber, et al., Using social network measures in wildlife disease ecology, epidemiology, and management, BioScience, 67 (2017), 245-257. |
[112] | M. E. Craft, Infectious disease transmission and contact networks in wildlife and livestock, Phil. Trans. R. Soc. B, 370 (2015), 20140107. |
[113] | R. H. Griffin, C. L. Nunn, Community structure and the spread of infectious disease in primate social networks, Evol. Ecol., 26 (2012), 779-800. |
[114] | C. L. Nunn, F. Jordan, C. M. McCabe, J. L. Verdolin, J. H. Fewell, Infectious disease and group size: More than just a numbers game, Phil. Trans. R. Soc. B, 370 (2015), 20140111. |
[115] | S. S. Godfrey, C. M. Bull, R. James, K. Murray, Network structure and parasite transmission in a group living lizard, the gidgee skink, Egernia stokesii, Behav. Ecol. Sociobiol., 63 (2009), 1045-1056. |
[116] | K. L. VanderWaal, E. R. Atwill, S. Hooper, K. Buckle, B. McCowan, Network structure and prevalence of Cryptosporidium in Belding's ground squirrels, Behav. Ecol. Sociobiol., 67 (2013), 1951-1959. |
[117] | T. Porphyre, M. Stevenson, R. Jackson, J. McKenzie, Original article Influence of contact heterogeneity on TB reproduction ratio R0 in a free-living brushtail possum Trichosurus vulpecula population, Vet. Res., 39 (2008), 31. |
[118] | J. Rushmore, D. Caillaud, R. J. Hall, R. M. Stumpf, L. A. Meyers, S. Altizer, Network-based vaccination improves prospects for disease control in wild chimpanzees, J. R. Soc. Interface, 11 (2014), 20140349. |
[119] | J. A. Drewe, K. T. D. Eames, J. R. Madden, G. P. Pearce, Integrating contact network structure into tuberculosis epidemiology in meerkats in South Africa: Implications for control, Prevent. Vet. Med., 101 (2011), 113-120. |
[120] | M. D. J. Blyton, S. C. Banks, R. Peakall, D. B. Lindenmayer, D. M. Gordon, Not all types of host contacts are equal when it comes to E. coli transmission, Ecol. Lett., 17 (2014), 970-978. |
[121] | C. R. Webb, Farm animal networks: Unraveling the contact structure of the British sheep population, Prevent. Vet. Med., 68 (2005), 3-17. |
[122] | F. Natale, A. Giovannini, L. Savini, D. Palma, L. Possenti, G. Fiore, et al., Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread, Prevent. Vet. Med., 92 (2009), 341-350. |
[123] | C. Dubé, C. Ribble, D. Kelton, B. Mcnab, A Review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development, Transbound. Emerg. Dis., 56 (2009), 73-85. |
[124] | H. Chen, G. Smith, S. Zhang, K. Qin, J. Wang, S. Li, et al., H5N1 virus outbreak in migratory waterfowl, Nature, 436 (2005), 191-192. |
[125] | B. J. Hoye, V. J. Munster, H. Nishiura, R. A. M. Fouchier, J. Madsen, M. Klaassen, Reconstructing an annual cycle of interaction: Natural infection and antibody dynamics to avian influenca along a migratory flyway, Oikos, 120 (2011), 748-755. |
[126] | K. R. Finn, M. J. Silk, M. A. Porter, N. Pinter-Wollman, The use of multilayer network analysis in animal behaviour, Anim. Behav., 149 (2019), 7-22. |
[127] | K. Robert, D. Garant, F. Pelletier, Keep in touch: Does spatial overlap correlate with contact rate frequency?, J. Wildl. Manag., 76 (2012), 1670-1675. |
[128] | M. L. Gilbertson, L. A. White, M. E. Craft, Trade‐offs with telemetry‐derived contact networks for infectious disease studies in wildlife, Meth. Ecol. Evol., 2020. |
[129] | S. E. Perkins, F. Cagnacci, A. Stradiotto, D. Arnoldi, P. J. Hudson, Comparison of social networks derived from ecological data: implications for inferring infectious disease dynamics, J. Anim. Ecol., 78 (2009), 1015-1022. |
[130] | J. Krause, A. D. M. Wilson, D. P. Croft, New technology facilitates the study of social networks, Trends Ecol. Evol., 26 (2011), 5-6. |
[131] | C. Rutz, Z. T. Burns, R. James, S. M. H. Ismar, J. Burt, B. Otis, et al., Automated mapping of social networks in wild birds, Curr. Biol., 22 (2012), R669-R671. |
[132] | I. Psorakis, B. Voelkl, C. J. Garroway, R. Radersma, L. M. Aplin, R. A. Crates, et al., Inferring social structure from temporal data, Behav. Ecol. Sociobiol., 69 (2015), 857-866. |
[133] | J. R. Ginsberg, T. P. Young, Measuring associations between individuals or groups in behavioural studies, Anim. Behav., 44 (1992), 377-379. |
[134] | L. Beijder, D. Fletcher, S. Brager, A method for testing association patterns of social animals, Anim. Behav., 56 (1998), 719-725. |
[135] | L. A. White, J. D. Forester, M. E. Craft, Using contact networks to explore mechanisms of parasite transmission in wildlife, Biol. Rev., 92 (2017), 389-409. |
[136] | R. K. Hamede, J. Bashford, H. McCallum, M. Jones, Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: Using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease, Ecol. Lett., 12 (2009), 1147-1157. |
[137] | T. C. Germann, K. Kadau, I. M. Longini, C. A. Macken, Mitigation strategies for pandemic influenza in the United States, Proc. Natl. Acad. Sci. USA, 103 (2006), 5935-5940. |
[138] | S. E. Robinson, M. G. Everett, R. M. Christley, Recent network evolution increases the potential for large epidemics in the British cattle population, J. R. Soc. Interface, 4 (2007), 669-674. |
[139] | J. C. Gibbens, C. E. Sharpe, J. W. Wilesmith, L. M. Mansley, E. Michalopoulou, J. B., et al., Descriptive epidemiology of the 2001 foot-and-mouth disease epidemic in Great Britain: The first five months, Vet. Rec., 149 (2001), 729-743. |
[140] | I. Z. Kiss, D. M. Green, R. R. Kao, The network of sheep movements within Great Britain: Network properties and their implications for infectious disease spread, J. R. Soc. Interface, 3 (2006), 669-677. |
[141] | D. M. Green, I. Z. Kiss, R. R. Kao, Modelling the initial spread of foot-and-mouth disease through animal movements, Proc. R. Soc. B, 273 (2006), 2729-2735. |
[142] | M. C.Vernon, M. J. Keeling, Representing the UK's cattle herd as static and dynamic networks, Proc. R. Soc. B, 276 (2009), 469-476. |
[143] | P. Sah, S. T. Leu, P. C. Cross, P. J. Hudson, S. Bansal, Unravelling the disease consequences and mechanisms of modular structure in animal social networks, Proc. Natl. Acad. Sci. USA, 114 (2017), 4165-4170. |
[144] | R. Pastor-Satorras, A. Vespignani, Epidemic spreading in scale-free networks, Phys. Rev. Lett., 86 (2001), 3200-3203. |
[145] | A. L. Lloyd, R. M. May, How viruses spread among computers and people, Science, 292 (2001), 1316-1317. |
[146] | D. C. Bell, J. S. Atkinson, J. W. Carlson, Centrality measures for disease transmission networks, Soc. Netw., 21 (1999), 1-21. |
[147] | M. J. Keeling, The effects of local spatial structure on epidemiological invasions, Proc. R. Soc. B, 266 (1999), 859-867. |
[148] | K. T. D. Eames, M. J. Keeling, Modeling dynamic and network heterogeneities in the spread of sexually transmitted diseases, Proc. Natl. Acad. Sci. USA, 99 (2002), 13330-13335. |
[149] | C. Buckee, L. Danon, S. Gupta, Host community structure and the maintenance of pathogen diversity, Proc. R. Soc. B, 274 (2007), 1715-1721. |
[150] | M. Salathé, J. H. Jones, Dynamics and control of diseases in networks with community structure, PLoS Comp. Biol., 6 (2010), e1000736. |
[151] | S. M. Firestone, M. P. Ward, R. M. Christley, N. K. Dhand, The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis, Prevent. Vet. Med., 102 (2011), 185-195. |
[152] | J. Frössling, A. Ohlson, C. Björkman, N. Hakansson, M. Nöremark, Application of network analysis parameters in risk-based surveillance-Examples based on cattle trade data and bovine infections in Sweden, Prevent. Vet. Med., 105 (2012), 202-208. |
[153] | L. García Álvarez, C. R. Webb, M. A. Holmes, A novel field-based approach to validate the use of network models for disease spread between dairy herds, Epidemiol. Infect., 139 (2011), 1863-1874. |
[154] | R. Biek, A. G. Rodrigo, D. Holley, A. Drummond, C. R. Anderson, H. A. Ross, et al., Epidemiology, genetic diversity, and evolution of endemic feline immunodeficiency virus in a population of wild cougars, J. Virol., 77 (2003), 9578-9589. |
[155] | B. T. Grenfell, O. G. Pybus, J. R. Gog, J. L. N. Wood, J. M. Daly, J. A. Mumford, et al., Unifying the epidemiological and evolutionary dynamics of pathogens, Science, 303 (2004), 327-333. |
[156] | R. Biek, A. Drummond, M. Poss, A virus reveals population structure and recent demographic history of its carnivore host, Science, 311 (2006), 538-542. |
[157] | E. A. Archie, G. Luikart, V. O. Ezenwa, Infecting epidemiology with genetics: A new frontier in disease ecology, Trends Ecol. Evol., 24 (2008), 21-30. |
[158] | C. M. Bull, S. S. Godfrey, D. M. Gordon, Social networks and the spread of Salmonella in a sleepy lizard population, Mol. Ecol., 21 (2012), 4386-4392. |
[159] | K. L. VanderWaal, E. R. Atwill, L. A. Isbell, B. McCowan, Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis), J. Anim. Ecol., 83 (2014), 406-414. |
[160] | K. L. VanderWaal, E. R. Atwill, L. A. Isbell, B. McCowan, B. Quantifying microbe transmission networks for wild and domestic ungulates in Kenya, Biol. Conserv., 169 (2014), 136-146. |
[161] |
J. S. Lee, E. W. Ruell, E. E. Boydston, L. M. Lyren, R. S. Alonso, J. L. Troyer, et al., Gene flow and pathogen transmission among bobcats (Lynx rufus) in a fragmented urban landscape, Mol. Ecol., 21 (2012), 1617-1631. doi: 10.1111/j.1365-294X.2012.05493.x
![]() |
[162] | B. Y. Reis, I. S. Kohane, K. D. Mandl, An epidemiological network model for disease outbreak detection, PLoS Med, 4 (2007), e210. |
[163] | F. Schirdewahn, V. Colizza, H. H. Lentz, A. Koher, V. Belik, P. Hövel, Surveillance for outbreak detection in livestock-trade networks, in Temporal Network Epidemiology (eds. M. Naoki, P. Holme), Springer, 2017, 215-240. |
[164] | P. Skums, A. Kirpich, P. I. Baykal, A. Zelikovsky, G. Chowell, Global transmission network of SARS-CoV-2: From outbreak to pandemic, medRxiv, 2020. |
[165] | D. Lusseau, H. Whitehead, S. Gero, Incorporating uncertainty into the study of animal social networks, Anim. Behav., 75 (2008), 1809-1815. |
[166] | R. James, D. P. Croft, J. Krause, Potential banana skins in animal social network analysis, Behav. Ecol. Sociobiol., 63 (2009), 989-997. |
[167] | B. Voelkl, C. Kasper, C. Schwab, Network measures for dyadic interactions: Stability and reliability, Am. J. Primatol., 73 (2011), 731-740. |
[168] | J. Krause, S. Krause, R. Arlinghaus, I. Psorakis, S. Roberts, C. Rutz, Reality mining of animal social systems, Trends Ecol. Evol., 28 (2013), 541-551. |
[169] | M. Berdoy, J. P. Webster, D. W. Macdonald, Fatal attraction in rats infected with Toxoplasma gondii, Proc. R. Soc. B, 267 (2000), 1591-1594. |
[170] | A. Vyas, S. Kim, N. Giacomini, J. C. Boothroyd, R. M. Sapolsky, Behavioral changes induced by Toxoplasma infection of rodents are highly specific to aversion of cat odors, Proc. Natl. Acad. Sci. USA, 104 (2007), 6442-6447. |
[171] | D. P. Croft, M. Edenbrow, S. K. Darden, I. W. Ramnarine, C. van Oosterhout, J. Cable, Effect of gyrodactylid ectoparasites on host behaviour and social network structure in guppies Poecilia reticulata, Behav. Ecol. Sociobiol., 65 (2011), 2219-2227. |
[172] | F. J. Theis, L. V. Ugelvig, C. Marr, S. Cremer, Opposing effects of allogrooming on disease transmission in ant societies, Phil Trans. R. Soc. B, 370 (2015), 20140108. |
[173] | V. O. Ezenwa, E. A. Archie, M. E. Craft, D. M. Hawley, L. B. Martin, J. Moore, et al., Host behaviour-parasite feedback: an essential link between animal behaviour and disease ecology, Proc. R. Soc. B, 283 (2016), 20153078. |
[174] | L. A. White, J. D. Forester, M. E. Craft, Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges, J. Anim. Ecol., 87 (2018), 559-580. |
[175] | K. Büttner, J. Salau, J., Krieter, Quality assessment of static aggregation compared to the temporal approach based on a pig trade network in Northern Germany, Prevent. Vet. Med., 129 (2016), 1-8. |
[176] | M. J. Silk, D. J., Hodgson, C. Rozins, D. P. Croft, R. J. Delahay, M. Boots, et al., Integrating social behaviour, demography and disease dynamics in network models: applications to disease management in declining wildlife populations, Phil. Trans. R. Soc. B, 374 (2019), 20180211. |
[177] | M. E. Craft, E. Volz, C. Packer, L. A. Meyers, Disease transmission in territorial populations: The small-world network of Serengeti lions, J. R. Soc. Interface, 8 (2011), 776-786. |
[178] | N. Weber, S. P. Carter, S. R. X. Dall, R. J. L. Delahay, J. L. McDonald, S. Bearhop, et al., Badger social networks correlate with tuberculosis infection, Curr. Biol., 23 (2013), R915-R916. |
[179] | K. P. Huyvaert, R. E. Russell, K. A. Patyk, M. E. Craft, P. C. Cross, M. G. Garner, et al., Challenges and opportunities developing mathematical models of shared pathogens of domestic and wild animals, Vet. Sci., 5 (2018), 92. |
[180] | S. Kraberger, N. M. Fountain-Jones, R. B. Gagne, J. Malmberg, N.G. Dannemiller, K. Logan, et al., Frequent cross-species transmissions of foamy virus between domestic and wild felids, Virus Evol., 6 (2020), vez058. |
[181] | R. K. Plowright, C. R. Parrish, H. McCallum, P. J. Hudson, A. I. Ko, A. L. Graham, et al., Pathways to zoonotic spillover, Nat. Rev. Microbiol., 15 (2017), 502. |
[182] | B. J. Coburn, B. G. Wagner, S. Blower, Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1). BMC Med., 7 (2009), 30. |
[183] | C. M. Scoglio, C. Bosca, M. H. Riad, F. D. Sahneh, S. C. Britch, L. W. Cohnstaedt, et al., Biologically informed individual-based network model for Rift Valley fever in the US and evaluation of mitigation strategies, PloS One, 11 (2016), e0162759. |
[184] | S. K. Lau, P. C. Woo, K. S. Li, Y. Huang, H. W. Tsoi, B. H. Wong, et al., Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats, Proc. Natl. Acad. Sci. USA, 102 (2005), 14040-14045. |
[185] | C. M. Luo, N. Wang, X. L. Yang, H. Z. Liu, W. Zhang, B. Li, et al., Discovery of novel bat coronaviruses in south China that use the same receptor as Middle East respiratory syndrome coronavirus, J. Virol., 92 (2018), e00116-18. |
[186] | N. Wang, S. Y. Li, X. L. Yang, H. M. Huang, Y. J. Zhang, H. Guo, et al., Serological evidence of bat SARS-related coronavirus infection in humans, China, Virol. Sin., 33 (2018),104-107. |
[187] | L. E. Escobar, R. Moen, M. E. Craft, K. L. VanderWaal, Mapping parasite transmission risk from white-tailed deer to a declining moose population, Eur. J. Wildl. Res., 65 (2019), 60. |
[188] | P. Sah, J. Mann, S. Bansal, Disease implications of animal social network structure: A synthesis across social systems, J. Anim. Ecol., 87 (2018), 546-558. |
1. | HECHAO LI, SHASHANK KAIRA, JAMES MERTENS, NIKHILESH CHAWLA, YANG JIAO, Accurate stochastic reconstruction of heterogeneous microstructures by limited x-ray tomographic projections, 2016, 264, 00222720, 339, 10.1111/jmi.12449 | |
2. | Ali Haghverdi, Majid Baniassadi, Mostafa Baghani, Abolfazl Alizadeh Sahraei, Hamid Garmestani, Masoud Safdari, A modified simulated annealing algorithm for hybrid statistical reconstruction of heterogeneous microstructures, 2021, 197, 09270256, 110636, 10.1016/j.commatsci.2021.110636 |
N° | DESCRIPTION | Volume (m3) | Density(Kg/m3) | Qty(Kg) | Embodied Energy(EE)Intensity(MJ/Kg) | Embodied Carbon (EC)Intensity KgCO2/Kg | EE Emissions(MJ) | EC Emissions(KgCO2) |
O | SITE INSTALLATION | No data | ||||||
Sub-total | ||||||||
I | FOUNDATION | |||||||
1.1 | Lean concrete mix at 150 Kg/m3 of thickness = 5 cm | 3.000 | 2200 | 6600 | 0.58 | 0.0755 | 3828 | 498.3 |
1.2 | Damp proof course/membrane of thickness=0.05 cm(plastic-general) | 0.050 | 960 | 48 | 80.5000 | 2.7300 | 3864 | 131.04 |
1.3 | Solid foundation wall of dimension 20 cm × 20 cm × 40 cm | 11.650 | 1900 | 22135 | 1.3300 | 0.2080 | 29439.55 | 4604.08 |
1.4 | Mortar for wall joints | 1.930 | 1650 | 3184.5 | 1.1100 | 0.1710 | 3534.795 | 544.5495 |
1.5 | Concrete mix at 350 Kg/m3 for footing, ground beam, substructure column | 4.000 | 2400 | 9600 | 1.0250 | 0.1505 | 9840 | 1444.8 |
1.6 | Timber for formwork(hardwood unspecified) | 0.700 | 90 | 63 | 10.0000 | 0.7100 | 630 | 44.73 |
1.7 | Concrete slab mix at 300 Kg/m3 of thickness = 10 cm | 8.690 | 2400 | 20856 | 0.91 | 0.131 | 18978.96 | 2732.136 |
1.8 | Substrate made of gravel and crushed rocks of thickness = 20 cm | 0.170 | 2240 | 380.8 | 0.0830 | 0.0048 | 31.6064 | 1.82784 |
1.9 | S and of thickness 5 cm | 0.040 | 2240 | 89.6 | 0.0810 | 0.0048 | 7.2576 | 0.43008 |
Sub-total | 70, 154.17 | 10, 001.89 | ||||||
II | ELEVATIONS | |||||||
2.1 | Brick walls(mud) | 29.400 | 1730 | 50862 | 0.0000 | 0.0000 | 0 | 0 |
2.2 | Mortar for wall joints | 5.250 | 1650 | 8662.5 | 1.1100 | 0.1710 | 9615.375 | 1481.2875 |
2.3 | Concrete mix at 350 Kg/m3 for super-structural beams and columns | 3.000 | 2400 | 7200 | 1.0250 | 0.1505 | 7380 | 1083.6 |
2.4 | Timber for formwork(hardwood unspecified) | 1.400 | 90 | 126 | 10.0000 | 0.7100 | 1260 | 89.46 |
Sub-total | 18, 255.38 | 2, 654.35 | ||||||
III | CARPENTRY AND ROOFWORK | |||||||
3.1 | Timber joist of dimension 3 cm x 15 cm(sawn hard wood) | 0.850 | 90 | 76.5 | 10.4000 | 0.8900 | 795.6 | 68.085 |
3.2 | Roof battens of dimension 8 cm x 8 cm(sawn hardwood) | 0.250 | 90 | 22.5 | 10.4000 | 0.8900 | 234 | 20.025 |
3.3 | Aluminium roof covering | 0.060 | 2700 | 162.00 | 155.0000 | 8.2400 | 25110 | 1334.88 |
3.4 | Ceiling plywood | 0.320 | 540 | 172.8 | 15.0000 | 1.0700 | 2592 | 184.896 |
3.5 | Aluminium ridge board | 0.002 | 2700 | 5.4 | 155.0000 | 8.2400 | 837 | 44.496 |
3.6 | Wooden fascia(sawn hardwood) | 0.030 | 700 | 21 | 10.4 | 0.89 | 218.4 | 18.69 |
3.7 | Aluminium on fascia | 0.006 | 2700 | 16.2 | 155.0000 | 8.2400 | 2511 | 133.488 |
Sub-total | 32, 298.00 | 1, 804.56 | ||||||
IV | ELECTRICITY | No data | ||||||
Sub-total | ||||||||
V | PLUMBING | No data | ||||||
Sub-total | ||||||||
VI | TILES AND PAINTINGS | |||||||
6.1 | Bathroom wall ceramic tiles of dimension 30 cm × 30 cm | 0.040 | 2000 | 80 | 10.0000 | 0.66 | 800 | 52.8 |
6.2 | Bathroom tiles 15 cm × 15 cm | 0.008 | 1700 | 13.6 | 29.0000 | 1.5100 | 394.4 | 20.536 |
6.3 | Mortar for posing of tiles | 0.009 | 1900 | 17.1 | 1.3300 | 0.2080 | 22.743 | 3.5568 |
Sub-total | 1, 217.14 | 76.89 | ||||||
VII | WOOD AND STEEL WORKS | |||||||
7.1 | Wooden door panel of thickness 4 cm including frames | 0.220 | 700 | 154 | 10.4 | 0.89 | 1601.6 | 137.06 |
7.2 | Aluminium locks | 3 | 155.0000 | 8.2400 | 465 | 24.72 | ||
7.3 | Timber window including frames | 0.200 | 700 | 140 | 10.4 | 0.89 | 1456 | 124.6 |
7.4 | Glass louvers | 0.090 | 25 | 2.25 | 15.0000 | 0.86 | 33.75 | 1.935 |
7.5 | Aluminium glass louvers' holders | 4 | 155.0000 | 8.24 | 620 | 32.96 | ||
7.6 | Steel window protectors | 0.075 | 7850 | 588.75 | 20.1 | 1.37 | 11833.875 | 806.5875 |
Sub-total | 16, 010.23 | 1, 127.86 | ||||||
Gr and -total | 137, 934.91 | 15, 665.56 |
N° | DESCRIPTION | Volume(m3) | Density(Kg/m3) | Qty(Kg) | Embodied Energy(EE)Intensity(MJ/Kg) | Embodied Carbon(EC)Intensity KgCO2/Kg | EE Emissions(MJ) | EC Emissions(KgCO2) |
O | SITE INSTALLATION | No data | ||||||
Sub-total | ||||||||
I | FOUNDATION | |||||||
1.1 | Lean concrete mix at 150 Kg/m3 of thickness = 5 cm | 3.850 | 2200 | 8470 | 0.58 | 0.0755 | 4912.60 | 639.49 |
1.2 | Damp proof course/membrane of thickness = 0.05 cm(plastic-general) | 0.070 | 960 | 67.20 | 80.5000 | 2.7300 | 5409.60 | 183.46 |
1.3 | Solid foundation wall of dimension 20 cm × 20 cm × 40 cm | 16.640 | 1900 | 31616 | 1.3300 | 0.2080 | 42049.28 | 6576.13 |
1.4 | Mortar for wall joints | 2.400 | 1650 | 3960 | 1.1100 | 0.1710 | 4395.60 | 677.16 |
1.5 | Concrete mix at 350 Kg/m3 for footing, ground beam, substructure column | 5.840 | 2400 | 14016 | 1.0250 | 0.1505 | 14366.40 | 2109.41 |
1.6 | Timber for formwork(hardwood unspecified) | 0.900 | 90 | 81.00 | 10.0000 | 0.7100 | 810.00 | 57.51 |
1.7 | Concrete slab mix at 300 Kg/m3 of thickness = 10 cm | 11.500 | 2400 | 27600 | 0.91 | 0.1310 | 25116.00 | 3615.60 |
1.8 | Substrate made of gravel and crushed rocks of thickness = 20 cm | 0.240 | 2240 | 537.60 | 0.0830 | 0.0048 | 44.62 | 2.58 |
1.9 | S and of thickness 5 cm | 0.060 | 2240 | 134.40 | 0.0810 | 0.0048 | 10.89 | 0.65 |
Sub-total | 97, 114.99 | 13, 861.97 | ||||||
II | ELEVATIONS | |||||||
2.1 | Cement blocks for walls(Cement-s and mix ratio 1:3) | 38.000 | 1900 | 72200 | 1.3300 | 0.2080 | 96026.00 | 15017.60 |
2.2 | Wall joint mortar(Cement-s and ration 1:4) | 6.100 | 1650 | 10065 | 1.1100 | 0.1710 | 11172.15 | 1721.12 |
2.3 | Concrete mix at 350 Kg/m3 for super-structural beams and columns | 4.000 | 2400 | 9600 | 1.0250 | 0.1505 | 9840.00 | 1444.80 |
2.4 | Timber for formwork(hardwood unspecified) | 1.700 | 90 | 153 | 10.0000 | 0.7100 | 1530.00 | 108.63 |
2.5 | Mortar for wall plastering | 4.300 | 1900 | 8170 | 1.3300 | 0.2080 | 10866.10 | 1699.36 |
Sub-total | 129, 434.25 | 19, 991.51 | ||||||
III | CARPENTRY AND ROOFWORK | |||||||
3.1 | Timber joist of dimension 3 cm x 15 cm(sawn hardwood) | 1.050 | 90 | 94.50 | 10.4000 | 0.8900 | 982.80 | 84.11 |
3.2 | Roof battens of dimension 8 cm x 8 cm(sawn hardwood) | 0.350 | 90 | 31.50 | 10.4000 | 0.8900 | 327.60 | 28.04 |
3.3 | Aluminium roof covering | 0.080 | 2700 | 216 | 155.0000 | 8.2400 | 33480.00 | 1779.84 |
3.4 | Ceiling plywood | 0.480 | 540 | 259.20 | 15.0000 | 1.0700 | 3888.00 | 277.34 |
3.5 | Aluminium ridge board | 0.003 | 2700 | 8.10 | 155.0000 | 8.2400 | 1255.50 | 66.74 |
3.6 | Wooden fascia(sawn hardwood) | 0.026 | 700 | 18.20 | 10.4 | 0.89 | 189.28 | 16.20 |
3.7 | Aluminium on fascia | 0.007 | 2700 | 18.90 | 155.0000 | 8.2400 | 2929.50 | 155.74 |
Sub-total | 43, 052.68 | 2, 408.00 | ||||||
IV | ELECTRICITY | No data | ||||||
Sub-total | ||||||||
V | PLUMBING | No data | ||||||
Sub-total | ||||||||
VI | TILES AND PAINTINGS | |||||||
6.1 | Bathroom wall ceramic tiles of dimension 30 cm × 30 cm | 0.075 | 2000 | 150.00 | 10.0000 | 0.66 | 1500.00 | 99.00 |
6.2 | Bathroom tiles 15 cm × 15 cm | 0.015 | 1700 | 25.50 | 29.0000 | 1.5100 | 739.50 | 38.51 |
6.3 | Mortar for posing of tiles | 0.020 | 1900 | 38.00 | 1.3300 | 0.2080 | 50.54 | 7.90 |
Sub-total | 2, 290.04 | 145.41 | ||||||
VII | WOOD AND STEEL WORKS | |||||||
7.1 | Wooden door panels of thickness 4cm including frames | 0.220 | 700 | 154.00 | 10.4 | 0.89 | 1601.60 | 137.06 |
7.2 | Aluminium locks | 4.00 | 155.0000 | 8.2400 | 620.00 | 32.96 | ||
7.3 | Timber window including frames | 0.200 | 700 | 140 | 10.4 | 0.89 | 1456.00 | 124.60 |
7.4 | Glass louvers | 0.130 | 25 | 3.25 | 15.0000 | 0.8600 | 48.75 | 2.80 |
7.5 | Aluminium glass louvers' holders | 6.00 | 155.0000 | 8.24 | 930.00 | 49.44 | ||
7.6 | Steel window protectors | 0.100 | 7850 | 785 | 20.1 | 1.37 | 15778.50 | 1075.45 |
Sub-total | 20, 434.85 | 1, 422.31 | ||||||
Gr and -total | 292, 326.81 | 37, 829.19 |
N° | DESCRIPTION | Volume (m3) | Density(Kg/m3) | Qty(Kg) | Embodied Energy(EE)Intensity(MJ/Kg) | Embodied Carbon (EC)Intensity KgCO2/Kg | EE Emissions(MJ) | EC Emissions(KgCO2) |
O | SITE INSTALLATION | No data | ||||||
Sub-total | ||||||||
I | FOUNDATION | |||||||
1.1 | Lean concrete mix at 150 Kg/m3 of thickness = 5 cm | 3.000 | 2200 | 6600 | 0.58 | 0.0755 | 3828 | 498.3 |
1.2 | Damp proof course/membrane of thickness=0.05 cm(plastic-general) | 0.050 | 960 | 48 | 80.5000 | 2.7300 | 3864 | 131.04 |
1.3 | Solid foundation wall of dimension 20 cm × 20 cm × 40 cm | 11.650 | 1900 | 22135 | 1.3300 | 0.2080 | 29439.55 | 4604.08 |
1.4 | Mortar for wall joints | 1.930 | 1650 | 3184.5 | 1.1100 | 0.1710 | 3534.795 | 544.5495 |
1.5 | Concrete mix at 350 Kg/m3 for footing, ground beam, substructure column | 4.000 | 2400 | 9600 | 1.0250 | 0.1505 | 9840 | 1444.8 |
1.6 | Timber for formwork(hardwood unspecified) | 0.700 | 90 | 63 | 10.0000 | 0.7100 | 630 | 44.73 |
1.7 | Concrete slab mix at 300 Kg/m3 of thickness = 10 cm | 8.690 | 2400 | 20856 | 0.91 | 0.131 | 18978.96 | 2732.136 |
1.8 | Substrate made of gravel and crushed rocks of thickness = 20 cm | 0.170 | 2240 | 380.8 | 0.0830 | 0.0048 | 31.6064 | 1.82784 |
1.9 | S and of thickness 5 cm | 0.040 | 2240 | 89.6 | 0.0810 | 0.0048 | 7.2576 | 0.43008 |
Sub-total | 70, 154.17 | 10, 001.89 | ||||||
II | ELEVATIONS | |||||||
2.1 | Brick walls(mud) | 29.400 | 1730 | 50862 | 0.0000 | 0.0000 | 0 | 0 |
2.2 | Mortar for wall joints | 5.250 | 1650 | 8662.5 | 1.1100 | 0.1710 | 9615.375 | 1481.2875 |
2.3 | Concrete mix at 350 Kg/m3 for super-structural beams and columns | 3.000 | 2400 | 7200 | 1.0250 | 0.1505 | 7380 | 1083.6 |
2.4 | Timber for formwork(hardwood unspecified) | 1.400 | 90 | 126 | 10.0000 | 0.7100 | 1260 | 89.46 |
Sub-total | 18, 255.38 | 2, 654.35 | ||||||
III | CARPENTRY AND ROOFWORK | |||||||
3.1 | Timber joist of dimension 3 cm x 15 cm(sawn hard wood) | 0.850 | 90 | 76.5 | 10.4000 | 0.8900 | 795.6 | 68.085 |
3.2 | Roof battens of dimension 8 cm x 8 cm(sawn hardwood) | 0.250 | 90 | 22.5 | 10.4000 | 0.8900 | 234 | 20.025 |
3.3 | Aluminium roof covering | 0.060 | 2700 | 162.00 | 155.0000 | 8.2400 | 25110 | 1334.88 |
3.4 | Ceiling plywood | 0.320 | 540 | 172.8 | 15.0000 | 1.0700 | 2592 | 184.896 |
3.5 | Aluminium ridge board | 0.002 | 2700 | 5.4 | 155.0000 | 8.2400 | 837 | 44.496 |
3.6 | Wooden fascia(sawn hardwood) | 0.030 | 700 | 21 | 10.4 | 0.89 | 218.4 | 18.69 |
3.7 | Aluminium on fascia | 0.006 | 2700 | 16.2 | 155.0000 | 8.2400 | 2511 | 133.488 |
Sub-total | 32, 298.00 | 1, 804.56 | ||||||
IV | ELECTRICITY | No data | ||||||
Sub-total | ||||||||
V | PLUMBING | No data | ||||||
Sub-total | ||||||||
VI | TILES AND PAINTINGS | |||||||
6.1 | Bathroom wall ceramic tiles of dimension 30 cm × 30 cm | 0.040 | 2000 | 80 | 10.0000 | 0.66 | 800 | 52.8 |
6.2 | Bathroom tiles 15 cm × 15 cm | 0.008 | 1700 | 13.6 | 29.0000 | 1.5100 | 394.4 | 20.536 |
6.3 | Mortar for posing of tiles | 0.009 | 1900 | 17.1 | 1.3300 | 0.2080 | 22.743 | 3.5568 |
Sub-total | 1, 217.14 | 76.89 | ||||||
VII | WOOD AND STEEL WORKS | |||||||
7.1 | Wooden door panel of thickness 4 cm including frames | 0.220 | 700 | 154 | 10.4 | 0.89 | 1601.6 | 137.06 |
7.2 | Aluminium locks | 3 | 155.0000 | 8.2400 | 465 | 24.72 | ||
7.3 | Timber window including frames | 0.200 | 700 | 140 | 10.4 | 0.89 | 1456 | 124.6 |
7.4 | Glass louvers | 0.090 | 25 | 2.25 | 15.0000 | 0.86 | 33.75 | 1.935 |
7.5 | Aluminium glass louvers' holders | 4 | 155.0000 | 8.24 | 620 | 32.96 | ||
7.6 | Steel window protectors | 0.075 | 7850 | 588.75 | 20.1 | 1.37 | 11833.875 | 806.5875 |
Sub-total | 16, 010.23 | 1, 127.86 | ||||||
Gr and -total | 137, 934.91 | 15, 665.56 |
N° | DESCRIPTION | Volume(m3) | Density(Kg/m3) | Qty(Kg) | Embodied Energy(EE)Intensity(MJ/Kg) | Embodied Carbon(EC)Intensity KgCO2/Kg | EE Emissions(MJ) | EC Emissions(KgCO2) |
O | SITE INSTALLATION | No data | ||||||
Sub-total | ||||||||
I | FOUNDATION | |||||||
1.1 | Lean concrete mix at 150 Kg/m3 of thickness = 5 cm | 3.850 | 2200 | 8470 | 0.58 | 0.0755 | 4912.60 | 639.49 |
1.2 | Damp proof course/membrane of thickness = 0.05 cm(plastic-general) | 0.070 | 960 | 67.20 | 80.5000 | 2.7300 | 5409.60 | 183.46 |
1.3 | Solid foundation wall of dimension 20 cm × 20 cm × 40 cm | 16.640 | 1900 | 31616 | 1.3300 | 0.2080 | 42049.28 | 6576.13 |
1.4 | Mortar for wall joints | 2.400 | 1650 | 3960 | 1.1100 | 0.1710 | 4395.60 | 677.16 |
1.5 | Concrete mix at 350 Kg/m3 for footing, ground beam, substructure column | 5.840 | 2400 | 14016 | 1.0250 | 0.1505 | 14366.40 | 2109.41 |
1.6 | Timber for formwork(hardwood unspecified) | 0.900 | 90 | 81.00 | 10.0000 | 0.7100 | 810.00 | 57.51 |
1.7 | Concrete slab mix at 300 Kg/m3 of thickness = 10 cm | 11.500 | 2400 | 27600 | 0.91 | 0.1310 | 25116.00 | 3615.60 |
1.8 | Substrate made of gravel and crushed rocks of thickness = 20 cm | 0.240 | 2240 | 537.60 | 0.0830 | 0.0048 | 44.62 | 2.58 |
1.9 | S and of thickness 5 cm | 0.060 | 2240 | 134.40 | 0.0810 | 0.0048 | 10.89 | 0.65 |
Sub-total | 97, 114.99 | 13, 861.97 | ||||||
II | ELEVATIONS | |||||||
2.1 | Cement blocks for walls(Cement-s and mix ratio 1:3) | 38.000 | 1900 | 72200 | 1.3300 | 0.2080 | 96026.00 | 15017.60 |
2.2 | Wall joint mortar(Cement-s and ration 1:4) | 6.100 | 1650 | 10065 | 1.1100 | 0.1710 | 11172.15 | 1721.12 |
2.3 | Concrete mix at 350 Kg/m3 for super-structural beams and columns | 4.000 | 2400 | 9600 | 1.0250 | 0.1505 | 9840.00 | 1444.80 |
2.4 | Timber for formwork(hardwood unspecified) | 1.700 | 90 | 153 | 10.0000 | 0.7100 | 1530.00 | 108.63 |
2.5 | Mortar for wall plastering | 4.300 | 1900 | 8170 | 1.3300 | 0.2080 | 10866.10 | 1699.36 |
Sub-total | 129, 434.25 | 19, 991.51 | ||||||
III | CARPENTRY AND ROOFWORK | |||||||
3.1 | Timber joist of dimension 3 cm x 15 cm(sawn hardwood) | 1.050 | 90 | 94.50 | 10.4000 | 0.8900 | 982.80 | 84.11 |
3.2 | Roof battens of dimension 8 cm x 8 cm(sawn hardwood) | 0.350 | 90 | 31.50 | 10.4000 | 0.8900 | 327.60 | 28.04 |
3.3 | Aluminium roof covering | 0.080 | 2700 | 216 | 155.0000 | 8.2400 | 33480.00 | 1779.84 |
3.4 | Ceiling plywood | 0.480 | 540 | 259.20 | 15.0000 | 1.0700 | 3888.00 | 277.34 |
3.5 | Aluminium ridge board | 0.003 | 2700 | 8.10 | 155.0000 | 8.2400 | 1255.50 | 66.74 |
3.6 | Wooden fascia(sawn hardwood) | 0.026 | 700 | 18.20 | 10.4 | 0.89 | 189.28 | 16.20 |
3.7 | Aluminium on fascia | 0.007 | 2700 | 18.90 | 155.0000 | 8.2400 | 2929.50 | 155.74 |
Sub-total | 43, 052.68 | 2, 408.00 | ||||||
IV | ELECTRICITY | No data | ||||||
Sub-total | ||||||||
V | PLUMBING | No data | ||||||
Sub-total | ||||||||
VI | TILES AND PAINTINGS | |||||||
6.1 | Bathroom wall ceramic tiles of dimension 30 cm × 30 cm | 0.075 | 2000 | 150.00 | 10.0000 | 0.66 | 1500.00 | 99.00 |
6.2 | Bathroom tiles 15 cm × 15 cm | 0.015 | 1700 | 25.50 | 29.0000 | 1.5100 | 739.50 | 38.51 |
6.3 | Mortar for posing of tiles | 0.020 | 1900 | 38.00 | 1.3300 | 0.2080 | 50.54 | 7.90 |
Sub-total | 2, 290.04 | 145.41 | ||||||
VII | WOOD AND STEEL WORKS | |||||||
7.1 | Wooden door panels of thickness 4cm including frames | 0.220 | 700 | 154.00 | 10.4 | 0.89 | 1601.60 | 137.06 |
7.2 | Aluminium locks | 4.00 | 155.0000 | 8.2400 | 620.00 | 32.96 | ||
7.3 | Timber window including frames | 0.200 | 700 | 140 | 10.4 | 0.89 | 1456.00 | 124.60 |
7.4 | Glass louvers | 0.130 | 25 | 3.25 | 15.0000 | 0.8600 | 48.75 | 2.80 |
7.5 | Aluminium glass louvers' holders | 6.00 | 155.0000 | 8.24 | 930.00 | 49.44 | ||
7.6 | Steel window protectors | 0.100 | 7850 | 785 | 20.1 | 1.37 | 15778.50 | 1075.45 |
Sub-total | 20, 434.85 | 1, 422.31 | ||||||
Gr and -total | 292, 326.81 | 37, 829.19 |