Research article Topical Sections

An economic comparison of dedicated crops vs agricultural residues as feedstock for biogas of vehicle fuel quality

  • The vast majority of the biofuels presently used in the EU are so called first generation biofuels produced from crops. Concerns of food security, displacement of food crop production and indirect land use change (iLUC) has led to the introduction of measures to reduce the use of first generations biofuels and promote so called advanced biofuels based on feedstock that does not compete with food/feed crops, such as waste and agricultural residues. In Sweden, 60% of the biofuel consumption is already based on waste/residual feedstock, and a unique feature of the Swedish biofuel supply is the relatively large use of biogas for transport, representing 9% of the current use of biofuels. The use of waste/residues dominates the biogas production, but agricultural residues, representing a large domestic feedstock potential, are barely used at present. This could indicate that biofuels from such feedstock is non-competitive compared both to fossil fuels and to biofuels produced from crops and waste under existing policy framework. This study show that without subsidies, the production cost of biogas as biofuel from all non-food feedstocks investigated (grass, crop residues and manure) is higher than from food crops. A shift from food crops to residues, as desired according to EU directives, would thus require additional policy instruments favoring advanced biofuel feedstock. Investment or production subsidies must however be substantial in order for biogas from residues to be competitive with biogas from crops.

    Citation: Mikael Lantz, Emma Kreuger, Lovisa Björnsson. An economic comparison of dedicated crops vs agricultural residues as feedstock for biogas of vehicle fuel quality[J]. AIMS Energy, 2017, 5(5): 838-863. doi: 10.3934/energy.2017.5.838

    Related Papers:

    [1] Yong X. Gan . A review of electrohydrodynamic casting energy conversion polymer composites. AIMS Materials Science, 2018, 5(2): 206-225. doi: 10.3934/matersci.2018.2.206
    [2] Qinghua Qin . Applications of piezoelectric and biomedical metamaterials: A review. AIMS Materials Science, 2025, 12(3): 562-609. doi: 10.3934/matersci.2025025
    [3] Elena Kossovich . Theoretical study of chitosan-graphene and other chitosan-based nanocomposites stability. AIMS Materials Science, 2017, 4(2): 317-327. doi: 10.3934/matersci.2017.2.317
    [4] Timothy K. Mulenga, Albert U. Ude, Chinnasamy Vivekanandhan . Concise review on the mechanical characteristics of hybrid natural fibres with filler content. AIMS Materials Science, 2020, 7(5): 650-664. doi: 10.3934/matersci.2020.5.650
    [5] Ruby Maria Syriac, A.B. Bhasi, Y.V.K.S Rao . A review on characteristics and recent advances in piezoelectric thermoset composites. AIMS Materials Science, 2020, 7(6): 772-787. doi: 10.3934/matersci.2020.6.772
    [6] Saeid Saberi, Azam Abdollahi, Fawad Inam . Reliability analysis of bistable composite laminates. AIMS Materials Science, 2021, 8(1): 29-41. doi: 10.3934/matersci.2021003
    [7] Christian M Julien, Alain Mauger, Ashraf E Abdel-Ghany, Ahmed M Hashem, Karim Zaghib . Smart materials for energy storage in Li-ion batteries. AIMS Materials Science, 2016, 3(1): 137-148. doi: 10.3934/matersci.2016.1.137
    [8] Nikolaos D. Papadopoulos, Polyxeni Vourna, Pinelopi P. Falara, Panagiota Koutsaftiki, Sotirios Xafakis . Dual-function coatings to protect absorbent surfaces from fouling. AIMS Materials Science, 2023, 10(6): 981-1003. doi: 10.3934/matersci.2023053
    [9] Erkan Oterkus, Selda Oterkus . Recent advances in peridynamic theory: A review. AIMS Materials Science, 2024, 11(3): 515-546. doi: 10.3934/matersci.2024026
    [10] Loh Guan Hong, Nor Yuliana Yuhana, Engku Zaharah Engku Zawawi . Review of bioplastics as food packaging materials. AIMS Materials Science, 2021, 8(2): 166-184. doi: 10.3934/matersci.2021012
  • The vast majority of the biofuels presently used in the EU are so called first generation biofuels produced from crops. Concerns of food security, displacement of food crop production and indirect land use change (iLUC) has led to the introduction of measures to reduce the use of first generations biofuels and promote so called advanced biofuels based on feedstock that does not compete with food/feed crops, such as waste and agricultural residues. In Sweden, 60% of the biofuel consumption is already based on waste/residual feedstock, and a unique feature of the Swedish biofuel supply is the relatively large use of biogas for transport, representing 9% of the current use of biofuels. The use of waste/residues dominates the biogas production, but agricultural residues, representing a large domestic feedstock potential, are barely used at present. This could indicate that biofuels from such feedstock is non-competitive compared both to fossil fuels and to biofuels produced from crops and waste under existing policy framework. This study show that without subsidies, the production cost of biogas as biofuel from all non-food feedstocks investigated (grass, crop residues and manure) is higher than from food crops. A shift from food crops to residues, as desired according to EU directives, would thus require additional policy instruments favoring advanced biofuel feedstock. Investment or production subsidies must however be substantial in order for biogas from residues to be competitive with biogas from crops.


    1. Introduction

    Regardless of long service life of civil engineering infrastructures, they cannot be considered as maintenance-free. These engineering structures are the most expensive investments and assets of any nation. Worldwide incidents of tragic failures of civil infrastructures remind that suitable measures are required to avoid sudden collapse of civil structures and associated loss of money and lives. Concrete is the most extensively used material in civil engineering structures. Due to some inherent drawbacks of concrete, these structures weaken with time. The weakening and failure of concrete structures occur mainly due to ageing of materials, aggressive environmental conditions, prolonged usage, overloading, difficulties involved in proper inspection methods, and lack of maintenance [1,2]. Within the microstructure of concrete, it contains numerous cracks in nano-scale. These cracks are formed during manufacturing or use. With time, nano-cracks join to form micro-cracks, which in turn, leads to formation of macro-cracks and failure of structures [3]. Through early detection of these inherent damages, sudden collapse and accidents can be avoided. Timely detection of damages and proper maintenance can greatly enhance the service life of concrete structures.

    The process of monitoring of deformation and damage that occur within civil engineering structures is commonly known as Structural Health Monitoring (SHM) [1,2,4,5,6]. SHM is highly essential for important civil structures such as nuclear power plants, dams, bridges, high-rise buildings, and power utilities. An active monitoring system can, in real time and online, recognize different defects and monitor damage, strain, and temperatures so that the optimal maintenance of the structures can be undertaken to provide enough safety and life span [1,2]. In general, a typical SHM system consists of three major components: a sensor system, a data processing system (containing data procuring, storage and transmission systems), and an evaluation system (comprising information management and diagnostic algorithms). The primary step to set-up an SHM system is to use stable and reliable sensing tools or sensors) [1,2]. Different sensors such as fibre optic sensors, piezoelectrics, magnetostrictive sensors, self-sensing composite materials, etc. possess capabilities of sensing various physical and chemical parameters related to the health of civil structures [7,8,9,10,11].

    2. SHM Systems for Civil Engineering Structures

    2.1. Fibre Optic Sensors

    Fibre optic sensors (FOS) are suitable for health monitoring of civil structure due to several reasons such as (a) due to their small size, they can easily embedded within civil structures without affecting their performance, (b) distributed sensing technology can be used to monitor civil structures at various locations, (c) electromagnetic interference does not have any effect on the sensing behavior, (d) can be used to monitor various parameters such as strain, displacement, vibration, cracks, corrosion, and chloride ion concentration, etc. [1,2,4]. However, optical fibres may be fragile and should be encapsulated within a protective material and also it is quite difficult to repair the damages. Many attempts have been made to incorporate FOS in pavements, bridges and buildings and field trials have been taken [12,13]. Figure 1 shows the use of Fibre Bragg Grating (FBG) sensor for monitoring of pavements [13].

    Figure 1. Schematic design of fine aggregate asphalt mixture encapsulated fibre optic sensor (a), real picture (b), and strain sensing capability (c).

    2.2. Piezoelectric Sensors

    Piezoelectric sensors also offer a number of advantages and are suitable for SHM of civil engineering structures. The advantageous features include their variety of sizes and possibility to incorporate them in very remote and inaccessible locations [1]. They can also be used to harvest energy from pavements due to the movement of the vehicles and generated pressure.

    2.3. Self-sensing Composites

    Self-diagnosing or self-sensing is the property by which a material can sense its own conditions such as stress, strain, damage, temperature, and so on. A self-sensing composite has the ability to sense its own deformation and damage and this ability makes them an excellent material for health monitoring of civil engineering structures. Strain and damage sensing in a composite material is usually achieved through detecting change in their electrical resistivity, i.e. self-sensing composite works based on piezoresistivity principle. One major advantage with the self-sensing composites is the possibility to achieve sensing as well as strengthening of civil structures simultaneously.

    To achieve piezo-resistivity in a composite material, it should contain a conducting element. Different types of conducting components have been used in the existing self-sensing composite materials. Short and continuous carbon fibres (CFs), carbon particles as well as carbon nanomaterials such as carbon nanofibers (CNFs) and nanotubes (CNTs) have been utilized for this purpose [14,15,16,17], as shown in Figure 2.

    Figure 2. Different electrically conducting elements used for fabricating self-sensing composites: (a) short carbon fibre, (b) carbon yarn, (c) carbon fabric and (d) carbon nanotube.

    These conducting components form a conducting electrical network within the composites. When the composites are subjected to deformation or damage, this conducting network is disturbed leading to a change in the electrical resistivity. Theconducting network and resulting change in resistivity are highly dependent on the type of conducting component, their amount as well as their distribution. One of the biggest advantages of self-sensing composites is their design flexibility. The type of response can be tailored easily through proper designing of the composite structure. As mentioned earlier, in civil infrastructures, composites are already in use as strengthening material. Therefore, these composites can also be designed as self-sensing so that they can perform both strengthening and health monitoring functions. This eliminates the need for incorporating sensors from outside for health monitoring of structures. Self-sensing composites can also be based on cementitious materials. Conducting fibres can be introduced directly within the structural elements to obtain the sensing behaviour. Alternatively, sensing composites can be developed by introducing conducting fibres or nanomaterials within polymers or cementitious materials and these sensors can be subsequently introduced within structural elements to perform health monitoring. Different types of self-sensing composites used for the health monitoring of civil structural elements are presented in Figure 3.

    Figure 3. Different types of self-sensing composites for health monitoring of civil structures.

    2.4. Characterization of Self-sensing Behaviour

    Usually, self-sensing performance of composite materials is quantified by measuring the fractional change in electrical resistance, which is expressed as follows [18]:

    @Fractional \, change \, in \, resistance\left( {FCR} \right) = \frac{{({\rm{R}} - {R_{0)}}}}{{{R_0}}}@ (1)

    Where R0 and R are the initial and final electrical resistances. Also, gauge factor is another frequently used parameter to quantify the self-sensing behavior of composites. Using the mechanical strain (@\varepsilon @), gauge factor can be calculated as follows [18]:

    @Gauge \, Factor\left( {GF} \right) = \frac{{FCR}}{\varepsilon }*100@ (2)

    Electrical resistance in a self-sensing composite can be measured in different ways as shown in Figure 4 [19].

    Figure 4. Different methods of electrical resistance measurement in self-sensing laminates.

    If the current contacts are on the same surface in the plane of the composite (Figure 4a), the current penetration is in the surface region only. When the current contacts are on the opposite surfaces in the plane of composites, but not located directly opposite to each other, the current penetration can be oblique, as shown in Figure 4b. When the current contacts are on the edge of the composites or located in the holes that goes through the thickness of the composites, the current penetrates through the entire cross-section of the composites, as presented in Figure 4c and 4d. When resistance is measured in the plane of the composites in the direction parallel to the fibres, it represents mainly the fiber breakage. On the other hand, when the resistance is measured through the thickness direction it represents the delamination damage. The oblique resistance, however, represents both of these damages and therefore, oblique resistance measurement is the most suitable method of detecting damage in composites.

    3. Carbon Short Fibre Based Self-Sensing

    Carbon short fibres (CSF) are used as admixtures in cement mixture to improve mechanical performance and incorporate functionalities in to the cementitious materials. When dispersed within cement, short carbon fibres introduce piezo-resistivity and self-sensing property. A self-sensing CSF based cementitious composite has been reported by Wen and Chung [20]. CSFs (0.5 wt.%) with diameter and length of 15 µm and 5 mm were dispersed within Portland cement using a rotary mixer. The self-sensing behaviour of the produced samples was tested under cyclic compressive stress in both longitudinal and transverse directions. The test results demonstrated the damage sensing capability of CSF dispersed cement samples. The damage was sensed by the irreversible increase in resistivity of the specimens under compression. An irreversible increase in both longitudinal and transverse resistivity (Figure 5a and 5b) occurred due to major damages such as breakage of fibres

    Figure 5. Variation of the fractional change in resistivity (thick curve) with time and strain (thin curve) with time during uniaxial compression at progressively increasing stress amplitudes: (a) longitudinal resistivity and (b) transverse resistivity.

    that bridged the micro-cracks. The major damage was sensed by the irreversible increase in the specimen resistivity in the range of 10 to 30%. On the contrary, smaller change in resistivity ranging from 1 to 7% indicated smaller damages in the structure.

    Similarly, Wang et al. [21] developed an innovative CSF (5 mm) reinforced concrete beam for sensing of fatigue damage. In this reinforcedconcrete (RC) beam (shown schematically in Figure 6), CSF reinforced concrete (CFRC) was used as a layer for both self-sensing and strengthening purpose.

    Figure 6. Schematic diagram of CFRC strengthened RC elements.

    The trend of fractional resistance change with load under monotonic flexural loading for this type of CFRC is shown in Figure 7. At lower loads, the inherent flaws in the specimens slowly merged to develop new micro-cracks which continued to expand in a stable manner with increasing load. Consequently, due to disturbance in the conducting network, the electrical resistance also continued to increase in a stable manner due to these minor damages. However, when the load increased considerably to the failure load of the specimens, due to the formation of continuous cracks fractional resistance change increased sharply. Under cyclic loading, when the stress amplitude was lower (80% of first cracking stress), only slight damage occurred in the beam resulting in only 7% increase in the fractional resistance change in 50 cycles. On the other hand, when a stress amplitude of 80% of the ultimate stress was applied, the fractional resistance increased irreversibly with the loading cycles, reaching 179% during failure of the beam in 38 cycles, as shown in Figure 8. The irreversibly increased electrical resistance, which is called the “residual resistance”, increased with fatigue damage and therefore, can be a useful parameter to monitor fatigue damage in the RC beams. Damage monitoring in concrete structure using CSF has also been reported by Chen and Liu [22].

    Figure 7. Variation of fractional change in resistance with load of CFRC reinforced beams with different CFRC layer thickness: 30 mm (J-2), 60 mm (J3) and 90 mm (J4).
    Figure 8. Fractional change in resistance during cyclic flexural loading at last 5 cycles.

    Recently, short carbon fibre reinforced polymeric composites have been developed for health monitoring of civil engineering elements [23]. For this purpose, short carbon fibres (1 mm and 3 mm lengths) at various weight % (0.5, 0.75, and 1.25) were dispersed in an unsaturated polyester resin through mechanical stirring. After curing, the short fibre dispersed composites showed excellent strain sensing behaviour, as shown in Figure 9. Chopped fibres with different lengths exhibited similar strain sensitivity which, however, enhanced with the decrease in their concentrations (0.5%). Gauge factor as high as about 36 was obtained with the optimized composites. Therefore, these short fibre dispersed composites can have good potential for strain sensing of civil engineering structures.

    Figure 9. Variation of fractional resistivity of CSF dispersed polyester matrix composites with compressive strain at differentconcentrations: (a) 0.5%, (b) 0.75% and (c) 1.25%.

    4. Continuous Carbon Fibre Based Self-Sensing

    4.1. Carbon Fibre Reinforced Polymeric Composites

    Polymeric composites of carbon fibres have been investigated extensively for strengthening as well as self-sensing of structural elements. Differentarrangements of carbon fibres such as unidirectional tows and textile fabrics (either woven or knitted) have been used for this purpose. Moreover, hybrid composites of carbon and other fibres (such as glass, aramid, etc.) have also been developed to improve the sensing as well as strengthening capability.

    Carbon fibre reinforced polymer composites exhibit piezoresistive behaviour under different types of loading [24]. Unidirectional carbon fibre reinforced epoxy composites have been found to sense their own strain in the fibre direction. Upon tensile loading, the longitudinal electrical resistance decreases reversibly with strain and transverse electrical resistance increases [25]. The reason behind the change in electrical resistance with tensile strain is the change in electrical contacts due to change in the fibre alignment. Under tensile loading, the fibres become more aligned in the loading direction leading to increase in the electrical contacts and decrease in resistance. The alignment of the fibres in longitudinal direction, however, decreaseselectrical contacts in the transverse direction and consequently, increases the transverse electrical resistance. This type of continuous carbon fibre composites could provide gauge factor from −35.7 to −37.6 and from +34.2 to +48.7 in the longitudinal and transverse direction, respectively. Therefore, they can be highly useful for sensing application in civil engineering structures. Carbon fibre reinforced plastic laminates were also found effective in sensing delamination, cracks and different types of damages occurred within the composite structures [26,27,28,29,30].

    However, continuous carbon fibre/epoxy composites have low ductility. Nowadays, civil engineers are looking for light weight ductile reinforcements which can replace steel to avoid its corrosion and other problems. For this purpose, hybrid composites have been designed and developed with tailorable mechanical properties. Hybrid composites can exhibit higher breaking strains and ductility due to the so called “pseudo-ductile” behaviour [31]. Therefore, hybrid composites of carbon with other fibres have been investigated for their strengthening as well as self-sensing behaviour [31,32].

    Currently existing hybrid composites exhibited continuous strain monitoring capability and can also be used to generate alarm signal well before the breakage of the composites. Both pseudo-ductility and self-sensing behaviours are highly dependent on the properties of the constituent fibres, their proportion and arrangement within the composites. Properly designed carbon fibre-glass fibre (CF-GF) hybrid composites were found to generate vital alarm signal representing the damage occurred in their structure [33,34]. These composites were designed by incorporating an internal carbon fibre core wrapped externally by glass fibre bundle, as shown in Figure 10. These composites showed reliable sensing capability under both monotonic and cyclic loading conditions. A sharp rise in the electrical resistance during the breakage of carbon fibres can be considered as the alarm signal, as shown in Figure 11. It was observed that the load at which the alarm signal was obtained could be designed by changing the relative proportion of carbon and glass fibres. At higher carbon fibre (CF-2.4%, GF-49%), the alarm signal was obtained almost at the breaking load of composites and therefore, these composites were not able to produce warning signal well before the breakage of composites. However, when the carbon fibre was used at lower quantity (CF-0.6%, GF-48% or CF-0.2%, GF-48%), the sharp rise in the electrical resistance was achieved at much lower load than the breaking load, as shown in Figure 11. Therefore, these self-sensing composites can be a suitable candidate for health monitoring of civil engineering structures.

    Figure 10. Structure of CF-GF hybrid composite rods for self-sensing: (a) schematic diagram (b) morphology.

    Figure 11. Variation of fractionalresistance (dotted line) and stress (solid line) during monotonic tensile testing of CF-GF hybrid rods with CF-0.2%, GF-48%.

    However, one of the major drawbacks of CF-GF self-sensing composites is their inability to detect early stage of damage. The change in electrical resistance at low strain (below 0.6%) was found to be only 1%. Good strain sensitivity at low strain through measurement of residual resistance could be obtained for CF-GF composites only in pre-stressed conditions [35]. To overcome this limitation, an innovative composite material with excellent low strain sensitivity has been developed. In this type of hybrid composites, carbon particles were used instead of carbon fibre in combination with glass fibres [36]. Figure 12 shows the schematic of these composites. A fractional change in resistance of 6.2% was obtained at 0.6% strain and therefore, these composites are able to detect early stage of damage. The higher resistance change achieved in this case was attributed to the significant change in the conducting network formed by the carbon particles even at low strain level.

    Figure 12. Schematic of CP-GF hybrid self-sensing rods.

    Improved strain sensitivity at low strain was also obtained with recently developed carbon fibre reinforced braided composite rods (BCRs) [37,38]. In BCRs, carbon fibres were axially introduced and over-braided using polyester filaments. Carbon fibres were impregnated with a polymeric resin before introducing to the braiding process and the produced structures were cured subsequently to produce the composite rods (Figure 13). The uniqueness of this technique is that the braiding of polyester yarns introduces certain degree of misalignment to the axial carbon fibres. Therefore, the change in the alignment of axial fibres under loading conditions and the resulting change in the electrical contacts lead to substantial change in electrical resistance even at low strain levels. The extent of misalignment introduced in the carbon fibres can be controlled by adjusting the braiding process parameters (such as speed, tension, etc.). Similar to the other sensing composites, strain sensitivity was found better with lower carbon fibre % and the best self-sensing BCR provided a gauge factor of 23.4 at a flexural strain of 0.55%.

    Figure 13. Braided surface and cross-section of BCR.

    4.2. Continuous Carbon Fibre Reinforced Concrete Structures

    Recently, self-sensing concretes have been developed to detect their own strain and damage using continuous carbon fibre based materials. Smart concretes incorporating carbon fibre textiles (Figure 14a) showed the capability to effectively monitor their strain [18]. Very good correlation was observed between the readings obtained from the textile sensor and conventional strain gauges, as shown in Figure 14b. The difference between the two readings was lower than 5%. The carbon textile based smart concretes provided gauge factor of around 10 and therefore, these smart materials can be advantageously utilized in the construction of self-sensing civil engineering structures.

    Figure 14. (a) carbon fibre textile for developing smart concrete and (b) correlation between the readings obtained from textile sensor and strain gauges.

    Hybrid carbon/glass fabric reinforced concrete beams are also able to detect strain and monitor its interaction with a wet environment [39]. An electromechanical sensing with a gauge factor in the order of 1 can be obtained. These smart concrete beams also show detectable correlation between electrical resistance with the load, displacement and strain responses. The wet environment can also be detected by a fractional resistance change in the order of 10−5, which can be detected effectively using the Wheatstone bridge principle.

    5. Carbon Nano materials Based Self-Sensing

    CNF and CNT are nanostructures made of carbon atoms. CNF comprises of graphene layers arranged as stacks of cones, plates or cups to create cylindrical nanostructures, whereas CNT comprises of graphene layers wrapped into perfect cylinders. These nanostructures possess outstanding mechanical properties and excellent electrical and thermal conductivities [40]. These characteristics of carbon nanomaterials make them attractive engineering materials for construction applications.

    Carbon nano materials can be used for strengthening as well as sensing in construction applications. These nanostructures form conducting networks within the matrix at nanoscale and any change in this network at nano or micro-scale leads to change in the electrical resistivity of the matrix. Consequently, CNT or CNF reinforced composites are able to detect nano and micro-scale damages present in their structure. In addition, changes in the electrical network at very low strain enables the detection of micro strains by these nanostructures.

    Different types of mechanical sensors have been developed until today using CNT/CNF or other carbon nano particle (CnP) based composite materials for sensing stress, strain, pressure, and so on. Among them, a few have been demonstrated for civil engineering applications. Nanni et al. [41] developed hybrid self-sensing composite rods consisting of internal conductive core surrounded by an external insulating skin (Figure 15). The conductive core was made of glass fibres impregnated with CnP/epoxy mixture. This sensing part was shielded by an outer GFRP skin, both to increase mechanical performance and to assure electric isolation. The used CnPs were spherical in shape with an average diameter of 30 nm and 5% CnP was used to produce the hybrid composites with good electrical conductivity. Concrete elements incorporating these hybrid rods exhibited good sensing behaviour, as shown in Figure 16a. The discontinuity in the resistance variation curve at points

    Figure 15. CnP based hybrid composite rods: transverse section scheme, SEM micrograph, and longitudinal view.
    Figure 16. Self-monitoring performance (a) and cracking pattern (b) of concrete specimens.

    1, 2 and 3 represented various changes occurring in the concrete specimens such as initial concrete cracking (point 1), formation and propagation of additional cracks (point 2) and severe cracking and failure of concrete specimens (point 3), as shown in Figure 16b.

    Self-sensing ability of CNF reinforced concrete has also been reported [42]. Under compressive strain, electrical resistance variation up to 80% wasobtained using the most conducting nanofibers at 1 vol.% concentration. The strain monitoring capability of CNF/concrete specimens was observed to be highly dependent on the type, conductivity and concentration of CNF and optimum conditions resulted in strain sensitivity suitable for practical applications.

    Under reverse cyclic loading also, CNF reinforced concrete showed good strain and damage sensing ability [43]. At smaller strains, the peaks and valleys in the electrical resistance of CNF reinforced concrete matched well with that of the applied force and the strain in the concrete, as shown in Figure 17. However, when the strain became high and the specimen was severely damaged, no correlation was observed between the electrical resistance and strain/force and electrical resistance increased quite irreversibly. This change in electrical resistance pattern indicated the occurrence of damage in the concrete specimens.

    Figure 17. Electrical resistance variation with force and strain of CNF reinforced concrete.

    Hybrid cement composites of CNTs and carbon fibres (Figure 18) also exhibit good sensing behaviour [44]. In these composites, 1 vol% of multi-walled CNTs was used in combination with 15 vol% of carbon fibres. Under cyclic compressive loading, the changes in electrical resistance could mimic both the changes in load and strain with high reliability. However, the response was nonlinear and rate dependant. Nevertheless, for a particular loading rate, the strain in the developed materials could be predicted from the fractional change in resistivity using a non-linear calibration curve. As compared to only carbon fibre sensor, hybrid sensor exhibited better results with good repeatability. This can be observed from the lower scatter of FCR values in case of hybrid sensors than the carbon fibre sensor, as presented in Figure 19.

    Figure 18. CF-CNF hybrid cement composite (a), CF distribution within cement (b) and MWCNT within the cement hydration product (c).

    Figure 19. Change of FCR with load for CF concrete sensor (left) and hybrid concrete sensor (right).

    Recently, CNT/cement composite sensors were developed and demonstrated their application in pavement monitoring [45]. For this purpose, carboxyl functionalized CNTs were dispersed within cement using ultrasonication process with help of a surfactant (sodium dodecyl benzene sulfonate). At 0.1% MWNT concentration, very good sensing behaviour was achieved, as shown in Figure 20. These CNT sensors were installed in the road for testing the pavement monitoring capability (Figure 21). Figure 22 shows the response of pre-cast and cast-in-place CNT sensors while a truck passes over the road and compares the response with that obtained in case of strain gauges. It can be observed that an abrupt change in voltage occurs when a wheel passes over the road and each wheel represents one signal peak. As compared to the signals obtained with the strain gauges, the CNT/cement sensors showed higher detection accuracy, as some signals were missed in case of the strain gauges.

    Figure 20. Sensing performance of CNT/cement sensor.

    Figure 21. Installation of CNT/cement sensor in roads for testing monitoring capability.
    Figure 22. Response of cast-in-place and pre-cast CNT sensors (a, b) and strain gauges (c and d), while a truck passes over the road.

    Self-sensing hybrid polymeric composites have also been developed using CNT. These composites are commonly known as multi-scale composites as they are fabricated combining macro and nano scale reinforcements [46,47,48,49]. The conventional macro-scale reinforcements (such as glass, carbon, etc.) are used for the strengthening purpose, whereas CNTs are incorporated to achieve sensing behaviour. In addition, CNTs can also improve mechanical properties of these composites. As compared to other hybrid composites, one major advantage with CNT based multi-scale composites is that they can detect micro-scale damages in the composite structure [50]. This is possible as damages even in nano and micro-scales can alter the conducting network of CNTs, resulting in change of resistivity of the composites. Figure 23 shows the sensing behaviour of a CNT based multi-scale braided composites.

    Figure 23. Change of electrical resistance with strain in multi-scale 3D braided composites.

    It can be observed that the change in the slope of the resistance curve represents different types of damages in the composites. The microscale damages such as transverse cracks or micro delamination starts at stage 2 and accumulates in stage 3, resulting in considerable increase in the electrical resistance change. In stage 4, the saturation of micro-damages occurs and they close due to Poisson’s contraction and jamming of yarns in stage 5.

    Braided composites using continuous CNT yarns have also been developed for developing self-monitoring systems [51]. These advanced braided composites have huge potential for application in structural applications. Braided composites present tailorable mechanical properties and surface characteristics and have been demonstrated as very good strengthening materials of concrete or masonry structures [52,53,54,55]. Therefore, sensing braided composites can be extensively utilized for both strengthening and health monitoring of civil engineering structures.

    6. Conclusions

    In this paper, an overview of carbon composites developed for health monitoring of civil engineering structures is presented. Carbon materials in different forms such as short fibre, particle, tows, fabrics and nanomaterials have been extensively studied for developing health monitoring systems. They have been either directly incorporated within cementitious materials for developing smart concrete or have been incorporated within polymers to fabricate self-sensing composites. Self-sensing polymeric composites can be advantageously utilized for strengthening as well as health monitoring of civil structures. Hybrid composites of carbon with other fibres offer the possibility of achieving higher ductility and generating alarm signal well before the composite’s failure. Therefore, they are useful to avoid sudden collapse of structures. However, they are not capable of detecting early stage damages in the structures. The low strain sensitivity of carbon composites can be greatly enhanced by using carbon nanoparticles, nanofibers or nanotubes. Formation of conducting networks at nano-scale offer them the possibility to detect very low strain and micro-damages in the structures. Smart concretes incorporating carbon nanomaterials also exhibit very good sensing performance. However, although carbon based self-sensing materials offer huge possibility to develop effective health monitoring systems, there exists a few critical issues which need to be solved in near future. More research and developments are required to develop self-sensing systems which can identify the location of damage. Although carbon nanomaterials based self-sensing materials offer better sensing performance, they are expensive and have processing difficulties. Enough information is also not available in the existing literature on the effect of environmental and usage conditions on the self-sensing performance of the developed composites. Therefore, for practical application of carbon based SHM systems, further research work is extremely essential for overcoming the practical problems in implementing these systems and reducing the cost and improving affordability of these materials.

    Conflict of Interest

    Authors declare that there is not conflict of interest.

    [1] EU (2009) DIRECTIVE 2009/28/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European Union.
    [2] Eurostat (2017) Energy from renewable sources. Available from: http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_from_renewable_sources.
    [3] EurObservÉR (2016) Biofuels Barometer 2016. EurObservÉR.
    [4] Fritsche UR, Wiegmann K (2011) Indirect land use change and biofuels. Committee on Environment, Public Health and Food Safety, European Parliament, Brussels.
    [5] EU (2015) DIRECTIVE (EU) 2015/1513 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 9 September 2015 amending Directive 98/70/EC relating to the quality of petrol and diesel fuels and amending Directive 2009/28/EC on the promotion of the use of energy from renewable sources. Official Journal of the European Union.
    [6] SEA (2016) Transportsektorns energianvändning 2015 [Use of energy in the transportation sector 2015]. The Swedish Energy Agency. ES 2016: 03.
    [7] SEA (2016) Produktion och användning av biogas och rötrester 2015 [Production and utilization of biogas and digestate 2015]. The Swedish Energy Agency.
    [8] EurObservÉR (2016) The state of renewable energies in Europe-Edition 2016, 16th EurObservÉR Report. EurObservÉR.
    [9] FNR (2016) Bioenergy in Germany-Facts and Figures 2016. Fachagentur Nachwachsende Rohstoffe e.V.
    [10] Grahn M, Hansson J (2015) Prospects for domestic biofuels for transport in Sweden 2030 based on current production and future plans. Wires Energy Environ 4: 290–306. doi: 10.1002/wene.138
    [11] STA (2016) Styrmedel och åtgärder för att minska transportsystemets utsläpp av växthusgaser–med fokus på transportinfrastrukturen [Policy instruments and measures to reduce emissions of GHG from the transport system-with focus on transport infrastructure]. The Swedish Transport Administration.
    [12] STA (2016) Åtgärder för att minska transportsektorns utsläpp av växthusgaser–ett regeringsuppdrag [Measures to reduce GHG emissions from the transport sector-a government assignment]. The Swedish Transport Administration.
    [13] SOU (2016) En klimat-och luftvårdsstrategi för Sverige [A climate and air pollution strategi] Swedish Government Official Report.
    [14] Gissén C, Prade T, Kreuger E, et al. (2014) Comparing energy crops for biogas production–yields, energy input and costs in cultivation using digestate and mineral fertilisation. Biomass Bioenerg 64: 199–210. doi: 10.1016/j.biombioe.2014.03.061
    [15] Börjesson P, Prade T, Lantz M, et al. (2015) Energy crop-based biogas as vehicle fuel: the impact of crop selection on energy efficiency and greenhouse gas performance. Energies 8: 6033–6058. doi: 10.3390/en8066033
    [16] FNR (2010) Biogas Messprogramm II-61 Biogasanlagen im Vergleich. Gulzow: Bundesministerium fur Ernährung, Landwirtshafts und Verbraucherschutz.
    [17] Hjort-Gregersen K (2015) Udvikling og effektivisering af biogasproduktionen i Danmark - Ökonomi, nögletal og benchmark, Energistyrelsens Biogas Taskforce. AgroTech.
    [18] Möller HB, Nielsen KJ (2015) Udvikling og effektivisering af biogasproduktionen i Danmark, Faglig rapport, Biogas Taskforce. Inst. for Ingeniörvidenskab, Aarhus Universitet.
    [19] SJV (2016) Rötning av animaliska biprodukter, 2016-10-24 Jordbruksverket.
    [20] Lantz M, Ekman A, Börjesson P (2009) Systemoptimerad produktion av fordonsgas, Report 69. Lund, Sweden: Department of Technology and Society, Lund University. 69. 110 p.
    [21] Petersson A, Wellinger A (2009) Biogas upgrading technologies–development and innovations. IEA Bioenergy Task 37, European Commission Joint Research Centre, Petten, The Netherlands.
    [22] IEA (2016) Biogas upgrading plant list, data up to the end of 2015. IEA Bioenergy Task 37, European Commission Joint Research Centre, Petten, The Netherlands.
    [23] Bauer F, Hultenberg C, Persson T, et al. (2013) Biogas upgrading–Review of commercial technologies. Malmö: Svenskt Gastekniskt Center. Rapport 2013: 270.
    [24] Hoyer K, Hultenberg C, Svensson M, et al. (2016) Biogas Upgrading–Technical Review. Energiforsk.
    [25] Urban W, Girod K, Lohmann H (2008) Technologien und Kosten der Biogasaufbereitung und Einspeisung in das Erdgasnetz. Ergebnisse der Markterhebung 2007–2008. Fraunhofer-Institut für Umwelt- Sicherheits- und Energietechnik, Oberhausen, Germany.
    [26] Berglund P, Bohman M, Svensson M, et al. (2012) Teknisk och ekonomisk utvärdering av lantbruksbaserad fordonsgasproduktion. Swedish Gas Technology Centre.
    [27] Colnerud GS, Gåverud H, Glimhall A (2010) Förändrade marknadsvillkor för biogasproduktion. The Energy Market Inspectorate.
    [28] Walla C, Schneeberger W (2008) The optimal size for biogas plants. Biomass Bioenerg 32: 551–557. doi: 10.1016/j.biombioe.2007.11.009
    [29] Börjesson P, Lantz M, Andersson J, et al. (2016) METHANE AS VEHICLE FUEL–A WELL-TO-WHEEL ANALYSIS (METDRIV), report 2016:06 f3 The Swedish Knowledge Centre for Renewable Transportation Fuels, Sweden.
    [30] Berglund M, Börjesson P (2006) Assesment of energy performance in the life-cycle of biogas production. Biomass Bioenerg 30: 254–266.
    [31] Brown J, Nizami AS, Thamsiriroj T, et al. (2011) Assessing the cost of biofuel production with increasing penetration of the transport fuel market: A case study of gaseous biomethane in Ireland. Renew Sust Energ Rev 15: 4537–4547. doi: 10.1016/j.rser.2011.07.098
    [32] Jury C, Benetto E, Koster D, et al. (2010) Life cycle assessment of biogas production by monofermentation of energy crops and injection into the natural gas grid. Biomass Bioenerg 34: 54–66.
    [33] FNR (2006) Handreichung Biogasgewinnung und nutzung. Fachagentur Nachwachsende Rohstoffe e.V., Bundesministerium für Ernährung, Landwirtschaft and Verbraucherschutz, Gülzow-Prüzen, German.
    [34] Murphy J, Braun R, Weiland P, et al. (2011) Biogas from Crop digestion. Available from: https://www.nachhaltigwirtschaften.at/resources/iea_pdf/reports/iea_bioenergy_task37_biogas_from_crop_digestion.pdf.
    [35] FNR (2005) Ergebnisse des Biogas-Messprogram. Fachagentur Nachwachsende Rohstoffe e.V., Bundesministerium für Ernährung, Landwirtschaft and Verbraucherschutz, Gülzow-Prüzen, Germany.
    [36] Lundqvist P (2009) Fjärrvärme för utökad biogasproduktion–Teknisk och ekonomisk utvärdering av fjärrvärme för uppvärmning av biogasprocesser. Thermal Engineering Research Institute, Stockholm, Sweden.
    [37] Norin E (2007) Alternativa hygieniseringsmetoder. Swedish Gas Technology Centre, Malmö, Sweden.
    [38] Rosenqvist H (2017) Kalkyler för energigrödor 2017-fastbränsle, biogas, spannmål och raps. Jordbruksverket.
    [39] Björnsson L, Prade T, Lantz M (2016) Grass for biogas-Arable land as carbon sink, Report 2016:280. Energiforsk.
    [40] Valin H, Peters D, Berg Mvd, et al. (2015) The land use change impact of biofuels consumed in the EU - Quantification of area and greenhouse gas impacts. ECOFYS.
    [41] Kreuger E, Prade T, Björnsson L, et al. (2014) Biogas från Skånsk betblast - potential, teknik och ekonomi [Biogas from beet tops in Scania-potential, technology and economy]. Environmental and Energy System Studies, Lund University.
    [42] SCB (2017) Price Indices and Prices in the Food Sector-Annual and Monthly Statistics-2017:06. Statistics Sweden. JO 49 SM 1708.
    [43] Energimyndigheten (2017) Trädbränsle- och torvpriser Nr 3 2017. Swedish Statistics.
    [44] Hansen MT (2015) Standardforutsaetninger-til VE til process-ansögningsmateriale. FORCE Technology.
    [45] Odhner PB, Svensson SE, Prade T (2015) Extruder för ökad biogasproduktion [Extrusion increases the production of biogas], Report 2015: 26 Sveriges lantbruksuniversitet, Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap.
    [46] FNR (2010) Guide for biogas-From production to use Nachwachsende Rohstoffe e.V., Bundesministerium für Ernährung, Landwirtschaft and Verbraucherschutz, Gülzow-Prüzen, Germany.
    [47] FNR (2010) Leitfaden Biogas–Von der Gewinnung zur Nutzung. Nachwachsende Rohstoffe e.V. Gülzow-Prüzen, Germany: Bundesministerium für Ernährung, Landwirtschaft and Verbraucherschutz.
    [48] FNR (2016) Leitfaden Biogas–Von der Gewinnung zur Nutzung. Nachwachsende Rohstoffe e.V. Gülzow-Prüzen, Germany: Bundesministerium für Ernährung, Landwirtschaft and Verbraucherschutz.
    [49] Lantz M, Björnsson L (2011) Biogas från gödsel och vall-analys av föreslagna styrmedel. The Federation of Swedish Farmers, Stockholm, Sweden: LRF.
    [50] Berglund P (2010) Biogödselhandbok–Biogödsel från storskaliga biogasanläggningar. Swedish Waste Management.
    [51] SEA (2017) Trädbränsle och torvpriser [Wood fuel and peat prices], Nr 1 2017. The Swedish Energy Agency.
    [52] SCB (2016) Priser på el för industrikunder [Electricity price for industries]. Available from: http://www.scb.se/hitta-statistik/statistik-efter-amne/energi/prisutvecklingen-inom-energiomradet/energipriser-pa-naturgas-och-el/pong/tabell-och-diagram/genomsnittspriser-per-halvar-2007/priser-pa-el-for-industrikunder-2007/, accessed 2017-05-23.
    [53] Smyth B, H S, Murphy J (2010) Can grass biomethane be an economically viable biofuel for the farmer and the consumer? Biofuel Bioprod Bio 4: 519–537. doi: 10.1002/bbb.238
    [54] Lantz M (2013) Biogas in Sweden-opportunities and challenges from a systems perspective. Lund, Sweden: Lund University.
    [55] SJV (2016) Rekommendationer för gödsling och kalkning 2017, Report JO16:24. The Swedish Board of Agriculture. Jönköping, Sweden.
    [56] Nutrients Fo (2011) Stallgödselkalkyl (Manure calculator) version 2011-03-31. accessed 2012-10-18.
    [57] Lantz M, Svensson M, Björnsson L, et al. (2007) The prospects for an expansion of biogas systems in Sweden–Incentives, barriers and potentials. Energy Policy 35: 1830–1843.
    [58] EC (2015) Statligt stöd-Skattebefrielser och skattenedsättningar för flytande drivmedel, C(2015) 9344. European Commission.
    [59] EC (2015) Statligt stöd-Skattebefrielser för biogas som används som motorbränsle, C(2015) 9345. European Commission.
    [60] Regeringen (2017) Reduktion av växthusgasutsläpp genom inblandning av biodrivmedel i bensin och dieselbränslen, Lagrådsremiss In: energidepartementet M-o, editor: The government of Sweden.
    [61] SJV (2017) Gödselgasstöd. Available from: http://www.jordbruksverket.se/amnesomraden/ stod/andrastod/godselgasstod.4.ac526c214a28250ac23333e.html, accessed 20170613.
    [62] SJV (2017) Investeringsstöd till biogas. Available from: http://www.jordbruksverket.se/amnesomraden/stod/stodilandsbygdsprogrammet/investeringar/biogas.4.6ae223614dda2c3dbc44f95.html, accessed.
    [63] EC (2014) COMMISSION REGULATION (EU) No 651/2014 of 17 June 2014 declaring certain categories of aid compatible with the internal market in application of Articles 107 and 108 of the Treaty In: COMMISSION TE, editor. Official Journal of the European Union.
    [64] SPBI (2017) Priser och skatter. Available from: http://spbi.se/statistik/priser/, accessed.
    [65] Karlsson S, Rodhe L (2002) Översyn av Statistiska Centralbyråns beräkning av ammoniakavgången i jordbruket–emissionsfaktorer för ammoniak vid lagring och spridning av stallgödsel.
    [66] Bengtsson B, Rasic Z (2005) Kadmium i odlingssystem med tillförsel av rötslam. Jord-skörd- och markvatten-analyser. Department of plant sciences, Swedish University of Agricultural Sciences, Alnarp, Sweden.
    [67] Roth U, Wulf S (2010) Gasausbeute in landwirtschaftlichen Biogasanlagen, KTBL-Heft 88. Kuratorium Fur Technik und Bauwesen in der Landwirtschaft e.V. (KTBL), Darmstadt, Germany.
    [68] ECN (2014) The database Phyllis2: Biomass and waste. Available from: https://www.ecn.nl/phyllis2, accessed 2017-04-03.
    [69] Tamaki Y, Mazza G (2010) Measurement of structural carbohydrates, lignins, and micro-components of straw and shives: Effects of extractives, particle size and crop species. Ind Crop Prod 31: 534–541. doi: 10.1016/j.indcrop.2010.02.004
    [70] Björnsson L, Castillo MdP, Gunnarsson C, et al. (2014) Förbehandling av lignocellulosarika råvaror för biogasproduktion-Nyckelaspekter vid jämförande utvärdering. Lund, Sweden: Environmental and Energy Systems Studies.
    [71] Moller HB, Sommer SG, Ahring B (2004) Methane productivity of manure, straw and solid fractions of manure. Biomass Bioenerg 26: 485–495. doi: 10.1016/j.biombioe.2003.08.008
    [72] Amon T, Amon B, Kryvoruchko V, et al. (2007) Biogas production from maize and dairy cattle manure-Influence of biomass composition on the methane yield. Agr Ecosyst Enviro 118: 173–182.
    [73] Mähnert P, Linke B (2009) Kinetic study of biogas production from energy crops and animal waste slurry: Effect of organic loading rate and reactor size. Environ Technol 30: 93–99.
    [74] McCarty PL (1964) Anaerobic waste treatment fundamentals, Part one, Chemistry and microbiology. Public Works 95: 107–112.
    [75] FNR (2010) Biogas-Messprogramm II, 61 Biogasanlagen im Vergleich. Fachagentur Nachwachsende Rohstoffe e.V., Bundesministerium für Ernährung, Landwirtschaft and Verbraucherschutz, Gülzow-Prüzen, Germany.
    [76] Ljung E, Palm O, Rodhe L (2013) Ökad acceptans för biogödsel inom lantbruket. Uppsala, Sweden: JTI-Institutet för jordbruks-och miljöteknik.
    [77] Procházka J, Dolejš P, Máca J, et al. (2012) Stability and inhibition of anaerobic processes caused by insufficiency or excess of ammonia nitrogen. Appl Microbiol Biot 93: 439–447. doi: 10.1007/s00253-011-3625-4
    [78] Schnürer A, Nordberg Å (2008) Ammonia, a selective agent for methane production by syntrophic acetate oxidation at mesophilic temperature. Water Sci Technol 57: 735–740.
    [79] Scherer P, Lippert H, Wolff G (1983) Composition of the major elements and trace elements of 10 methanogenic bacteria determined by inductively coupled plasma emission spectrometry Biol Trace Elem Res 5: 149–163.
    [80] Banks CJ, Zhang Y, Jiang Y, et al. (2012) Trace element required for stable food waste digestion at elevated ammonia concentrations. Bioresource Technol 104: 127–135. doi: 10.1016/j.biortech.2011.10.068
    [81] Overend R (1982) Haul distance and transportation work factors for biomass. Biomass 2: 75–79. doi: 10.1016/0144-4565(82)90008-7
    [82] Börjesson P, Gustavsson L (1996) Regional production and utilization of biomass in Sweden. Energy 21: 747–764. doi: 10.1016/0360-5442(96)00029-1
    [83] Björnsson L, Lantz M, Murto M, et al. (2011) Biogaspotential i Skåne-inventering och planeringsunderlag på översiktsnivå [The biogas potential in Scania-inventory and data for planning]. The County Administrative Board of Skåne.
  • This article has been cited by:

    1. Thanyarat Buasiri, Karin Habermehl-Cwirzen, Andrzej Cwirzen, State of the Art on Sensing Capability of Poorly or Nonconductive Matrixes with a Special Focus on Portland Cement–Based Materials, 2019, 31, 0899-1561, 03119003, 10.1061/(ASCE)MT.1943-5533.0002901
    2. A.K. Roopa, Anand M. Hunashyal, Pallavi Venkaraddiyavar, Sharanabasava V. Ganachari, Smart hybrid nano composite concrete embedded sensors for structural health monitoring, 2020, 27, 22147853, 603, 10.1016/j.matpr.2019.12.071
    3. Giulia D'Ambrogio, Omar Zahhaf, Yoann Hebrard, Minh Quyen Le, Pierre‐Jean Cottinet, Jean‐Fabien Capsal, Micro‐Structuration of Piezoelectric Composites Using Dielectrophoresis: Toward Application in Condition Monitoring of Bearings, 2021, 23, 1438-1656, 2000773, 10.1002/adem.202000773
    4. Xiaoying Cheng, Hongshui Zhou, Zhenyu Wu, Xudong Hu, An investigation into self-sensing property of hat-shaped 3D orthogonal woven composite under bending test, 2019, 38, 0731-6844, 149, 10.1177/0731684418808093
    5. Nazrul Islam Khan, Sudipta Halder, Jialai Wang, Diels-Alder based epoxy matrix and interfacial healing of bismaleimide grafted GNP infused hybrid nanocomposites, 2019, 74, 01429418, 138, 10.1016/j.polymertesting.2018.12.021
    6. Enrique García-Macías, Filippo Ubertini, Seismic interferometry for earthquake-induced damage identification in historic masonry towers, 2019, 132, 08883270, 380, 10.1016/j.ymssp.2019.06.037
    7. D.D.L. Chung, Self-sensing concrete: from resistance-based sensing to capacitance-based sensing, 2021, 12, 1947-5411, 1, 10.1080/19475411.2020.1843560
    8. Min Kim, Dong Kim, Electromechanical Response of High-Performance Fiber-Reinforced Cementitious Composites Containing Milled Glass Fibers under Tension, 2018, 11, 1996-1944, 1115, 10.3390/ma11071115
    9. Greta Donati, Antonio De Nicola, Gianmarco Munaò, Maksym Byshkin, Luigi Vertuccio, Liberata Guadagno, Ronan Le Goff, Giuseppe Milano, Simulation of self-heating process on the nanoscale: a multiscale approach for molecular models of nanocomposite materials, 2020, 2, 2516-0230, 3164, 10.1039/D0NA00238K
    10. Tejendra K Gupta, S Kumar, Amal Z Khan, Kartik M Varadarajan, Wesley J Cantwell, Self-sensing performance of MWCNT-low density polyethylene nanocomposites, 2018, 5, 2053-1591, 015703, 10.1088/2053-1591/aa9f9e
    11. Baofei Cao, Erik Maehle, Norbert Stoll, Chao-Hsien Chu, Computer structural model analysis and civil engineering testing technology, 2019, 19, 14727978, 285, 10.3233/JCM-191041
    12. Asma A. Eddib, D.D.L. Chung, First report of capacitance-based self-sensing and in-plane electric permittivity of carbon fiber polymer-matrix composite, 2018, 140, 00086223, 413, 10.1016/j.carbon.2018.08.070
    13. Lukáš Fiala, Michaela Petříková, Wei-Ting Lin, Luboš Podolka, Robert Černý, Self-Heating Ability of Geopolymers Enhanced by Carbon Black Admixtures at Different Voltage Loads, 2019, 12, 1996-1073, 4121, 10.3390/en12214121
    14. Zbigniew Rozynek, Khobaib Khobaib, Alexander Mikkelsen, Opening and Closing of Particle Shells on Droplets via Electric Fields and Its Applications, 2019, 11, 1944-8244, 22840, 10.1021/acsami.9b05194
    15. Santoshi Mohanta, Yashwanth Padarthi, Jeetendra Gupta, Swati Neogi, In-Situ Determination of Degree of Cure by Mapping with Strain Measured by Embedded FBG and Conventional Sensor during VIM Process, 2020, 21, 1229-9197, 2614, 10.1007/s12221-020-1064-5
    16. B. Nivetha, D. Suji, 2021, Chapter 20, 978-981-15-9808-1, 239, 10.1007/978-981-15-9809-8_20
    17. Sohel Rana, Shama Parveen, Subramani Pichandi, Raul Fangueiro, 2018, Chapter 15, 978-3-319-64640-4, 159, 10.1007/978-3-319-64641-1_15
    18. Ayman I. Madbouly, M.M. Mokhtar, M.S. Morsy, Evaluating the performance of rGO/cement composites for SHM applications, 2020, 250, 09500618, 118841, 10.1016/j.conbuildmat.2020.118841
    19. Panga Narasimha Reddy, Bode Venkata Kavyateja, Bharat Bhushan Jindal, Structural health monitoring methods, dispersion of fibers, micro and macro structural properties, sensing, and mechanical properties of self‐sensing concrete—A review, 2020, 1464-4177, 10.1002/suco.202000337
    20. Sarah Aguasvivas Manzano, Dana Hughes, Nikolaus Correll, Wireless Online Impact Source Localization on a Composite, 2018, 24, 23519789, 319, 10.1016/j.promfg.2018.06.021
    21. Dong-Hui Kim, Wan-Shin Park, Sun-Woo Kim, Moon-Sung Lee, Soo-Yeon Seo, Hyun-Do Yun, Steel Reinforcing Bar and Steel Fibers Content Effect on Tensile and Electrical Behaviors of Strain-Hardening Cement Composite (SHCC) with MWCNTs in Direct Tension, 2021, 11, 2076-3417, 2446, 10.3390/app11052446
    22. Enrique García-Macías, Filippo Ubertini, Earthquake-induced damage detection and localization in masonry structures using smart bricks and Kriging strain reconstruction: A numerical study, 2019, 48, 00988847, 548, 10.1002/eqe.3148
    23. Tran Thanh Tung, Md J. Nine, Melinda Krebsz, Tibor Pasinszki, Campbell J. Coghlan, Diana N. H. Tran, Dusan Losic, Recent Advances in Sensing Applications of Graphene Assemblies and Their Composites, 2017, 27, 1616301X, 1702891, 10.1002/adfm.201702891
    24. A.K Roopa, Anand M. Hunashyal, Evaluating Self-sensing Property of Carbon Fibre Cement Composite by experimental study and Finite Element Modelling For Structural Health Monitoring Applications, 2021, 1070, 1757-8981, 012041, 10.1088/1757-899X/1070/1/012041
    25. Andrea Meoni, Antonella D'Alessandro, Nicola Cavalagli, Massimiliano Gioffré, Filippo Ubertini, Shaking table tests on a masonry building monitored using smart bricks: Damage detection and localization, 2019, 48, 0098-8847, 910, 10.1002/eqe.3166
    26. Hocine Siad, Mohamed Lachemi, Mustafa Sahmaran, Habib A. Mesbah, Khandakar Anwar Hossain, Advanced engineered cementitious composites with combined self-sensing and self-healing functionalities, 2018, 176, 09500618, 313, 10.1016/j.conbuildmat.2018.05.026
    27. Xue Xin, Ming Liang, Zhanyong Yao, Linping Su, Jizhe Zhang, Peizhao Li, Changjun Sun, Hongguang Jiang, Self-sensing behavior and mechanical properties of carbon nanotubes/epoxy resin composite for asphalt pavement strain monitoring, 2020, 257, 09500618, 119404, 10.1016/j.conbuildmat.2020.119404
    28. Shima Taheri, A review on five key sensors for monitoring of concrete structures, 2019, 204, 09500618, 492, 10.1016/j.conbuildmat.2019.01.172
    29. Kaveh Barri, Behnam Jahangiri, Omid Davami, William G. Buttlar, Amir H. Alavi, Smartphone-based molecular sensing for advanced characterization of asphalt concrete materials, 2020, 151, 02632241, 107212, 10.1016/j.measurement.2019.107212
    30. Iftekar Gull, Manzoor A. Tantray, Self‐damage sensing of electrically conductive self‐compacting concrete incorporating short carbon fibers, 2021, 1545-2255, 10.1002/stc.2735
    31. B. Nivetha, D. Suji, 2021, Chapter 64, 978-981-33-4589-8, 691, 10.1007/978-981-33-4590-4_64
    32. Guido Goracci, David M. Salgado, Juan J. Gaitero, Jorge S. Dolado, Electrical Conductive Properties of 3D-PrintedConcrete Composite with Carbon Nanofibers, 2022, 12, 2079-4991, 3939, 10.3390/nano12223939
    33. Tamas Rev, Meisam Jalalvand, Jonathan Fuller, Michael R. Wisnom, Gergely Czél, A simple and robust approach for visual overload indication - UD thin-ply hybrid composite sensors, 2019, 121, 1359835X, 376, 10.1016/j.compositesa.2019.03.005
    34. Min Kyoung Kim, Dong Joo Kim, Electromechanical response of strain-hardening fiber-reinforced cementitious composites (SH-FRCCs) under direct tension: A review, 2023, 349, 09244247, 114096, 10.1016/j.sna.2022.114096
    35. Aleksandra Jelić, Milan Travica, Vukašin Ugrinović, Aleksandra Božić, Marina Stamenović, Dominik Brkić, Slaviša Putić, 2022, Chapter 13, 978-3-030-86008-0, 239, 10.1007/978-3-030-86009-7_13
    36. Mohammadmahdi Abedi, Omid Hassanshahi, Joaquim A.O. Barros, António Gomes Correia, Raul Fangueiro, Three-dimensional braided composites as innovative smart structural reinforcements, 2022, 297, 02638223, 115912, 10.1016/j.compstruct.2022.115912
    37. Amir A. E. Elseady, Ivan Lee, Yan Zhuge, Xing Ma, Christopher W. K. Chow, Nima Gorjian, Piezoresistivity and AC Impedance Spectroscopy of Cement-Based Sensors: Basic Concepts, Interpretation, and Perspective, 2023, 16, 1996-1944, 768, 10.3390/ma16020768
    38. Mohammadmahdi Abedi, Raul Fangueiro, António Gomes Correia, Effects of multiscale carbon-based conductive fillers on the performances of a self-sensing cementitious geocomposite, 2021, 43, 23527102, 103171, 10.1016/j.jobe.2021.103171
    39. Zere Bekzhanova, Shazim Ali Memon, Jong Ryeol Kim, Self-Sensing Cementitious Composites: Review and Perspective, 2021, 11, 2079-4991, 2355, 10.3390/nano11092355
    40. Xiaoyan Huang, Lu Han, Xiao Yang, Zhiwen Huang, Jun Hu, Qi Li, Jinliang He, Smart dielectric materials for next-generation electrical insulation, 2022, 1, 2771-9197, 19, 10.23919/IEN.2022.0007
    41. Akbar Shojaei, Samaneh Salkhi Khasraghi, 2021, 9780128205129, 307, 10.1016/B978-0-12-820512-9.00015-0
    42. Namgyu Kim, Jong-Jae Lee, Noncontact stress measurement technique for concrete structure using photoluminescence piezospectroscopy, 2021, 11, 2190-5452, 1189, 10.1007/s13349-021-00501-z
    43. Andrea Meoni, Antonella D’Alessandro, Massimo Mancinelli, Filippo Ubertini, A Multichannel Strain Measurement Technique for Nanomodified Smart Cement-Based Sensors in Reinforced Concrete Structures, 2021, 21, 1424-8220, 5633, 10.3390/s21165633
    44. D. D. L. Chung, A review to elucidate the multi-faceted science of the electrical-resistance-based strain/temperature/damage self-sensing in continuous carbon fiber polymer-matrix structural composites, 2023, 58, 0022-2461, 483, 10.1007/s10853-022-08106-7
    45. Giacomo Selleri, Francesco Mongioì, Emanuele Maccaferri, Riccardo D’Anniballe, Laura Mazzocchetti, Raffaella Carloni, Davide Fabiani, Andrea Zucchelli, Tommaso Maria Brugo, Self-Sensing Soft Skin Based on Piezoelectric Nanofibers, 2023, 15, 2073-4360, 280, 10.3390/polym15020280
    46. Chuanyi Ma, Xue Xin, Ning Zhang, Jianjiang Wang, Chuan Wang, Ming Liang, Yunfeng Zhang, Zhanyong Yao, Encapsulation for Sensing Element and Its Application in Asphalt Road Monitoring, 2023, 13, 2079-6412, 390, 10.3390/coatings13020390
    47. Giacomo Selleri, Maria Elena Gino, Tommaso Maria Brugo, Riccardo D'Anniballe, Johnnidel Tabucol, Maria Letizia Focarete, Raffaella Carloni, Davide Fabiani, Andrea Zucchelli, Self-sensing composite material based on piezoelectric nanofibers, 2022, 219, 02641275, 110787, 10.1016/j.matdes.2022.110787
    48. Nazire Deniz Yilmaz, 2022, 9781119654780, 1, 10.1002/9781119654872.ch1
    49. Shama Parveen, Bruno Vilela, Olinda Lagido, Sohel Rana, Raul Fangueiro, Development of Multi-Scale Carbon Nanofiber and Nanotube-Based Cementitious Composites for Reliable Sensing of Tensile Stresses, 2021, 12, 2079-4991, 74, 10.3390/nano12010074
    50. A. Meoni, C. Fabiani, A. D’Alessandro, A.L. Pisello, F. Ubertini, Strain-sensing smart bricks under dynamic environmental conditions: Experimental investigation and new modeling, 2022, 336, 09500618, 127375, 10.1016/j.conbuildmat.2022.127375
    51. Tangfeng Feng, Yunfei Wang, Junjie Yang, Yunlong Li, Peng Xu, Huan Wang, Hua-Xin Peng, Faxiang Qin, Real-time self-monitoring and smart bend recognizing of fiber-reinforced polymer composites enabled by embedded magnetic fibers, 2023, 232, 02663538, 109869, 10.1016/j.compscitech.2022.109869
    52. Krzysztof Lalik, Mateusz Kozek, Ireneusz Dominik, Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect, 2021, 14, 1996-1944, 4116, 10.3390/ma14154116
    53. Mohammadmahdi Abedi, António Gomes Correia, Raul Fangueiro, Geotechnical and piezoresistivity properties of sustainable cementitious stabilized sand reinforced with recycled fibres, 2021, 6, 2666691X, 100096, 10.1016/j.treng.2021.100096
    54. Zhuang Tian, Shaoqi Li, Yancheng Li, Aligning conductive particles using magnetic field for enhanced piezoresistivity of cementitious composites, 2021, 313, 09500618, 125582, 10.1016/j.conbuildmat.2021.125582
    55. Yunlong Zhang, Jianxin Wang, Jing Wang, Xuesong Qian, Preparation, mechanics and self-sensing performance of sprayed reactive powder concrete, 2022, 12, 2045-2322, 10.1038/s41598-022-11836-y
    56. Rajani Kant Rao, Saptarshi Sasmal, 2022, Chapter 17, 978-981-16-9092-1, 203, 10.1007/978-981-16-9093-8_17
    57. Minxiao Lin, Shijun Guo, Shun He, Wenhao Li, Daqing Yang, Structure health monitoring of a composite wing based on flight load and strain data using deep learning method, 2022, 286, 02638223, 115305, 10.1016/j.compstruct.2022.115305
    58. Cecílie Mizerová, Ivo Kusák, Pavel Rovnaník, Patrik Bayer, Conductive Metakaolin Geopolymer with Steel Microfibres​, 2021, 321, 1662-9779, 59, 10.4028/www.scientific.net/SSP.321.59
    59. Sahar Hassani, Mohsen Mousavi, Amir H. Gandomi, Structural Health Monitoring in Composite Structures: A Comprehensive Review, 2021, 22, 1424-8220, 153, 10.3390/s22010153
    60. Xin Qian, Heng Yang, Jialai Wang, Yi Fang, Mengxiao Li, Eco-friendly treatment of carbon nanofibers in cementitious materials for better performance, 2022, 16, 22145095, e01126, 10.1016/j.cscm.2022.e01126
    61. Anthony Palumbo, Eui-Hyeok Yang, 2022, 9780128234426, 361, 10.1016/B978-0-12-823442-6.00008-8
    62. Maria Elena Gino, Giacomo Selleri, Davide Cocchi, Tommaso Maria Brugo, Nicola Testoni, Luca De Marchi, Andrea Zucchelli, Davide Fabiani, Maria Letizia Focarete, On the design of a piezoelectric self-sensing smart composite laminate, 2022, 219, 02641275, 110783, 10.1016/j.matdes.2022.110783
    63. Gouri Sankar Das, Vijayendra Kumar Tripathi, Jaya Dwivedi, Lokesh Kumar Jangir, Kumud Malika Tripathi, Nanocarbon-based sensors for the structural health monitoring of smart biocomposites, 2024, 16, 2040-3364, 1490, 10.1039/D3NR05522A
    64. Xinyue Wang, Siqi Ding, Yi-Qing Ni, Liqing Zhang, Sufen Dong, Baoguo Han, Intrinsic self-sensing concrete to energize infrastructure intelligence and resilience: A review, 2024, 3, 27729915, 100094, 10.1016/j.iintel.2024.100094
    65. Liangsheng Qiu, Siqi Ding, Danna Wang, Baoguo Han, Self-sensing GFRP-reinforced concrete beams containing carbon nanotube-nano carbon black composite fillers, 2023, 34, 0957-0233, 084003, 10.1088/1361-6501/accc20
    66. Ayushi Thakur, Ruchira Srivastava, Preeti Singh Bahadur, Ajay Rana, 2024, chapter 8, 9798369343975, 249, 10.4018/979-8-3693-4397-5.ch008
    67. Mohammadmahdi Abedi, Raul Fangueiro, António Gomes Correia, Javad Shayanfar, Smart Geosynthetics and Prospects for Civil Infrastructure Monitoring: A Comprehensive and Critical Review, 2023, 15, 2071-1050, 9258, 10.3390/su15129258
    68. Self-Sensing Potential of Metashale Geopolymer Mortars with Carbon Fiber/Graphite Powder Admixtures, 2024, 14, 2226-809X, 423, 10.46604/ijeti.2024.13570
    69. M. Rama, J.S. Sudarsan, N. Sunmathi, S. Nithiyanantham, Behavioral assessment of intrinsically formed smart concrete using steel fibre and carbon black composite, 2024, 10, 24058440, e26948, 10.1016/j.heliyon.2024.e26948
    70. António Gomes Correia, Mohammad Jawed Roshan, Self-sensing cementitious geocomposites in rail track substructures, 2024, 46, 22143912, 101260, 10.1016/j.trgeo.2024.101260
    71. Vo Minh Chi, Nguyen Lan, Nguyen Minh Hai, Nguyen Van Huong, Compression self-sensibility of the concrete using high content carbon black with various measurement conditions, 2023, 1289, 1757-8981, 012033, 10.1088/1757-899X/1289/1/012033
    72. Said Quqa, Sijia Li, Yening Shu, Luca Landi, Kenneth J Loh, Crack identification using smart paint and machine learning, 2024, 23, 1475-9217, 248, 10.1177/14759217231167823
    73. Anthony Palumbo, Zheqi Li, Eui-Hyeok Yang, Trends on Carbon Nanotube-Based Flexible and Wearable Sensors via Electrochemical and Mechanical Stimuli: A Review, 2022, 22, 1530-437X, 20102, 10.1109/JSEN.2022.3198847
    74. Qi Cui, Zhen-gang Feng, Ruoting Shen, Xiangnan Li, Zhuang Wang, Dongdong Yao, Xinjun Li, Piezoresistive response of self-sensing asphalt concrete containing carbon fiber, 2024, 426, 09500618, 136121, 10.1016/j.conbuildmat.2024.136121
    75. Hashim Hassan, William A Crossley, Tyler N Tallman, Hybrid optimization schemes for solving the piezoresistive inversion problem in self-sensing materials, 2024, 33, 0964-1726, 065033, 10.1088/1361-665X/ad49ec
    76. Ziyan Hang, Zhi Ni, Jinlong Yang, Yucheng Fan, Chuang Feng, Shuguang Wang, Nonlinear Vibration of FG-GNPRC Dielectric Beam with Kelvin–Voigt Damping in Thermal Environment, 2024, 24, 0219-4554, 10.1142/S021945542450130X
    77. Ely Leburu, Yuting Qiao, Yanshen Wang, Jiakuan Yang, Sha Liang, Wenbo Yu, Shushan Yuan, Huabo Duan, Liang Huang, Jingping Hu, Huijie Hou, Flexible electronics for heavy metal ion detection in water: a comprehensive review, 2024, 26, 1387-2176, 10.1007/s10544-024-00710-5
    78. Yifei Gong, Zhiyu Xie, Guanhao Chen, Dawei Zhang, Influence of Cementitious Material Infiltration on Piezoresistive Effect of Carbon Fiber Bundle, 2023, 35, 0899-1561, 10.1061/JMCEE7.MTENG-14701
    79. Omolayo M. Ikumapayi, Temitayo S. Ogedengbe, Sunday A. Afolalu, Adebayo T. Ogundipe, Emeka S. Nnochiri, 2024, 3007, 0094-243X, 100010, 10.1063/5.0197101
    80. Francisco Alfonso Alvarez del Castillo Manzanos, Robert R. Hughes, Anthony J. Croxford, Passive Wireless Mechanical Overload Sensing: Proof of Concept Using Agarose Hydrogels, 2023, 72, 0018-9456, 1, 10.1109/TIM.2023.3291741
    81. Michela Rossi, Dionysios Bournas, Structural Health Monitoring and Management of Cultural Heritage Structures: A State-of-the-Art Review, 2023, 13, 2076-3417, 6450, 10.3390/app13116450
    82. Hao Chen, Inge Hoff, Gang Liu, Xuemei Zhang, Diego Maria Barbieri, Fusong Wang, Jianan Liu, Development of finite element model based on indirect tensile test for various asphalt mixtures, 2023, 394, 09500618, 132085, 10.1016/j.conbuildmat.2023.132085
    83. Abdalaziz Al-Maeeni, Mikhail Lazarev, Nikita Kazeev, Kostya S Novoselov, Andrey Ustyuzhanin, Review on automated 2D material design, 2024, 11, 2053-1583, 032002, 10.1088/2053-1583/ad4661
    84. Omar Shabbir Ahmed, Abdul Aabid, Jaffar Syed Mohamed Ali, Meftah Hrairi, Norfazrina Mohd Yatim, Progresses and Challenges of Composite Laminates in Thin-Walled Structures: A Systematic Review, 2023, 8, 2470-1343, 30824, 10.1021/acsomega.3c03695
    85. Rajani Kant Rao, S. Gautham, Saptarshi Sasmal, A Comprehensive Review on Carbon Nanotubes Based Smart Nanocomposites Sensors for Various Novel Sensing Applications, 2024, 64, 1558-3724, 575, 10.1080/15583724.2024.2308889
    86. Md. Zobair Al Mahmud, Moyeen Khan, Md. Faysal Ahamed Dewan Refati, Md Hosne Mobarak, Md. Abdullah Al Shafi, Nayem Hossain, A. K. M. Foysal Ahmed, Advances and Significances of Nanoparticles as Concrete Additives: A Comprehensive Review, 2024, 14, 1793-9844, 10.1142/S179398442440004X
    87. Zhizhong Deng, Wengui Li, Wenkui Dong, Zhihui Sun, Jayantha Kodikara, Daichao Sheng, Multifunctional asphalt concrete pavement toward smart transport infrastructure: Design, performance and perspective, 2023, 265, 13598368, 110937, 10.1016/j.compositesb.2023.110937
    88. Zhuang Wang, Zhen-gang Feng, Qi Cui, Genmiao Guang, Xinjun Li, Evaluation of piezoresistive response and mechanical performance of self-sensing asphalt concrete mixed with different lengths of carbon fiber, 2025, 462, 09500618, 139942, 10.1016/j.conbuildmat.2025.139942
    89. Xue Xin, Junyao Hui, Lin Chen, Ming Liang, Zhanyong Yao, Monitoring the Internal Conditions of Road Structures by Smart Sensing and In Situ Monitoring Technology: A Review, 2025, 15, 2076-3417, 3945, 10.3390/app15073945
    90. Manish Baboo Agarwal, Manu Mehrotra, Manish Kumar Panday, Pankaj Mittal, Vinay Kumar Jadon, Seema Agarwal, 2025, Chapter 4, 978-3-031-77295-5, 69, 10.1007/978-3-031-77296-2_4
    91. Shu‐Yang Wang, Gui‐Hua Xie, Hong‐Yun Xia, Shuai Xu, Zi‐Han Lin, Shi‐Quan Li, A Review on Resistance‐Based Self‐Sensing of Carbon Fiber‐Reinforced Polymer Subjected to Loads, 2025, 1438-1656, 10.1002/adem.202500244
    92. Xue Xin, Xingchi Zhao, Jing Gao, Zhanyong Yao, Yunzhen Li, Mechanism of Strain-Resistance Response of CNT/Polymer Composite Materials for Pavement Strain Self-Sensing Based on the Molecular Dynamics Simulation Method, 2025, 17, 2073-4360, 1427, 10.3390/polym17111427
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6482) PDF downloads(1325) Cited by(7)

Article outline

Figures and Tables

Figures(7)  /  Tables(8)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog