Fractional Change In Resistance Research Articles

Piezoresistive and capacitive cement-based stress sensors designed by incorporating carbon nanotubes doped with graphene nanopowder for structural health monitoring

Piezoresistive and capacitive cement-based smart composites are promising candidates for stress monitoring applications in buildings. However, the drawbacks of designing cement-based smart composites with a piezoresistive mechanism sensitive to temperature variations or a capacitive mechanism sensitive to vibration can be solved with sensor designs based on two principles. To address this issue, cement-based smart composites with piezoresistive and capacitive stress sensitivity and significantly improved compressive strength were designed using carbon nanotubes (CNT) doped with graphene nanopowder. A maximum fractional change in electrical resistivity of 90.08% with a high linearity of 98% was obtained with a blend of 0.01 wt%, and the maximum fractional change in capacitance was 163.11% with a high linearity of 97% at 0.05 wt%. Cement-based smart composites have absolute piezoresistive stress sensitivities that vary from 1.38% MPa−1 to 4.16% MPa−1 and 2.54% MPa−1 to 2.57% MPa−1 for blended and impregnated low doses of 0.01 wt% to 0.05 wt% CNT doped with graphene. In addition, capacitive stress sensitivities of 22.74% MPa−1 and 5.1% MPa−1 were achieved by blending and impregnating 0.01 wt% CNT doped with graphene in cementitious composites. Furthermore, the conductive path, overlapping, and tunneling effect created by CNT doped with graphene in cementitious composites and the porous structure in the dielectric layer are responsible for the increased stress sensitivity.

Anti-Oxidized Self-Assembly of Multilayered F-Mene/MXene/TPU Composite with Improved Environmental Stability and Pressure Sensing Performances.

MXenes, as emerging 2D sensing materials for next-generation electronics, have attracted tremendous attention owing to their extraordinary electrical conductivity, mechanical strength, and flexibility. However, challenges remain due to the weak stability in the oxygen environment and nonnegligible aggregation of layered MXenes, which severely affect the durability and sensing performances of the corresponding MXene-based pressure sensors, respectively. Here, in this work, we propose an easy-to-fabricate self-assembly strategy to prepare multilayered MXene composite films, where the first layer MXene is hydrogen-bond self-assembled on the electrospun thermoplastic urethane (TPU) fibers surface and the anti-oxidized functionalized-MXene (f-MXene) is subsequently adhered on the MXene layer by spontaneous electrostatic attraction. Remarkably, the f-MXene surface is functionalized with silanization reagents to form a hydrophobic protective layer, thus preventing the oxidation of the MXene-based pressure sensor during service. Simultaneously, the electrostatic self-assembled MXene and f-MXene successfully avoid the invalid stacking of MXene, leading to an improved pressure sensitivity. Moreover, the adopted electrospinning method can facilitate cyclic self-assembly and the formation of a hierarchical micro-nano porous structure of the multilayered f-MXene/MXene/TPU (M-fM2T) composite. The gradient pores can generate changes in the conductive pathways within a wide loading range, broadening the pressure detection range of the as-proposed multilayered f-MXene/MXene/TPU piezoresistive sensor (M-fM2TPS). Experimentally, these novel features endow our M-fM2TPS with an outstanding maximum sensitivity of 40.31 kPa-1 and an extensive sensing range of up to 120 kPa. Additionally, our M-fM2TPS exhibits excellent anti-oxidized properties for environmental stability and mechanical reliability for long-term use, which shows only ~0.8% fractional resistance changes after being placed in a natural environment for over 30 days and provides a reproducible loading-unloading pressure measurement for more than 1000 cycles. As a proof of concept, the M-fM2TPS is deployed to monitor human movements and radial artery pulse. Our anti-oxidized self-assembly strategy of multilayered MXene is expected to guide the future investigation of MXene-based advanced sensors with commercial values.

Piezoresistive performance of cement-based composites with two-dimensional MXene subjected to various loading conditions and water content

Two-dimension MXene was applied in cement to fabricate MXene cement-based sensor used in structural health monitoring. The effect of MXene content on the piezoresistive performance of MXene cement-based composites (MCBC) was investigated. The fractional change in resistivity (FCR) under different loading rate, water content and failure load were also measured. Microanalysis results proves that MXene can construct electrical network in cement composites. The results demonstrated that the stress sensitivity has a maximum at 1.0 wt% MXene content. When loading rate increases, the sensitivities of composites decrease linearly, while the water content increases, the decrease of sensitivities is not completely linear. The relationship between compressive stress and fractional change in resistivity conforms to quadratic polynomial under failure load.

Smart ultra-high-performance concrete under cyclic high compressive stress

The self-stress sensing response of smart ultra-high-performance concrete (S-UHPC) under high cyclic compressive stress was investigated at different temperatures between 15 °C and 35 °C as a basic study for understanding the effects of temperature on the self-stress sensing response of S-UHPC. The S-UHPC has maintained the quasi-linear relationship between stress and fractional change in electrical resistivity (FCR) even under cyclic stress. The stress-sensing coefficient (SSC) of S-UHPC almost linearly decreased from 0.2450 %/MPa to 0.1722%/MPa with increasing the temperature from 15 °C to 35 °C. The lower SSC at higher temperature is based on the decreased electrical resistivity (∆ρ) of S-UHPCs owing to more activated ion movements in S-UHPCs.

An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials

Abstract In this study, hybrid functional fillers were used to create an electrical network that is used to develop self-damage sensing ability within conventional cementitious mixtures. This electrical network was used to examine the self-sensing properties of cementitious composites under different loading scenarios such as compression, splitting tensile, and cyclic loading for three ages, those are 7, 14, and 28. With the help of a single type of functional filler or fiber, the self-sensing capabilities of the majority of previous works are demonstrated. This study incorporates two types of functional fillers for sustainability and low cost: micro-scale carbon fibers (CF) and waste iron (Ir) powder in the form of microparticles. The purpose of the current work is to fill up the subject’s gap using two different types of functional fillers as a hybrid form. Three hybrid proportions of a micro-scale CF (vol% of mixture) and waste iron powder (Ir) (wt% of cementitious materials) are utilized. These are (0.33, 15), (0.67, 10), and (1.00, 5), respectively. Unlike carbon-based materials, polyvinyl alcohol is used as a mechanical reinforcing fiber 2% by volume of the cementitious mixture. Additionally, a control combination without any fillers has been created. The electrical resistivity of the fabricated samples was monitored during various loading applications for every second to confirm their self-sensing capabilities. Regarding the fractional change in electrical resistivity, the self-sensing behavior was superior for mixes containing high dosages either of waste iron powder or CF in the same hybrid matrix. Quite the contrary to that, the piezoresistivity was modest in the middle hybrid ratio.

Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches

Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the durability and strength of the concrete. Nevertheless, the complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints in comprehensively capturing these intricate compositions. Thus, posing challenges in delivering precise and dependable estimations. Therefore, the current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS), in conjunction with experimental investigation to study the effect of the integration of graphene nanoplatelets (GNPs) on the electrical resistivity (ER) and compressive strength (CS) of concrete containing GNPs. Concrete containing GNPs demonstrated an improved fractional change in resistivity (FCR) and strength. The experimental measures depict that strength enhancement was notable at GNP concentrations of 0.05% and 0.1%, showcasing increases of 13.23% and 16.58%, respectively. Simultaneously, the highest observed FCR change reached −12.19% and −13%, respectively. The prediction efficacy of the three models proved to be outstanding in forecasting the characteristics of concrete containing GNPs. For CS, the GEP, ANN, and ANFIS models demonstrated impressive correlation coefficient (R) values of 0.974, 0.963, and 0.954, respectively. For electrical resistivity, the GEP, ANN, and ANFIS models exhibited high R-values of 0.999, 0.995, and 0.987, respectively. The comparative analysis of the models revealed that the GEP model delivered precise predictions for both ER and CS. The mean absolute error (MAE) of the GEP-CS model demonstrated a 14.51% reduction compared to the ANN-CS model and a substantial 48.15% improvement over the ANFIS-CS model. Similarly, the ANN-CS model displayed an MAE that was 38.14% lower compared to the ANFIS-CS model. Moreover, the MAE of the GEP-ER model demonstrated a 56.80% reduction compared to the ANN-CS model and a substantial 82.47% improvement over the ANFIS-CS model. The Shapley Additive explanation (SHAP) analysis provided that curing age exhibited the highest SHAP score. Thus, indicating its predominant contribution to CS prediction. In predicting ER, the graphene content exhibited the highest SHAP score, signifying its predominant contribution to ER estimation. This study highlights ML's accuracy in predicting the properties of concrete with graphene nanoplatelets, offering a fast and cost-effective alternative to time-consuming experiments.

Graphene nanoplatelet/polypropylene pellets in cement-based composites: Mechanical, piezoresistive and microstructural properties

Graphene nanoplatelet–reinforced polypropylene is considered a promising conductive polymer-based additive to provide unique features to cementitious composites. This study comprehensively investigated the collective effects of graphene and polypropylene in the form of graphene nanoplatelet/polypropylene pellets (GrPp). Adding GrPp increased the flexural strength of the cementitious composites due to its high tensile strength. Moreover, the GrPp-enhanced cementitious composites show a high piezoresistive absolute maximum fractional change in electrical resistivity (FCR) of 96.50 %, stress sensitivity of 4.13 %/MPa, and repeatability of 90.81 % with the addition of 2–3.5 wt% GrPp on average. Furthermore, adding GrPp improved the piezoresistive behavior by forming a conductive path and increasing the deformability of the cementitious composites. These findings demonstrate that Gr-reinforced Pp pellets present an economical and efficient way to develop piezoresistive stress sensors for smart cement-based composites.

Evaluation of industrial byproduct iron ore tailings and carbon fiber cement-based materials under sulfate freeze-thaw cycles: Durability, piezoresistivity, and microscopic mechanism

This study investigated the feasibility of utilizing industrial byproduct iron ore tailings and carbon fiber cement-based materials (ICCM) in sulfate freeze-thaw (F-T) environments. The effects of carbon fiber (CF) and iron ore tailing (IOT) on the sulfate F-T resistance, piezoresistivity, and pore distribution were tested. The results revealed that CF (0.6 vol%) and IOT (30%) significantly improved the compressive strength (51.6 MPa) and resistance to sulfate F-T of ICCM. The stress sensitivity was obviously enhanced to 1.389 %·MPa−1. Furthermore, the stress sensitivity is closely related to the sulfate F-T environment. After the sulfate F-T cycles, the fractional change in resistivity (FCR) and compressive stress agreed with a first-order exponential decay relationship. Replacement with IOT compensated for the degradation in piezoresistivity by formation of balanced pore distribution over 200 F-T cycles. The combination of IOT and CF in cement material yields excellent performance in terms of stress sensing, cost, and environmental impact (EI). This approach is expected to be developed for self-sensing in extreme environments with a recycled conductive filler.

Monitoring damage of concrete beams via self-sensing cement mortar coating with carbon nanotube-nano carbon black composite fillers

Self-sensing concrete used in coating form for structural health monitoring of concrete structures has the merits of cost-effectiveness, offering protective effect on structural components, enabling electrical measurements unaffected by steel reinforcement and is also convenient to maintain and replace. This paper investigates the feasibility of using self-sensing cement mortar coating containing carbon nanotube-nano carbon black (CNT-NCB) composite fillers (CNCFs) for damage monitoring of concrete beams. The self-sensing cement mortar coated to concrete beams demonstrated outstanding electrical conductivity (resistivity ranging from 18 to 85 Ω·cm). Under monotonic flexural loadings, self-sensing cement mortar coating with 1.8 vol.% CNCFs featured sensitive self-sensing performance in terms of capturing the initiation of vertical cracks at pure bending span of concrete beams, with fractional change in resistivity (FCR) reaching up to 60.6%. Moreover, FCR variations of self-sensing cement mortar coating exhibited good synchronization and stability with the variation of mid-span deflections of concrete beams during cyclic flexural loadings irrespective of the contents of CNCFs and cyclic amplitudes. Remarkably, it was found that FCR of cement mortar coating basically showed a progressive upward tendency, representing irreversible increase in the resistance during cyclic loading. The irreversible residual FCR indicated the crack occurrence and damage accumulation of concrete beams.

Development of self-sensing cementitious composites by incorporating a two-dimensional carbon-fibre textile network for structural health monitoring

Many challenges persist with traditional self-sensing cementitious composites, such as selecting conductive fillers, determining optimal aspect ratios, controlling dosage, achieving filler dispersion, designing practical electrodes, and overcoming fabrication difficulties. Therefore, this paper proposes a novel self-sensing technique for cementitious composites by incorporating a 2-dimensional (2-D) carbon-fibre textile network to address these challenges. Additionally, instead of using the entire composite volume as the sensor, an alternative approach is explored, which involves utilising the interlaminar interface by incorporating the 2-D carbon-fibre textile network. This approach provides an integrated self-sensing system, including electrical leads and conductive pathways, which can be tailored based on the design requirements. The paper introduces fundamental concepts and measurement circuit design, followed by a comprehensive study covering measurement techniques, electromechanical properties, and microstructural analysis. Furthermore, it discusses the impact of ambient conditions, such as temperature and relative humidity, on the measurements. Experimental results demonstrate a remarkable maximum fractional change in contact resistivity, reaching up to 70%. The reversibility during cyclic compression is excellent, with a maximum negative gauge factor of − 2500. These findings represent a significant step toward achieving a practical and simplified method for manufacturing self-sensing cementitious composites and open avenues for self-sensing, sustainable textile-reinforced concrete structures (TRC).

A novel method for temperature self-sensing of reinforced concrete

In this work, a temperature-sensing reinforced concrete has been developed using capacitance and resistance-based measurements. No additives are required for self-temperature sensing. Steel bars in the reinforced concrete are used as electrodes. To determine the self-sensing properties of the reinforced concrete specimen, it is cooled from room temperature to −9 °C and heated from room temperature until it reaches 50.9 °C. The varying capacitance and resistance values of the sample concerning temperature changes are evaluated. Fractional changes in capacitance and resistance upon cooling are 80.6% and 276%, respectively. Fractional changes in capacitance and resistance upon warming are 60% and 35%, respectively. Temperature coefficients of capacitance and resistance (fractional change in capacitance and resistance per unit change in temperature) upon cooling are 2.5 × 10−2/°C and 8.6 × 10−2/°C, while upon warming they are 2.2 × 10−2/°C and 1.3 × 10−2/°C. This study aims to use capacitance and resistance-based sensing techniques to detect temperature variations in steel-reinforced concrete structures. Further studies are planned to investigate various factors such as moisture, porosity, aggregate proportion, and water content that affect the self-temperature-sensing performance of reinforced concrete.

Prediction of mechanical and electrical properties of carbon fibre-reinforced self-sensing cementitious composites

The transmission of signal values in self-sensing concrete allows us to precisely locate damaged structures and prevent disasters. Currently, there are over ten functional materials used in self-sensing concrete applications. Carbon fibre (CF) is a well-known functional material that has been extensively studied for its reproducibility and accuracy in self-sensing concrete experiments. In contrast, this study is based on finite element modelling to rapidly predict the impact of the functional filler material, CF, on concrete performance. This paper simulates the mechanical and piezoresistive properties of concrete with unsized and desized short-cut CFs at lengths of 3, 6, and 12 mm. Four different weight ratios were considered for each type of CF. The results indicate that the inclusion of CF increases compressive strength by approximately 2 MPa, while flexural strength can increase by up to 39%, with the unsized 6 mm fibres performing best at a concentration of 0.7 wt%. In the piezoresistive model, the higher fractional change in resistivity between 0 and 2 MPa is attributed to the formation of a conductive circuit. The predicted trends in mechanical strength and resistance variation align well with experimental observations. Therefore, this proposed model holds the potential to aid in the development of design strategies aimed at fine-tuning the microstructure of these self-sensitive materials to enhance performance and facilitate the design and application of carbon fibres in cementitious composites.

Experimental investigation and predictive modeling of compressive strength and electrical resistivity of graphene nanoplatelets modified concrete

Multipurpose cement composites are required for the sustainable development of the construction industry. Incorporation of nanomaterials can produce high-performing and multipurpose cement composites (CC). However, predicting the properties of these composites is challenging due to their complex composite nature and nonlinear behavior. This study investigates the integration of graphene nanoplatelets (GNPs) in concrete to enhance compressive strength and electrical properties. In addition to the experimental study, three machine learning techniques i.e., gene expression programming (GEP), support vector machine (SVM), and random forest regression (RFR), are employed to predict the compressive strength and electrical resistivity of graphene nanoplatelet modified concrete (GrNCC). The experimental results showed that GNP was evenly distributed throughout the matrix, and GrNCC exhibited enhanced compressive strength (CS) and fractional change in resistivity (FCR). GNP concentrations of 0.1% and 0.5% depict strength enhancement of 16.58% and 13.23% respectively. Similarly maximum FCR change of − 13%, and − 12.19% is observed. Moreover, GEP was the most robust ML model with mean R2 values of 0.988 and 0.74 for CS and ER respectively. In addition, SVM has R2 values of 0.93 and 0.71, and RFR shows R2 values of 0.899 and 0.671 for CS and ER respectively. GEP performed 52% and 67.5% better than SVM and RFR for CS, and 34.92% and 35.1% better than SVM and RFR for ER in terms of MAE. Furthermore, sensitivity analysis revealed that graphene content is the most important parameter contributing almost 27.3% and 29.2% for CS and ER respectively, followed by graphene dispersion and water content. Moreover, a graphical user interface (GUI) is made using a model that has been trained to predict the output values based on the input parameters. This simplifies the process and offers a valuable tool for utilizing the model's capabilities in civil engineering.

Self-sensing nano-engineered ultra-high performance concrete (UHPC) under tension

With the addition of electrically conductive steel or carbon fibers, Ultra-High-Performance Concrete (UHPC) possesses an intrinsic self-sensing capability. This opens up the possibility of combining the resilience and sustainability of UHPC with the development of self-sensing solutions for structural applications. In this study, the self-sensing behaviour of a proprietary nano-engineered UHPC material subjected to tension was investigated. To assess the self-sensing performance of the material, bulk resistivity measurements were used on direct tension and pure flexure tests, while a novel wireless approach that operates on was used on out-of-plane bending tests. The wireless approach used alternate current (AC) measurements while the bulk resistivity methods were performed through direct current (DC) and the four-probe method. In both methods, the fractional change in resistance was correlated to the state of deformation. The disposition of the actual strain field was evaluated using Digital Image Correlation (DIC). It was found that in the case of direct tension and pure flexure, the fractional change of resistance was initially decreased up to the onset of strain localization, while it gradually increased in the post-peak range, where the separation of the localized crack gradually increased. In the case of the wireless approach using AC, the onset of cracking was successfully predicted with an abrupt increase in resistivity. The wireless strain-sensing approach also captured the Poisson’s effect due to the loading.

Electrical properties of low temperature-sensitive coating sensor for fatigue crack monitoring in steel bridges

Coating sensors are used for high-precision monitoring of fatigue cracks in steel bridges. However, the time-variation temperature has a considerable impact on the monitoring precision of fatigue cracking of steel bridges. This study developed a coating sensor using silver-plated copper powder (conductive filler)/acrylate composites as a sensing layer of a coating sensor to monitor fatigue cracks considering variable ambient temperatures. The synthesis and preparation of the composite were presented, and the microstructures of the coating sensor composites were observed by scanning electron microscopy. The bulk conductivity of the sensing layer at different conductive filler contents was measured by the four-probe method to investigate the effect of the concentration of the conductive filler on the electrical conductivity and temperature sensitivity of the resistivity. Moreover, the fractional change in resistivity (FCR) of the sensing layer was tested over a range of temperatures to investigate the temperature sensitivity of resistivity at different conductive filler contents. Finally, the composite with the lowest temperature sensitivity of resistivity was selected to prepare a coating sensor for fatigue crack monitoring. The composite of the sensing layer with 60 wt% of conductive filler content had the lowest temperature sensitivity of resistivity with a minimum value (5.1%) of FCR. The maximum range of the FCR did not exceed 6.4% under cyclic loading and variation-time ambient temperatures. The temperature variation had minimal effects on the output signal of the coating sensor compared with the output signal variation caused by fatigue cracking. The proposed coating sensor can be used for high-precision monitoring of fatigue cracking in steel bridges under different ambient temperatures.

Piezoresistive behavior in cement-based sensors: Nonlinear modeling and preliminary application

Self-sensing cement sensors detect stress/strain by measuring the fractional change in electrical resistivity. However, neglecting the nonlinear portion of the FCR-stress/strain curves may limit monitoring range. Existing functional models lack versatility and physical meanings of parameters. A new model based on Ramberg-Osgood relation, incorporating the gauge factor, fractional change in resistance, and strain was proposed to cover linear and nonlinear regions. To validate the model, compression and tension cement sensors made of ultra-high toughness cementitious composite and multi-walled carbon nanotubes were tested. Results from various types of cement-based sensors validated the model's versatility. A four-point bending test on a C70 steel-reinforced concrete beam with an embedded cement sensor confirmed the feasibility of the proposed functional model.

An Experimental Approach to Assess the Sensitivity of a Smart Concrete

Structural health monitoring of concrete infrastructure is a critical concern for timely repair and maintenance. This study provides an innovative approach utilizing smart concrete integrated with multi-walled carbon nanotubes (MWCNTs) to enhance electrical conductivity. The smart concrete’s self-sensing capability is assessed through fractional change in electrical resistance (FCR) measured using a four-probe technique. Four-point bending and compressive tests explore the material’s response to cyclic and monotonic loads. Additionally, the impact of saturation levels on self-sensing sensitivity is investigated through compressive tests on varying saturation degrees. Remarkably, a substantial correlation between crack mouth opening displacement (CMOD) and FCR is observed during cyclic bending tests, where FCR increases significantly (from 0.019% to 154%) as CMOD rises from 0.004 mm to 0.55 mm. Digital image correlation (DIC) further validates CMOD measurements and their correlation with FCR. Moreover, this study reveals that amplitude of loading and degree of saturation have a significant effect on the self-sensing of the smart concrete. In saturated conditions, the self-sensing response of the material is insensitive to the mechanical strain, while with reduction in the saturation degree, a quasi-linear response is observed. To assess the sensitivity of the smart concrete, stress and strain sensitivities were evaluated, revealing a noteworthy enhancement of approximately 33% and 50% in stress and strain sensitivity, respectively, as saturation levels decreased. The self-sensing response of the material is very sensitive to the mechanical strain during monotonic loading and damage. These findings indicate the potential of smart concrete as a promising tool for comprehensive, real-time structural health monitoring for infrastructure during its entire life.

Effect of micro steel fibre contents on compressive and self-sensing properties of polyethylene fibre-reinforced cement composite (PE-FRCC) in cyclic compression

This study is conducted to develop a cement-based strain-detecting sensor with high cost-effectivity, large strain measuring range and good compatibility with concrete infrastructures. The paper describes experimental results on the compressive properties and piezoresistive behaviour of polyethylene(PE) fibre-reinforced cement composite(PE-FRCC) with different contents of micro steel fibres (SFs) under cyclic compression. The synthetic PE fibres were added into cement-based mortar to improve the strain capacity in compression and micro SFs at 0.25-1.5% by volume fraction of cement were incorporated to improve the compressive properties and electrical conductivity. Test results show that for the PE-FRCC with compressive strength of 32MPa, the increase in the micro SF content to 0.5% increased about 45% in the compressive strength compared to PE-FRCC without SFs. The addition of micro SFs into PE-FRCC improved the compressive properties, such as modulus of elasticity and lateral confinement. The incorporation of micro SFs is also effective to reduce the signal noise in the relationship between fractional change in resistance (FCR) and axial strain of PE-FRCC. PE-FRCC sensor with more 1.0% micro SF (PE0.75+SF1.00 composite) possess a strong linear relationship between FCR and axial strain during repeated compression at different stress amplitudes.

Carbon nanotubes-coated cement particles for cement-based sensors with excellent piezoresistivity

Typically, self-sensing cement composites are prepared by directly mixing cement particles with conductive additives, such as carbon black, carbon nanotubes (CNT), and graphene. This study proposes a novel approach to directly coat cement particles with CNT as the conductive filler (CNT@cement) before contact with water, aiming to develop a high-performance self-sensing cement-based sensor. CNT@cement nano/micro hybrid fabrication relies on a simple and efficient method that enables the uniform absorption of CNT on the surface of cement particles without any surfactants, thereby not diminishing the workability and mechanical properties of cement composites. This cement composite exhibits improved microstructure, bending strength, and electrical conductivity. Remarkably, the formulation with the addition of 0.1 wt% CNT@cement hybrid increases the bending strength by about 23% and improves the electrical conductivity by approximately two orders of magnitude, outperforming previously reported cement composites with similar CNT content. Additionally, outstanding piezoresistivity with a fractional change in resistivity of 1.26 is achieved for our cement-based sensor. These findings represent a major step toward the practical application of CNT in structural health monitoring.

Relation between electrically conductive properties and flexural behaviors of ultra‐high‐performance concrete

AbstractThe ability of the resistivity measurement to study the crack propagation behavior of the ultra‐high‐performance concrete (UHPC) under flexural loading was analyzed, and focused on investigating the effects of crack length and the fracture process zone (FPZ) on the change in electrical resistivity in the post‐cracking stage. The results indicated that the strain‐dependent resistivity characteristic was able to drawn by a linear‐fitting equation before the crack initiated (stage I). After the crack initiated, the change in electrical resistance of the UHPC can be good to reflect the crack development in the post‐cracking stages (II and III), then, the relationship between the crack propagation behavior and the fractional change in electrical resistance (FCR) can be quantitative analysis by using an exponential function. It was also found that the evolution of the crack length and FPZ also had an appreciable impact on the variation tendency of the FCR. The crack length increased dramatically at first and then increased smoothly with the increase of FRC, while the FPZ showed the reverse change trend.