Self-sensing Cementitious Composites Research Articles

3D printed self-sensing cementitious composites using graphite and carbon microfibers

Cementitious materials possessing self-sensing properties conferred through the incorporation of conductive fillers have attracted significant interest in the research community due to their potential application to structural health monitoring of civil infrastructure. A key advantage of cementitious sensors is that they can be organically embedded within cementitious structures to provide extremely robust monitoring capabilities. Of interest to this paper, the integration of cementitious sensors could be critical in empowering the 3D printing of cementitious composites by enabling real-time monitoring of additive manufacturing. Such integration could be achieved by 3D printing self-sensing cementitious nodes during the fabrication process. 3D printed self-sensing cementitious sensors could also be topologically engineered to achieve new opportunities in sensing of existing and new concrete and other cementitious structures. This paper presents one of the earliest studies, if not the first, on the 3D printing of self-sensing functionalized composites. The self-sensing cementitious composite sensors are fabricated using a cementitious matrix doped with graphite (G) and carbon microfibers. Various mixtures are studied, in particular G-only, G with milled carbon microfibers (MCMF), G with chopped carbon microfibers (CCMF), and G with MCMF and CCMF. A study of the percolation curves reveals that it is possible to attain electrical percolation using these conductive particles to create strain-sensitive sensors by leveraging the boosted piezoresistive effect. A study of strain sensing performance conducted on various specimens fabricated using different mixture types reveals the optimal concentration of dopants under different types of conductive particles. It is found that the optimal mixture that yields optimal performance also improves the Young’s Modulus of the specimens by 42.2% compared to cement-only specimens. Overall, this optimal mixture composed of 10% weight-to-cement G (10 wt% G), 0.250 wt% MCMF, and 0.125 wt% CCMF yielded a static gauge factor of 622, a resolution of 167 με , an accuracy of 19.24 με , and a Young’s Modulus of 798 MPa.

Performance of Self-Sensing Cement-Stabilized Sand under Various Loading Conditions.

Numerous elements, such as the composition and characteristics of carbon nanomaterials, the composition and characteristics of the matrix material, moisture levels, temperature, and loading circumstances, influence the piezoresistive behavior of self-sensing cementitious composites. While some past research has explored the impact of some of these factors on the performance of self-sensing cementitious composites, additional investigations need to be conducted to delve into how loading conditions affect the sensitivity of self-sensing cement-stabilized composites. Therefore, this study explores the influences of various loading conditions (i.e., location of loading regarding the location of recording electrodes, and loading level) on the electromechanical performance of self-sensing cement-stabilized sand. To this end, firstly, the evaluation of the percolation threshold based on 10% cement-stabilized sand specimens containing various multiwall carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) was performed. Then, 10% cement-stabilized sand containing 4% MWCNTs/GNPs was tested under various cyclic compressive stresses. The results suggested that the distance between the loading area and the electrode location used for recording the electrical resistance significantly impacted the sensitivity of cement-stabilized sand. Optimal sensitivity was achieved when the electrodes were positioned directly beneath the loading area. Moreover, the study showed that the stress sensitivity of self-sensing cement-stabilized sand increased proportionally with the stress level. An examination through scanning electron microscopy (SEM) demonstrated that the loading condition influences the bridging characteristics of carbon nanomaterials in cement-stabilized sand, leading to diverse electromechanical behaviors emerging based on the loading condition. This study underscores the importance of considering specific parameters when designing self-sensing cement-stabilized sand for application in practical field use.

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).

Piezoresistive performance of self-sensing cement-based composites filled with multi-layer graphene

Development of self-sensing cementitious composites is promising for structural health monitoring. This study aims at the piezoresistive performance of Multi-layer Graphene filled cement-based composites (GCBC). Experiments were conducted to evaluate the sensitivity, repeatability, linearity and hysteresis of GCBC working as a sensor under different loading systems. Percolation characteristic of GCBC, with one order of magnitude decrease in resistivity in line with the MLGs content increased from 2 vol.% to 6 vol.%, was obtained. The piezoresistive ability of GCBC containing 5 vol.% MLGs showed high sensitivity and fidelity under both cyclic and monotonic loading, with the stress sensitivity of 1.89%/MPa and gauge factor of 266.4. Meanwhile, the resistivity response of GCBC demonstrated four distinct behaviours under the monotonic loading. The piezoresistive model for GCBC dominated by tunnelling effect was proposed and compared with the experimental results. The comparative result showed good agreement between predictions with the experimental curves.

Pressure-Sensitive Capability of AgNPs Self-Sensing Cementitious Sensors.

Intelligent monitoring approaches for long-term, real-time digitalization in structural health monitoring (SHM) are currently attracting significant interest. Among these, self-sensing cementitious composites stand out due to their easy preparation, cost-effectiveness, and excellent compatibility with concrete structures. However, the current research faces challenges, such as excessive conductive filler, difficulties in filler dispersion, and insufficient stress sensitivity and instability. This study presents a novel approach to these challenges by fabricating self-sensing cementitious sensors using silver nanoparticles (AgNPs), a new type of conductive filler. The percolation threshold of AgNPs in these materials was determined to be 0.0066 wt%, marking a reduction of approximately 90% compared to traditional conductive fillers. Moreover, the absorbance test with a UV spectrophotometer showed that AgNPs were well dispersed in an aqueous solution, which is beneficial for the construction of conductive pathways. Through various cyclic loading tests, it was observed that the self-sensing cementitious sensors with AgNPs exhibited robust pressure-sensitive stability. Additionally, their stress sensitivity reached 11.736, a value significantly surpassing that of conventional fillers. Regarding the conductive mechanism, when encountering the intricate environment within the cementitious material, AgNPs can establish numerous conductive pathways, ensuring a stable response to stress due to their ample quantity. This study provides a significant contribution to addressing the existing challenges in self-sensing cementitious materials and offers a novel reference for further research in this domain.

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 12mm. Four different weight ratios were considered for each type of CF. The results indicate that the inclusion of CF increases compressive strength by approximately 2MPa, while flexural strength can increase by up to 39%, with the unsized 6mm fibres performing best at a concentration of 0.7wt%. In the piezoresistive model, the higher fractional change in resistivity (FCR) between 0-2MPa 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.

Performance of 3D-Printed Beams and Slabs Using Self-Sensing Cementitious Composites and DIC Method.

This paper aims to explore the structural performance of 3D-printed and casted cement-based steel-reinforced concrete beams and one-way slabs incorporating short carbon fibre and activated carbon powder, which have been shown to enhance concrete's flexural strength and reduce its electrical resistivity. The samples are cast and printed in 250 × 325 × 3500 mm beams and 150 × 400 × 3500 mm one-way slabs and mechanical, electrical, and piezoresistivity properties were measured. This length of beams and one-way slabs with rebars have been considered as they can magnify the flexural and cracking behaviour and make them easier to be monitored and analysed. The samples were loaded up to 80% of maximum stress. Crack propagation and strain was assessed using the 2D digital image correlation (DIC) method. The results compared samples under continuously increasing loads between 3D-printed and cast samples. The 3D-printed composites had a better piezoresistive response due to the enhanced anisotropic behaviour. DIC analysis illustrated similar results among different samples, while 3D-printed blocks had lower cracking performance due to the horizontal case fracture in lower stress.

Self-sensing cementitious composites incorporating hybrid NGPs/CNTs/NCBs for structural health monitoring

Self-sensing cementitious composites incorporating hybrid NGPs/CNTs/NCBs for structural health monitoring

Measurement techniques for self-sensing cementitious composites under flexure

Intrinsic self-sensing concrete has gained substantial interest recently as a promising construction material for smart structures. Most of the research focuses on testing their sensing behaviour under pure compression, but in practice, damage to concrete structures due to bending is more commonly seen. It is thus necessary to also test the sensing performance of the composites under flexural loading. Due to the variation of the stress distribution in a concrete element subject to bending, the influence of the geometry of electrodes on measured results is more complex. This study investigated the self-sensing behaviour of prismatic graphite-cementitious specimens under three- and four-point bending with different electrode placements. Composites were tested both in the elastic and plastic range by applying cyclic and monotonic flexural loading. Prisms with different sample depth, sample width, electrode penetration and electrode diameter were further modelled numerically using Finite Element Analysis (FEA) to study the electrical behaviour. Experimental results show that to obtain accurate measurements for composites under flexure, the electrodes need to be at close spacings with shallow penetration at the tensile side of the specimen in order to measure behaviour in the tensile zone. An equation for the relationship between the measured resistance and the geometry variables has been found through numerical simulation that can estimate the influence zone within the sample and hence enable optimisation of the electrode placement for self-sensing cementitious composites under flexure.

Characterization and piezo-resistivity studies on graphite-enabled self-sensing cementitious composites with high stress and strain sensitivity

Carbon-based conductive fillers have recently been incorporated into a cement matrix to develop an intrinsic self-sensing concrete for data monitoring without the need for embedded, attached, or remote sensors while maintaining or improving its mechanical properties and durability. This paper studies cementitious composites filled with graphite as a novel self-sensing construction material. Experiments were systematically conducted to investigate the dispersion, chemical, mechanical, electroconductivity and piezo-resistivity properties of the composites. Experimental results showed an effective mixing with uniform dispersion of the graphite which acted as an inert filler in the mix and did not alter the microstructure of cement hydration products. Isothermal calorimetry, TGA and rheology tests showed good hydration and adequate workability of the composites with low graphite concentration (≤ 10%), while the effect of adding graphite on the compressive strength is insignificant for graphite concentrations of up to 10%. Monotonic and cyclic compressive test results indicated a repeatable piezo-resistivity performance for low graphite concentration cementitious composites whose stress sensitivity values vary from 0.75 to 7.25%/MPa, and strain sensitivity/gauge factor (GF) 150–1250 with some hysteresis. These combinations showed a stable and reliable piezo-resistivity and the ability to detect damage upon failure.

Concrete units for strain-monitoring in civil structures: Installation of cement-based sensors using different approaches

Cementitious materials with self-sensing properties have been recently proposed for strain-monitoring of civil structures. Although different methods have been used to install cement-based sensors in concrete elements, there is a lack of systematic experimental comparisons between self-sensing mechanisms provided by these different methodologies. The present work investigated the bonding performance of different interface agent types (cement-based bonding agent, polyurethane-based interface and epoxy-based structural adhesive) between self-sensing cementitious materials and concrete substrates. It also reported the strain-monitoring performance and the compressive behavior of self-sensing cementitious composites embedded into solid concrete units using three different installation methods: (i) the cement-based sensor was in the hardened state and the concrete structural element was in the fresh state during the installation process (HS/FC method - hardened sensor/fresh concrete); (ii) the fresh cement-based sensor was installed in hardened concrete units (FS/HC method - fresh sensor/hardened concrete); and (iii) both components were in the hardened state during the installation process (HS/HC method - hardened sensor/hardened concrete). Results indicated that the epoxy-based structural adhesive provided the best bonding performance and ensured suitable transference of stresses between cement-based sensors and the monitored concrete element. The HS/FC and the FS/HC approaches were found to be appropriate for future cement-based sensors installations because they provided composites with appropriate values of average gauge factor and mechanical performance.

Development and evaluation of conductive ultra-lightweight cementitious composites for smart and sustainable infrastructure applications

The stability of electrical conductivity of self-sensing cementitious composites under different environments is the key to the application of structural health monitoring using electrical resistivity/impedance tomography. In addition, the lightweight of concrete is becoming a new requirement for sustainable development. This study aims to develop long carbon fibre (LCF) reinforced ultra-lightweight cementitious composites (ULCC) to address these issues. The mechanical properties and electrical and thermal conductivities of composites were investigated. The conductivity of LCF-reinforced ULCC under different testing methods (AC and DC), frequencies, temperatures, water content in the mixture, and curing ages were examined. The electromechanical properties of LCF-reinforced ULCC under compression (monotonic, cyclic loading under different loading peaks and loading rates) and tension were also tested. Except for the content, the effects of the pre-heating treatment of carbon fibre on the conductivity and electromechanical properties were compared. The experimental results revealed that LCF can remove the polarization effect and maintain the stability of the electrical conductivity of LCF-ULCC under different environments. The LCF can build a complete and continuously 3D conductive pathway, and free electrons can transport stably in the matrix, which can diminish the effect of electrolytic ions on the conductivity. The LCF-reinforced ULCC can show sensitive electromechanical ability where LCF is better used for self-sensing under tension when compared with compression. The conductivity of composites increases with increasing LCF content but initially increases and then decreases with increasing the pretreatment heating temperature of LCF. It is efficient to increase the conductivity and mechanical properties of composites by heating LCF to 300 °C. The composites (Oven-dry density: 990–1100 kg/m3) are a good thermal insulation material and the thermal conductivity is between 0.35 and 0.45 W/mK. Finally, microstructural analysis was conducted and the mechanisms of the improved electrical conductivity and thermal insulation of the composites were discussed.

Performance of 3D printed columns using self-sensing cementitious composites

Self-sensing cementitious composites can be used as an economical method of structural health monitoring. This is achieved by using the piezoresistivity effect to assist in the detection of defects and enabling repairs to be conducted early which can therefore avoid structural failures. This study uses an established self-sensing cementitious composite mix to 3D print column to examine its structural performance and sensing capabilities in components of large-scale concrete structures. An extrusion-based 3D printer was used to print hollow square column segments. These segments were then joined and filled with steel rebar reinforcement and cast concrete. Another column was created using mould cast concrete for comparison. The specimens were tested under static axial compression to determine their compressive properties, electrical resistivity and piezoresistivity response. The results showed that the 3D printed column performance can be monitored using the self-sensing composite until the printed shell cracks. The 3D printed column did show superior electrical properties with a lower electrical resistance of 274.2 Ω.cm. These results indicate that a different manufacturing approach to creating a 3D printed column should utilise the advantages of 3D printing for use in self-sensing concrete structures.

A novel measurement system for self-sensing graphite-cement composites

Carbon-based conductive fillers have been incorporated into cement matrix to develop smart self-sensing materials with piezoresistive properties. However, accurately measuring the sensing property of the cement composite without compromising its mechanical performance is not easy to achieve in practical engineering. Therefore, in this study, a novel experimental setup for measuring the self-sensing properties of conductive fillers embedded cementitious composites was developed. This multi-functional measurement system is able to measure specimens under compressive and flexural stress with different loading profiles, apply various loading rates, obtain the electrical properties, and measure the strain using both LVDT and Particle Image Velocimetry (PIV) or Digital Image Correlation (DIC) techniques with all the data synchronised to one file sharing the same time stamp controlled by Python codes. In this study, the piezoresistivity and the performance on damage detection of the cementitious composites with low graphite concentration (5%) in a bulk form were investigated through monotonic compressive and flexural tests. Experiment results include the specimens’ stress, strain and Fractional Change in Resistivity (FCR). Data analysis showed that the set-up and methodology developed in this study are effective to test self-sensing cementitious composites, and the graphite-cement composites used in this study have a stable piezoresistivity and able to detect damage upon failure.

Structural performance of reinforced concrete beams with 3D printed cement-based sensor embedded and self-sensing cementitious composites

Cementitious composites containing conductive functional fillers can achieve self-sensing and continuous monitoring of the damages and cracks through piezoresistive characteristics defined as the electrical resistivity change under external deformation. Previous studies developed two types of self-sensing cementitious composites expressing superior piezoresistive characteristics, as well as good linearity, repeatability, and sensitivity. This research further investigates the flexural damage sensing capability of the developed self-sensing cementitious composites by applying them to reinforced concrete beams. The 3D printed self-sensing cementitious composites containing carbon fibre (CF) at 0.7 wt% and activated carbon powder (ACP) at 0.25 wt% were tested as the embedded sensors in this study. Mould cast self-sensing cementitious composites developed by 100 wt% of magnetite aggregate (MA), 0.3 wt% of CF, and 0.05 wt% of carbon nanotube (CNT) were examined in bulk form. The corresponding fractional change in resistivity (FCR) was attained on the tension face of the reinforced concrete beams when subjected to flexural loading. Experimental findings suggest that the embedded 3D printed cement-based sensors in beam elements can contribute to the load-carrying capacity of the reinforced concrete beam, achieving an average load of yielding at 88.65 kN and a maximum loading capacity of 111.09 kN, which is 10% and 12% higher compared with the controlled reinforced concrete beam, respectively. In terms of sensing performance, both 3D printed self-sensing cementitious composites in sensor form and mould cast self-sensing cementitious composites in bulk form could reflect stress and strain changes and detect crack formation in reinforced concrete beams, which demonstrate gage factors at 318 and 527, respectively. However, 3D printed cementitious sensors are more effective when reinforced concrete beams are severely damaged. In terms of self-sensing cementitious composites in bulk form, the randomly distributed CF and CNT could provide a more pronounced crack and damage sensing capability, with fluctuations that can be observed at both low and high levels of damage.

Preparation, property determination and bridge health monitoring applications of self-sensing cement nanocomposites

Graphene has local superconductivity, very high electron mobility, good mechanical properties and high stability, and is therefore an excellent filler material for self-sensing cementitious composites. The incorporation of graphene provides a broad application prospect for the development of self-sensing cementitious composites. This work explored the effects of polyethylene (PE) fiber size and surface modifiers on the compressive and tensile properties of ultra-high performance concrete (UHPC). Then, graphene was added on this basis to observe the effect of graphene doping on the electrical properties of UHPC and to select the optimal doping amount. Finally, the mechanical properties and tensile and compressive self-sensing of the UHPC added with graphene were tested. Finally, the bridge with higher stress under self-weight is simulated by finite element to find the location of sensor placement and design a simple bridge health monitoring system.

An Experimental Study on Electrical Properties of Self-Sensing Mortar

Self-sensing cementitious composites are a combination of conventional materials used in the construction industry along with any type of electrically conductive filler material. Research has already been carried out with various types of conductive fillers incorporated into cement mortars to develop a self-sensing material. Carbon fibres have been used as conductive fillers in the past, which is uneconomical. In order to overcome this drawback, brass fibres have been introduced. This study concentrates on the behaviour of self-sensing mortar under two different curing conditions, including air and water curing. The main aim of this paper is to determine the self-sensing ability of various types of smart mortars. For this purpose, an experimental study was carried out, with the addition of various brass fibres of 0.10%, 0.15%, 0.20%, 0.25%, and 0.30% by volume, to determine the electrical properties of cementitious mortar. In addition, different combinations of brass and carbon fibres were considered, such as 95% brass fibre with 5% carbon fibre, 90% brass fibre with 10% carbon fibre, and 85% brass fibre with 15% carbon fibre by volume, to determine the piezoresistive behaviour. A fractional change in electrical resistance was determined for all the mortar cubes. A fractional change in electrical resistance (fcr) is defined as the change in its electrical resistance with respect to its initial resistance (ΔR/R). Additionally, the temperature effects on self-sensing mortar under compressive loading were observed for various temperatures from room temperature to 800 °C (at room temperature, 200 °C, 400 °C, 600 °C, and 800 °C). It was observed that the addition of brass fibre to the cement mortar as an electrically conductive filler improved the self-sensing ability of the mortar. After 28 days of water curing, when compared to conventional mortar, the percentage increase in change in electrical resistance (fcr) was observed to be 26.00%, 26.87%, 27.87%, 38.55%, and 35.00% for 0.10%, 0.15%, 0.20%, 0.25%, and 0.30% addition of brass fibres, respectively. When the smart mortar was exposed to elevated temperatures, the compressive strength of the mortar was reduced. Additionally, the fractional change in electrical resistance values was also reduced with the increase in temperature. In addition to this, the self-sensing ability of smart mortars showed improved performance in water curing rather than in air-cured mortars. Compressive strengths, stress, strain, and change in electrical resistance (fcr) values were determined in this study. Finally, microstructural analysis was also performed to determine the surface topography and chemical composition of the mortar with different fibre combinations.

Development of 3D printable self-sensing cementitious composites

This paper explores the potential of fabricating self-sensing cementitious composites using the 3D printing technology. In this study, carbon fibre (CF) and activated carbon powder (ACP) were studied at various concentrations as functional fillers. An extrusion-based 3D printer was used for 3D printed samples preparation and the mould cast samples were fabricated as a direct comparison. The mechanical properties, electrical resistivity and piezoresistive performance in two directions were measured. The results indicate that 3D printing technology can achieve fibre alignment within the printed filament and, consequently, result in anisotropic behaviour of the 3D printed composites. The optimal mechanical strength with compressive strength of 74.9 MPa and flexural strength of 16.4 MPa were achieved by 3D printed composite with hybrid CF at 0.7 wt% and ACP at 0.25 wt%. The optimal 3D printed composite shows an excellent piezoresistive response with improved linearity, repeatability and signal quality when the loading direction is perpendicular to the printing direction, demonstrating a strong fit with the predicted equation. On the microstructural level, scanning electron microscopy images indicated the fibre alignment and enhanced fibre–matrix bonding of 3D printed composites.

In-situ synthesizing carbon nanotubes on cement to develop self-sensing cementitious composites for smart high-speed rail infrastructures

Self-sensing cementitious composites (SSCCs) with carbon nanotubes (CNTs) have attracted extensive attention because of their excellent mechanical and durability properties combined with multifunctional benefits. However, their performance modulation, as well as scalable manufacturing and application, are limited by the uniform dispersion of CNTs inside them. Here, a straightforward approach to in-situ synthesizing CNTs on cement (CNT@Cem) toward alleviating the CNTs’ dispersion issue and enhancing their composite efficiency and effectiveness is explored. Due to the inherently containing silicate and ferrite phases, microscale cement particles act as effective substrate-bound catalysts, facilitating high-yield and strongly anchored CNTs growth. The hierarchically structured CNT@Cem integrates the dual functions of reinforcement and conduction, significantly affecting early-age hydration, mechanical, electrical, and self-sensing properties of the final SSCCs with CNT@Cem. The CNT@Cem structure can promote early-age hydration while slowing the later hydration rate and hindering the strength development of the SSCCs. The addition of CNT@Cem can be effective in tailoring the electrical microstructures to enhance the electrical conductivity and self-sensing sensitivity of the SSCCs. The SSCCs with CNT@Cem achieved a maximum stress sensitivity of 2.87%/MPa with a gauge factor of 748. They demonstrated excellent repeatability and stability, outstanding adaptability to various applied conditions, and fast response and recovery. The developed SSCCs-engineered smart track slab is competent in axle counting and speed detection. It opens up a new territory to develop high-performance and versatile SSCCs-engineered smart components/structures for long-term, wide distribution, and low-cost monitoring of high-speed rail (HSR) infrastructures.

The Utilization of Pearson’s Method to Analyze Piezoresistive Effect in Self-Sensing Cement Composite with Graphite

Structural health monitoring (SHM) techniques aim to detect and prevent failures in constructions, although their use may require many sensors, which makes this technique expensive and laborious. In this sense, the use of self-sensing cementitious composites based on the piezoresistivity effect could be a solution to some monitoring problems. Thus, the evaluation of the piezoresistive effect is commonly performed by analyzing the linearity between mechanical forces and the variation of electrical resistivity, through the coefficient of determination (R2). However, this work has been used to perform the analysis through Pearson's correlation in samples of self-sensing cementitious composites with graphite addition. The results obtained have shown that Pearson's correlation has the potential to be used for the evaluation of the correlation between electrical resistivity and mechanical forces to verify the piezoresistive effect in the cases studied.