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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. [ABSTRACT FROM AUTHOR]

An extensive experimental study was conducted to investigate the co-effects of surface area and distance between electrodes as well as filler scales on the percolation threshold of piezoresistive cement-stabilised sand. In this route, the electrical resistivity of numerous specimens of different sizes and composed of different content of carbon-based conductive fillers was measured, including carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), and carbon fibres (CFs) with different aspect ratios. In addition, the numerical relations between the electrical percolation threshold and matrix dimensions were expressed for different conductive fillers. Furthermore, the electrical percolation threshold of two large-scale specimens with different shapes (a 10 × 10 × 85 cm3 beam, and a 15 cm size cube) were predicted through numerical relations, and their piezoresistivity performances were investigated under compression cyclic loading (cube) and flexural cyclic loading (beam). The mechanical properties of the specimens were also evaluated. The results showed that the changes in the length, width, and thickness of the matrix surrounded between electrodes had a significant effect on the electrical percolation threshold. However, the effects of length changes on the percolation threshold were greater than the width and thickness changes. Generally, increasing the aspect ratio of the conductive fillers caused a reduction in the electrical percolation threshold of the cementitious geocomposite. The appropriate piezoresistivity response of the large-scale specimens composed of filler content equal to their percolation threshold (obtained by the numerical relation presented in this study) showed the adequacy of the results in terms of threshold dosage prediction and self-sensing geocomposite design. The results of this study addressed a crucial factor for the design of self-sensing composites and pave the way for the development of field-applicable, smart, cementitious geocomposite. [ABSTRACT FROM AUTHOR]

Self-sensing composites are becoming highly attractive for civil engineering applications to improve the safety and performance of structures. These smart composites show a detectable change in their electrical resistivity with applied stress or strain and this unique characteristic make them useful for health monitoring of structures. Till date, different forms of carbon composites, i.e. short fibre, continuous fibre, particles, nano fibres, nanotubes, etc. have been utilized for this purpose. In this context, the present paper reports an overview of different self-sensing composite systems used for the health monitoring of civil engineering structures. [ABSTRACT FROM AUTHOR]

In this study, a self-sensing cementitious stabilized sand (CSS) was developed by the incorporation of hybrid carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) based on the piezoresistivity principle. For this purpose, different concentrations of CNTs and GNPs (1:1) were dispersed into the CSS, and specimens were fabricated using the standard compaction method with optimum moisture. The mechanical and microstructural, durability, and piezoresistivity performances, of CSS were investigated by various tests after 28 days of hydration. The results showed that the incorporation of 0.1%, 0.17%, and 0.24% CNT/GNP into the stabilized sand with 10% cement caused an increase in UCS of about 65%, 31%, and 14%, respectively, compared to plain CSS. An excessive increase in the CNM concentration beyond 0.24% to 0.34% reduced the UCS by around 13%. The addition of 0.1% CNMs as the optimum concentration increased the maximum dry density of the CSS as well as leading to optimum moisture reduction. Reinforcing CSS with the optimum concentration of CNT/GNP improved the hydration rate and durability of the specimens against severe climatic cycles, including freeze–thaw and wetting–drying. The addition of 0.1%, 0.17%, 0.24%, and 0.34% CNMs into the CSS resulted in gauge factors of about 123, 139, 151, and 173, respectively. However, the Raman and X-ray analysis showed the negative impacts of harsh climatic cycles on the electrical properties of the CNT/GNP and sensitivity of nano intruded CSS. [ABSTRACT FROM AUTHOR]

In this paper a hybrid combination of carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) was used for developing cementitious self-sensing composite with high mechanical, microstructural and durability performances. The mixture of these two nanoparticles with different 1D and 2D geometrical shapes can reduce the percolation threshold to a certain amount which can avoid agglomeration formation and also reinforce the microstructure due to percolation and electron quantum tunneling amplification. In this route, different concentrations of CNT + GNP were dispersed by Pluronic F-127 and tributyl phosphate (TBP) with 3 h sonication at 40 °C and incorporated into the cementitious mortar. Mechanical, microstructural, and durability of the reinforced mortar were investigated by various tests in different hydration periods (7, 28, and 90 days). Additionally, the piezoresistivity behavior of specimens was also evaluated by the four-probe method under flexural and compression cyclic loading. Results demonstrated that hybrid CNT + GNP can significantly improve mechanical and microstructural properties of cementitious composite by filler function, bridging cracks, and increasing hydration rate mechanisms. CNT + GNP intruded specimens also showed higher resistance against climatic cycle tests. Generally, the trend of all results demonstrates an optimal concentration of CNT (0.25%) + GNP (0.25%). Furthermore, increasing CNT + GNP concentration leads to sharp changes in electrical resistivity of reinforced specimens under small variation of strain achieving high gauge factor in both flexural and compression loading modes. [ABSTRACT FROM AUTHOR]

[Display omitted] • The trends of FCR under monotonic loading are contingent on the health of the cement composite. • A novel measurement coefficient (β t) is proposed for monitoring the initiation and propagation of cracks. • DIC and SEM experiments confirm the effectiveness of β t in crack detection. Health and safety are relevant in transportation infrastructures. Most health evaluations are performed by spot measurements using conventional transducers. This study aims to explore a self-sensing composite that can be integrated into a structural layer of a pavement or rail track to continuously evaluate the full layer performance in critical zones and anticipate damage occurrence. For this purpose, a 10 % cement-stabilized standard sand used in conventional structural layers was transformed into a self-sensing composite by synergistic incorporation of 3 % and 4 % multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs). In addition, Pluronic F-127 and tributyl phosphate 97 % (TBP) were used for better dispersion of carbon nanomaterials in the mixture, and these materials were compacted at maximum density and optimum moisture conditions. Through a detailed laboratory investigation, including piezoresistivity measurements, digital image correlation (DIC), and microstructure analysis through scanning electron microscopy (SEM), it was possible to propose a novel method to detect the propagation of damage under monotonic loading based on changing the slope of normalized electrical resistance against time. This was supported by a theoretical background able to explain that the emergence and propagation of damage result in increasing electrical resistance and hence fractional changes in resistance (FCR). [ABSTRACT FROM AUTHOR]

In this study, a novel multifunctional grid-shaped polymeric composite (MGPC) with reinforcing, autonomous strain, stress, and damage sensing and localizing in addition to targeted heating capabilities, has been developed. Distinct from preceding composites, this grid synthesizes these features collectively for the first time and concurrently addresses current challenges in multifunctional composites, such as environmental impact, data reliability, complexity, and production costs. The fabrication process entails 3D printing an electrical circuit with conductive filaments within a polylactic acid (PLA) host polymer. The conductive filament was composed of a thermoplastic polymer (TPU) infused with carbon nanotubes (CNT)-grafted carbon fibres (CFs) produced via chemical vapour deposition. The mechanical, microstructural, and electrical properties of the grid elements and cementitious slabs reinforced with MGPC were comprehensively examined. The MGPC's performance in traffic flow monitoring, mechanical behaviour prediction, and damage localization was assessed through wheel tracking, asymmetric punch tests, piezoresistivity response evaluation, and digital image correlation techniques. Furthermore, the self-warming ability of the MGPC in cementitious composites was investigated using different voltages. The extruded TPU containing CNT-grafted CFs exhibited an electrical percolation threshold of approximately 5.0 wt%, resulting in a conductivity of around 70 S/m for the filaments. Incorporating MGPC as reinforcement within cementitious composite slabs led to notable enhancements, with flexural strength increasing by approximately 15 % and failure strain by up to 350 %. Wheel tracking tests revealed changes in the electrical: 5.8 % for 520 N and 7.8 % for 700 N wheel loads, with roughly 5.0 % average error in velocity detection. Transverse elements precisely detected wheel locations demonstrating the MGPC capabilities in accurately detecting wheel speed weight, and location. The study established strong correlations between electrical resistance changes, mechanical behaviour, and damage detection, affirming the MGPC's reliability and efficacy for damage monitoring and localization. The cementitious slab reinforced with MGPC reached around 52°C through a 20 V direct current, with a heating rate of 0.25°C/s and a power density of 142 W/m², showing its potential for practical applications such as self-healing and de-icing. However, design parameters such as mesh and conductive circuit configuration, long-term performance, as well as Life Cycle Assessment need further investigation. [Display omitted] • A Polymeric Composite grid with self-sensing, self-heating, and reinforcing functions was developed. • The smart grid potential for traffic flow, type, speed, and density monitoring was demonstrated. • The grid's capability to predict damage intensity and location was effectively showcased. • The smart grid's targeted self-heating abilities for de-icing and self-healing functions were drawn. [ABSTRACT FROM AUTHOR]

This study introduces an innovative multifunctional cementitious-based geocomposite with self-sensing and self-heating capability, generated for transportation infrastructural health monitoring and traffic recognition. In this route, hybrid graphene nanoplatelets (GNP) and carbon nanotubes (CNT) were uniformly dispersed within a matrix of stabilized sand containing 10% cement. The mechanical and microstructural performances of the composite were examined using various tests. The self-sensing capabilities of the composite were also investigated. In addition, the effects of temperature, moisture content, and loading shape, speed and magnitudes on the strain and stress sensing capability of the specimens were also investigated. The self-sensing performance of this cementitious geocomposite for the asphalt layer strain detection was assessed in a hybrid section including the asphalt mixture layer and smart geocomposite. Moreover, the research also examined the composite's capacity for self-heating, a highly relevant application within the transportation sector. The 0.98 ˚C/s heating rate, 526 mW/˚C heating power, and 251 ˚C maximum temperature proved the high heating potential of this geocomposite for deicing, self-healing, and temperature sensing applications. The specimens showed a supreme correlation between strain changes and fractional changes in electrical resistance (FCR) values in different loading patterns. Although increasing temperature amplified the piezoresistivity response of the composite, the introduction of humidity had adverse impacts. An appropriate correlation was observed between gauge factor, modulus, and digital image correlation analysis. The piezoresistive response of this geocomposite in the hybrid section including smart geocomposite and asphalt layer, representative of the common structural layers of urban pavements, showed the ability of this geocomposite in terms of strain and stress detection of itself and even in the asphalt layer. This study provides a bright horizon for smart materials development in civil infrastructures, particularly transportation and railways. [Display omitted] • A self-sensing, self-heating cement geocomposite was developed for infrastructure health monitoring and traffic recognition. • The effects of temperature, moisture, loadings' shapes, speeds and magnitudes on composite sensing ability were investigated. • The damage detection ability of the composite was also examined using the DIC technique. • Composite sensing ability for asphalt strain detection was assessed in a hybrid section of asphalt mixture and geocomposite. • The composite's self-heating potential, along with its mechanical and microstructural properties, was examined. [ABSTRACT FROM AUTHOR]

[Display omitted] • Innovative results of strain sensing and damage sensing behaviour of a self-sensing cement-stabilized sand under cyclic loading from a small load to the failure. • Fractional changes in resistance (FCR) changes can identify the trends of elastic behaviour to failure in cementitious composites. • The digital image correlation (DIC) test precisely detected the emergence of cracks in synchronization with FCR changes. • Scanning electron microscope (SEM) analysis showed that multi wall carbon nanotube and graphene nanoplates (MWCNT/GNP) bridges the voids and cracks, resulting in random conductive pathways. • The present study is a contribution for application of self-sensing cementitious composites to autonomously monitor the health of infrastructures. The sensing ability induced by carbon nanomaterials has attracted considerable attention in the scientific community for use in cement and concrete. However, the application of carbon nanomaterials in stabilized soils has been scarce. Therefore, in this study, the use of multiwalled carbon nanotubes (MWCNTs) and graphene nanoplates (GNPs) in cement-stabilized sand was investigated. MWCNTs and GNPs not only improve the mechanical characteristics of cement-stabilized sand but also can change conventional cement-stabilized sand into an intrinsic self-sensing material, which is important for structural health monitoring (SHM). The proposed composite was submitted from small to large stresses under cyclic loading to detect strain, cracks, and damage. To identify different levels of damage, various macro- and microscale analyses were carried out to explain the composite self-sensing ability. The key results used in the analysis were from the fractional changes in resistivity (FCR) and digital image correlation (DIC) and scanning electron microscope (SEM) measurements. These results demonstrate the capability of the proposed MWCNT/GNP cement-stabilized sand for detecting the strain, stress, and damage of materials through different self-sensing trends in the elastic and plastic regions. It is very important to monitor the serviceability of a composite in a real structure through different self-sensing trends in the elastic and plastic regions. [ABSTRACT FROM AUTHOR]

The development of innovative sensing technologies is essential for implementing smart road pavement monitoring systems. The design of asphalt-based materials with intrinsic self-sensing capabilities represents a promising solution in that regard. Despite this, this technology is still not mature and further efforts should be made for its development. With this aim, the present paper evaluates the self-sensing response of asphalt mixtures incorporating graphene nanoplatelets (GNPs), and proposes the use of a digital signal processing technique, based on wavelet transform, for the analysis of the electrical signals generated by the mixture. The results showed that the mixtures exhibited both stress and damage sensing functions, albeit some issues related to the dispersion of GNPs should be further investigated. In addition, wavelet transform analysis seems to be able to capture insightful information about the electrical response of the mixture, as well as its structural condition, useful for traffic and pavement health monitoring purposes. [Display omitted] • The incorporation of GNPs endows asphalt mixtures with stress and damage-sensing functions. • Self-sensing asphalt pavements can be used for pavement health monitoring operations. • The electrical response was analyzed with digital signal processing (DSP) analysis. • SEM analysis revealed agglomerations of GNPs inside the bitumen. [ABSTRACT FROM AUTHOR]

[Display omitted] • A field-applicable smart planar braid composite (SPC) was developed. • Self-sensing, self-heating and reinforcing abilities of the composite were assessed. • The SPC potential to be used in different structural elements was demonstrated. Allocating different capabilities to structural elements simultaneously is still challenging. In this study, a field-applicable multifunctional planar braided composite with the abilities of reinforcing, self-sensing and self-heating was developed for the first time. In this route, three commercial fabrics were used, including cotton, cotton/polyamide, and polyester. The fabrics were first chemically treated and then coated with a carbon nanomaterial-based polymeric conductive paste using screen printing with different concentrations and layers. The samples were then covered and sealed with a thermoplastic polyurethane-based polymer to avoid environmental factors effects. Smart planar composites (SPC) were also used as reinforcement for cementitious specimens. The electrical conductivity and joule heating capability of the samples were also evaluated. The microstructure of the SPCs was investigated using various tests. The mechanical and self-sensing performances of the cementitious composite reinforced with different SPCs were assessed using different load patterns. The results showed a heating rate of 0.44 ˚C/s, a joule heating power of 0.7 W/˚C, and a maximum temperature of 44 ˚C which proved the proper heating capability of the cementitious composites reinforced with SPCs. The great correlation between electrical resistivity changes and strain values indicated the high potential of the composite in strain sensing for different applications. The SPCs also improved the post-crack behaviour of the specimen and its flexural strength and failure strain by approximately 50% and 118%, respectively. The outcomes of this study draw a bright horizon in multifunctional braided composite development with different applications in civil infrastructures, which is a crucial step for intelligent cities' advances. [ABSTRACT FROM AUTHOR]

[Display omitted] • In this study different polymer-based braided composite rods (BCRs) were successfully developed to reinforce and monitoring of structural elements. • Piezoresistivity and mechanical behaviour of different BCRs reinforced with short carbon fibre, hybrid carbon nanotube/graphene nanoplatelets, and directed carbon fibre were investigated. • The proper piezoresistivity behaviour of the concrete beam reinforced with smart BCRs demonstrated a high potential of this elements in terms of strain and stress detection. This study is a systematic effort focused on the development of feasible self-sensing braided composite rods (BCRs) for structural reinforcement and damage monitoring. In this route, BCRs were produced and reinforced with different carbon-based conductive fillers, including hybrid carbon nanotubes (CNTs)/graphene nanoplatelets (GNPs), chopped carbon fibres (CCFs), and directed (unidirectional) carbon fibres (DCFs) using a specific process. The mechanical and self-sensing performances of the BCRs were evaluated using various tests. Furthermore, a honeycomb 3D system of DCF/BCRs was used to reinforce a 100 mm × 100 mm × 850 mm concrete beam, and the piezoresistivity of the specimen was investigated under cyclic flexural loading with different patterns. The maximum tensile strength and elastic modulus were observed for the BCR reinforced with 1.0% (wt) CCFs. However, the maximum sensitivity was attained for the BCRs with 3.0% hybrid CNTs/GNPs. The analysis of the results and economic assessment showed that DCF-reinforced BCRs are the optimum choice for developing a feasible, reliable, and economic structural health monitoring technique. [ABSTRACT FROM AUTHOR]