Civil Engineering Applications Research Articles

Microstructural Properties of Asian Hornet Nest Paper-Like Materials: Preliminary Step Towards Biomimicry Materials for Civil Engineering Applications

Microstructural Properties of Asian Hornet Nest Paper-Like Materials: Preliminary Step Towards Biomimicry Materials for Civil Engineering Applications

Integrating AI and statistical methods for enhancing civil structural practices: current trends, practical issues, and future direction

The integration of artificial intelligence (AI) and statistical methods has revolutionized civil engineering by enhancing accuracy, efficiency, and reliability in various processes. This review systematically examines how advanced optimization techniques, including artificial neural networks (ANNs), Design of Experiments (DOE), and fuzzy logic (FL), are transforming civil engineering practices. It emphasizes the significant roles these methods play in addressing modern challenges such as structural health monitoring, damage detection, seismic design optimization, and concrete condition assessment. The review delves into case studies and real-world applications, showcasing the potential of these methods to create more resilient, sustainable, and cost-effective infrastructures. It critically examines the limitations and scalability of these techniques, identifying gaps in current research and practical challenges in real-world applications. The investigation also highlights the need for substantial computational resources, data privacy, security, and software interoperability. By addressing these issues, the review not only shows advancements in optimization techniques but also outlines future research directions, aiming to bridge the gap between theoretical developments and practical applications in civil engineering. This review serves as an essential resource for researchers, professionals, and policymakers interested in leveraging optimization techniques to advance civil engineering practices.

Prediction of Scour Depth for Diverse Pier Shapes Utilizing Two-Dimensional Hydraulic Engineering Center’s River Analysis System Sediment Model

Examining scouring around bridge piers is crucial for ensuring water-related infrastructure’s long-term safety and stability. Accurate forecasting models are essential for addressing scour, especially in complex water systems where traditional methods fall short. This study investigates the application of the HEC-RAS 2D sedimentation model, which has recently become available for detailed sediment analysis, to evaluate its effectiveness in predicting scoring around various pier shapes and under different water conditions. This study offers a comprehensive assessment of the model’s predictive capabilities by focusing on variables such as water velocity, shear stress, and riverbed changes. Particular attention was paid to the influence of factors like floating debris and different pier geometries on scour predictions. The results demonstrate that while the HEC-RAS 2D model generally provides accurate predictions for simpler pier shapes—achieving up to 85% precision—it shows varied performance for more complex designs and debris-influenced scenarios. Specifically, the model overpredicted scouring depths by approximately 20% for diamond-shaped piers and underpredicted by 15% for square piers in debris conditions. Elliptical piers, in contrast, experienced significantly less erosion, with scour depths up to 30% shallower compared to other shapes. This study highlights the novel application of the HEC-RAS 2D model in this context and underscores its strengths and limitations. Identified issues include difficulties in modeling water flow and debris-induced bottlenecks. This research points to the improved calibration of sediment movement parameters and the development of advanced computational techniques to enhance scour prediction accuracy in complex environments. This work contributes valuable insights for future research and practical applications in civil engineering, especially where traditional scour mitigation methods, such as apron coverings, are not feasible.

A graphics-based digital twin (GBDT) framework for accurate UAV localization in GPS-denied environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure’s geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure.

Heat transfer inspection and entropy optimization in water/MoS2-SiO2 Casson hybrid nanofluid flow with quadratic radiation ray over an exponentially contracting permeable Riga surface: Duality and stability analysis

In this study, we have examined the primary behaviour of a hybridized electro-magneto-non-Newtonian MoS2-SiO2/H2O nanofluid over a porous Riga surface that is exponentially contracting, in particular its flow dynamics and heat transfer characteristics. The key factors of this research are the presence of quadratic thermal radiation over the Riga surface under velocity slip and convective boundary. We have solved the highly circuitous coupled partial differential equations system by utilizing the bvp4c numerical method with MATLAB programming. A dual solution is determined based on a certain amount of mass suction parameter and shrinking parameter under the critical value range; beyond that value, no solution exists. We investigated the stability of the dual solutions by determining the minimal eigenvalue. An evaluation of the temporal stability of these solutions reveals that only the first solution remains stable. This is supported by the positivity of the smallest eigenvalues (β1> 0), indicating stability. Conversely, for the second solution, the smallest eigenvalues (β1< 0) are negative, signifying its instability. It can be seen that the nanofluid temperature is substantially enhanced for both branches of solution for the quadratic thermal radiating parameter, Eckert number, and Biot number. It was found that, in the first solution branch, the velocity layout was boosted with the velocity slip factor while reduced by enhancing the Casson parameter, whereas, in the second solution branch, the opposite behavior was observed. As the suction parameter increases, the heat transfer coefficient for the first solution is decreased by about 0.5 % and increases by about 0.75 % for the second solution branch. Quadratic thermal radiation parameter Nr significantly impacts the Nusselt number, as it is reduced by approximately 27.44 % and 28.72 % for the first and second solution branches, respectively. The shear stress coefficient is decreased by about 29.32 % for the first solution branch with contracting parameter λ. Because the Riga plate, paired with Casson nanofluid flows, has industrial and civil engineering applications, this study's thermal relevance makes it applicable to a wide range of industrial and engineering disciplines.

Review Paper on AI and Ml Application in Civil Engineering

Review Paper on AI and Ml Application in Civil Engineering

Ensemble machine learning models for compressive strength and elastic modulus of recycled brick aggregate concrete

This study delivers an in-depth analysis of predicting the compressive strength and elastic modulus of recycled brick aggregate concrete (RBAC). It assembles an extensive database of 633 compression test results and develops five ensemble learning models—random forest (RF), gradient boosting regression trees (GBRT), extreme gradient boosting (XGB), light gradient boosting machine (LGBM) and stacking (St). These models are evaluated against existing empirical formulas with respect to their effectiveness. The findings reveal that machine learning (ML) models outperform existing formulas: for compressive strength prediction, traditional models achieve a maximum determination coefficient R² of 0.38, whereas ML models attain an R² range of 0.91–0.94. For elastic modulus, the highest R² values are 0.44 for traditional models and 0.97 for ML models. Notably, the LGBM and St models excel in predicting compressive strength and elastic modulus, respectively. The study also identifies critical parameters influencing RBAC’s compressive behavior and highlights their impact trends. Remarkably, while the mass-weighted water absorption of coarse aggregates and the replacement ratio of recycled brick aggregates (RBAs) have less impact on compressive strength compared to the effective water-to-cement ratio, they become more influential for elastic modulus. Additionally, the decrease in elastic modulus due to higher RBA replacement ratios or increased mass-weighted water absorption of coarse aggregates exceeds the corresponding reduction in compressive strength. This research not only deepens the understanding of RBAC’s mechanical properties but also provides valuable predictive tools for civil engineering applications.

Development of engineered polymeric reinforced cementitious composite (EPRC) using nature-inspired hollow architectures: Flexural experimental and numerical evaluations

This study explores the integration of nature-inspired architectural designs into Mechanics of Materials (MoM)-based reinforcement layouts for reinforced cementitious composites (RCs). While traditional MoM layouts focus on longitudinal rebars for tensile strength, this study aims to enhance the flexural properties of RCs by incorporating nature-inspired elements like hollow tubes found in plant stems. The research utilizes both experimental and numerical methods to evaluate the flexural performance of engineered polymeric reinforced cementitious composites (EPRCs) with integrated nature-inspired motifs. A numerical model, based on the material properties of reinforcing elements and the cementitious matrix, was developed to simulate the flexural performance of EPRCs reinforced with both solid and hollow bars, including MoM-based solid and hollow rebars. The model's predictions were validated through experimental testing of 3D-printed MoM-based solid and hollow rebars under three-point bending conditions. The results demonstrated that EPRCs reinforced with hollow rebars exhibited significantly improved flexural properties compared to those with solid rebars. Specifically, the hollow-reinforced samples achieved an average modulus of rupture of 8.68 MPa, toughness of 11.76 N m, and mid-point deflection of 1.99 mm, representing increases of 34 %, 55 %, and 93 %, respectively, over their solid counterparts. These enhancements are attributed to improved shear reinforcement and bond strength despite the reduced moment of inertia of the hollow rebars. The study provides valuable insights for optimizing the mechanical properties of RCs in civil engineering applications through the incorporation of nature-inspired architectural designs.

Piezoelectric Energy Harvesting for Civil Engineering Applications

This work embarks on an exploration of piezoelectric energy harvesting (PEH), seeking to unravel its potential and practicality. PEH has emerged as a promising technology in the field of civil engineering, offering a sustainable approach to generating energy from ambient mechanical vibrations. We will explore the applications and advancements of PEH within the realm of civil engineering, focusing on publications, especially from the years 2020 to 2024. The purpose of this study is to thoroughly examine the potential and practicality of PEH in civil engineering applications. It delves into the fundamental principles of energy conversion and explores its use in various areas, such as roadways, railways, bridges, buildings, ocean wave-based energy harvesting, structural health monitoring, and even extraterrestrial settings. Despite the potential benefits of PEH in these domains, there are significant challenges that need to be addressed. These challenges include inefficient energy conversion, limitations in scalability, concerns regarding durability, and issues with integration. This review article aims to address these existing challenges and the research gap in the piezoelectric field.

Bivariate cubic normal distribution for non-Gaussian problems

Probabilistic models play critical role in various engineering fields. Numerous critical issues exist in probabilistic modeling, especially for non-Gaussian correlated random variables. However, conventional parameter-based bivariate distribution models are generally developed for specific types of random variables, limiting their flexibility and applicability. A flexible bivariate distribution model is proposed, in which the joint cumulative distribution function is derived with the probability equivalently expressed as the summation of three basic probabilities corresponding to simple functions. These three basic probabilities are calculated with the aid of univariate cubic normal distribution, and thus the proposed model is named as bivariate cubic normal (BCN) distribution. The proposed BCN distribution has been applied in modeling several common bivariate distributions and actual engineering datasets. Results show that the BCN model accurately models both typical theoretical distributions and the JCDF of practical datasets, offering a significant improvement over existing models. Furthermore, the proposed BCN distribution has been utilized in seismic reliability assessment and the calculation of the mean recurrence interval and hazard curve of hurricane wind speed and storm size. Results demonstrate that the BCN distribution excels in modeling and matching capabilities, proving its accuracy and effectiveness in civil engineering applications.

Experimental Investigation and Machine Learning Prediction of Mechanical Properties of Rubberized Concrete for Sustainable Construction

Concrete is widely used in civil engineering applications and the natural aggregates which used in concrete are scarce, but its demand is increasing. The disposal of rubber tyres poses a significant environmental challenge, as their decomposition releases harmful chemicals into the soil and water bodies over many years. Decomposition of tyres should be done in a smart way and hence came the emergence of mixing recycled rubber crumbs into concrete as Rubberised Concrete (RC). This paper provides an in-depth analysis of the mechanical properties of concrete such as Compressive Strength (fck), Tensile Strength (ft), Flexural Strength (fcr) of 7, 14, 28 days in replacement of fine aggregate with fine rubber (FR), and Coarse Aggregate with Coarse Rubber (CR). The results indicate that RC is more suitable for structural applications, including Reinforced Concrete columns, beams, slabs, than conventional concrete. The primary objective of the article is to explore the potential use of recycled rubber crumbs in concrete, referred to as Rubberised Concrete (RC), and to analyze its mechanical properties such as compressive strength, tensile strength, and flexural strength over different curing periods. Additionally, machine learning (ML) based prediction model has been developed for various strength characteristics of concrete mixtures at 28 days. The hyperparameter optimization using Grid Search CV with fivefold cross-validation have been performed to obtain the best hyperparameters. The model’s performance is evaluated using metrics like MAE, MSE, RMSE, and R-squared values. Results reveal varying performances among different ML algorithms for predicting flexural, tensile, and compressive strengths.

Use of Various Industrial and Eggshell Wastes for the Sustainable Construction Sector

The alternative composites’ production alleviates the serious problem generated by global warming. Methods to reduce the amount of cement used in concrete production, for example, are being investigated to determine how to reduce carbon dioxide emissions in many applications. Egg shells and various industrial wastes, which are recommended to use in the construction sector at an appropriately high rate, also cause serious environmental damage. Bottom ash (BA) and marble powder (MP) wastes are used today in civil engineering applications. In addition, it is important to increase the use of eggshells due to their rich calcium carbonate content. In this work, BA and MP wastes were blended with eggshells to produce cement paste composites. Two different sets of composites were prepared during this study. The composites were prepared with cement (80%), BA (20%), and MP (20%) wastes by weight with 0.3%, 0.75%, 1.5%, and 2.5% eggshell waste. The fresh (flow table), physical (dry unit mass, apparent specific gravity, and porosity), mechanical (unconfined compressive strength and flexural strength), and durability (water absorption, seawater resistance) tests were conducted. According to the experimental results, the composites can be classified as lightweight construction materials. The test results showed that 0.75% eggshell by weight of cement in bottom ash and marble powder can be used as an optimum value for better performance. The bottom ash mixtures groups are higher water absorption and porosity values when referring to the marble powder mixture groups. The highest compressive strength value was found at 56.03 MPa in the MP mixture group and 52.79 MPa in the BA mixture groups with these optimum eggshell combinations at 56 days. The MP mixture group showed better resistance to seawater when referring to the bottom ash blended mixtures. Laboratory-produced composites are possible candidates for cost-effective and environmentally friendly building materials. The eggshells have a promising alternative binder for concrete in the near future and they are utilized together with industrial wastes such as BA and MP in sustainable concrete construction.

Shear and bonding performances between Fe-SMA and steel for strengthening of steel structures

Iron-based shape memory alloy (Fe-SMA) has been employed in China and the Czech Republic to reinforce steel bridges due to its outstanding self-prestressing characteristics. Nevertheless, the lack of theoretical guidelines necessitates this study to align with the practical Fe-SMA-reinforced applications in civil engineering. This study experimentally investigates the shear performance and load-transfer mechanism of 45 Fe-SMA/steel adhesive joints. Specifically, the shear characteristics of the adhesive joints affected by the various parameters of bonding layer thickness, steel plate thickness, bonding length and adhesive type are explored, the failure mode and load-displacement relationship are assessed, and some suggestions for optimization design are proposed. The experimental results demonstrate that the Fe-SMA/steel adhesive joints experiencing interface failure exhibit a linear load-displacement relationship. Contrarily, the load-displacement relationship of the adhesive joints under cohesive failure modes contains a yield segment. Longer bonding length results in more considerable failure evolution distances for Sika S02 and LPdur 03 specimens with cohesive failure, indicating better ductility of these specimens. Additionally, the epoxy adhesives of Sika S02 and LPdur 03 are preferred while employing Fe-SMA plates for bonding reinforcement of steel structures, which can maximize the characteristics of Fe-SMA to obtain stable and superior reinforcement performance. The experimental achievements have remarkable potentials for improving the in-service performance of old and damaged steel structures and other infrastructures.

AI optimization and mathematical simulation validated by nondestructive testing for resonance frequency in advanced composite structures for bridge applications

This paper presents a novel approach integrating artificial intelligence (AI) optimization and mathematical simulation to predict the resonance frequency of nanoclay-reinforced concrete cylindrical shell structures intended for bridge applications. These composite structures, known for their enhanced mechanical properties, require precise evaluation of their vibrational behavior to ensure structural stability and longevity. Traditional methods for predicting resonance frequencies are often time-consuming and prone to inaccuracies, especially in complex materials like nanoclay composites. To address this, an AI-based optimization algorithm was developed, incorporating Particle Swarm Optimization (PSO) and a mathematical modeling to simulate resonance characteristics under varying material and geometric parameters. The mathematical modeling is validated using nondestructive testing (NDT) techniques, such as modal analysis, which provided real-world resonance data without damaging the structure. The nondestructive testing results is compared against the mathematical model to ensure accuracy and reliability. The integration of nanoclay into the concrete matrix significantly altered the vibrational properties, enhancing the stiffness and reducing damping losses, which is crucial for bridge applications where dynamic loads are prevalent. The optimized model not only predicted resonance frequencies with high accuracy but also demonstrated its potential for large-scale bridge infrastructure. This methodology offers a streamlined and robust tool for engineers, reducing the need for physical prototyping and providing enhanced design capabilities. Future research will focus on expanding the model to incorporate additional material behaviors and load conditions, furthering its application in civil engineering.

A graph network-based learnable simulator for spatial-temporal prediction of rigid projectile penetration

Predicting plate penetration by rigid projectiles (PPRP) is crucial in terminal ballistics, with broad applications in civil and military engineering. Empirical and analytical methods face challenges in predicting field variables like displacement and stress in target plates. Although numerical methods offer high accuracy, they suffer from low computational efficiency. Herein, we introduce an efficient data-driven machine learning (ML) method based on graph neural networks (GNNs), named PGN, specifically tailored to address the PPRP problem. Unlike traditional ML methods that establish direct input-output mappings, PGN predicts comprehensive spatial-temporal information pertaining to the projectile-target interaction process. A thorough analysis of PGN's performance in terms of accuracy, computational efficiency and generalization ability was performed. Compared to validated results of numerical simulations, PGN maintained high precision with RMSE for displacement, stress, and strain predictions below 0.5 %, 9.5 %, and 2.1 %, respectively. It also achieved R2 values exceeding 0.92 for the time history of projectile velocity and acceleration, while requiring only 9.8 % of the computation time compared to LS-DYNA. In generalization tests, PGN exhibited remarkable adaptability in tackling challenging scenarios that extend far beyond the training data distribution, with overall RMSE between 11 % and 13 %. Furthermore, we find that the maximum information propagation capacity of a simulated physical system must meet or exceed the information propagation need of the real-world physical phenomenon it aims to replicate. Consequently, an approach was proposed to determine the critical connectivity radius of the massage passing method directly from the wave speed in the target medium, which greatly improved the accuracy and efficiency of PGN.

Influence of Foam Content and Concentration on the Physical and Mechanical Properties of Foam Concrete

Foam concrete has been used in various real-life applications for decades. Simple manufacturing methods, lightweight, high flowability, easy transportability, and low cost make it a useful construction material. This study aims to develop foam concrete mixtures for various civil and geotechnical engineering applications, such as in-fill, wall backfill and soil replacement work. A blended binder mix containing cement, fly ash and silica fume was produced for this study. Its compressive strength performance was compared against conventional general purpose (GP) cement-based foam concrete. Polypropylene (PP) fibre was used for both mixtures and the effect of various percentages of foam content on the compressive strength was thoroughly investigated. Additionally, two types of foaming agents were used to examine their impact on density, strength and setting time. One foaming agent was conventional, whereas the second foaming agent type can be used to manufacture permeable foam concrete. Results indicate that an increase in foam content significantly decreases the strength; however, this reduction is higher in GP mixes than in blended mixes. Nevertheless, the GP mixes attained two times higher compressive strength than the blended mix’s compressive strengths at any foam content. It was also found that the foaming agent associated with creating permeable foam concrete lost its strength (reduced by more than half), even though the density is comparable. The compressive stress–deformation behaviour showed that densification occurs in foam concrete due to its low density, and fibres contributed significantly to crack bridging. These two effects resulted in a long plateau in the compressive stress–strain behaviour of the fibre-reinforced foam concrete.

Mechanical performance and reinforcing mechanisms of foamed concrete strengthened by carbon fibers

Foamed concrete (FC) is a lightweight and environmentally friendly building material that offers versatile applications in civil engineering. However, FC is inherently weak in withstanding tensile and compressive loads. While incorporating fibers into FC has been shown to resist crack propagation and enhance its mechanical properties, the specific mechanisms through which carbon fibers affect FC's tensile and compressive behaviors remain unclear. This study aims to address this gap by incorporating short-cut carbon fibers into FC to achieve a micro-reinforcement effect and enhance its compressive and tensile performance. The mechanical properties, failure modes, and microstructure evolution of carbon fiber-reinforced FC at different density levels, carbon fiber lengths, and contents were comprehensively explored. Digital Image Correlation (DIC) was utilized to analyze crack propagation, and an optimal mix ratio experiment was conducted. The results demonstrate that incorporating carbon fibers effectively enhances the mechanical performance of FC, with a more significant impact on specimens with a density of 500 kg/m3. Furthermore, the maximum improvements in tensile and compressive strengths reached 250 % and 88.5 %, respectively. This study suggests that optimal reinforcement ratio can be achieved through mix ratio adjustments and curve fitting formulas, thereby optimizing the tensile and compressive performance of the specimens.

A combined RF–GRNN algorithm for monitoring complex damage of bolted joints with high-level robustness

Bolted joints in aerospace, rail transportation, civil engineering, and other applications are prone to various forms of damage, such as bolt loosening and crack, due to the harsh service environments. The difficulties arising from crosstalk and nonlinear evaluation between composite damage and single damage at bolted joints in practical monitoring environments are particularly pronounced. In response to this challenge, we propose an integrated algorithm, namely the random forest-generalized regression neural network (RF–GRNN) algorithm, characterized by high-level robustness. The proposed algorithm adheres to the core concept of “specificity” and forms a multi-task prediction framework of “classification before regression.” This approach enables efficient damage assessment and mitigates the nonlinear complexity of the prediction model. To evaluate the effectiveness of the RF–GRNN algorithm, its generalization, robustness, and prediction accuracy based on the piezoelectric ultrasonic guided wave principle under 0%–10% noise (the corresponding minimum signal-to-noise ratio was 10.4 dB) interference were investigated. Additionally, we clarify the impact of feature selection and its combination method on the algorithm’s prediction performance, deciphering the intrinsic link between prediction performance and feature distribution. The results demonstrate that the presented algorithm achieves the accurate classification and quantification of multiple bolted structural states, including no damage, crack, loosening, and crack-loosening composite damage. Therefore, the RF–GRNN algorithm is an important attempt to solve the bottleneck present in existing damage monitoring techniques which fail to balance the identification and quantification of multiple damage models.

Investigating resonance frequency in advanced composite structures as the main part of bridge construction: a multi-physics simulation approach

This paper investigates the nonlinear vibration characteristics and resonance frequencies of a graphene oxide powders (GOP) reinforced beam structure, which is in contact with fluid, serving as a crucial component in bridge construction. Utilizing a multi-physics simulation approach, this study integrates both mathematical modeling and COMSOL simulations to analyze the behavior of the GOP-reinforced beam. The fluid in the analysis is modeled as incompressible, non-viscous, and irrotational, contributing to the interaction with the beam structure. The isogeometric approach, coupled with the harmonic balance method and the arc-length technique, is employed to solve the complex nonlinear partial differential equations (PDEs). This coupled method ensures the accurate capture of the nonlinear response and resonance frequencies of the beam, which are critical for the safe design and performance of bridge structures. The findings reveal the significant influence of GOP reinforcement on the vibration and stability characteristics of the beam, highlighting its potential to enhance structural performance under dynamic loading conditions. The study also emphasizes the importance of considering fluid-structure interactions in the design process, as the fluid’s presence substantially affects the vibration behavior. This research contributes to the understanding of the dynamic behavior of advanced composite structures in fluid environments, offering valuable insights for the design and analysis of bridge components. The results demonstrate the efficacy of the proposed simulation approach in addressing the complexities of nonlinear vibrations in fluid-structure interaction systems, paving the way for future developments in civil engineering applications.

Self-Sensing Potential of Metashale Geopolymer Mortars with Carbon Fiber/Graphite Powder Admixtures

Multifunctional building materials with self-sensing capability have great potential for civil engineering applications. The self-sensing capability of typically calcium aluminosilicate matrices of cementitious or geopolymer materials is adopted by admixing electrically conductive admixtures in an amount that ensures optimal electrical properties and their proportionality to mechanical loading. The paper aims to evaluate the self-sensing capability of 4 metashale geopolymer mortars with graphite powder (GP) and carbon fibers (CF) in different ratios, including MGF 5/0, MGF 4.5/0.5, MGF 4/1, and MGF 3/0. The 4-probe measurements at 21 V DC input voltage on (100 × 100 × 100) mm3 samples with embedded copper-grid electrodes evaluate the gauge factor, which corresponds to the monitored changes in electrical resistivity. Despite the limitations of DC measurements, the self-sensing capability is observed for all the mixtures. The most promising response to dynamic loading with an FCR of 0.018%, is observed for the MGF 4.5/0.5 sample.