Auxetic cementitious cellular composites (ACCCs) offer high deformability that is attractive for mechanical energy harvesting when integrated with flexible piezoelectric materials. However, the intrinsic brittleness of cement-based materials and the complex coupling between auxetic geometry and damage evolution hinder the efficient design of ACCC energy harvesters. This study proposes a novel learning-driven design framework that, for the first time, integrates a physics-based energy harvesting model with Bayesian Optimization (BO) to directly optimize the recoverable hinge-like strain capacity of ACCCs for enhanced electrical output. The optimization maximizes the voltage generated by piezoelectric materials bonded at hinge regions, while using constraints to prevent splitting failure and non-auxetic behavior under compression. The energy harvesting model combines the concrete damage plasticity (CDP) model for pre-compression damage with a secondary elastic model for cyclic loading, enabling prediction of recoverable strain in generalized ACCC geometries. The learning-driven approach proved far more efficient than random generation in identifying optimal ACCC configurations. Experimental validation of the optimized design achieved a peak-to-peak voltage of nearly 15.0 V per cycle, about 2.7 times higher than a reference design. This study provides a learning-driven approach to designing enhanced compliant auxetic cementitious energy harvesters for smart infrastructure applications.

This study presents an integrated finite-element–machine-learning framework for predicting early-age stress evolution in concrete materials/structures by combining an enhanced thermo-chemo-mechanical (TCM) model, deep sequential learning (DSL), and active learning (AL). The proposed TCM model incorporates experimentally informed viscoelasticity, a stable exponential creep–relaxation conversion, and an efficient exponential algorithm for the Maxwell-chain formulation in finite element analysis, which is further validated by a temperature stress testing machine. This model generates high-fidelity stress–time data across diverse mixtures, temperatures, and structural configurations. These simulations are used to train a Gated Recurrent Unit with Monte Carlo Dropout (GRU-MCD) model, whose predictive performance surpasses conventional point-wise approaches such as Light Gradient Boosting Machine and Gaussian Process Regression, yielding higher accuracy with reduced overfitting. The AL strategy further enhances efficiency by enabling the GRU-MCD model to achieve the accuracy of ∼900 Latin Hypercube samples using only ∼200 samples selected by active learning. Although demonstrated on a wall–base structure, the proposed framework is general and applicable to other cementitious material or structural systems, providing an effective tool for cracking-risk evaluation, reliability analysis, and the design of low-carbon concrete structures.

The use of Additive Manufacturing (AM) to create reinforcements for cementitious composites has become a popular research topic in recent years. One illustrative example is the integration of 3D-printed auxetic reinforcements into cementitious matrices, which exhibit superior energy absorption due to their negative Poisson's ratio. This presents the opportunity to tailor the Poisson's ratio of reinforcements to align with local stress distributions and enhance structural efficiency. In this study, Tailored Poisson's Ratio-reinforcements (TPR) were proposed, characterized by a linear gradient of Poisson's ratios along the height of the reinforcement to accommodate varying stress profiles within beams. Specifically, the top chords of TPR exhibit negative Poisson's ratios (auxetic), undergoing lateral contraction under compression and providing confinement to the surrounded matrix. Conversely, the bottom chords possess positive Poisson's ratios, contributing to lateral contraction under tension. These lateral deformations cause a shift in the principal stress state of the confined matrix, extending the loading path in stress space and actively delaying failure. Three novel Tailored Poisson's Ratio-reinforced Cementitious Composite (TPRCC) designs are developed and tested under four-point bending in this study. Experimental recordings indicate increases in load capacity and toughness of up to 191% and 6900% with respect to plain mortar, respectively.

This study evaluates the performance of damaged concrete beams retrofitted with a purpose-designed mechanochromic composite, which provides structural reinforcement and visual feedback for structural health monitoring (SHM). The retrofitting process utilizes externally bonded reinforcement (EBR) on pre-damaged concrete prisms. The mechanochromic composite, a thin-ply hybrid material made of unidirectional ultra-high modulus (UHM) carbon/epoxy and S-glass/epoxy layers, changes color to indicate structural overload when the UHM carbon layer fractures due to excessive strain. Eighteen concrete specimens were prepared and subjected to four-point bending tests, assessing various combinations of damaged, undamaged, retrofitted, and non-retrofitted configurations. Results showed that the mechanochromic composite functions effectively as both a passive visual sensor and reinforcement. For instance, a 5 % crack depth reduced load-bearing capacity by 30 %, however, retrofitting with the mechanochromic composite improved load-bearing capacity by up to 208 % compared to undamaged beams. The study further discusses the effects of different damage levels on load-bearing capacity through flexural strength, load-displacement curves, and failure modes.

The microstructure of cement paste determines the overall performance of concrete and therefore obtaining the microstructure is an essential step in concrete studies. Traditional methods to obtain the microstructure, such as scanning electron microscopy (SEM) and X-ray computed tomography (XCT), are time-consuming and expensive. Herein we propose using Denoising Diffusion Probabilistic Models (DDPM) to synthesize realistic microstructures of cement paste. A DDPM with a U-Net architecture is employed to generate high-fidelity microstructure images that closely resemble those derived from SEM. The synthesized images are subjected to comprehensive image analysis, phase segmentation, and micromechanical analysis to validate their accuracy. Findings demonstrate that DDPM-generated microstructures not only visually match the original microstructures but also exhibit similar greyscale statistics, phase assemblage, phase connectivity, and micromechanical properties. This approach offers a cost-effective and efficient alternative for generating microstructure data, facilitating advanced multiscale computational studies of cement paste properties.

Accurate and reliable strain measurement is essential for effective condition monitoring of engineering structures. This study presents an analytical and experimental investigation into the performance of piezoelectric sensors for structural strain measurements, evaluating the effect of attachment strategy and the properties of the substrate and the sensor. Lead zirconate titanate (PZT) and polyvinylidene fluoride (PVDF) sensors were evaluated in two attachment configurations: Fully Attached (FA) and Two-End Attached (TEA). A voltage-strain relationship was developed based on principles of piezoelectricity, electrical circuit modelling, and solid mechanics. Results indicate that sensor performance is significantly influenced by the attachment method. Specifically, the TEA configuration reduced the impact of substrate properties and improved uniaxial strain measurement accuracy by up to 32 % compared to the FA configuration. The FA configuration exhibited sensitivity to the substrate's Poisson ratio, leading to a nonlinear voltage-strain response. In contrast, the TEA configuration provided pure uniaxial strain measurements by reducing the effects of shear lag and substrate elasticity. These findings provide a comprehensive approach to using piezoelectric sensors for structural strain measurement, allowing for the placement of sensors on various substrates without the need for calibration by effectively utilizing sensor and substrate properties along with the attachment strategy. The study provides a novel analytical–experimental comparison of sensor attachment methods, showing how TEA significantly improves uniaxial strain accuracy and reduces substrate dependency in piezoelectric strain measurements.

Additive manufacturing (AM) has revolutionized the fabrication of complex geometries, enabling efficient material use and innovative applications across sectors such as biomedical, automotive, and aerospace. A significant development is the emergence of 3D-printed lattice structures (LSs), which combine lightweight design with tailored mechanical properties, making them highly suitable for civil engineering applications, including bridge elements, façade systems, and reinforcement of concrete structures. Recent research has increasingly explored the integration of LSs into cementitious composites, though findings remain diverse and primarily experimental. This paper provides a comprehensive review of lattice structures in cement-based materials, examining both their classifications–by dimensionality (2D vs. 3D) and configuration (cellular vs random)–and their role in enhancing ductility, reducing weight, and improving overall performance. It also surveys materials commonly used in 3D printing, such as polymers (PLA, PEEK, ABS), ceramics, and composites, along with relevant printing techniques. Evidence demonstrates that LSs significantly improve the mechanical behavior of cementitious composites, transforming failure modes from brittle to ductile and increasing energy absorption. These findings highlight the potential of 3D-printed lattices as effective reinforcements, offering promising pathways for advancing structural performance in construction.

Structural fatigue can lead to catastrophic failures in various engineering applications and must be properly monitored and effectively managed. This paper provides a state-of-the-art review of recent developments in structural fatigue monitoring using piezoelectric-based sensors. Compared to alternative sensing technologies, piezoelectric sensors offer distinct advantages, including compact size, lightweight design, low cost, flexible formats, and high sensitivity to dynamic loads. The paper reviews the working principles and recent advancements in passive piezoelectric-based sensors, such as acoustic emission wave and strain measurements, and active piezoelectric-based sensors, including ultrasonic wave and dynamic characteristic measurements. These measurements, captured under in-service dynamic strain, can be correlated to the remaining structural fatigue life. Case studies are presented, highlighting applications of fatigue life monitoring in metals, polymeric composites, and reinforced concrete structures. The paper concludes by identifying challenges and opportunities for advancing piezoelectric-based sensors for fatigue life monitoring in engineering structures.

Due to the gradual diffusion of CO₂ under natural exposure, areas with varied degrees of carbonation exist at different depths from the surface of slag-rich cement paste. While extensive research has been dedicated to investigating the fully carbonated zone as identified by phenolphthalein spray, the transitional zone, located between the fully carbonated and the uncarbonated regions, has received comparatively less attention. This study thus aims to address this research gap by exploring its microstructural, micromechanical, and mineralogical properties. The results reveal that carbonation-induced damage extends beyond the fully carbonated zone as identified by phenolphthalein. Particularly in the transitional area close to the carbonated zone, nanoindentations results reveal that micromechanical properties of this area are even lower to that of the fully carbonated zone. In addition, mineralogical investigation suggest that the depth of carbonation stays within the range where slag-containing blends loses its green coloration. By comparing specimens with different slag composition, it was found that the depth of this faded green area can be an important indicator to assess the carbonation resistance of slag-containing blends.

The concept of self-healing asphalt has been developed to implement an extrinsic crack repair system, reduce maintenance efforts, and extend the service life of asphalt pavements. Various self-healing asphalt methods have been proposed and demonstrated, however, it is difficult to compare and finalize an optimum self-healing design for an upscaled application. To provide a better understanding of the prospects of each self-healing technology, this study investigates the physical properties and ranks the healing efficiency of each self-healing asphalt technology. Four self-healing systems were investigated, including alginate capsule system, conductive alginate capsule system, induction system, and a hybrid system (alginate capsule & induction). Laboratory tests, including Indirect Tensile test (ITT), Water Sensitivity test (WS), Binder Drainage test (BD), Triaxial test, and Semi-circular Bending test (SCB), were conducted to assess the physical performance of the asphalt mixtures. The healing efficiency of each mix was evaluated with a SCB bending and healing program. The results indicate that the addition of self-healing additives affects the physical properties of the SMA mix. The capsules reduce the mixture strength, stiffness and high-temperature stability, while the steel fibers have the opposite effect. The healing efficiency results show that the capsule healing system and conductive capsule healing system can be repeated twice, while the induction system and hybrid healing system showed a healing index above 60 % in all eight bending-healing cycles, demonstrating a promising and durable healing effect for the SMA mix.

Plasters and renders in historic buildings are often damaged by salt crystallization, because of their relatively low mechanical strength and their location at the surface of the wall, where evaporation and salt accumulation take place. Current solutions to provide plasters and renders with a better resistance with respect to salt decay are mostly based on the use of stronger binders, such as cement, or of water repellent additives. Unfortunately, both these solutions show often a low compatibility with the materials used in historic buildings.

In the past decade, research has been carried to improve the durability of plasters against salt damage by the use of crystallization inhibitors. Crystallization inhibitors are ions or molecules able to delay crystal nucleation and growth of the crystal by preferentially adsorbing on specific crystal faces. Sodium ferrocyanide (NaFeC) is a well-known inhibitor of sodium chloride. Past research has shown that NaFeC, is able to provide hydrated lime-based mortars with an improved resistance to salt decay? However, leaching of this water-soluble inhibitor may compromise its effect in time. Recently, encapsulation of NaFeC in chitosan-calcium alginate capsules was proven effective to control the release of the inhibitor in mortar. In this paper, the durability of a natural hydraulic lime plaster with encapsulated NaFeC crystallization inhibitor is discussed based on the results of laboratory accelerated salt weathering test and monitoring of test panels applied on site.

Portland cement paste has a highly heterogenous evolving microstructure that complicates the development of stronger and greener cementitious materials. Microstructure is the fundamental input of multiscale studies on material behaviors. Herein, we propose a conditional generative AI framework for synthesizing high-fidelity 3D microstructures of hydrating cement paste (1–28 days) with varying water-to-cement ratios and Blaine fineness values. A latent diffusion transformer, operating within a compact two-stage latent space derived via a vector quantized variational autoencoder, efficiently captures and reproduces experimentally measured microstructural patterns. Statistical analyses confirm strong consistency in grey value distributions, micromechanical properties, hydration phase evolution, and particle size distributions, with only minor boundary-related discrepancies. Validation using a pretrained classifier further corroborates the fidelity of generated microstructures. This approach provides a robust tool for realistic cement paste microstructure generation, supporting multiscale modeling and advancing the design of sustainable cementitious materials.

Self-healing concrete, with its ability to autonomously repair damages, holds promise in enhancing its structural durability and resilience. Research on self-healing concrete in the past decade has advanced in understanding the mechanisms behind healing, exploring various healing agents, and assessing their effectiveness in concrete structures. However, the full potential of self-healing concrete remains untapped unless its effects are effectively integrated into the design practices of reinforced concrete structures. Realizing this challenge, this paper synthesizes the current research progress and discusses the possibilities to consider self-healing into design codes. The focus was placed on two specific benefits of applying self-healing concrete: one centered on durability and the other on mechanical performance. Specifically, the effect of self-healing on impeding chloride penetration into cracked reinforced concrete was discussed first. Modifications of parameters in existing predictive models based on different types of healing approaches were recommended. Furthermore, the possible impact of the self-healing capacity in mitigating the stiffness reduction of concrete was also discussed. Equations that can describe the stiffness regained due to healing action are presented. In each part of the case study, limitations and challenges still hindering standardization and wider application in the construction field are discussed.

Auxetic cementitious cellular composites (ACCCs) exhibit hinge-type recoverable deformation during auxetic behavior phase, a rare pseudo-elastic property in cementitious materials. However, their low load-bearing capacity during this phase restricts their use in high-load applications. This study developed ACCCs using strain-hardening cementitious composites (SHCCs) with short (SHCC-SS) and long (SHCC-LS) softening tails, fabricated by additive manufacturing-assisted casting. Uniaxial compression tests employing Digital Image Correlation (DIC) evaluated their compressive behavior, peak strength, Poisson's ratio variation, and energy dissipation. Cyclic tests after pre-compression assessed their recoverable deformation resilience, with fiber bridging at joint cracks examined using digital optical microscope. Results were compared to a reference using fiber-reinforced cementitious materials with strain softening (SS). Compared to the reference (SS), ACCCs using SHCC mixtures exhibit superior load-bearing capacity and stable auxetic behavior under compression. After self-contact, they maintain a negative Poisson's ratio up to a considerably high compressive strain, preventing splitting failure and preserving structural integrity. This is because incorporating SHCC enables greater joint rotation by promoting multiple cracks with strain hardening, which delays primary crack formation and reduces its opening. During cyclic tests, P1-shaped ACCCs with SHCC-LS and SHCC-SS enhance the elasticity modulus of recoverable deformation by 4.8 and 3.0 times, respectively, compared to SS. SHCC-LS outperforms SHCC-SS in compressive resilience due to its prolonged softening tail, which improves fiber bridging in primary cracks and increases rotational stiffness in hinge joints. SHCC mixtures with initial strain hardening and extended softening enable scalable design of advanced auxetic cementitious materials across various load levels.

Accurate evaluation of air-void content in hardened concrete is important for assessing durability and long-term performance. Traditional methods often require time-consuming surface preparation and depend on imaging techniques that are sensitive to surface texture. This study investigates the potential of a high-resolution tactile sensing approach for non-destructive void detection with minimal surface preparation. To understand the effect of surface roughness, void detection was first performed on paired concrete specimens with identical internal surfaces, while one was left unpolished and the other polished. Despite the presence of surface irregularities, the tactile sensor was still able to identify most of the voids larger than a defined threshold, demonstrating its effectiveness even under challenging surface conditions. In the second phase, the sensor’s void quantification performance was benchmarked on a polished specimen using a standardized digital microscopy-based method. The tactile system estimated porosity at 4.58% with an average void area of 0.013 mm², closely matching the reference digital microscopy-based results of 5.05% and 0.011 mm². These results highlight the sensor’s capability to produce consistent and quantitative measurements on both rough and smooth surfaces. The approach offers a practical alternative to traditional void analysis methods, with the potential to simplify inspection workflows and support future automation in concrete surface evaluation.

The concrete recycling industry faces significant challenges due to the uncontrolled mixing of parent concretes with varying properties during demolition, resulting in inconsistent recycled concrete aggregate (RCA) quality and limiting its potential for use in new concrete production. Existing literature typically characterizes parent concrete solely based on compressive strength, neglecting other critical parameters. Consequently, RCA is often labelled as inherently heterogeneous, without fully considering the variability introduced by mixed-source demolition. This study introduces an novel protocol for systematic parent concrete characterization, combining an Artificial Intelligence (AI)-based segmentation approach with complementary techniques like polarized light and fluorescence microscopy (PFM). The proposed methodology quantifies critical properties of parent concrete, including water-to-cement (W/C) ratio, cement and aggregate content, air void content, and aggregate gradation. This enables a detailed evaluation of parent concrete variability, providing the basis for selective demolition strategies that reduce RCA heterogeneity and enhance the predictability of its properties. To illustrate its applicability, the protocol was applied to structural components of a Dutch viaduct, including prestressed beams, heavily reinforced columns, foundations, and abutment-wall. Results revealed significant differences in estimated water-to-cement ratios, ranging from 0.29 ± 0.03 in beams to 0.38 ± 0.03 in abutment walls, while hydration degrees varied between 0.80 ± 0.08 and 0.93 ± 0.02, indicating differences in cement maturity. Cement content ranged from 316 ± 11 kg/m³ in foundations to 390 ± 10 kg/m³ in beams, and air void content ranged significantly from 0.9 % in abutment walls to 4.3 % in foundations. Microstructural composition also differed substantially: paste volume ranged from 17 % to 27 %, and coarse aggregate content from 37 % to 52 % depending on the component. These quantitative differences confirm that structural concretes, even within the same structure, exhibit substantial internal variation. As such, uncontrolled demolition leads to the mixing of materials with fundamentally different properties—supporting the argument that RCA heterogeneity is not intrinsic, but largely a result of conventional demolition. This reinforces the value of the proposed protocol in identifying material variability and informing targeted demolition to enhance the predictability and uniformity of RCA.

The buckling mode in piezoelectric materials offers advantages such as an increased measurable strain range, ease of installation, and extended service life. This paper investigates the potential of piezoelectric sensors operating in buckling mode for structural strain measurement by evaluating key factors including boundary conditions, sensor response linearity under dynamic loading, and impedance engineering to optimize the voltage–strain relationship. A structural extension was developed to facilitate sensor integration and to enable the application of different buckling boundary conditions. Results show that the clamped–clamped configuration generated at least 1.65 times higher output voltage, and three times greater peak strain compared to other boundary conditions. An experimentally validated analytical model was employed to assess and improve the performance of buckled piezoelectric sensors in dynamic environments. The findings highlight that introducing initial buckling reduces signal perturbations, enhances voltage linearity across loading frequencies, and extends the effective strain measurement range. Furthermore, impedance engineering was used to successfully mitigate the nonlinear effects of transient response, thereby improving signal stability and accuracy in dynamic strain monitoring applications.

This paper reports the development, optimization, and real-world application of an innovative wobbling triboelectric nanogenerator to harvest energy from wind or vibrations. The harvester features a spring-supported structure, purposely designed to become unbalanced and reversibly shift from a steady to a non-steady state in response to minimal wind or vibration stimuli. Unlike conventional wind turbines, this approach transforms wind energy into contact-separation events via a wobbling structure. The harvester’s mechanisms are engineered to enhance power generation efficiency, optimizing parameters like electrode dimensions and contact-separation quality. Experimental findings showcase a maximum output power density of 1.6 W/m2 under optimal conditions, employing a fixed suspending mechanism for heightened impact energy during contact. Moreover, the harvester efficiently charges a 3.7 V lithium-ion battery with over 4.5 μA, showcased in a self-powered light mast as a practical demonstration. The harvester provides cost-effectiveness by utilizing inexpensive, easily accessible materials without complex fabrication. This research paves the way for future exploration in integrating triboelectric nanogenerators with wobbling mechanisms, offering a promising pathway for sustainable power generation across various applications, including lighting and IoT sensor nodes.

Anisotropy in 3D-printed concrete structures has persistently raised concerns regarding structural integrity and safety. In this study, an architected 3D printing strategy, “stitching”, was proposed to mitigate anisotropy in 3D-printed Engineered Cementitious Composites (ECC). This approach integrates the direction-dependent tensile resistance of extruded ECC, the mechanical interlocking between three-dimensional layers, and a deliberately engineered interwoven interface system. As a result, the out-of-plane direction of the printed structure can be self-reinforced without external reinforcements. Four-point bending tests demonstrated that the “stitching” pattern induced multi-cracking and flexural-hardening behavior in the out-of-plane direction, boosting its energy dissipation to 343 % of the reference “parallel” printing and achieving 48.6 % of cast ECC. Additionally, micro-CT scanning and acoustic emission tests further validated the controlled crack propagation enabled by the engineered interface architecture. The proposed strategy has been proven to substantially alleviate anisotropy and enhance structural integrity.

Fatigue Life Monitoring is crucial to ensuring the safety and durability of engineering structures. This paper presents an innovative approach for fatigue life monitoring using a PZT (Lead Zirconate Titanate) piezoelectric sensor operating in buckling mode to measure applied cyclic strains. A significant focus is placed on the utilization of a 3D-printed 'Extension' platform upon which the PZT sensor is installed, allowing it to operate in buckling mode while subjected to cyclic strains. An analytical framework is developed to establish a direct relationship between dynamic strain values and the sensor's output, considering critical strain attributes such as amplitude and strain rate. The analytical solutions are validated through a series of experimental tests conducted under various dynamic loading conditions. Integrating the piezoelectric sensor with the 3D-printed extension demonstrates high sensitivity and serves as a passive dynamic strain measurement sensor, with promising potential for application as sensor nodes in fatigue life monitoring of engineering structures.