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.

The true triaxial stress are typical stress state in the deep underground. 3D surface flaws, one of the most common type of flaws, extensively existed in the rocks. Therefore, the study on evolution of the 3D surface flaw under true triaxial stress is crucial to determine the fracture behaviors of the rock in some deep underground spaces. Gypsum, as a rock-like material, has been extensively used in studies of crack initiation and propagation. In this study, we prefabricate a pair of 3D surface flaws at 45° in the cubic gypsum specimen and investigates the effect of the intermediate principal stress on initiation and peak stresses (characteristic stress thresholds) of flaws and crack propagation patterns of 3D surface flaws parallel to the intermediate principal stress. The true triaxial apparatus and acoustic emission (AE) technique were used to test and monitor the mechanical behaviors of the samples. The internal crack propagation pattern was observed by X-ray CT scan. The results demonstrate that the intermediate principal stress strongly affects crack patterns but has a limited influence on characteristic stress thresholds. Both the intermediate and minimum principal stresses affect the difference in the crack peak and initiation stress, which elucidates how the true triaxial stress affects the fracture behavior of the specimen. Additionally, the intermediate principal stress effect on characteristic stresses is closely related to the magnitude of minimum principal stress. When the magnitude of minimum principal stress is small, with the rising intermediate principal stress, the characteristic stresses increase slowly. When the magnitude of minimum principal stress is large, the intermediate principal stress almost has no effect on characteristic stresses. The surface wing cracks and anti-wing cracks initiate from the flaw when the magnitude of intermediate principal stress is relatively small. With the intermediate principal stress increasing, the surface crack propagation pattern is shift from tensile crack to shear crack. Through the CT image reconstruction technique, the propagation patterns of the inner tips of single 3D surface flaw were illustrated in this paper. It is observed that the large intermediate principal stress can restrict the crack wrapping and even make the internal flaw propagation patterns same with that on the specimen surface, providing insights into the validity of simplifying 3D flaws as 2D flaws for analyzing and computing crack propagation.

This study introduces a novel approach that integrates Acoustic Emission monitoring with fractal analysis to assess and predict damage progression in FRP-strengthened reinforced concrete beams subjected to corrosion-induced deterioration. By combining AE signals with fractal measures, specifically the correlation dimension, the research provides an effective tool for tracking internal damage evolution and offering early-warning indicators for structural health. The developed damage model identifies three distinct stages of damage: initial damage, damage evolution, and sustained growth. The study reveals that corrosion accelerates both the accumulation and rate of damage, with AE ring counts significantly increasing in moderately to severely corroded beams, indicating heightened crack activity and reduced structural capacity. The correlation dimension shows a strong relationship with the degree of damage, with higher values corresponding to more disordered internal damage. The correlation dimension evolves from an initial increase to a decrease as damage progresses, marking the transition from early to advanced degradation. These findings highlight that corrosion not only accelerates damage but also lowers the detection threshold for significant structural damage.

Recycling waste tires for the production of concrete materials with good toughness is a green and economical solution, but the severe deterioration of rubber under high temperatures limits its application in engineering practice. Therefore, to examine the impact of elevated temperature on the fracture characteristics of rubber concrete (RC), three-point bending fracture tests were conducted on RC with five rubber replacement rates and five treatment temperatures. The purpose was to correlate the fracture parameters of RC with the rubber replacement rate and the temperature. Then, by employing the digital image correlation (DIC) technology and microscopic testing methods, the crack evolution trend and the potential mechanism were analyzed in detail. The results indicate that rubber particles can effectively improve the toughness, deformation capacity, and fracture energy of concrete, but have a significant weakening effect on the load and fracture performance. When the treatment temperature is below 400 ℃, rubber particles mainly affect the initiation and propagation of cracks by alleviating the stress concentration phenomenon at the crack tip and improving the crack propagation path. Rubber particles may initiate cracks earlier, but significantly delay their propagation process. When the treatment temperature is above 400 ℃, rubber particles tend to exert a weakening effect on the fracture performance. As the temperature rises, the microstructure of rubber particles gradually changes from a relatively uniform state in close contact with the cement matrix to a fragmented state filled with pores separated from the matrix. This process will lead to severe deterioration of concrete performance. It is anticipated that the findings of this study will provide a theoretical basis for predicting the performance of RC in high-temperature environments.

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.

Carbon fiber reinforced polymer (CFRP) has emerged as an effective material for strengthening reinforced concrete (RC) structures due to its high tensile strength, corrosion resistance, and ease of installation. However, in square or rectangular RC columns, stress concentrations at corners hinder the development of uniform confinement, thereby reducing strengthening efficiency. This study presents a comprehensive experimental and theoretical investigation into the performance of CFRP-confined RC square columns with varying anchor configurations. Six full-scale column specimens were tested under monotonic axial compression, each externally wrapped with one layer of CFRP sheet and installed with zero to four CFRP anchors. All columns were chamfered with a 30 mm radius to mitigate corner stress concentrations. The experimental results demonstrated that CFRP anchors significantly enhanced load-bearing capacity and ductility, improved lateral confinement, and modified the failure mechanisms. The specimen with three anchors exhibited optimal performance, with a 51.5 % increase in peak load (from 879.9 kN to 1333.2 kN) and a 29.9 % improvement in ductility index compared to the unconfined control. The failure mode transitioned from brittle global instability to ductile localized damage, accompanied by more uniform hoop strain distribution. However, excessive anchoring introduced stress interference and local cracking, leading to performance degradation. To characterize the mechanical response, a modified stress–strain model was developed, incorporating a reduction factor to account for confinement weakening caused by anchor installation. The model exhibited strong agreement with experimental data (R² > 87 %) in predicting both peak and ultimate stresses. This study provides valuable insights into the mechanical enhancement mechanisms of CFRP anchoring systems and offers a rational design basis for strengthening non-circular RC columns in structural rehabilitation.

Lattice reinforcement (LR) demonstrates great potential in enhancing cementitious matrices due to its ability to be strategically designed and additively manufactured to optimize composite properties. To fully exploit the synergy between LR and cementitious matrix, a deep understanding of the reinforcing mechanisms is essential. In this study, five lattice designs with various configurations and sizes were examined through uniaxial tensile tests on dog-bone specimens. It was observed that geometric characteristics, including auxetic behavior, significantly influenced the mechanical properties of lattice structures. At the composite level, the flexural performance of lattice-reinforced cementitious composites (LRCC) was investigated through four-point bending tests. It was found that up to 23-fold enhancements in energy absorption capacity can be achieved with a low reinforcing ratio of 3.5 %. Acoustic emission tests and CT scanning provided valuable insights into the distinct reinforcing mechanisms between auxetic and non-auxetic lattice designs. Furthermore, Finite Element Method (FEM) simulations confirmed that auxetic LR effectively mitigated interfacial debonding.

This study explores the damage evolution and crack behavior exhibited by CFRP-strengthened corroded RC beams subjected to bending loads, with the application of AE monitoring. The results show that three primary failure modes were observed: concrete cover separation, debonding of the CFRP sheet with anchor pullout and matrix cracking and fiber tearing, with the middle mode exhibiting greater ductility. AE ringing counts analysis effectively divided the damage process into three stages: initial damage, damage development, and continuous damage. Signal intensity analyses provided insights into damage severity, revealing enhanced crack propagation in corroded beams, while severe corrosion reduced AE signal frequency and intensity in later stages, indicating initial crack activity inhibition and accelerated damage in mild to moderate corrosion. The Ib-value demonstrated trends in ductility and damage severity, with higher ductility in less corroded beams and restricted damage development in heavily corroded ones. RA-AF crack classification and Gaussian Mixture Clustering identified an increased proportion of shear cracks with higher corrosion levels, reducing shear load-carrying capacity. These findings highlight AE-based monitoring as an effective tool for real-time damage assessment in CFRP-strengthened RC beams.

The next generation of acoustic emission (AE) applications in concrete structural health monitoring (SHM) relies upon a reliable and quantitative relationship between AE measurements and corresponding AE sources. To achieve this, it is a prerequisite to accurately model the whole AE process that is a multiscale coupling process between local material fracturing and induced elastic wave propagation at structural level. Such a complex process, however, cannot be well addressed in currently available modelling methods. To fill this research gap, this study proposes a lattice modelling approach that achieves for the first time the explicit simulation of complete waveforms of transient AE signals induced by concrete fracture. The proposed approach incorporates an explicit time integration technique with a novel proportional-integral-derivative (PID) control algorithm for reducing spurious oscillations and a Rayleigh damping-based calculation and calibration method for the attenuation of AE waves. In this paper, the proposed lattice modelling approach is implemented to simulate the concrete Mode-I fracturing process in a three-point bending test. Besides the mechanical behaviors and AE hit number, a comparison was conducted between numerically and experimentally obtained AE waveforms. The AE waveforms and their attenuation characteristics simulated by the proposed lattice modelling method turn out to be comparable to experimental results. The proposed approach is of significance for a deep understanding of AE-related fracture mechanisms and a more reliable application of AE technique.

Damage localization in civil infrastructure, such as large-scale reinforced concrete (RC) pedestrian bridges, is essential for conducting precise maintenance and avoiding catastrophic failures. In this study, multiple one-dimensional convolutional neural networks (1-D CNNs) are developed for automatically extracting implicit damage-sensitive features from the structural raw dynamic responses to localize damage in the pile foundations of pedestrian bridges considering uncertainties such as environmental and operational variations (EOVs) inherent in dynamic responses. For this purpose, transient dynamics numerical computation models are established to simulate the multi-point dynamic response of the structure under different typical damage scenarios, forming the baseline dataset. Then, on-site vibration tests are conducted on the structural prototype. Ambient vibrations of the real intact bridge are considered EOVs and integrated into the baseline dataset, forming the test dataset. Additionally, the intact structural dynamic response with measured EOVs replaces the simulated intact structural dynamic response in the baseline dataset to form a reference dataset. The network architectures based on one-dimensional convolutional layers proposed in this paper are trained on the baseline dataset and reference datasets to obtain baseline and reference models. Subsequently, model performance evaluation is conducted on the test dataset, and the results indicate a significant decrease in the performance of damage models based on a single deep learning when EOVs are present. However, integrating the baseline and reference models achieves zero false negative/positive predictions which is safety-oriented and an exemplary classification accuracy of up to 97.2 %.

To date, there is no comprehensive approach available that can explicitly model the complete transient waveforms of acoustic emissions (AE) induced by fracture processes in brittle and quasi-brittle materials like concrete. The complexity of AE modelling arises from the intricate coupling between the local discontinuity of material fracturing and the global continuity of elastic wave propagation in solids. Among others, the lattice type models are promising approaches, as they are known to be a matured modelling approach to simulate the fracturing process in concrete-like materials. Nevertheless, like other discrete element methods (DEM), they are currently limited to describing the number and rate of AE events (broken elements) in the fracture process and cannot explicitly model wave generation and propagation. In this study, we propose a lattice modeling framework to simulate the propagation of complete waveforms of fracture-induced AE signals in concrete. A proportional-integral-derivative (PID) control algorithm is incorporated in an explicit time integration procedure to reduce dynamic noise from spurious oscillations. Additionally, a Rayleigh damping-based calculation method and corresponding calibration procedure are proposed to model the attenuation of AE signals due to material damping. Using the developed approach, we systematically investigate the feasibility of lattice models for elastic wave propagation simulation, the dependence of lattice mesh sizes and the choice of numerical damping parameters. These results lead to a systematic framework which can be employed in simulating wave propagation with attenuation using DEM models in general including lattice models.

The Stiffness Damage Test (SDT), a cyclic test in compression, is considered as a reliable tool for assessing concrete structures affected by ASR. Depending on the extent of ASR damage in concrete, loading levels up to 40% of the compressive strength may contribute to increasing internal damage during testing. Nevertheless, previous research found that no additional damage was induced by the SDT. This confirmed the non-destructive character of the SDT making it valid to determine the compressive strength on the same test specimens following the SDT. However, other research suggests that loading levels above 15% of the compressive strength could lead to load-induced damage in the first load cycle. The implication of the non-destructive character and the loading level of the SDT needs more attention, especially when testing anisotropically ASR-damaged concrete structures. This paper thus presents a critical evaluation of the non-destructive character of the SDT by utilizing Acoustic Emission (AE) measurements. The SDT was used to evaluate an ASR affected concrete structure after 60 years in use. Several cores from cantilever slabs were extracted enabling damage assessment of the concrete structure in use. AE allowed to measure crack occurrence with a higher accuracy. Therefore, the critical load level could be more accurately identified using AE. The magnitude of enhancing internal damage during the SDT is related to the extent of ASR. From this study it can be concluded that the non-destructive character of the stiffness damage test depends the critical load level in relation to the internal degree of damage, which can be determined by means of Acoustic Emission.

Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel deep learning algorithm called deep residual network (DRN), a structural health monitoring (SHM) strategy is proposed for AE source localization through classifying and recognizing the AE signals generated in different sub-regions of critical areas in structures. Hammer hits and pencil-leak break (PLB) tests were carried out on a steel-concrete composite slab specimen to register time-domain AE signals under multiple structural damage conditions. The obtained time-domain AE signals were then converted into time-frequency images as inputs for the proposed DRN architecture using the continuous wavelet transform (CWT). The DRNs were trained, validated and tested by AE signals generated from different source types at various damage states of the slab specimen. The proposed DRN architecture shows an effective potential for AE source localization. The results show that the DRN models pre-trained by the AE signals obtained in the undamaged specimen are able to accurately classify and identify the locations of different types of AE sources with 3–4.5 cm intervals even when multiple cracks with widths up to 4–6 mm are present in the wave travel paths. Moreover, the influence factors on the model performance are investigated, including structural damage conditions, sensor-to-source distances and AE sensor mounting positions; in accordance with the parametric analyses, recommendations are proposed for the engineering application of the proposed SHM strategy.

This study presents a damage pattern recognition approach for corroded steel beams strengthened by CFRP anchorage system based on acoustic emission clustering analysis. The proposed method includes four steps: acoustic emission signal acquisition, feature extraction, clustering analysis, and damage pattern recognition. Four corroded beams with different corrosion levels and strengthening schemes were tested under four-point bending loading. The acoustic emission signals were collected during the loading process and analyzed using Gaussian mixture model clustering method. The results showed that the collected AE data were analyzed using clustering analysis, successfully distinguishing the distinct damage patterns associated with each mode. The AE signals exhibited distinct characteristics for different damage modes: concrete matrix damage had high-frequency and low-energy characteristics, CFRP-matrix debonding showed intermediate values for all parameters, and CFRP tearing had longer durations, lower peak frequencies, and high-energy characteristics. Besides, the study identified three stages of the damage process: an initial stage with fewer low-intensity AE signals, a damage development stage characterized by an increase in concrete-matrix damage and CFRP – matrix debonding signals, and a continuous damage growth stage with significant AE signals associated with three damage modes. Furthermore, the degree of corrosion significantly influenced the cumulative AE energy of damage modes. Lower degrees of corrosion led to higher cumulative energy from concrete matrix damage and CFRP-matrix debonding. These findings provide valuable insights for understanding the damage evolution and failure mechanisms of CFRP-strengthened corroded beams. The use of AE techniques for damage pattern recognition can enhance the evaluation and design of CFRP anchorage systems, leading to more effective rehabilitation strategies for corroded structures.

This study investigated the relationship between the acoustic emission (AE) signals parameter sequence fractal characteristics and the damage evolution information of corroded reinforced concrete (RC) beams under four-point bending. Strength deterioration behavior and AE data can be obtained by coupling the four-point bending test and the AE monitoring. The results show that AE ringing counts of corroded and uncorroded beams had prominent fractal characteristics. The fractal dimension values of corroded RC beams all showed a fluctuating rise to a peak and then a sharp drop before the failure. The damage index corresponding to the peak point decreases with the increase of corrosion degree. Fractal dimension peak point could be used as an early warning point for corroded RC beams' failure. Moreover, the AE fractal dimension analysis can effectively reflect the pattern of crack development, which have an important value for evaluating the process of corroded RC beams rupture.