Recently, structural health monitoring (SHM) methods for civil structures have been investigated widely, especially Deep Learning (DL)-based methods. However, it is usually difficult to fully train a deep neural network, and thus, typical DL-based SHM methods are limited in terms of performance. While addressing these issues, in this paper, a novel methodology is proposed for smart damage identification of frame structures. The newly proposed SHM method is based on raw time-domain structural response signals and deep residual network (DRN). The introduced DRN algorithm has been designed and tested in an effective way for extracting and learning the optimum features of the 1D raw ambient vibration acceleration signals, without any need for engineered features. Also, the network’s performance has been optimized using Bayesian optimization, which clearly enhances the network’s accuracy and information flow across it. Next, the outputs of DRNs are further utilized through new methods for damage size estimation and damage localization. The proposed methodology has been evaluated using the datasets of numerical and experimental frames of the SHM benchmark problem and the dataset of a real-world full-scale truss bridge. The results show that the proposed method is capable of detecting, localizing, and quantifying structural damage accurately for all of the simulated cases of the two examples. Furthermore, conducted comparison studies have approved that the new approach is more efficient than other machine learning-based methods, and it can overcome the major limitations of Artificial intelligence-based SHM models.

Deep learning models are widely used to extract features from data through supervised or unsupervised methods. However, when these two methods are used separately, their generalization ability is insufficient. In this study, a structural health monitoring technology based on a novel hybrid deep learning (HDL) model and time-domain electromechanical impedance (TEMI) has been proposed. The HDL is a highly efficient combination of deep auto-encoder (DAE) and two-dimensional convolutional neural network (2D CNN). The DAE is used for unsupervised reconstruction of the high-level features of 1D TEMI signals, and then, the features extracted by DAE are arranged and fed into a supervised 2D CNN that is designed to learn the optimal feature representations of the multi-label signals. This increases effectively the model’s learnability and generalizability. The robustness of the developed approach has been tested using TEMI signals that are collected from a scaled model of a rectangular pipe gallery subjected to transversal joint damage. Results indicate that the maximal identification errors of joint damage severity are 2.4% and 9.33% when the corresponding structural conditions are used and unused by the training process of HDL, respectively.

To keep pace with rapid developments in infrastructure, an AI-based damage detection tool is crucial for ensuring safety. However, the rapid deterioration of civil infrastructure necessitates intelligent, autonomous structural health monitoring (SHM) systems capable of real-time damage detection, localization, and quantification. In this article, a novel method for smart structural damage identification is proposed. a novel hybrid architecture integrating electromechanical impedance ( ) sensing with multi-scale attention-based deep learning. Unlike conventional approaches reliant on hand-crafted features, our method employs a Squeeze-and-Excitation Residual Convolutional Network ( ) coupled with multi-head self-attention mechanisms for automatic feature extraction from raw PZT-EMI signals. The model was designed and trained using an effective approach for optimal feature learning from the raw PZT-EMI response signals. Further, Bayesian optimization has increased the network’s reliability. It substantially increases the network performance and knowledge acquisition process. To validate the proposed approach experimentally, PZT-EMI signals have been obtained from a beam with various structural conditions. The model demonstrates 100% classification accuracy across four damage severity levels (healthy, 25%, 50%, 75% stiffness reduction), with an RMSE of 0.137 cm for damage localization. Comparative study against state-of-the-art methodologies, such as GNN reviles the superior efficiency of the introduced approach. This research establishes a new paradigm for intelligent SHM, bridging the gap between data-driven pattern recognition and physics-aware structural assessment

Objective: This study investigates the shear performance of reinforced concrete beams containing longitudinal hollow cores, focusing on hollow-core diameter, shear span-to-effective depth ratio (a/d), and environmental benefits from reduced concrete consumption. Methodology: An experimental program was conducted on six reinforced concrete beam specimens subjected to four-point bending with two symmetrically applied concentrated loads until failure. Hollow-core diameter and a/d were varied. Measured shear capacities were compared with ACI 318 predictions. Material Production Energy (MPE) and CO ₂ emissions were evaluated from the material quantities used in each specimen. Results: Increasing hollow-core diameter reduced beam stiffness and ultimate shear capacity, with reductions of up to 19.4% relative to the solid beam. Increasing a/d from 2.5 to 3.0 reduced ultimate shear capacity by approximately 10%. All specimens failed in a brittle diagonal shear mode. ACI 318 consistently underestimated measured shear capacities, providing conservative predictions. Reduced concrete quantities also decreased MPE and CO ₂ emissions. Discussion: The results show that hollow-core diameter and a/d affect shear strength, cracking behavior, and stiffness. Longitudinal hollow cores can reduce material production impacts while maintaining acceptable shear performance within the investigated range. Conclusion: Longitudinal hollow cores provide a viable approach for reducing concrete consumption and environmental impacts without disproportionate loss of shear capacity within the investigated geometric limits. Further studies with larger specimen sizes and repeated specimens are required to verify these findings.

In this study, a novel physics-informed hybrid deep learning (PI-HDL) algorithm is proposed for intelligent condition assessment of metro tunnel structures. The algorithm integrates state-of-the-art deep learning models, including auto-encoder and convolutional neural network (CNN) models. Physics-inspired constraints derived from structural dynamics have been embedded into the loss functions of these models. Via an effective learning, merging, and training methodology, the designed model reconstructs high-level features of 1D time-domain vibration response signals in an unsupervised manner. Then, these physics-consistent high-level features are fed into a supervised 2D CNN trained with a composite loss function that incorporates both data fidelity and physics-based residuals derived from the stiffness–frequency relationship and moving load characteristics. The developed methodology is evaluated using vibration signals collected numerically from a concrete metro tunnel structure under various moving train loads and damage scenarios. The proposed PI-HDL framework embeds the underlying physics of wave propagation and structural dynamics directly into the learning process. This enables accurate identification of predefined damage conditions and stiffness reduction quantification within a closed set of ten structural scenarios, even with limited training data and noisy measurements. Comparisons prove that the PI-HDL algorithm achieves superior closed-set classification performance compared to purely data-driven HDL approaches.