Armin Dadras Eslamlou

Accurate time synchronization is essential for vibration-based structural health monitoring (SHM), particularly when acceleration responses are collected using distributed wireless sensors. Residual timing errors may remain after clock-level synchronization due to clock drift, triggering jitter, communication latency, and hardware-dependent acquisition delays, introducing phase distortion and reducing the reliability of modal identification and damage-sensitive analysis. This paper proposes a post-processing synchronization framework for multi-sensor tri-axial bridge acceleration signals under ambient vibration. The method combines learned waveform representations, graph-based sensor interaction, classical delay-estimation evidence, and physics-informed consistency constraints. A shared convolutional encoder extracts sensor-level latent features, while graph message passing models inter-sensor dependencies over a complete sensor graph. In parallel, correlation- and phase-based edge features provide physically meaningful delay cues for each sensor pair. Raw pairwise delay estimates are projected into cycle-consistent sensor offsets using a graph-based least-squares formulation, and the predicted offsets are used to compensate the measured signals. A two-stage inference strategy is further introduced, where the model first estimates delays from the original asynchronous signals, synchronizes the signals using the extracted offsets, and then performs a second inference step to refine the final estimates. The model is trained using healthy and mass-varied finite element simulations with artificial timing offsets, while damaged scenarios are reserved for testing to evaluate generalization to unseen structural states. The results show that the proposed two-stage framework provides more accurate and robust synchronization than direct inference and the classical GCC-PHAT baseline, supporting its use as a damage-insensitive signal-level synchronization approach for wireless bridge SHM.

Monitoring cracks in concrete surfaces is essential for structural safety. While machine vision techniques have received significant interest in this domain, selecting optimal models and tuning hyperparameters remain challenging. This paper proposes a Distributed Automated Machine Learning (AutoML) framework for efficiently designing and optimizing deep learning models for concrete crack segmentation. The framework simultaneously optimizes multiple objectives, including segmentation accuracy and mean intersection over union (mIoU), while minimizing model parameters, depth, and computational complexity. Applied to the optimization of five U‐Net model variations, the framework is compared against non‐dominated sorting genetic algorithm (NSGA‐II), Gene Manipulation Genetic Algorithm (GMGA), grid search, and random search. Results show that the proposed method produces 55% to 177% more optimal models, with 79% to 99% of its models outperforming those generated by alternative approaches. The findings highlight the potential of distributed AutoML for real‐world structural health monitoring applications, especially in edge devices.

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Crack detection is an important task in bridge health monitoring, and related detection methods have gradually shifted from traditional manual methods to intelligent approaches with convolutional neural networks (CNNs) in recent years. Due to the opaque process of training and operating CNNs, if the learned features for identifying cracks in the network are not evaluated, it may lead to safety risks. In this study, to evaluate the recognition basis of different crack detection networks; several crack detection CNNs are trained using the same training conditions. Afterwards, several crack images are used to construct a dataset, which are used to interpret and analyze the trained networks and obtain the learned features for identifying cracks. Additionally, a crack identification performance criterion based on interpretability analysis is proposed. Finally, a training framework is introduced based on the issues reflected in the interpretability analysis.