Intelligent structural health monitoring using convolutional neural networks and IoT sensor fusion

A SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN STRUCTURAL HEALTH MONITORING OF CIVIL INFRASTRUCTURE

This systematic review synthesizes recent advances in AI applications for SHM across civil infrastructure including bridges, buildings, tunnels, and dams and identifies interdisciplinary opportunities including federated learning for decentralized monitoring, explainable AI for stakeholder trust, and autonomous inspection systems.

Structural Health Monitoring Using Wireless IoT Sensors

Structural Health Monitoring (SHM) has become an important science to help keep civil infrastructure like bridges, buildings, dams, and industrial plants safe, reliable and lasting. Traditional methods of SHM have depended primarily on wired sensor networks and periodic manual inspections that are regularly expensive, labor-intensive and lack real-time responsiveness. The present-day breakthroughs in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have made it possible to create intelligent, scalable, and energy-efficient SHM systems that would allow continuous monitoring and remote diagnostics. This paper provides an extensive exploration of Structural Health Monitoring with wireless IoT sensors in terms of system architecture, sensing modalities, communication protocols, data processing methods, and damage detection methods. The intricate literature survey reveals the development of the SHM technologies and outlines the gaps in the research. The suggested methodology will combine low-power wireless sensors, edge computing, and cloud-based analytics to track such structural parameters as strain, vibration, displacement, and temperature. Damage detection and feature extraction mathematical models are discussed and the experimental validation strategies. The findings support the idea that IoT-based SHM systems can greatly improve the accuracy of fault detection, decrease the cost of maintenance, and predictive maintenance. The paper has reached the conclusion that wireless IoT-based SHM is an innovative solution to smart infrastructure management and offers the research perspectives on its future development in terms of scalability and artificial intelligence incorporation.

An edge-enabled lightweight intelligent framework for structural health monitoring with strain-temperature sensing using wavelet scattering and 1D convolutional autoencoders

Gated Multi-GPT Fusion for Unsupervised Structural Health Monitoring of Bridge Structures

Smart infrastructure systems require monitoring frameworks that can function autonomously, remain effective under varying operational conditions, and issue dependable warnings without requiring labeled damage information. This study presents an unsupervised structural health monitoring (SHM) framework for transportation infrastructure that integrates LLM-inspired representation learning with a gated multi-GPT fusion autoencoder for robust intact-only anomaly detection using networked strain measurements. In the proposed pipeline, raw multi-channel strain-gauge signals are first processed through an adversarial autoencoder (AAE) to extract compact and informative feature representations, which are then fed into the downstream model. Multiple GPT-based reconstruction experts analyze these segmented AAE-derived features in parallel, while a compact gating network adaptively combines their outputs. The proposed workflow supports calibration using only intact-condition data, adaptive threshold setting, and continuous monitoring under non-stationary loading scenarios. The framework is validated on a laboratory pedestrian bridge instrumented with multichannel strain gauges and exposed to walking-induced excitation, where it achieves high anomaly detection performance across two damage levels. Robustness studies under both white and colored noise, together with threshold sensitivity analysis, confirm stable class separability and reliable operational performance. Comparative evaluation against leading unsupervised anomaly detection methods further highlights improved robustness to correlated noise and a reduced number of false alarms, demonstrating a scalable sensing-to-decision solution for resilient SHM.

AI-Based Structural Damage Detection in Civil Infrastructure

Structural Health Monitoring (SHM) has become increasingly important in civil engineering due to the aging of infrastructure such as bridges, buildings, tunnels, and dams. Traditional inspection methods rely on manual visual assessments, which are time-consuming, labor-intensive, and often unable to detect early-stage damage. Recent advances in Artificial Intelligence (AI) have enabled the development of intelligent damage detection systems that improve the accuracy and efficiency of structural assessments. AI techniques, including Machine Learning, Deep Learning, Computer Vision, and Pattern Recognition, analyze sensor data, vibration signals, and images to identify defects and predict structural failures. This study presents a comprehensive survey of AI-based damage detection methods for civil infrastructure. The proposed framework integrates sensor data acquisition, feature extraction, machine learning classification, and automated damage assessment. Various AI algorithms such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), and Convolutional Neural Networks (CNN) are evaluated for detecting structural deterioration. Experimental results demonstrate that AI-based approaches, particularly deep learning models, achieve higher damage detection accuracy than conventional inspection techniques. Furthermore, AI-powered SHM systems enable real-time monitoring, reduce maintenance costs, and support proactive infrastructure management, highlighting their potential to transform modern civil infrastructure maintenance and safety.

AI-assisted structural health monitoring for high-performance mechanical components

Abstract. High-performance mechanical component structural health monitoring (SHM) is a vital issue in contemporary engineering, especially in the aerospace, automotive, and industrial turbomachinery sectors where component failure may be disastrous. This article introduces a new AI-aided SHM framework with multimodal sensor fusion and ensemble deep learning architecture with one-dimensional convolutional neural networks (1D-CNN) and bidirectional long short-term memory (Bi-LSTM) networks, which can be used to detect fault, classify fault, and predict remaining useful life (RUL) in real-time. The proposed system takes in time-series streams of vibration, acoustic emission, and strain gauge data, runs them through an adaptive signal preprocessing pipeline, and derives hierarchical features of fault relevance without using manually specified features. The tests are done on two benchmark datasets, which include the CWRU bearing fault dataset and a custom gas turbine blade fatigue dataset that were obtained under controlled laboratory settings and a real gas turbine compressor testbed. The CNN-BiLSTM ensemble suggested has an accuracy of fault classification of 98.7 and a mean absolute percentage error (MAPE) of 3.14 to predict RUL with average inference latency of 12.3 ms, which is appropriate to be integrated into embedded systems in real-time. These findings constitute a statistically significant step forward compared to the current state-of-the-art baselines and they generalize and scale to provide an AI-SHM paradigm of safety-critical mechanical systems.