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

Artificial Intelligence-Based Structural Health Monitoring of Bridges and Tunnels: A Comprehensive Review

This review has organized improvements in SHM along the lines of vibration-based anomaly detection, vision-based defect recognition, and multi-modal data fusion along the lines of vibration-based anomaly detection, vision-based defect recognition, and multi-modal data fusion.

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

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of critical civil infrastructure such as bridges, buildings, and dams. Traditional SHM approaches rely heavily on manual inspection and threshold-based alarm systems, which are prone to high false alarm rates and limited sensitivity to incipient damage. This paper proposes an intelligent SHM framework that integrates one-dimensional Convolutional Neural Networks (1D-CNN) with Long Short-Term Memory (LSTM) networks for automated damage detection from vibration sensor data acquired through Internet of Things (IoT) sensor networks. The proposed architecture employs a dual-branch feature extraction strategy: the 1D-CNN branch captures local spatial patterns from raw acceleration signals, while the LSTM branch models temporal dependencies across sequential sensor readings. An attention-based sensor fusion module aggregates information from heterogeneous sensor types including accelerometers, strain gauges, and temperature sensors, enabling comprehensive structural state assessment. Transfer learning is applied to adapt models pre-trained on large-scale simulated datasets to real-world bridge monitoring scenarios. Extensive experiments on the LANL structural damage detection dataset, the Z24 bridge benchmark, and a custom simulated structural dataset demonstrate that the proposed method achieves damage detection accuracy of 96.5%, significantly outperforming conventional machine learning baselines including SVM (84.1%), Random Forest (85.5%), and standard MLP (87.2%), while maintaining a false alarm rate below 3.2%. Ablation studies confirm the contribution of each architectural component to the overall performance.

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.

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.

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

Artificial Intelligence–Driven Predictive Systems for Civil Engineering: Advancing Smart Infrastructure and Structural Health Insights

Structural health information from continuous monitoring of vibration makes an important contribution to the development of a proactive approach to the management of civil infrastructure. These studies propose an artificial intelligence driven predictive monitoring framework of vibration based structural health assessment under real conditions including where the labelled damage data is unavailable. Structural behavior is evaluated according to baseline referenced deviation based on physics informed vibration features. A Structural Deviation Index is introduced to quantify deviation where median values range from the baseline measurements 0.42-0.45 to 3.68-3.92 in later monitoring tests representing progressive structural change. One-Class Support Vector Machine deviation scores reveal a corresponding deviation from close to zero to -0.76 which indicates that the classifier is highly sensitive to deviation in early stages. Band-limited spectral energy and dominant frequency are the most influential indicators with the value of permutation importance up to 0.231 and correlation coefficients up to 0.78, according to explainability analysis. The results validate that the proposed framework allows for interpretable, scalable and data-driven predictive monitoring. The study is an illustration of the possibilities of artificial intelligence to facilitate early warning, decision-making and smart infrastructure management with continuous structural health assessment.