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.
Abstract
The rapid deterioration of civil infrastructure, particularly tunnels and bridges, poses a global socio-economic challenge. As an alternative to traditional inspection methods, Structural Health Monitoring (SHM) has become a key part of identifying deterioration of components and ensuring public safety and serviceability. The last decade has seen the introduction of Artificial Intelligence (AI) to SHM, which has included Machine Learning (ML) and Deep Learning (DL). These methods have revolutionised the discipline and shifted the focus of SHM from a manual and reactive practice to the automation and prediction of maintenance works. This paper provides a detailed overview of the applications of AI in the SHM of bridges and tunnels. The research surveyed 99 of the most cited papers in the discipline and focused on the contributions those papers made to the field. The use of DL, particularly Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), has provided significant advances. The reported research has a 95% success rate for the detection of structural damage in high complexity situations. This review has organized improvements in SHM along the lines of vibration-based anomaly detection, vision-based defect recognition, and multi-modal data fusion. This review has also focused on the combination of AI with rapidly emerging and advanced technologies, such as Digital Twins (DT), Physics-Informed Neural Networks (PINNs), and Explainable AI (XAI). This review provides an innovative research guide for SHM systems that focuses on SHM systems that use AI and modern supports to address challenges such as sparse data and class imbalance.
Structural Health Monitoring (SHM) has evolved from traditional periodic inspection methods to intelligent, data-driven systems powered by artificial intelligence (AI). This systematic review synthesizes recent advances in AI applications for SHM across civil infrastructure including bridges, buildings, tunnels, and dams. A comprehensive literature search covering 2020-2025 across Web of Science, Scopus, and IEEE Xplore identified 31 high-quality studies for qualitative analysis. The review examines AI methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and physics-informed neural networks (PINNs) applied to damage detection, localization, classification, and prognostic tasks. Sensor technologies—including accelerometers, fiber optics, piezoelectric transducers, and vision-based systems—are integrated with IoT platforms and digital twins for real-time monitoring. Key findings indicate that AI-driven SHM achieves damage detection accuracies exceeding 95%, with significant reductions in inspection time and maintenance costs. However, critical challenges persist: data scarcity in operational environments, model generalization across diverse infrastructure types, uncertainty quantification, cybersecurity vulnerabilities, and limited model interpretability for safety-critical decisions. Physics-informed and hybrid AI-physics models emerge as promising solutions to enhance robustness and transferability. This review identifies interdisciplinary opportunities including federated learning for decentralized monitoring, explainable AI for stakeholder trust, and autonomous inspection systems. Future research must prioritize standardized benchmark datasets, field-validated deployment studies, and sustainable practices for infrastructure resilience in the context of aging, climate-stressed, and increasingly complex built environments.
M. Khan, Muhammad Shoaib Ashraf, Muhammad Jahanzeb et al.· International journal of com...· 0 citations
Pavements are essential elements of transportation networks and are instrumental to the growth and development of a country. To harness the full potential of this infrastructure, it is necessary to maintain it in proper structural and functional conditions. Conventional techniques, such as manual inspection can be used to determine the functional conditions of the pavement. However, these techniques have different shortcomings, including high monitoring costs, time consumption and requirement of traffic control during pavement surveying. These shortcomings can be mitigated by utilizing various Artificial Intelligence (AI) techniques such as machine learning and deep learning, for pavement surface inspection. This study aims to systematically review state-of-the-art deep learning techniques such as vision transformer model for pavement distress detection. This research contributes to the knowledge body by summarizing the application of vision transformer model for pavement distress detection. Moreover, this research provides a brief description of the application of other deep learning architectures including You Only Look Once (YOLO), and Convolutional Neural Networks (CNNs) for pavement distress detection. Deep learning techniques can autonomously detect various types of pavement distress including longitudinal and transverse cracks, rutting, faulting, patching, shoving, raveling and potholes from the pavement surface. The research was performed in three different steps namely: keyword selection, identification of databases, literature acquisition and summarizing the literature based on different deep learning models. The findings from the review indicate that YOLO and CNN were extensively employed by researchers, however in recent times, vision transformers gained popularity among researchers and pavement engineers. Overall, this study highlights the critical role played by different deep learning techniques in transforming pavement monitoring, leading to safer, more resilient, and sustainable transportation infrastructure.
Civil structures such as buildings, roads, railways, bridges, tunnels, and dams are present in every society, and the safest and most durable ones are those that are properly managed and maintained. Health monitoring plays an important role in ensuring safety, but engineers still depend on traditional inspection tools like crack gauges or comparator cards, which are slow, labour-intensive, and differ based on the inspector's judgment. There is also no proper tool that systematically analyses this data or suggests solutions based on standard codes using AI. Considering these limitations, this project aims to develop an AI model that processes images to classify whether a defect is structural or non-structural, identify the type of defect, assign a severity score where 0 is worst and 100 is best, suggest causes and remedies using an LLM, and estimate the remaining service life. Literature supports the need for such a system, as studies have focused on deep-learning-based detection of concrete damage, CNN-based comparison between traditional and AI inspection, and 3D scanning with machine learning for defect detection. Surveys on NDT tools like UPV and X-ray show improved efficiency over manual methods but still remain time-consuming and less precise.
Most research concentrates on a single defect such as cracks, and although some models can detect or localize them, there is still a major gap in systems that quantify severity or provide causes and repair suggestions. Many approaches stop at detection without offering guidance for maintenance, making it difficult to translate inspection data into decisions. To address this, datasets in this project are collected from public sources and field surveys, followed by preprocessing such as histogram equalization and median filtering. Images are then manually labelled into structural and non-structural categories and further classified by defect type. An image classification model is trained on this dataset to automatically detect and classify defects. Severity scoring is done using the formula Intensity × Extent, where intensity depends on measurable parameters like crack width or spall area, and extent depends on defect length or percentage affected. The model uses an LLM aligned with Indian Standard codes to suggest possible causes and remedies and predicts remaining service life based on severity. The novelty of this work lies in combining multiple functions that previous studies treat separately: multi-defect classification, quantitative severity scoring, LLM-based engineering interpretation, and service-life estimation. No existing system integrates all these components into a single workflow dedicated to structural inspection.
Buildings are long-term investments, and regular monitoring helps extend their lifespan and prevent failures. This project aligns with SDG 9, SDG 11, and SDG 12 by supporting safer, more resource-efficient, and sustainable infrastructure management. By bringing together defect detection, severity assessment, interpretation, and life prediction, the work addresses limitations of manual inspections and gaps in current automated tools. It supports better maintenance planning and contributes to a more comprehensive AI-based structural health-monitoring framework.
S. Moharana, Prashant Yadav· e-Journal of Nondestructive...· 0 citations
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.
Rahul Mehta· International Journal of Mod...· 0 citations
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.
Jasjeet Singh· Materials Research Proceedin...· 0 citations
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.
Yijin Zhang· International Conference on...· 0 citations