2022· International Journal of Modern Research in Science & Engineering· 0 citations
Abstract
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.
With the rapid development of artificial intelligence, the Internet of Things, and computer vision technologies, bridge structural health monitoring is increasingly evolving toward intelligent and automated inspection. As one of the most common structural defects, bridge surface cracks require timely and accurate identification, which is of great significance for bridge safety assessment, maintenance decision-making, and long-term service performance evaluation. However, conventional crack detection approaches mainly rely on manual inspection and traditional image processing algorithms, which are often limited by low efficiency, insufficient detection accuracy, strong subjectivity, and poor adaptability to complex environmental conditions. To address these limitations, this paper proposes an intelligent bridge surface crack detection method based on an improved convolutional neural network and metric representation learning. Specifically, a crack detection model is constructed using the TensorFlow framework and the Keras deep learning library. The proposed model employs convolutional neural networks to automatically extract edge, texture, and morphological features from bridge crack images. Meanwhile, the loss function and model parameters are further optimized, and metric representation learning is introduced to transform crack detection into an anomaly detection problem in the pixel embedding space, which improves the ability to distinguish subtle cracks from complex backgrounds by constructing a discriminative feature manifold. Experimental results demonstrate that the proposed method can effectively identify cracks on bridge surfaces, achieves a detection accuracy of 98.23% on the self-built dataset, and provides a feasible technical paradigm for the application of deep learning in bridge structural health monitoring.
Yuyao Liu· International Conference on...· 0 citations
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, suggest causes and remedies using an LLM, and estimate the remaining service life.
S. Moharana, Prashant Yadav· e-Journal of Nondestructive...· 0 citations
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.
M. Khan, Muhammad Shoaib Ashraf, Muhammad Jahanzeb et al.· International journal of com...· 0 citations
Structural health monitoring is needed to gauge the safety and sustainability of civil infrastructure. Conventional crack detection methods are through manual inspection which is time consuming, labor intensive and can easily be compromised through human error. The article offers a powerful method of identifying cracks in concrete buildings through transfer learning with deep convolutional neural networks. Models like ResNet50 and MobileNetV2, which are pretrained, are trained on a dataset of real crack images of concrete to be able to classify between cracked and non-cracked surfaces. On model generalization and performance, data augmentation and preprocessing methods are implemented. The experimental findings indicate that the suggested approach is highly accurate, precise and recalls, despite having a small training set. It is an effective system that may be used in real-time structural health monitoring applications.
Thushar S. Shetty, G. P. Dharshini, K. Kowsalyadevi et al.· International Conference on...· 0 citations
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 is proposed.
Yijin Zhang· International Conference on...· 0 citations
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.
Bellal Mia, Md Umar Faruk, M. Hasan et al.· Scientia. Technology, Scienc...· 0 citations
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