Applications of Artificial Intelligence in Bridge Structural Health Monitoring: A Systematic Review of the Last Decade


== Abstract ==

<p>Bridges are critical components of transportation networks whose structural integrity is often threatened by aging, environmental loads, and insufficient maintenance. Structural Health Monitoring (SHM) supported by Artificial Intelligence (AI) offers a transformative approach to early damage detection, predictive maintenance, and operational safety. This study presents a systematic literature review, conducted in accordance with PRISMA 2020 guidelines, on the application of AI algorithms to bridge SHM between 2015 and 2025. A total of 70 peer reviewed articles were analyzed, covering diverse geographic contexts, structural types, and sensing technologies. The review categorizes studies by AI technique (ANN, CNN, LSTM, SVM, hybrid models, and emerging methods such as Transformers and Graph Neural Networks), sensor architecture (accelerometers, fiber optic sensors, UAV based imaging, IoT modules), and performance metrics. Results indicate that convolutional and recurrent neural networks achieve detection accuracies above 95% and R2values exceeding 0.90 in displacement prediction, while hybrid approaches combining deep learning with traditional classifiers enhance robustness. Sensor integration with IoT and multimodal data fusion improves detection sensitivity, with correlation values above 0.99 in some cases. However, over 90% of studies lack robust cross validation, real world deployment, or standardized performance reporting, limiting replicability. This review highlights current trends, technical challenges, and research opportunities, including the need for interoperable sensor&ndash; algorithm platforms, explainable AI models, and broader implementation in developing regions. By consolidating existing knowledge, the study provides a technical reference for researchers, practitioners, and policymakers aiming to implement intelligent, predictive, and resource efficient bridge SHM systems.OPEN ACCESS Received: 27/08/2025 Accepted: 13/11/2025</p>



== Document ==
<pdf>Media:Draft_content_880792241-8052-document.pdf</pdf>