AI-Optimized Structural Health Monitoring: A Review of Intelligent Systems for Infrastructure Assessment

Abstract

Structural Health Monitoring (SHM) plays a vital role in the assessment, maintenance, and safety assurance of infrastructure systems. As structures become more complex and the demand for real-time, accurate, and automated monitoring increases, traditional SHM methods are proving inadequate in many scenarios. The integration of Artificial Intelligence (AI) with SHM offers a transformative solution by enabling intelligent data interpretation, damage detection, pattern recognition, and predictive analytics. AI-optimized SHM harnesses machine learning algorithms, deep learning techniques, and advanced signal processing methods to enhance the performance, scalability, and accuracy of monitoring systems. This paper provides a comprehensive review of current developments in AI-optimized SHM, highlighting recent advances, methodologies, challenges, and future opportunities. The paper begins with an in-depth introduction to SHM and the evolution of AI techniques in civil infrastructure monitoring. It proceeds to analyze recent literature that exemplifies various AI implementations across different SHM systems. The methodology section outlines core strategies including data acquisition, AI model selection, training processes, and performance evaluation. A discussion on future directions identifies emerging technologies such as edge AI,
federated learning, and explainable AI, and explores their potential to address the limitations of current systems. Ultimately, the integration of AI into SHM is poised to revolutionize how engineers perceive, maintain, and design structures for safety and longevity.