Advanced Structural Health Monitoring and Digital Performance Evaluation of Civil Infrastructure for Enhanced Resilience and Service Life Extension

Abstract

Advanced Structural Health Monitoring (SHM) and digital performance evaluation have emerged as critical strategies for enhancing the resilience and service life of civil infrastructure. This study investigated the integration of Digital Twins (DT), Internet of Things (IoT) sensors, and artificial intelligence (AI) algorithms to monitor, assess, and predict structural performance in real time. Traditional inspection methods were found to be episodic, subjective, and limited in detecting early-stage structural anomalies. The research employed a combination of IoT-enabled sensor networks, multi-modal data integration, and AI-based predictive modeling to identify deterioration patterns, simulate dynamic load responses, and optimize maintenance planning. Results demonstrated that DT-enabled systems significantly improved condition assessment accuracy, facilitated proactive maintenance, and reduced operational risks associated with infrastructure failure. Machine learning models accurately predicted structural degradation trends, while multi-modal integration of remote sensing, satellite, and ground-based data enhanced the contextual understanding of complex infrastructure systems. The study also identified key challenges, including high implementation costs, data interoperability issues, and cybersecurity concerns, which may hinder widespread adoption. Recommendations focused on standardized deployment protocols, long-term monitoring strategies, and integration of emerging technologies to support scalable and cost-effective SHM systems. Overall, the findings underscored the importance of data-driven, predictive frameworks in transforming infrastructure management from reactive to proactive approaches, enabling optimized service life, improved resilience, and informed decision-making.