Advanced Structural Health Monitoring (SHM) and Intelligent Prognostics for Enhancing Safety, Performance, and Long-Term Reliability of Critical Civil and Mechanical Infrastructure

Abstract

ABSTRACT Structural Health Monitoring (SHM) has emerged as an essential technology for assessing the real-time performance, deterioration, and future reliability of civil, aerospace, mechanical, and marine systems. As infrastructure ages and operational loads increase, the need for accurate, continuous, and automated structural evaluation becomes increasingly important. Prognostics, a complementary discipline to SHM, extends this capability by predicting the Remaining Useful Life (RUL) and estimating when maintenance should be performed. This paper presents a comprehensive review of SHM principles, sensing technologies, data acquisition approaches, analytical methods, and intelligent prognostic models. It further discusses the challenges, opportunities, and future scope of integrating SHM with artificial intelligence and digital twin frameworks. The study emphasizes the significance of SHM-based decision making in creating sustainable, resilient, and safer infrastructure systems.

KEYWORDS: Structural Health Monitoring, Prognostics, Sensors, Damage Detection, Remaining Useful Life, Machine Learning, Vibration Analysis, Infrastructure Safety, Digital Twin, Condition Monitoring