Bridge Safety Assessment and Management Strategies Under Long-Term Operating Conditions

Junjie Qi

doi:10.54097/8857w621

Authors

Junjie Qi

DOI:

https://doi.org/10.54097/8857w621

Keywords:

Structural health monitoring; bridge safety management; advanced monitoring technologies; Machine Learning; risk-based maintenance strategies.

Abstract

In the critical field of civil engineering, maintaining the safety and integrity of bridge structures under long-term operating conditions is a major challenge. This paper examines multifaceted approaches to bridge safety assessment and management, and analyzes the dynamic interactions between environmental factors, material aging, and human impacts on structural health. Key elements of the study include the application of advanced structural health monitoring (SHM) technologies such as wireless sensor networks, fiber optic sensors, and digital image-related technologies. The effectiveness of machine learning and Internet of Things (IoT) systems in improving the predictability and efficiency of security assessments is highlighted. The results of this paper show that technological advances are combined with regular inspections and strategic material use. The combined application of these technologies can significantly improve the long-term safety and reliability of bridge infrastructure. This paper can provide an effective reference for bridge safety evaluation and management strategy under long-term operation conditions.

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