Advancing Bridge Infrastructure Management through Artificial Intelligence: A Comprehensive Review

Abstract

Bridge infrastructure serves as a vital component of global transportation systems, yet its aging condition and exposure to increasing environmental and operational stressors necessitate smarter, faster, and more objective approaches to inspection, deterioration modeling, and maintenance management. Traditional methods often suffer from subjectivity, inefficiency, and data limitations. This comprehensive review explores how recent advancements in Artificial Intelligence (AI), including computer vision, natural language processing, deep learning, predictive modeling, robotics, and large language models (LLMs), are revolutionizing the entire bridge management lifecycle. AI-based systems are examined for automated condition detection and rating, data-driven deterioration forecasting, and maintenance prioritization using multi-modal data inputs. Special emphasis is placed on the LLMs for extracting actionable insights from unstructured inspection records and facilitating automated decision support. In addition, the review covers AI-driven training and quality assurance tools for inspectors and demonstrates the potential of LLM-powered bots for real-time bridge condition communication. By benchmarking these innovations against traditional practices, this paper identifies current capabilities, integration challenges, and future research directions essential for realizing intelligent, sustainable, and scalable bridge infrastructure management.