Computer Vision and Artificial Intelligence-Driven Structural Health Monitoring in Water Conservancy: Intelligent Early Warning and Economic Benefit Optimization

Abstract

Water conservancy structures serve as the core infrastructure for the operation of water conservancy projects; their structural health directly impacts regional flood control safety, water resource supply security, and ecological environment safety. Traditional health monitoring of water conservancy structures relies on manual inspections and wired sensing devices, but this approach suffers from limitations such as limited coverage, insufficient detection timeliness, and high maintenance and operation costs. This paper investigates the application of computer vision and artificial intelligence (AI) technologies in the field of water conservancy structure health monitoring. It outlines the technical pathways employed by computer vision techniques to identify structural anomalies—such as surface cracks, deformation, or damage—and analyzes the predictive logic of AI algorithms for forecasting the evolution trends of structural health. The paper proposes a framework for an intelligent safety early-warning system tailored for water conservancy structures and systematically evaluates the economic benefits derived from the application of these technologies from both direct and indirect perspectives. Case studies based on actual engineering applications demonstrate that this technical framework can significantly enhance the accuracy of anomaly detection and the timeliness of early warnings for water conservancy structures, reduce manual maintenance and operation costs, and extend the service life of these structures. Finally, this paper identifies the challenges currently faced during the implementation phase of these technologies—particularly in data processing and privacy protection—and proposes corresponding solutions. It also offers insights into future technological development directions in this field, providing a valuable reference for the intelligent operation and maintenance of water conservancy projects.