Health Monitoring and Evaluation Method of Civil Engineering Structure Based on Machine Learning

The health monitoring and assessment of civil engineering structures are important for ensuring structural safety and can Sustainability is critical. Traditional monitoring methods often require a lot of time and resources, and have some limitations in real-time and accuracy. In order to overcome these problems, this paper proposes a method of health monitoring and evaluation of civil engineering structures based on machine learning. This method combines sensor data with machine learning algorithm to realize real-time and accurate monitoring and evaluation of structural health status. By training and learning the data of existing civil engineering structures, the correlation model between structural health and sensor data is established. Through field experiments and simulation verification, the results show that the proposed method can effectively detect structural abnormalities, early warning potential faults of the structure, and provide scientific decision-making basis for engineers, so as to improve the safety and reliability of civil engineering structures.