Building Structure Safety Assessment System Based on Deep Learning

In the construction industry, traditional structural safety assessment methods often rely on manual inspection and empirical judgment, which are inefficient and error-prone. This paper aims to establish a building structure safety assessment system based on LSTM. First, a large amount of building structure data is collected, including material strength, stress distribution, historical damage records and environmental factors, and the data is preprocessed and feature engineered to ensure the quality and validity of the data. Next, the LSTM model is used to conduct classification and regression analysis for structural safety. Cross-validation and hyperparameter adjustment are used during the training process to optimize model performance. The system's accuracy in structural safety assessment reaches 98.4%, and the assessment time is the shortest 2.8 seconds. Assessment systems based on deep learning can significantly improve the efficiency and accuracy of building structure safety assessments, providing important support for the intelligent development of the construction industry.