Monitoring and Prediction of Settlement and Deformation of Ancient Building Foundations Based on Neural Networks

Abstract

With the rapid development of the tourism industry, the visual value of ancient buildings gradually increases. And the prediction and protection of ancient building foundation settlement based on neural networks have been developed. When traditional methods are used to monitor and predict the settlement and deformation of ancient building foundations, complex factors such as water environment and geological conditions can bring noise to the experimental results. Deep belief was introduced into grey artificial neural networks to effectively denoise the corresponding model and enhance its ability to process data. And stress analysis was conducted on the ancient building foundation in the experiment to generate a composite model. Experiments were conducted on the Abfound dataset and three models were comparedto verify the predictive ability for ancient buildings, including random forest, to verify the superiority of the model. The dimensionality reduction capabilities of four models for building data were 4.6, 3.6, 3.2, and 3.9, respectively, indicating that the optimized model could effectively handle a large amount of data. The composite model had the highest accuracy in predicting the settlement of ancient building foundations, with an experimental data of 99.2%. These experiments confirmed that the proposed composite algorithm performed best in terms of noise reduction and prediction ability, and was suitable for predicting the settlement deformation of ancient building foundations.