Prediction of building subsidence in Vietnam using machine learning techniques based on leveling results

Abstract

Vietnam’s rapid urbanization and economic growth have led to an increase in high-rise buildings, making building subsidence a significant concern. Monitoring subsidence is crucial for ensuring building safety and reducing potential risks. The leveling method is commonly used in Vietnam to monitor subsidence, providing valuable data for predicting future subsidence behavior. However, traditional prediction methods based on mathematical models have limitations in capturing complex subsidence patterns. Machine learning techniques have shown promise in enhancing subsidence prediction accuracy. In this study, we analyze machine learning methods for predicting building subsidence using leveling results in Vietnam. We utilize a dataset from a subsidence monitoring network in Hoa Binh General Hospital and compare the performance of linear regression, decision tree regression, and random forest regression models. Our results show that the decision tree and random forest models produce consistent predicted subsidence values, aligning with the observed stability of the building. In contrast, the linear regression model fails to capture the diminishing nature of subsidence over time. We discuss the implications of these findings and highlight the advantages of machine learning in accurately forecasting subsidence. The study demonstrates the potential of machine learning in revolutionizing subsidence prediction and enhancing the monitoring and management of building stability and structural integrity in Vietnam.

Keywords

• building subsidence
• leveling
• machine learning
• linear regression
• decision tree regression
• random forest regression