PCA-NuSVR Framework for Predicting Local and Global Indicators of Tunneling-induced Building Damage

Today, numerous construction projects aimed at urban expansion, such as subway systems, underground utilities, and transportation tunnels, pose significant environmental challenges, including ground settlement, vibration, and alterations in groundwater flow. Accurately predicting potential building damage is vital for assessing and mitigating some of these impacts on nearby infrastructure, allowing safe development practices. Leveraging Machine Learning (ML) tools facilitates the creation of quick and efficient prediction models for building damage assessment. In this paper, the authors generated a comprehensive synthetic dataset by conducting nearly 1000 non-linear Finite Element Method (FEM) of building damage to tunneling simulations using High-Performance Computing. This dataset include eight local and global indicators crucial for evaluating building damage resulting from tunneling activities. To address this challenge, we devised a novel unsupervised-supervised framework by integrating Principal Component Analysis and Nu Support Vector Regression (PCA-NuSVR). We developed algorithms for training and applying the proposed framework. Modeling was conducted using 5-fold cross-validation and results were evaluated using different performance metrics. Comparative analysis against various existing ML methods, including ensemble techniques, revealed the superiority of the optimized PCA-NuSVR framework. Specifically, the utilization of this framework led to a notable enhancement in prediction accuracy. The increased accuracy offered by the PCA-NuSVR framework underscore its applicability in addressing numerous practical challenges within civil engineering.