Self-Tuning Inference Model for Settlement in Shield Tunneling: A Case Study of the Taipei Mass Rapid Transit System’s Songshan Line

Constructing tunnels in urban spaces usually uses shield tunneling. Because of numerous uncertainties related to underground construction, appropriate monitoring systems are required to prevent disasters from happening. This study collected the settlement monitoring data for Tender CG291 of the Songshan Line of the Taipei Mass Rapid Transit (MRT) system and considered that influential factors were examined to identify the correlations between predictor variables and settlement outcomes. An inference model based on symbiotic organisms search-least squares support vector machine (SOS-LSSVM) was proposed and trained on the collected data. Moreover, because the dataset used for this study contained far less data at the alert level than at the safe level, the class of the dataset was imbalanced, which could compromise the classification accuracy. This study also employed the probability distribution data balance sampling methods to enhance the forecast accuracy. The results showed that the SOS-LSSVM exhibited the most favorable accuracy compared to four other artificial intelligence-based inference models. Therefore, the proposed model can serve as an early warning reference in tunnel design and construction work.