Machine learning-based framework for predicting the fire-induced spalling in concrete tunnel linings

Abstract

Fire-induced spalling in concrete is a serious issue in tunnel lining design because it can reduce the load-bearing capacity of the tunnel and the cross-section area of the tunnel lining. The adverse consequences of concrete spalling can cause serious damage to the tunnel lining or even failure occasionally. Hence, concrete spalling at elevated temperatures particularly explosive spalling must be properly assessed by considering it as a crucial factor for fire resistance in concrete tunnel lining designs. In the last several years, there has been a surge of scientific studies aimed at explaining why concrete spalls when exposed to fire. Despite these attempts, a current evaluation method that can reliably forecast the average depth of spalling of concrete tunnel lining has not yet been developed, and a comprehensive analysis of this phenomenon has not been completed. Many areas of structural engineering have benefited from the use of machine learning, but no one has yet attempted to use it to predict the spalling depth of concrete tunnel lining. Most sophisticated techniques in machine learning such as ensemble learning approaches have not been adopted. This study also addressed this issue by developing a database of 415 spalling test results under 16 input variables to provide predictions about the spalling depth of concrete tunnel lining using ensemble learning approaches such as Random Forest (RF), Categorical gradient boosting algorithm (Catboost), Light gradient boosting algorithm (LightGBM) and Extreme gradient boosting algorithm (XGBoost). This research developed a novel machine learning-based framework to predict the spalling behaviour in tunnel lining exposed to fire. Based on the conclusions, XGBoost demonstrated the highest performance in predicting spalling depth in concrete tunnel linings.