Machine Learning Prediction of Structural Response for Reinforced Concrete Members under Blast Loading
Abstract
With increasing accidental and intentional explosions and blast events inflicting life loss and economic damage to civil infrastructure, greater attention is given to the analysis and design of blast-resistant structures. Accordingly, this thesis introduces state-of-the-art machine learning models dedicated to predicting the structural behavior of various reinforced concrete (RC) members under blast loading, including slabs, columns, and beams. Moreover, extended prediction models were developed for RC members that employ fiber-reinforced polymer (FRP) retrofitting and steel fiber-reinforced concrete as blast mitigation strategies. For each model, extensive validation was conducted through statistical performance measures and comparisons to existing prediction methods. Additionally, feature importance analyses were performed to investigate the extent to which each proposed model captured its respective application. Overall, the developed prediction models achieved accurate and computationally efficient performance for the complex application of blast-loaded structures.