An interpretable machine learning method for the prediction of R/C buildings' seismic response

Building seismic assessment is at the forefront of modern scientific research. Several researchers have proposed methods for estimating the damage response of buildings subjected to earthquake motions without conducting time-consuming analyses. The advancement of computer power has resulted in the development of modern soft computing methods based on the use of Machine Learning (ML) algorithms. However, a lack of expertise associated with the use of complex ML architectures can affect the performance of the intelligent model and, ultimately, reduce the algorithm's reliability and generalization which should characterize these systems. The current paper proposes a fully validated interpretable ML method for predicting seismic damage of R/C buildings. Specifically, the most efficient machine learning algorithms were used in a large-scale comparison study in a sophisticated dataset of 3D R/C buildings. Moreover, effective additional validation ensures that models are sound, have low complexity, are fair and provide clear explanations for decisions made. Also, extensive experiments were done to make the final machine learning model explainable and the decisions interpretable. The proposed method aims to suggest that the civil protection mechanisms must include scientific methodology and appropriate tech-nical tools into their technological systems, in order to make substantial innovative leaps in the new era.