Integrating automated machine learning and interpretability analysis in architecture, engineering and construction industry: A case of identifying failure modes of reinforced concrete shear walls

Machine learning (ML) has been recognized by researchers in the architecture, engineering, and construction (AEC) industry but undermined in practice by (i) complex processes relying on data expertise and (ii) untrustworthy 'black box' models. As a result, ML results of complex non-linear AEC problems, such as the failure mechanism of reinforced concrete (RC) shear walls, are not comparable with empirical and mechanics-based models. This paper aims to integrate automated ML (AutoML) and interpretability analysis to study the failure mechanism of RC shear walls. In this study, we collected a dataset of 351 comprehensive samples for the failure mode identification of RC shear walls. First, the AutoML model trained using the dataset outperformed a set of conventional ML methods in terms of the F1 accuracy score. Then, three model-agnostic interpretability analysis methods confirmed the trustworthiness of the AutoML model. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic identification of failure modes of RC shear walls. Second, the interpretability analysis can validate 'black-box' ML models against long-established domain knowledge in solving nonlinear AEC problems. Third, for AEC industrial practitioners, the whole process is automatic, accurate, less reliant on data expertise, and interpretable.

Automated summary

This paper integrates automated machine learning and interpretability analysis to study the failure mechanism of reinforced concrete shear walls. The results show that the AutoML model outperforms conventional ML methods in terms of accuracy, and the interpretability analysis confirms the trustworthiness of the model.