A novel explainable AI-based approach to estimate the natural period of vibration of masonry infill reinforced concrete frame structures using different machine learning techniques

Abstract

In this study, we used four different machine learning models - artificial neural network (ANN), support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF) - to predict the natural period of reinforced concrete frame structures with masonry infill walls. To interpret the models and their predictions, we employed Shapley additive explanations (SHAP), Local interpretable model-agnostic explanations (LIME), and partial dependency plots (PDP). All models showed good accuracy in predicting the fundamental period (T). The post-hoc explanations provided insights into (a) the importance of each feature, (b) their interaction, and (c) the underlying reasoning behind the predictions. For the first time, we have created a graphical interface that can predict the value of T along with its SHAP explanation. This interface can be useful in manually optimizing the design of reinforced concrete frame structures with masonry infill walls. However, the local explanations from SHAP and LIME exhibited significant discrepancies, and LIME underestimated the feature importance of dominant features compared to SHAP. These discrepancies observed in the explanations highlight the need for further research in the field of explainable artificial intelligence (XAI) in structural engineering.

Keywords

• Explainable AI
• Masonry infill
• Machine learning
• Regression
• Natural period