Development of fragility functions of low-rise steel moment frame by artificial neural networks and identifying effective parameters using SHAP theory

Estimating analytical fragility functions requires high computational costs due to numerous incremental non-linear dynamic analyses. This study employs a soft computing approach to reduce this process and increase estimation accuracy. Utilized a fragility database of low-rise steel moment structures designed for different versions of Iranian seismic design code, and six feedforward Artificial Neural Networks (ANN) with various architectures were trained to estimate the fragility function’s parameters to determine the most desired architectures. The neural networks used the design parameters (frames story, code edition of design, ductility, soil type, near- and far-field earthquake, and seismic zone factor and importance factor) as inputs. For each of the fragility function’s parameters (Median PGA and βs of damages: slight, moderate, extensive, and complete) as outputs, an ANN was trained. The best architect of ANN was selected based on the accuracy of the models from different statistical indicators and the most balanced of the inputs’ importance on the outputs using SHapley Additive explanations (SHAP) analysis. To demonstrate the accuracy of the selected architect, the coefficient of determination was estimated for all database values, and the fragility functions of four randomly chosen frames were compared. The high R2 value in most cases and the closeness of the predicted fragility curved with the actual one indicates the reliability of the ANN method for predicting the fragility function.