Interpretable machine-learning models for maximum displacements of RC beams under impact loading predictions. (15th April 2023)

• Record Type: Journal Article
• Authors: Lai, Dade; Demartino, Cristoforo; Xiao, Yan
• Is Part Of: Engineering structures. Volume 281(2023)
• Publication Date: 2023-04-15
• Subjects: Machine learning (ML) -- Interpretation models -- RC beam -- Maximum displacements -- Stacking model -- SHapley Additive exPlanation (SHAP); Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2023.115723
• Languages: English
• ISSNs: 0141-0296

Abstract

Abstract: The estimation of the maximum displacements of RC beams subjected to impact loads is of pivotal importance to define demand models to be employed in structural design. The dynamic behavior is quite complex and the maximum displacements can be obtained through experimental tests or high-fidelity FEM models, while simplified analytical models accounting for few degrees of freedom have generally low accuracy. In this context, this paper presents a study on the training and interpretation of a machine-learning model that strategically combines several algorithms for the said purpose. To train the model, a comprehensive database of 342 samples of rectangular simply supported beams under rigid impact loads is adopted. Seven ML regression algorithms are adopted in this study to develop a predictive model: (1) Support Vector Regression ( SV R ), (2) Gaussian Process Regression ( GPR ), (3) Random Forest Regressor ( RFR ), (4) XGBoost, (5) CatBoost, (6) LightGBM, and (7) Artificial Neural Networks ( ANN ). Finally, a stacking model combining SV R, GPR, RFR, XGBoost, CatBoost and ANN is built to improve accuracy, wherein 75% and 25% of the data are used for training and validation, respectively, in combination with the 5 cross-validation technique. This effort resulted in approximately 90% validation accuracy exceeding current mechanics-based/semi-empirical models. Finally, the SHapley Additive exPlanations (SHAP) algorithm is used to estimate the relative importance of the input features; the most influential factors are impact velocity and impactor mass which are representing the initial kinetic energy. Moreover, a full parametric analysis of the proposed model is provided and the influence of each input is discussed. The model is finally applied to perform a structural check for three damage levels. The proposed model can be used in practical applications where a reliable estimation of the maximum displacements of RC beams under impact loading is required. Highlights: Dataset of 342 samples of rectangular RC beams subjected to impact loads. Seven ML regression algorithms are compared to predict maximum displacements. Stacking model combining SV R, GPR, RFR, XGBoost, CatBoost and ANN . SHapley Additive exPlanations (SHAP) algorithm to estimate importance of the inputs. A full parametric analysis of the proposed model is provided.