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Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10, Pages 1249-1266 doi: 10.1007/s11709-022-0858-5

Abstract: The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer (GFRP) elastic gridshell structures. Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacement of GFRP elastic gridshell structures. Several ML algorithms, including linear regression (LR), ridge regression (RR), support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine (LightGBM), are implemented in this study. Output features of structural performance considered in this study are the maximum stress as f₁(x) and the maximum displacement to self-weight ratio as f₂(x). A comparative study is conducted and the Catboost model presents the highest prediction accuracy. Finally, interpretable ML approaches, including shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions. SHAP is employed to describe the importance of each variable to structural performance both locally and globally. The results of sensitivity analysis (SA), feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f₁(x) and f₂(x).

Keywords： machine learning gridshell structure regression sensitivity analysis interpretability methods

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Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4, Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application

Keywords： neural networks interpretability dynamical behavior network decouple

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Visual interpretability for deep learning: a survey Review

Quan-shi ZHANG, Song-chun ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 27-39 doi: 10.1631/FITEE.1700808

Abstract: Although deep neural networks have exhibited superior performance in various tasks, interpretabilityAt present, deep neural networks obtain high discrimination power at the cost of a low interpretabilityWe believe that high model interpretability may help people break several bottlenecks of deep learningrepresentations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability

Keywords： Artificial intelligence Deep learning Interpretable model