Explainable machine learning-based model for failure mode identification of RC flat slabs without transverse reinforcement. (November 2022)

• Record Type: Journal Article
• Authors: Shen, Yuanxie; Wu, Linfeng; Liang, Shixue
• Is Part Of: Engineering failure analysis. Volume 141(2022)
• Publication Date: 2022-11
• Subjects: Machine learning -- RC flat slab -- Failure mode identification -- SHAP; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106647
• Languages: English
• ISSNs: 1350-6307

Abstract

Graphical abstract: Highlights: Machine learning-based prediction models are established for identifying the failure modes of flat slabs. Machine learning-based models exhibit higher prediction accuracy than 3 empirical models. XGBoost is the best prediction model for predicting the failure mode of flat slab. The interpretation of SHAP indicates that the reinforcement ratio has the highest influence of the failure mode. Abstract: In this paper, an accurate prediction model, which screened from 8 machine learning-based models (LR, ANN, DT, SVC, RF, AdaBoost, GBDT, XGBoost), is established for identifying the failure mode of flat slabs. A database contains 610 experimental data is collected. The hyper-parameters are determined through grid search method with 10-fold cross validation, and precision, recall, F1 score and accuracy are utilized for appraising the prediction of each model. After comparison with other 7 machine learning-based models and 3 empirical models, XGBoost is selected as the best model, in which the precision, recall, F1 score and accuracy of which are 97.30%, 94.74%, 96.00% and 99.02%, respectively. The prediction of XGBoost is explained by SHAP, the results including global and individual interpretations and the feature dependency relationship between input variables. According to these results, the relationship between failure mode of flat slabs and influence factors is exhibited through another perspective.