A machine-learning-based model for predicting the effective stiffness of precast concrete columns. (1st June 2022)

• Record Type: Journal Article
• Authors: Wang, Zhen; Liu, Tongxu; Long, Zilin; Wang, Jingquan; Zhang, Jian
• Is Part Of: Engineering structures. Volume 260(2022)
• Publication Date: 2022-06-01
• Subjects: Precast concrete columns -- Effective stiffness -- Machine learning -- Voting ensemble learning -- Post-tensioning -- Partial dependence; 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.2022.114224
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Machine learning method is firstly used to systematically study the ES of PCCs. A database is assembled including 177 flexural-dominant PCCs with 42 features. A VEL model with feature selection is proposed to predict the ES of PCCs. The VEL model is experimentally verified and compared with empirical formulas. The VEL model is interpreted using the combination of PDA and ICE. Abstract: Predicting effective stiffness (ES) of precast concrete columns (PCCs) is an essential topic when PCCs are applied to structures in seismic zones. However, existing researches provide nearly no method especially for predicting the ES of PCCs, partly due to the high complexity of this issue. This study aims to firstly develop a machine learning (ML) model for predicting the ES of PCCs based on voting ensemble learning (VEL) algorithm which adopted different ML algorithms: support vector regression (SVR), random forest regression (RFR), and gradient boosting tree regression (GBTR) to respectively establish three base models. 177 flexural-dominant PCCs with various construction details were collected from the literature to assemble an experimental database, in which each specimen has 42 features. A ML model establishment proceeding was conducted, involving data preparation, feature selection, and hyperparameter tuning. Experimental verification was conducted to assess the VEL, SVR, RFR, and GBTR models, under the scenarios with/without feature selection. A comparison was conducted on the prediction performance between the existing empirical formulas and the VEL model with feature selection which was further interpreted using the combination of partial dependence analysis (PDA) and individual conditional expectation (ICE). Results show that the VEL algorithm can improve the accuracy and reliability of predicting the ES of PCCs. The VEL model with feature selection is proposed because it eliminates 21 negligible features and still presents far better prediction performance compared with the empirical formulas. The effect of each of the features considered in the proposed VEL model is recognized and depicted. Besides the four parameters considered in the existing formulas, another five parameters are also identified to have non-negligible influence on the ES of PCCs. Despite some limitations such as relatively insufficient number of data points, restricted range of input parameters, and lack of mechanical explanations, the proposed VEL model still firstly provides an accessible way to predict the ES of PCCs and can give inspiration for future researchers.