Application of bagging ensemble model for predicting compressive strength of hollow concrete masonry prism

In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism (f(p)). A matrix of input combinations is constructed based on several predictive variables, including mortar compressive strength (f(m)), concrete block compressive strength (f(b)), and height to thickness ratio (h/t). Three modeling scenarios based on the different data divisions (i.e., 80-20%, 75-25%, and 70-30%) for training-testing phases are evaluated. The proposed model is validated against classical support vector regression (SVR) and decision tree regression (DTR) models using statistical indicators and graphical presentations. Results indicate the superiority of the BGR over the other models. In quantitative terms, BGR attains minimum root mean square error (RMSE = 1.51 MPa) using the data division scenario of 80-20% in the testing phase, while DTR and standalone SVR models offer RMSE = 2.55 and 2.33 MPa, respectively. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Automated summary

In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism. The results show that BGR outperforms classical support vector regression (SVR) and decision tree regression (DTR) models in terms of minimum root mean square error.