A hybrid data analytics approach for high-performance concrete compressive strength prediction. Issue 2 (2nd July 2020)

• Record Type: Journal Article
• Authors: Simsek, Serhat; Gumus, Mehmet; Khalafalla, Mohamed; Issa, Tahir Bachar
• Is Part Of: Journal of Business Analytics. Volume 3:Issue 2(2020)
• Page Start: 158
• Page End: 168
• Publication Date: 2020-07-02
• Subjects: Statistical and Machine Learning -- decision support tool -- regression diagnostic -- sensitivity analysis -- high-performance concrete; Business intelligence -- Periodicals; Management -- Statistical methods -- Periodicals; Decision making -- Statistical methods -- Periodicals; 658.403
• DOI: 10.1080/2573234X.2020.1760741
• Languages: English
• ISSNs: 2573-234X

Abstract

ABSTRACT: Contrary to the popular belief cited in the literature, the proposed data analytics technique shows that multiple linear regression (MLR) can achieve as high a predictive power as some of the black box models when the necessary interventions are implemented pertaining to the regression diagnostic. Such an MLR model can be utilised to design an optimal concrete mix, as it provides the explicit and accurate relationships between the HPC components and the expected compressive strength. Moreover, the proposed study offers a decision support tool incorporating the Extreme Gradient Boosting (XGB) model to bridge the gap between black-box models and practitioners. The tool can be used to make faster, more data-driven, and accurate managerial decisions without having any expertise in the required fields, which would reduce a substantial amount of time, cost, and effort spent on measurement procedures of the compressive strength of HPC.