International Journal of Recent
Engineering Science

Research Article | Open Access | Download PDF
Volume 12 | Issue 6 | Year 2025 | Article Id. IJRES-V12I6P101 | DOI : https://doi.org/10.14445/23497157/IJRES-V12I6P101

Multiple Regression Models that Predict the Strength of Concrete Blocks

Chijioke Paul, Amulu, Lgiligi Chibueze Jonhbosco

Received Revised Accepted Published
01 Oct 2025 02 Nov 2025 18 Nov 2025 09 Dec 2025

Citation :

Chijioke Paul, Amulu, Lgiligi Chibueze Jonhbosco, "Multiple Regression Models that Predict the Strength of Concrete Blocks," International Journal of Recent Engineering Science (IJRES), vol. 12, no. 6, pp. 1-11, 2025. Crossref, https://doi.org/10.14445/23497157/IJRES-V12I6P101

Abstract

The study seeks to create a multiple regression model to forecast the strength of concrete blocks mass-made in the Abakaliki metropolis of Nigeria, employing critical elements that affect the strength of blocks produced by local enterprises. The technique comprises a survey and controlled trials on concrete block samples sourced from five distinct sectors, designated as groups G2, G3, G4, G5, and G6, in addition to a Control Group (CTG) constructed in accordance with the Nigerian Industrial Specification (NIS) blueprint. The study evaluated the block size, production techniques (mechanical or manual), material selection, mix ratios, and the strength of the block samples by controlled testing. These characteristics were employed to develop a multiple regression model for forecasting the strength of block mass produced in the state. The research indicated that the identified variables strongly influence block strength, and the created model accurately predicted block strength, achieving an R-squared value of 98.03%, which signifies an exceptional fit to the data. The T-test analysis indicated no significant difference between the actual strengths and those predicted by the model, as the t-statistic (0.693) is below the t-critical value (2.03) at p>0.005. The study recommends applying the established model to forecast concrete block strength in the urban area. Rather than doing destructive testing on block samples, it is advised that construction professionals utilize the model’s equation to estimate compressive strength by obtaining the requisite input data directly from block manufacturers. This research supports data-driven decision-making in maintaining quality control.

Keywords

Strength of Concrete Blocks, Material Selection and Proportions, Predictive model, Controlled experiment.

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