Analysis of factors influencing concrete resistance in construction industry: machine learning approach

Articles in Press

Authors

1 M.Sc, Civil Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan

2 Control Department. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz,, Iran

10.22034/ceej.2024.62446.2370

Abstract

Concrete, as one of the main materials in the construction industry, plays a vital role in the sustainability, safety, and welfare of urban spaces. This is because concrete has a direct impact on bearing gravitational and lateral loads, and improving the quality of concrete can prevent the premature destruction of buildings. Additionally, it can reduce the volume of construction waste and create a sustainable urban environment. However, numerous factors affect the compressive strength of concrete, and failing to identify these factors can lead to premature building destruction and adverse outcomes during natural disasters. A proper understanding of these factors is essential for enhancing concrete quality and ensuring the optimal performance of structures. Accordingly, the aim of this article is to analyze the factors influencing the quality and strength of concrete to improve the sustainability, safety, and welfare of urban spaces and to protect the urban environment. In this article, to achieve the research objectives, in addition to using a machine learning model based on the Extreme Gradient Boosting algorithm, metaheuristic algorithms have been employed to create an accurate predictive model.

Keywords

Journal of Civil and Environmental Engineering

Articles in Press, Accepted Manuscript
Available Online from 28 December 2024

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