Using multivariate regression and ANN models to predict properties of concrete cured under hot weather: a case of Rawalpindi Pakistan

Maqsoom, Ahsen and Aslam, Bilal and Gul, Muhammad Ehtisham and Ullah, Fahim ORCID: https://orcid.org/0000-0002-6221-1175 and Kouzani, Abbas Z. and Mahmud, M. A. Parvez and Nawaz, Adnan (2021) Using multivariate regression and ANN models to predict properties of concrete cured under hot weather: a case of Rawalpindi Pakistan. Sustainability, 13:10164. pp. 1-28.

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Abstract

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Date Deposited: 14 Sep 2021 01:59
Last Modified: 14 Sep 2021 05:06
Uncontrolled Keywords: artificial neural network; concrete properties; hot climate; regression analysis; Rawalpindi Pakistan
Fields of Research (2008): 12 Built Environment and Design > 1299 Other Built Environment and Design > 129999 Built Environment and Design not elsewhere classified
09 Engineering > 0905 Civil Engineering > 090503 Construction Materials
09 Engineering > 0905 Civil Engineering > 090506 Structural Engineering
09 Engineering > 0905 Civil Engineering > 090502 Construction Engineering
Fields of Research (2020): 40 ENGINEERING > 4005 Civil engineering > 400510 Structural engineering
40 ENGINEERING > 4005 Civil engineering > 400504 Construction engineering
33 BUILT ENVIRONMENT AND DESIGN > 3302 Building > 330201 Automation and technology in building and construction
40 ENGINEERING > 4005 Civil engineering > 400505 Construction materials
Identification Number or DOI: https://doi.org/10.3390/su131810164
URI: http://eprints.usq.edu.au/id/eprint/43650

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