Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather

Ahsen Maqsoom, Bilal Aslam, Muhammad Ehtisham Gul, Fahim Ullah, Abbas Z. Kouzani, M. A. Parvez Mahmud and Adnan Nawaz
Additional contact information
Ahsen Maqsoom: Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, Pakistan
Bilal Aslam: Department of Earth Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
Muhammad Ehtisham Gul: Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, Pakistan
Fahim Ullah: School of Civil Engineering and Surveying, University of Southern Queensland, Springfield Central, QLD 4300, Australia
Abbas Z. Kouzani: School of Engineering, Deakin University, Geelong, VIC 3216, Australia
M. A. Parvez Mahmud: School of Engineering, Deakin University, Geelong, VIC 3216, Australia
Adnan Nawaz: Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, Pakistan

Sustainability, 2021, vol. 13, issue 18, 1-28

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.

Keywords: artificial neural network; concrete properties; hot climate; regression analysis; Rawalpindi Pakistan (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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