Using multivariate regression and ANN models to predict properties of concrete cured under hot weather: a case of Rawalpindi Pakistan
Article
Article Title | Using multivariate regression and ANN models to predict properties of concrete cured under hot weather: a case of Rawalpindi Pakistan |
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ERA Journal ID | 41498 |
Article Category | Article |
Authors | Maqsoom, Ahsen (Author), Aslam, Bilal (Author), Gul, Muhammad Ehtisham (Author), Ullah, Fahim (Author), Kouzani, Abbas Z. (Author), Mahmud, M. A. Parvez (Author) and Nawaz, Adnan (Author) |
Journal Title | Sustainability |
Journal Citation | 13, pp. 1-28 |
Article Number | 10164 |
Number of Pages | 28 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2071-1050 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/su131810164 |
Web Address (URL) | https://www.mdpi.com/2071-1050/13/18/10164 |
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 |
ANZSRC Field of Research 2020 | 400510. Structural engineering |
400504. Construction engineering | |
330201. Automation and technology in building and construction | |
400505. Construction materials | |
Byline Affiliations | COMSATS University Islamabad, Pakistan |
Quaid-i-Azam University, Pakistan | |
School of Civil Engineering and Surveying | |
Deakin University | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q6q6z/using-multivariate-regression-and-ann-models-to-predict-properties-of-concrete-cured-under-hot-weather-a-case-of-rawalpindi-pakistan
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