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Using multivariate regression and ann models to predict properties of concrete cured under hot weather: A case of rawalpindi Pakistan

Maqsoom, A, Aslam, B, Gul, ME, Ullah, F, Kouzani, Abbas, Mahmud, M A Parvez and Nawaz, A 2021, Using multivariate regression and ann models to predict properties of concrete cured under hot weather: A case of rawalpindi Pakistan, Sustainability, vol. 13, no. 18, pp. 1-28, doi: 10.3390/su131810164.

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Title Using multivariate regression and ann models to predict properties of concrete cured under hot weather: A case of rawalpindi Pakistan
Author(s) Maqsoom, A
Aslam, B
Gul, ME
Ullah, F
Kouzani, AbbasORCID iD for Kouzani, Abbas orcid.org/0000-0002-6292-1214
Mahmud, M A ParvezORCID iD for Mahmud, M A Parvez orcid.org/0000-0002-1905-6800
Nawaz, A
Journal name Sustainability
Volume number 13
Issue number 18
Article ID 10164
Start page 1
End page 28
Total pages 28
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2021
ISSN 2071-1050
2071-1050
Summary 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 penetra-tion (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
Language eng
DOI 10.3390/su131810164
Field of Research 12 Built Environment and Design
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30156065

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.