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Prediction of Properties of Concrete Cured Under Hot Weather Using Multivariate Regression and ANN Models

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Abstract

Concreting in hot weather poses special problems, and the properties of concrete are significantly influenced by mix parameters, construction practices and environmental conditions. In this study, mix design parameters, casting temperature and curing conditions were varied to study the mechanical properties and durability characteristics with the ultimate aim of developing models relating the concrete properties and the experimental parameters. The effect of w/c ratio (0.3 to 0.45), in situ concrete temperature (25 to 45 °C) and curing method (ponding, covering with wet burlap or applying curing compound followed by curing under laboratory or field conditions) was assessed by measuring compressive strength (f′c), split tensile strength (ft), pulse velocity (PV) and depth of water penetration (WP) up to 180 days. Prediction models were developed utilizing quadratic regression models and artificial neural networks (ANNs). The regression models indicated that moist curing, in situ concrete temperature and curing period have a positive impact on the strength parameters and PV, while the former two variables had a negative effect on the WP. The w/c ratio had a significant positive impact on the WP. The concrete properties predicted by ANN models were more accurate than those predicted by regression models. A combined ANN model, developed for predicting strength and PV simultaneously, had a better correlation coefficient compared to ANN models developed for each variable separately. The developed models are expected to assist concrete technologists in the selection of appropriate concrete mixture parameters to obtain concrete with desired properties under hot weather conditions.

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Abbreviations

OPC:

Ordinary Portland cement

w/c :

Water-to-cement ratio

ANN:

Artificial neural network

ACI:

American Concrete Institute

ASTM:

American Society of Testing Materials

f′c:

Compressive strength of concrete, MPa

ft:

Split tensile strength of concrete, MPa

PV:

Ultrasonic pulse velocity, m/s

WP:

Depth of water penetration, mm

CGB:

Conjugate gradient backpropagation

RMSE:

Root-mean-square error

MAE:

Mean absolute error

T :

In situ concrete temperature, °C

t :

Curing period, days

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Acknowledgements

The authors acknowledge the support provided by King Fahd University of Petroleum and Minerals (KFUPM) for this study under KFUPM research grant # RG1101. Imam Abdulrahman Bin Faisal University is also thanked.

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Authors and Affiliations

  1. Department of Civil and Construction Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, 31451, Saudi Arabia

    Muhammad Nasir

  2. Department of Civil Engineering, College of Engineering, University of Bahrain, Isa town, Bahrain

    Uneb Gazder

  3. Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Mohammed Maslehuddin

  4. Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Omar S. Baghabra Al-Amoudi & Imran Ali Syed

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  1. Muhammad Nasir
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  2. Uneb Gazder
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  3. Mohammed Maslehuddin
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  4. Omar S. Baghabra Al-Amoudi
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Correspondence to Muhammad Nasir.

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Nasir, M., Gazder, U., Maslehuddin, M. et al. Prediction of Properties of Concrete Cured Under Hot Weather Using Multivariate Regression and ANN Models. Arab J Sci Eng 45, 4111–4123 (2020). https://doi.org/10.1007/s13369-020-04403-y

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  • DOI: https://doi.org/10.1007/s13369-020-04403-y

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