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Prediction of Strength of Plain and Blended Cement Concretes Cured Under Hot Weather Using Quadratic Regression and ANN Tools

  • Research Article-Civil Engineering
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Abstract

Concreting and curing under hot climatic conditions pose adverse effects on the characteristics of concrete. These challenges have prompted cement and concrete technologists to incorporate pozzolanic materials for the dual advantages from technical and sustainable perspectives. In this research, the impact of: (1) casting temperature between the range of 25–45 °C, (2) curing regimes, namely water ponding, burlap covering or curing compound, and (3) pozzolanic materials, namely fly ash, very fine fly ash, silica fume, natural pozzolan and ground granulated blast furnace slag on the long-term strength development of concrete have been investigated. Prediction models correlating the investigated variables and concrete strength were developed utilizing quadratic regression models and artificial neural networks (ANNs). ANN models were able to predict the compressive strength of concrete with higher accuracy than that of regression model. This model is expected to be applied for designing concrete of higher strengths under hot weather conditions.

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Fig. 1

Source: climatemps.com]

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Abbreviations

°C:

Unit for temperature, degree Celsius

Y :

Output of prediction models (compressive strength of concrete) (MPa)

X :

Vector of input variables

a, b, c :

Coefficients for regression model

W :

Vector of weight values for nodes in artificial neural network

Yi:

Actual value of compressive strength of concrete (MPa) for each sample

Y’i:

Predicted value of compressive strength of concrete (MPa) for each sample

fc’:

Compressive strength of concrete (MPa)

n :

Number of samples

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Acknowledgements

The authors acknowledge the financial support provided by King Fahd University of Petroleum and Minerals (KFUPM) under KFUPM research Grant # RG1101.

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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, Zallaq, Bahrain

    Uneb Gazder

  3. Department of Civil Engineering, Qurtuba University of Science and Information Technology, D.I. Khan, 29150, Pakistan

    Muhammad Umar Khan

  4. Digital Construction Technology Department, Sumitomo Mitsui Construction Co. Ltd., 2-1-6, Tsukuda, Chou-ku, Tokyo, 104-0051, Japan

    Mehboob Rasul

  5. Applied Research Center for Metrology, Standards and Testing, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Mohammed Maslehuddin

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

    Omar S. Baghabra Al-Amoudi

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  1. Muhammad Nasir
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  2. Uneb Gazder
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Correspondence to Mehboob Rasul.

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Nasir, M., Gazder, U., Khan, M.U. et al. Prediction of Strength of Plain and Blended Cement Concretes Cured Under Hot Weather Using Quadratic Regression and ANN Tools. Arab J Sci Eng 47, 12697–12709 (2022). https://doi.org/10.1007/s13369-022-06586-y

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