Applications of Gene Expression Programming for Estimating Compressive Strength of High‐Strength Concrete

Advances in Civil Engineering - Tập 2020 Số 1 - 2020
Fahid Aslam1, Furqan Farooq2, Muhammad Nasir Amin3, Kaffayatullah Khan3, Abdul Waheed2, Arslan Akbar4, Muhammad Faisal Javed2, Rayed Alyousef1, Hisham Alabduljabbar1
1Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
2Department of Civil Engineering, COMSATS University Islamabad, Abbottabad, 22060, Pakistan
3Department of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P.O. Box 380, Al-Hofuf, Al Ahsa 31982, Saudi Arabia
4Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong

Tóm tắt

The experimental design of high‐strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python‐based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.

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Advances in Civil Engineering

Tập 2020 Số 1

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