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Steel and Composite Structures Volume 52, Number 3, August 10 2024 , pages 293-312 DOI: https://doi.org/10.12989/scs.2024.52.3.293 |
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Predicting tensile strength of reinforced concrete composited with geopolymer using several machine learning algorithms |
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Ibrahim Albaijan, Hanan Samadi, Arsalan Mahmoodzadeh, Danial Fakhri, Mehdi Hosseinzadeh4, Nejib Ghazouani and Khaled Mohamed Elhadi
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| Abstract | ||
| Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength. | ||
| Key Words | ||
| geopolymer concrete; reinforced concrete; supervised learning algorithms; tensile strength | ||
| Address | ||
| Ibrahim Albaijan:Mechanical Engineering Department, College of Engineering at Al-Kharj, Prince Sattam Bin Abdulaziz University, Al Kharj 16273, Saudi Arabia Hanan Samadi:IRO, Civil Engineering Department, University of Halabja, Halabja, 46018, Iraq Arsalan Mahmoodzadeh:IRO, Civil Engineering Department, University of Halabja, Halabja, 46018, Iraq Danial Fakhri:IRO, Civil Engineering Department, University of Halabja, Halabja, 46018, Iraq Mehdi Hosseinzadeh:1)Institute of Research and Development, Duy Tan University, Da Nang, Vietnam 2)School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam Nejib Ghazouani:Department of Civil Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia Khaled Mohamed Elhadi:1)Civil Engineering Department, College of Engineering, King Khalid University, Saudi Arabia 2)Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi Arabia | ||