Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete

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@Article{ALARFAJ:2024:cscm,

• author = "Mohammed Alarfaj and Hisham Jahangir Qureshi and Muhammad Zubair Shahab and Muhammad Faisal Javed and Md Arifuzzaman and Yaser Gamil",
• title = "Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete",
• journal = "Case Studies in Construction Materials",
• volume = "20",
• pages = "e02836",
• year = "2024",
• ISSN = "2214-5095",
• DOI = "doi:10.1016/j.cscm.2023.e02836",
• URL = "https://www.sciencedirect.com/science/article/pii/S2214509523010173",
• keywords = "genetic algorithms, genetic programming, Gene expression programming, Fiber reinforced Recycled Aggregate Concrete, Machine Learning, Sustainability, Eco-friendly Concrete, Spilt Tensile Strength, Deep neural networks, ANN, Optimizable gaussian process regression",
• abstract = "The demand for concrete production has led to a significant annual requirement for raw materials, resulting in a substantial amount of waste concrete. In response, recycled aggregate concrete has emerged as a promising solution. However, it faces challenges due to the vulnerability of the hardened mortar attached to natural aggregates, leading to susceptibility to cracking and reduced strength. This study focuses on predicting the split tensile strength of fiber reinforced recycled aggregate concrete using five prediction models, including two deep neural network models DNN1 and DNN2, one optimizable Gaussian process regression (OGPR), and two genetic programming based GEP1 and GEP2 models. The models exhibited high accuracy in predicting spilt tensile strength with robust R2, RMSE, and MAE values. DNN2 has the highest R2 value of 0.94 and GEP1 has the lowest R2 value of 0.76. DNN2 model R2 was 3.3percent and 13.5percent higher than OGPR and GEP2. Similarly, DNN2 and GEP2 model performed 9.3percent and 9.21percent better than DNN1 and GEP1 respectively in terms of R2. DNN2 model performed 20.32percent and 31.5percent better than OGPR and GEP2 in terms of MAE. Similarly, GEP2 and DNN2 MAE were 13.1percent and 31.5percent better than GEP1 and DNN1. Sensitivity analysis using the relevance factor and permutation feature importance revealed that the most significant positive factors are cement, natural coarse aggregates, density of recycle aggregates, and superplasticizer while recycle aggregate concrete, max size, and water content of recycle aggregates and water content have the most negative effect on STS values. The proposed ML methods, especially DNN2 and OGPR can be effectively used in practical projects, saving time and cost for eco-friendly fiber reinforced recycled aggregate concrete mixes. However, it is required to study more input variables and use hybrid models to further enhance the accuracy and reliability of the models",

}

Genetic Programming entries for Mohammed Alarfaj Hisham Jahangir Qureshi Muhammad Zubair Shahab Muhammad Faisal Javed Md Arifuzzaman Yaser Gamil

Citations