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A new predictive model for compressive strength of HPC using gene expression programming |
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| Authors: | Seyyed Mohammad Mousavi Amir Hossein Alavi |
| Affiliation: | a Department of Geography and Urban Planning, Faculty of Humanities and Social Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran b Department of Civil Engineering, Islamic Azad University, Shahrood Branch, Shahrood, Iran c Young Researchers Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran d Young Researchers Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran e Department of Civil Engineering, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran |
| Abstract: | In this study, gene expression programming (GEP) is utilized to derive a new model for the prediction of compressive strength of high performance concrete (HPC) mixes. The model is developed using a comprehensive database obtained from the literature. The validity of the proposed model is verified by applying it to estimate the compressive strength of a portion of test results that are not included in the analysis. Linear and nonlinear least squares regression analyses are performed to benchmark the GEP model. Contributions of the parameters affecting the compressive strength are evaluated through a sensitivity analysis. GEP is found to be an effective method for evaluating the compressive strength of HPC mixes. The prediction performance of the optimal GEP model is better than the regression models. |
| Keywords: | High performance concrete Gene expression programming Compressive strength Regression analysis Sensitivity analysis Prediction |
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