An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement

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@Article{Ashrafian:2020:Measurement,

• author = "Ali Ashrafian and Amir H. Gandomi and Mohammad Rezaie-Balf and Mohammad Emadi",
• title = "An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement",
• journal = "Measurement",
• year = "2020",
• volume = "152",
• pages = "107309",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Gene expression programming, Evolutionary approach, Roller compacted concrete pavement, Compressive strength, Prediction",
• ISSN = "0263-2241",
• URL = "http://www.sciencedirect.com/science/article/pii/S026322411931173X",
• DOI = "doi:10.1016/j.measurement.2019.107309",
• abstract = "The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (fc) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the fc of RCCP. The fc is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on fc of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to use, and provide consequently new formulations for fc of RCCP.",
• notes = "also known as \cite{ASHRAFIAN2020107309}",

}

Genetic Programming entries for Ali Ashrafian A H Gandomi Mohammad Rezaie-Balf Mohammad Emadi

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