Ashrafian, A.; Amiri, M.J.T.; Masoumi, P.; Asadi-shiadeh, M.; Yaghoubi-chenari, M.; Mosavi, A.; Nabipour, N. Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement. Appl. Sci.2020, 10, 3707.
Ashrafian, A.; Amiri, M.J.T.; Masoumi, P.; Asadi-shiadeh, M.; Yaghoubi-chenari, M.; Mosavi, A.; Nabipour, N. Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement. Appl. Sci. 2020, 10, 3707.
Ashrafian, A.; Amiri, M.J.T.; Masoumi, P.; Asadi-shiadeh, M.; Yaghoubi-chenari, M.; Mosavi, A.; Nabipour, N. Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement. Appl. Sci.2020, 10, 3707.
Ashrafian, A.; Amiri, M.J.T.; Masoumi, P.; Asadi-shiadeh, M.; Yaghoubi-chenari, M.; Mosavi, A.; Nabipour, N. Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement. Appl. Sci. 2020, 10, 3707.
Abstract
In the field of pavement engineering, the determination of the mechanical characteristics is one of the essential process for reliable material design and highway sustainability. Early determination of mechanical characteristics of pavement is highly essential for road and highway construction and maintenance. Tensile strength (TS), compressive strength (CS) and flexural strength (FS) of roller compacted concrete pavement (RCCP) are very crucial characteristics as they are necessitated for many data from mixture proportions as input variables. In this research, the classification-based regression models named Random Forest (RF), M5rule model tree (M5rule), M5prime model tree (M5p) and Chi-square Automatic Interaction Detection (CHAID) are developed for simulation of the mechanical characteristics of RCCP. A comprehensive and reliable dataset comprising 621, 326 and 290 data records for CS, TS and FS experimental cases extracted from several open sources over the literature. The mechanical properties are developed based on influential inputs combination that processed using Principle Component Analysis (PCA). The applied PCA method as feature selection is specified that volumetric/weighted content forms of experimental variables (e.g., coarse aggregate, fine aggregate, supplementary cementitious materials, water and binder) and specimens’ age are the most effective inputs to generate the better performances. Several statistical metrics are measured to evaluate proposed classification-based regression models. RF model revealed an optimistic classification capacity of the CS, TS and FS prediction of the RCCP in comparison with the CHAID, M5rule, and M5p models. The research is extended for the results verification using Monte-carlo model for the uncertainty and sensitivity of variables importance analysis. Overall, the proposed methodology indicated a reliable soft computing model that can be implemented for the material engineering construction and design.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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