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Article

Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement

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Department of Civil Engineering, Tabari University of Babol, Babol P.O. Box 47139-75689, Iran
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Department of Civil Engineering, Higher Education Institute of Pardisan, Freidonkenar P.O. Box 47516-74715, Iran
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Department of Civil Engineering, Shomal University, Amol P.O. Box 46161-84596, Iran
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Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
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Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
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Department of Mathematics, J. Selye University, 94501 Komarno, Slovakia
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(11), 3707; https://doi.org/10.3390/app10113707
Received: 12 March 2020 / Revised: 13 May 2020 / Accepted: 25 May 2020 / Published: 27 May 2020
In the field of pavement engineering, the determination of the mechanical characteristics is one of the essential processes for reliable material design and highway sustainability. Early determination of the mechanical characteristics of pavement is 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 crucial characteristics. In this research, the classification-based regression models random forest (RF), M5rule model tree (M5rule), M5prime model tree (M5p), and chi-square automatic interaction detection (CHAID) are used 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 was extracted from several open sources in the literature. The mechanical properties are determined based on influential input combinations that are processed using principle component analysis (PCA). The PCA method specifies 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 better performance. Several statistical metrics were used to evaluate the proposed classification-based regression models. The 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. Monte-Carlo simulation was used to verify the results in terms of the uncertainty and sensitivity of variables. Overall, the proposed methodology formed a reliable soft computing model that can be implemented for material engineering, construction, and design. View Full-Text
Keywords: roller-compacted concrete pavement; classification-regression models; feature selection; mechanical properties; machine learning; Monte-Carlo uncertainty; data science; civil engineering; transportation; mobility; prediction model; random forest (RF); structural health monitoring; pavement management roller-compacted concrete pavement; classification-regression models; feature selection; mechanical properties; machine learning; Monte-Carlo uncertainty; data science; civil engineering; transportation; mobility; prediction model; random forest (RF); structural health monitoring; pavement management
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MDPI and ACS Style

Ashrafian, A.; Taheri Amiri, M.J.; 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. https://doi.org/10.3390/app10113707

AMA Style

Ashrafian A, Taheri Amiri MJ, 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. Applied Sciences. 2020; 10(11):3707. https://doi.org/10.3390/app10113707

Chicago/Turabian Style

Ashrafian, Ali, Mohammad J. Taheri Amiri, Parisa Masoumi, Mahsa Asadi-shiadeh, Mojtaba Yaghoubi-chenari, Amir Mosavi, and Narjes Nabipour. 2020. "Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement" Applied Sciences 10, no. 11: 3707. https://doi.org/10.3390/app10113707

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