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

Ali Ashrafian,Amir Mosavi,Mojtaba Yaghoubi-Chenari,Mahsa Asadi-Shiadeh,Narjes Nabipour,Parisa Masoumi,Mohammad Javad Taheri Amiri

doi:10.3390/app10113707

Abstract

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.

Highlights

In this technologically advanced world, along with advances in various scientific fields, the concrete industry has grown, and such advances have resulted in the production of roller-compacted concrete pavement (RCCP)
Classification-based regression methods based on the random forest (RF), M5rule, M5p, and
Classification-based methods basedmodels on theof RF, M5rule, M5p, and techniques were applied as a MLregression tools to develop new predictive the mechanical

Summary

Introduction

In this technologically advanced world, along with advances in various scientific fields, the concrete industry has grown, and such advances have resulted in the production of roller-compacted concrete pavement (RCCP). The high cost of producing bituminous pavement and the quantity of petroleum contaminants in the environment necessitate the use of alternative technologies for solving roading problems [3]. Lower cement paste content and higher aggregate volume in RCCP have led to its low consistency, which results in greater durability of RCCP than bituminous asphalt. The use of pozzolanic materials to ensure sufficient compaction in the mixtures with standard fine-grained aggregates in the production of RCCP has attracted interest due to lower production costs than cement and improved strength [5,6]. Pozzolans are mixed with the gels produced in the concrete and increase the concrete’s hydration, thereby increasing the density of produced concrete and enhancing the chemical and mechanical properties of RCCP

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Conclusion