Potential of Repurposing Recycled Concrete for Road Paving: Flexural Strength (FS) Modeling by a Novel Systematic and Evolved RF-FA Model

Shuwei Gu, Hao Shen (), Chuming Pang (), Zhiping Li, Long Liu, Huan Liu, Shuai Wang, Yaxin Song and Jiandong Huang
Additional contact information
Shuwei Gu: Lijiahao Coal Mine, Baotou Energy Co., Ltd., China Energy Investment Corporation, Hantai Town, Dongsheng District, Erdos 017008, China
Hao Shen: College of Resources, Shandong University of Technology, Taian 271000, China
Chuming Pang: College of Energy and Mining Engineering, Shandong University of Technology, Qiangangwan Road, Huangdao District, Qingdao 100027, China
Zhiping Li: Lijiahao Coal Mine, Baotou Energy Co., Ltd., China Energy Investment Corporation, Hantai Town, Dongsheng District, Erdos 017008, China
Long Liu: Lijiahao Coal Mine, Baotou Energy Co., Ltd., China Energy Investment Corporation, Hantai Town, Dongsheng District, Erdos 017008, China
Huan Liu: Lijiahao Coal Mine, Baotou Energy Co., Ltd., China Energy Investment Corporation, Hantai Town, Dongsheng District, Erdos 017008, China
Shuai Wang: Lijiahao Coal Mine, Baotou Energy Co., Ltd., China Energy Investment Corporation, Hantai Town, Dongsheng District, Erdos 017008, China
Yaxin Song: Lijiahao Coal Mine, Baotou Energy Co., Ltd., China Energy Investment Corporation, Hantai Town, Dongsheng District, Erdos 017008, China
Jiandong Huang: School of Civil Engineering, Guangzhou University, Guangzhou 510006, China

Sustainability, 2023, vol. 15, issue 4, 1-15

Abstract: Concrete can be recycled after certain processing technologies for use in pavement engineering but the flexural strength (FS) is difficult to predict accurately in the design process. This study proposes a novel systematic and evolved approach to estimate the FS of recycled concrete. The proposed methods are conducted based on the random forest (RF) model as well as the firefly algorithm (FA), where the latter is employed to tune the hyperparameters of the RF model. For this purpose, data sets were collected from previously published literature for the training and verification of the model, and the accuracy of the model was verified by the fitting effect of the predicted and actual values. The results showed that the proposed hybrid machine learning model has a good fitting effect on the predicted and actual values; the calculation and evaluation process demonstrated fast convergence and significantly lower values of RMSE for the proposed model to determine the FS of the recycling concrete. In addition, the study analyzed the sensitivity of the FS of recycled concrete to input variables, and the results showed that effective water-cement ratio (WC), water absorption of recycling concrete (WAR), and water absorption of natural aggregate (WAN) show more obvious influences on FS, so these factors should be paid more attention in future pavement design using the recycling of concrete.

Keywords: hybrid machine learning; flexural strength; recycled concrete; hyperparameters (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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