Preprint Article Version 2 This version is not peer-reviewed

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Version 1 : Received: 26 February 2020 / Approved: 27 February 2020 / Online: 27 February 2020 (16:08:35 CET)
Version 2 : Received: 8 June 2020 / Approved: 9 June 2020 / Online: 9 June 2020 (11:35:32 CEST)

How to cite: Karballaeezadeh, N.; Ghasemzadeh Tehrani, H.; Mohammadzadeh S., D.; Shamshirband, S. Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques. Preprints 2020, 2020020411 (doi: 10.20944/preprints202002.0411.v2). Karballaeezadeh, N.; Ghasemzadeh Tehrani, H.; Mohammadzadeh S., D.; Shamshirband, S. Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques. Preprints 2020, 2020020411 (doi: 10.20944/preprints202002.0411.v2).

Abstract

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

Subject Areas

transportation infrastructure; flexible pavement; structural number prediction; Gaussian process regression; m5p model tree; random forest

Comments (1)

Comment 1
Received: 9 June 2020
Commenter: Nader Karballaeezadeh
Commenter's Conflict of Interests: Author
Comment: Submission files were added.
+ Respond to this comment

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 1
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.