A Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during ConstructionSource: Journal of Transportation Engineering, Part B: Pavements:;2024:;Volume ( 150 ):;issue: 003::page 04024029-1Author:Mostafa A. Elseifi
,
Md. Tanvir Ahmed Sarkar
,
Ramchandra Paudel
,
Hossam Abohamer
,
Momen R. Mousa
DOI: 10.1061/JPEODX.PVENG-1411Publisher: American Society of Civil Engineers
Abstract: Pavement construction monitoring and quality assurance (QA) practices are based mostly on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in the case of the availability of an inertial profiler. However, most of these practices are costly and/or destructive. Therefore, the key objective of this study was to develop a quality assurance decision-making tool that can predict pavement roughness, in terms of the International Roughness Index (IRI), and aggregate segregation based on digital image analysis, image recognition, and deep machine learning models. The developed models were trained, tested, and validated using 600 pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS). Furthermore, the effectiveness of the convolution neural network (CNN) model was validated using pavement surface images collected at construction sites in Louisiana a few days after paving. The roughness model predicted the International Roughness Index values with a coefficient of determination R2 of 0.98 and a RMS error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved acceptable accuracy. To support the implementation of these results, the models were incorporated into a computer application that can be used by site engineers for quality assurance without the need for coding software on their device.
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contributor author | Mostafa A. Elseifi | |
contributor author | Md. Tanvir Ahmed Sarkar | |
contributor author | Ramchandra Paudel | |
contributor author | Hossam Abohamer | |
contributor author | Momen R. Mousa | |
date accessioned | 2024-12-24T09:59:30Z | |
date available | 2024-12-24T09:59:30Z | |
date copyright | 9/1/2024 12:00:00 AM | |
date issued | 2024 | |
identifier other | JPEODX.PVENG-1411.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4298089 | |
description abstract | Pavement construction monitoring and quality assurance (QA) practices are based mostly on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in the case of the availability of an inertial profiler. However, most of these practices are costly and/or destructive. Therefore, the key objective of this study was to develop a quality assurance decision-making tool that can predict pavement roughness, in terms of the International Roughness Index (IRI), and aggregate segregation based on digital image analysis, image recognition, and deep machine learning models. The developed models were trained, tested, and validated using 600 pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS). Furthermore, the effectiveness of the convolution neural network (CNN) model was validated using pavement surface images collected at construction sites in Louisiana a few days after paving. The roughness model predicted the International Roughness Index values with a coefficient of determination R2 of 0.98 and a RMS error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved acceptable accuracy. To support the implementation of these results, the models were incorporated into a computer application that can be used by site engineers for quality assurance without the need for coding software on their device. | |
publisher | American Society of Civil Engineers | |
title | A Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during Construction | |
type | Journal Article | |
journal volume | 150 | |
journal issue | 3 | |
journal title | Journal of Transportation Engineering, Part B: Pavements | |
identifier doi | 10.1061/JPEODX.PVENG-1411 | |
journal fristpage | 04024029-1 | |
journal lastpage | 04024029-9 | |
page | 9 | |
tree | Journal of Transportation Engineering, Part B: Pavements:;2024:;Volume ( 150 ):;issue: 003 | |
contenttype | Fulltext |