A Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during Construction-Reference-Cited by-同舟云学术

A Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during Construction

Published:2024-09 Issue:3 Volume:150 Page:
ISSN:2573-5438
Container-title:Journal of Transportation Engineering, Part B: Pavements
language:en
Short-container-title:J. Transp. Eng., Part B: Pavements

Author:

Elseifi Mostafa A.¹^ORCID,Sarkar Md. Tanvir Ahmed²^ORCID,Paudel Ramchandra³,Abohamer Hossam⁴^ORCID,Mousa Momen R.⁵

Affiliation:

1. Occidental Chemical Corporation Distinguished Professor, Dept. of Civil and Environmental Engineering, Louisiana State Univ., 3240N Patrick Taylor Hall, Baton Rouge, LA 70803 (corresponding author). ORCID: .

2. Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Louisiana State Univ., 3256 Patrick Taylor Hall, Baton Rouge, LA 70803. ORCID: .

3. Formerly, Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Louisiana State Univ., Newark, DE 19716.

4. Civil Staff Engineer, Applied Research Associates, 2217 W Braker Ln., Austin, TX 78758. ORCID: .

5. Assistant Professor, Dept. of Engineering Technology, Sam Houston State Univ., Huntsville, TX 77340.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JPEODX.PVENG-1411

Reference15 articles.

1. Development of a Deep Convolutional Neural Network for the Prediction of Pavement Roughness from 3D Images

2. Bode T. A. 2012. “An analysis of the impacts of temperature segregation on hot-mix asphalt.” Master’s thesis Dept. of Civil and Environmental Engineering Univ. of Nebraska.

3. Dhakal N. 2020. “Identification of top-down bottom-up and cement-treated reflective cracks using convolutional neural network and artificial neural network.” Ph.D. dissertation Dept. of Civil and Environmental Engineering Louisiana State Univ.

4. Classification of surface pavement cracks as top-down, bottom-up, and cement-treated reflective cracking based on deep learning methods;Dhakal N.;Can. J. Civ. Eng.,2021

5. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection