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

Author:

Abohamer Hossam1ORCID,Elseifi Mostafa2ORCID,Dhakal Nirmal3,Zhang Zhongjie4,Fillastre Christophe N.5

Affiliation:

1. Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Louisiana State Univ., 3252 Patrick Taylor Hall, Baton Rouge, LA 70803. ORCID: .

2. 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: .

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

4. Pavement Geotechnical Research Administrator, Louisiana Transportation Research Center, 4101 Gourrier Ave., Baton Rouge, LA 70808.

5. Pavement Management Engineer, Louisiana Dept. of Transportation and Development, 1201 Capitol Access Rd., Baton Rouge, LA 70804.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Reference31 articles.

1. AASHTO. 2010. Guide for the local calibration of the mechanistic-empirical pavement design guide. Washington, DC: AASHTO.

2. International Roughness Index prediction model for flexible pavements;Abdelaziz N.;Int. J. Pavement Eng.,2018

3. Abedin, T., Z. Chowdhury, A. R. Afzal, F. Yeasmin, and T. C. Turin. 2016. Application of binary logistic regression in clinical research. Calgary, Canada: Univ. of Calgary.

4. Development of roughness prediction models for low-volume road networks;Albuquerque F. S.;Transp. Res. Rec.,2011

5. Road Roughness and Whole Body Vibration: Evaluation Tools and Comfort Limits

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