In-Time Density Monitoring of In-Place Asphalt Layer Construction via Intelligent Compaction Technology

Author:

Zhang Weiguang1,Ahmad Kamal Nasir2ORCID,Tong Zheng1,Hu Zhaoguang3,Wang Haoyang4,Wu Meng5ORCID,Zhao Kai6,Yang Shunxin1,Farooq Hassan7,Mohammad Louay N.8ORCID

Affiliation:

1. Associate Professor, School of Transportation, Southeast Univ., Nanjing 211189, China.

2. Graduate Research Assistant, School of Transportation, Southeast Univ., Nanjing 211189, China. ORCID: .

3. Professorate Senior Engineer, China Road and Bridge Co., Ltd., No. 88, Andingmenwai St., Dongcheng District, Beijing 100011, China.

4. Research Engineer, RoadMaint Co., Ltd., Beijing 100095, China.

5. Graduate Research Assistant, School of Transportation, Southeast Univ., Nanjing 211189, China (corresponding author). ORCID: .

6. Research Engineer, Shanxi Transportation Holdings Group Co., Ltd., Taiyuan, Shanxi 030021, China.

7. Graduate Research Assistant, College of Engineering, Univ. of Louisiana Lafayette, Lafayette, LA 70504.

8. Professor, Transportation Engineering Faculty Group, Dept. of Civil and Environmental Engineering, Louisiana State Univ., Baton Rouge, LA 70803. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Reference32 articles.

1. Artificial Neural Network–Based Intelligent Compaction Analyzer for Real-Time Estimation of Subgrade Quality

2. Beainy F. S. Commuri and M. Zaman. 2010. “Asphalt compaction quality control using artificial neural network.” In Proc. 49th IEEE Conf. on Decision and Control. New York: IEEE.

3. Chang, G., Q. Xu, J. Rutledge, B. Horan, L. Michael, D. White, and P. Vennapusa. 2011. Accelerated implementation of intelligent compaction technology for embankment subgrade soils, aggregate base, and asphalt pavement materials. Washington, DC: Federal Highway Administration.

4. Chang, G. K., Q. Xu, J. L. Rutledge, and S. I. Garber. 2014. A study on intelligent compacting and in-place asphalt density. Rep. No. FHWA-IF-12-002. Washington, DC: Federal Highway Administration.

5. Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression

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