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A Gravel Loss Prediction Model Using Beta Regression

Date and Time: Wednesday, July 26: 10:30 AM - 12:00 PM
Location: Grand Ballroom B

 

Lead Presenter: Shafkat Alam-Khan

, Applied Pavement Technology, Inc.

Speaker Biography

Mr. Alam-Khan is an Engineering Associate at Applied Pavement Technology, Inc. (APTech). He is involved in a range of technical projects, from national research on pavement preservation and asset management to hands-on pavement evaluation and management projects. Mr. Alam-Khan has been involved in research projects for Federal and State clients such as the Federal Highway Administration (FHWA), the National Cooperative Highway Research Program (NCHRP), and a pavement evaluation project for the Illinois Department of Transportation (DOT). He worked on updating the Transportation Asset Management Plan (TAMP) of several state DOTs including the Alaska Department of Transportation and Public Facilities (DOT&PF), the Colorado DOT, the Georgia DOT, the Nevada DOT, the Kentucky Transportation Cabinet (KYTC), the New York State DOT, the North Dakota DOT, and the Tennessee DOT. He has extensive research experience in infrastructure asset management, engineering reliability analyses, climate data improvement for mechanistic-empirical (ME) pavement distress analyses, geomaterial characterization, and local calibration of mechanistic-empirical pavement design guide. Mr. Alam-Khan has authored multiple top-tier peer reviewed international journals and conference proceedings, along with presenting research findings in multiple international conferences.

Co-Authors 

Presentation Description/Paper Summary

Unpaved roads consist of considerable portions of the roadway network in many countries. These roads play crucial roles in the development of the infrastructure systems, advancements of the socio-economic activities and improvements of the agricultural and production sectors. Thereby, unpaved roads benefit the underdeveloped, rural and remote neighborhoods; and act as lifelines for these geographically disadvantaged communities. Frequent and regular maintenance activities keep the roadway system operational at a desired level of service. Resurfacing is one of the major maintenance treatments for unpaved roads. A gravel loss prediction model (GLPM) can evaluate the impacts of varying magnitude of resurfacing treatments on the roadway performance. Thus, a GLPM can provide valuable insights for roadway maintenance budget scheduling and decision-making tasks. In this paper; the backgrounds, input requirements and output results of three popular GLPMs were reviewed. These models were i) Highway Development and Management Model 4 (HDM-4), ii) South African Technical Recommendation for Highways Model 20 (TRH-20) and iii) Australian Road Research Board (ARRB) Model. In addition, the practicality of roadway resurfacing frequency charts which were developed based on these models were also evaluated. This study determined that the existing GLPMs and  the corresponding roadway resurfacing frequency charts were often unreliable and impractical. In this study, a beta regression (BR) analyses methodology was utilized to develop and calibrate a GLPM for Iowa. Due to its simplified yet effective nature, BR model outperformed the popular GLPMs and offered a practical approach to quantify annual roadway gravel loss.

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