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Re-calibration of Rigid Pavement Performance Models and Development of Traffic Inputs for Pavement-ME Design in Michigan
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Re-calibration of Rigid Pavement Performance Models and Development of Traffic Inputs for Pavement-ME Design in Michigan. |
| 개인저자 | Musunuru, Gopi Krishna. |
| 단체저자명 | Michigan State University. Civil Engineering - Doctor of Philosophy. |
| 발행사항 | [S.l.]: Michigan State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 270 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05A. Dissertation Abstract International |
| ISBN | 9781088390511 |
| 학위논문주기 | Thesis (Ph.D.)--Michigan State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: A.
Advisor: Haider, Syed W. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | The mechanistic-empirical pavement design guide (AASHTOWARE Pavement-ME) incorporates mechanistic models to estimate stresses, strains, and deformations in pavement layers using site-specific climatic, material, and traffic characteristics. These structural responses are used to predict pavement performance using empirical models (i.e., transfer functions). The transfer functions need to be calibrated to improve the accuracy of the performance predictions, reflecting the unique field conditions and design practices. The existing local calibrations of the performance models were performed by using version 2.0 of the Pavement-ME software. However, AASHTO has released versions 2.2 and 2.3 of the software since the completion of the last study. In the revised versions of the software, several bugs were fixed.Consequently, some performance models were modified in the newer software versions. As a result, the concrete pavement IRI predictions and the resulting PCC slab thicknesses have been impacted. The performance predictions varied significantly from the observed structural and function distresses, and hence, the performance models were recalibrated to enhance the confidence in pavement designs. Linear and nonlinear mixed-effects models were used for calibration to account for the non-independence among the data measured on the same sections over time. Also, climate data, material properties, and design parameters were used to develop a model for predicting permanent curl for each location to address some limitations of the Pavement-ME. This model can be used at the design stage to estimate permanent curl for a given location in Michigan.Pavement-ME also requires specific types of traffic data to design new or rehabilitated pavement structures. The traffic inputs include monthly adjustment factors (MAF), hourly distribution factors (HDF), vehicle class distributions (VCD), axle groups per vehicle (AGPV), and axle load distributions for different axle configurations. During the last seven years, new traffic data were collected, which reflect the recent economic growth, additional, and downgraded WIM sites. Hence it was appropriate to re-evaluate the current traffic inputs and incorporate any changes. Weight and classification data were obtained from 41 Weigh-in-Motion (WIM) sites located throughout the State of Michigan to develop Level 1 (site-specific) traffic inputs. Cluster analyses were conducted to group sites for the development of Level 2A inputs. Classification models such as decision trees, random forests, and Naive Bayes classifier were developed to assign a new site to these clusters |
| 일반주제명 | Civil engineering. Transportation. |
| 언어 | 영어 |
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