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020 ▼a 9781088390511
035 ▼a (MiAaPQ)AAI22619234
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 385
1001 ▼a Musunuru, Gopi Krishna.
24510 ▼a Re-calibration of Rigid Pavement Performance Models and Development of Traffic Inputs for Pavement-ME Design in Michigan.
260 ▼a [S.l.]: ▼b Michigan State University., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 270 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-05, Section: A.
500 ▼a Advisor: Haider, Syed W.
5021 ▼a Thesis (Ph.D.)--Michigan State University, 2019.
506 ▼a This item must not be sold to any third party vendors.
506 ▼a This item must not be added to any third party search indexes.
520 ▼a 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
590 ▼a School code: 0128.
650 4 ▼a Civil engineering.
650 4 ▼a Transportation.
690 ▼a 0543
690 ▼a 0709
71020 ▼a Michigan State University. ▼b Civil Engineering - Doctor of Philosophy.
7730 ▼t Dissertations Abstracts International ▼g 81-05A.
773 ▼t Dissertation Abstract International
790 ▼a 0128
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493608 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK