Date of Award

2012-01-01

Degree Name

Master of Science

Department

Civil Engineering

Advisor(s)

Carlos M. Chang Albitres

Second Advisor

Soheil Nazarian

Abstract

Pavement performance models exist in various forms to cater to the pavement management agencies' needs and resources. Well-calibrated models are needed to accurately predict future pavement conditions, and to forecast and prioritize confidently the future rehabilitation and maintenance expenditures. Statistical tools are commonly used to develop the performance models. These statistical models may be impractical or misleading if not constrained with experts' opinions. This paper presents a hybrid technique where statistical tools and expert knowledge are combined for the calibration of pavement performance models. This technique was validated using historical pavement condition data for continuously-reinforced-concrete pavements (CRCP) from the Texas Department of Transportation's Pavement Management Information System (TxDOT-PMIS). The recalibrated CRCP performance models obtained with the hybrid technique represent an improvement when compared to the current models since they merge expert opinion and statistical analysis which allow to better reflect field observations regarding distress initiation, distress evolution rate, and maximum allowable amount of distress growth. Furthermore, this paper also discusses the application of this technique for the calibration of the Highway Development and Management Model (HDM-4) and Mechanistic-Empirical Pavement Design (MEPD) performance models.

Language

en

Provenance

Received from ProQuest

File Size

418 pages

File Format

application/pdf

Rights Holder

Alejandra Gallegos

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