A probabilistic and adaptive approach to modeling performance of pavement infrastructure

Li, Zheng, 1977-; Zhang, Zhanmin, 1962-; University of Texas at Austin. Center for Transportation Research

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A probabilistic and adaptive approach to modeling performance of pavement infrastructure

2007-08-01
By Li, Zheng, 1977- ; Zhang, Zhanmin, 1962-

[PDF-697.92 KB]

English

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Alternative Title:

Develop a dynamic system to simulate the life-cycle performance of pavements [project title];Develop a system to support preparation of life-cycle budget needs for highways [project title];
Creators:

Li, Zheng, 1977- ; Zhang, Zhanmin, 1962-

Li, Zheng, 1977- ; Zhang, Zhanmin, 1962- Less -
Corporate Creators:

University of Texas at Austin. Center for Transportation Research
Corporate Contributors:

Southwest Region University Transportation Center (U.S.) ; Texas

Southwest Region University Transportation Center (U.S.) ; Texas Less -
Subject/TRT Terms:

[+]

Life Cycle Analysis Pavement Management Systems Pavement Performance Structural Deterioration And Defects
Publication/ Report Number:

SWUTC/07/167267-1 ; 167267-1 ; NTIS-PB2008101548

SWUTC/07/167267-1 ; 167267-1 ; NTIS-PB2008101548 Less -
Resource Type:

Research Paper
TRIS Online Accession Number:

1080721
OCLC Number:

180193926
Corporate Publisher:

University of Texas at Austin. Center for Transportation Research ; Southwest Region University Transportation Center (U.S.)

University of Texas at Austin. Center for Transportation Research ; Southwest Region University Transportation Center (U.S.) Less -
NTL Classification:

NTL-HIGHWAY/ROAD TRANSPORTATION-Pavement Management and Performance;NTL-PLANNING AND POLICY-PLANNING AND POLICY;
Abstract:

Accurate prediction of pavement performance is critical to pavement management agencies. Reliable and accurate predictions of pavement infrastructure performance can save significant amounts of money for pavement infrastructure management agencies through better planning, maintenance, and rehabilitation activities. Pavement infrastructure deterioration is a dynamic, complicated, and stochastic process with its outcome as the aggregated impact from various factors such as traffic loading, environmental condition, structural capacities, and some unobserved factors. However, existing performance prediction models are still constrained by inadequate consideration of the dynamic and stochastic characteristics of pavement infrastructure deterioration. The goal of this research is to develop a probabilistic and adaptive methodological framework that is capable of capturing the dynamic and stochastic nature of pavement deterioration processes. The ordered probit model and the sequential logit model as probabilistic models are proposed to directly predict the performance of pavements in terms of their condition states by relating the performance to the structural, traffic, and environmental variables. The proposed probabilistic models were pilot-tested with pavement performance data collected during the AASHTO Road Test, yielding good prediction results. In addition, these models were further enhanced as mechanistic-empirical models and compared with existing performance models. The comparison results show that the proposed models yield better predictions than the previously developed models in terms of prediction accuracy. Then, a structural state space model is proposed to characterize the dynamic nature of pavement deterioration. The proposed structural model has the capability of adaptively updating the performance model with new inspection data by taking advantage of a polynomial trend filter and the Kalman filter algorithm. The results from a simulation case study indicate that the adaptive algorithm is robust and responsive to structural deviations of the pavement deterioration process. Therefore, it is concluded that the proposed probabilistic and adaptive methodological framework is reliable and robust to accurately predict pavement performance. /Abstract from report summary page/
Format:

PDF
Alternate URL:

http://ntl.bts.gov/lib/26000/26800/26891/167267-1.pdf
Funding:

10727
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:d694f8dd1885e4fbb718e8d4583966462cbc10f89bc6db5f9877be96f2919826
Download URL:

https://rosap.ntl.bts.gov/view/dot/16334/dot_16334_DS1.pdf
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A probabilistic and adaptive approach to modeling performance of pavement infrastructure

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