Details:

Creators:
Fu, Gongkang ; Devaraj, Dinesh
Fu, Gongkang ; Devaraj, Dinesh Less -
Corporate Creators:
Wayne State University. Center for Advanced Bridge Engineering
Subject/TRT Terms:
[+]
Bridges--Deterioration--Mathematical Models
Bridges--Maintenance And Repair--Management
Markov Processes.
Bridges
Deterioration
Highway Maintenance
Markov Processes

Resource Type:
Tech Report
Geographical Coverage:
Michigan
OCLC Number:
676698618
Edition:
Final report.
Corporate Publisher:
Michigan. Dept. of Transportation
NTL Classification:
NTL-HIGHWAY/ROAD TRANSPORTATION-Bridges and Structures
Abstract:
Bridge management is an important activity of transportation agencies in the US

and in many other countries. A critical aspect of bridge management is to reliably predict

the deterioration of bridge structures, so that appropriate or optimal actions can be

selected to reduce or minimize the deterioration rate and maximize the effect of spending

for replacement or maintenance, repair, and rehabilitation (MR&R). In the US, Pontis is

the most popular bridge management system used among the state transportation

agencies. Its deterioration model uses the Markov Chain, with a statistical regression to

estimate the required transition probabilities. This is the core part of deterioration

prediction in Pontis.

This report focuses on the Markov Chain model used in Pontis, which is vital to

the understanding and implementation of the Pontis software. Emphasis has been made

on the limitations of Pontis methodology, and establishing a new method for transition

probability estimation. This is because predicting deterioration is the basis for decisionmaking

with respect to MR&R. The next portion of this chapter presents a literature

review on the subject of estimating transition probability matrix using Markov Chain for

modeling deterioration in civil engineering facilities, such as bridges, pavements, and

waste water systems.


Format:
PDF
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:fa390120fb91401622eb59aaa11b028573371b6697d13c09eeb376f8e41d70cd
File Type:

Supporting Files

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