Stochastic Prediction of Road Network Degradation Based on Field Monitoring Data (Record no. 814331)

MARC details
000 -LEADER
fixed length control field 02564aab a2200241 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231115b20232023|||mr||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0733-9364
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Tran, Huu
9 (RLIN) 878912
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Robert, Dilan
9 (RLIN) 878913
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Gunarathna, Prageeth
9 (RLIN) 878914
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Setunge, Sujeeva
9 (RLIN) 171710
245 ## - TITLE STATEMENT
Title Stochastic Prediction of Road Network Degradation Based on Field Monitoring Data
300 ## - PHYSICAL DESCRIPTION
Extent 1-12 p.
520 ## - SUMMARY, ETC.
Summary, etc. Asset management of pavement network requires understanding of pavement deterioration rate for cost-effective maintenance and adequate budget allocation. The pavement industry has recognized the challenge of uncertainty or variation in deterioration processes that could not be captured by deterministic deterioration models. This study investigated the stochastic Markov chain theory for modeling deterioration of pavement network. The discrete condition data for the Markov model is obtained by a proposed maintenance-related condition rating scheme (MRCR) that combines three commonly inspected pavement distresses including cracking, rutting and roughness. The Markov model is calibrated by the proven Bayesian Markov chain Monte Carlo simulation method, and the statistical Chi-square test is used for testing model fitness. A case study with time series data of pavement distresses collected from regular inspection of a highway network is used in this study. Various influential factors to pavement deterioration are also investigated in this study to understand their impact on the deterioration rate of highways. The results on the case study show that the Markov model is suitable for modeling deterioration of highway network, and there are significant differences in deterioration rates of highways among influential factors including traffic volume, rainfall amount, demographic location, and prioritized maintenance. The outcomes of this study provide more understanding of pavement deterioration of road networks and demonstrate the forecasting of maintenance budget by the deterioration prediction of Markov model for supporting asset management of pavement network.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Pavements
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Rehabilitation
9 (RLIN) 125572
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Markov Deterioration
9 (RLIN) 878915
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Failure
9 (RLIN) 168120
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Inspection
773 0# - HOST ITEM ENTRY
Title ASCE: Journal of Construction Engineering and Management
International Standard Serial Number 07339364
Place, publisher, and date of publication Reston,Virginia, U.S.A : American Society of Civil Engineers/ American Concrete Institute
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1061/JCEMD4.COENG-13293 Abstract">https://doi.org/10.1061/JCEMD4.COENG-13293 Abstract</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Articles
-- 14993
-- Mr. Muhammad Rafique Al Haj Rajab Ali (Late)
Holdings
Not for loan Home library Serial Enumeration / chronology Total Checkouts Date last seen Koha item type
  Periodical Section Vol.149, No.10(Oct.2023)   15/11/2023 Articles