2026-06-19T13:06:27Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/98602025-01-11T01:05:29Zcom_10259_4204com_10259_5086com_10259_2604col_10259_5373
Repositorio Institucional de la Universidad de Burgos
author
Alonso-Solorzano, Ángela
author
Pérez Acebo, Heriberto
author
Findley, Daniel J.
author
Gonzalo Orden, Hernán
2025-01-10T09:35:55Z
2025-01-10T09:35:55Z
2023-11
1029-8436
http://hdl.handle.net/10259/9860
10.1080/10298436.2023.2278694
1477-268X
Probabilistic pavement models, with Markov chains as the most widely used type, are considered to capture an accurate representation of the in situ pavement performance. Homogeneous Markov chain models present the same transition probability matrix (TPM) for all the transitions of the period and require data from multiple duty cycles of one or two years. The aim of this paper is to explore the feasibility of developing homogeneous Markov chain models with variations of the duty cycle (in increments of either one or two years). Without considering maintenance and rehabilitation works, this research found that TPMs for a one-year duty cycle can be calculated from the two-year duty cycle, without a noticeable effect on accuracy using International Roughness Index (IRI) values from the Spanish State Road Network. However, for developing coherent TPMs, two primary assumptions were made: (1) heavy vehicle traffic volumes determine the traffic category (TC), and (2) only roads from the same climatic region were modelled. The satisfactory results verified the validity of the methodology and overcame the disadvantages of homogeneous Markov models. Furthermore, the results suggest that pavement sections are adequately designed in Spain for each TC because of the similar deterioration patterns.
eng
Atribución-NoComercial 4.0 Internacional
Pavement performance model
Probabilistic model
Homogeneous Markov chain
International Roughness Index
Pavement management system
Transition probability matrices for pavement deterioration modelling with variable duty cycle times
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/9860/4/Alonso-ijpe_2023.pdf
File
MD5
6a178303df810e249af385c0fad6dcbe
1749736
application/pdf
Alonso-ijpe_2023.pdf