Pavement maintenance and rehabilitation decisions derived by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Chang:2010:ICNC,
  author =       "Jia-Ruey Chang and Sao-Jeng Chao",
  title =        "Pavement maintenance and rehabilitation decisions
                 derived by genetic programming",
  booktitle =    "Sixth International Conference on Natural Computation
                 (ICNC), 2010",
  year =         "2010",
  month =        "10-12 " # aug,
  volume =       "5",
  pages =        "2439--2443",
  address =      "Yantai, Shandong, China",
  abstract =     "The application of genetic programming (GP) to
                 pavement performance evaluation is relatively new. GP
                 was first proposed by John R. Koza as an evolutionary
                 computation technique: a stochastic search method based
                 on the Darwinian principle of `survival of the
                 fittest', whereby intelligible relationships in a
                 system are automatically extracted and used to generate
                 mathematical expressions or `programs'. Nowadays, GP
                 has been used as an important problem-solving method
                 for function fitting and classification. In this paper,
                 an empirical study is performed to develop a pavement
                 maintenance and rehabilitation (M and R) decision model
                 by using GP. As part of the research, experienced
                 pavement engineers from the Taiwan Highway Bureau (THB)
                 conducted pavement distress surveys on seven county
                 roads. For each road section, the severity and coverage
                 of existing distresses that required M and R treatments
                 were separately identified and collated into an
                 analytical database containing 2,340 records. These
                 records were then used to train, validate, and apply
                 the M and R decision model. The finding shows that the
                 total accuracy of the evolved M and R decision model
                 was 0.903, 0.877, and 0.878 for the training,
                 validation, and application data set, respectively. It
                 proves that the GP-based M and R decision model process
                 makes the pavement knowledge extraction process more
                 systematic, easier to use and solvable with a higher
                 probability of success - even for complex M and R
                 decision problems.",
  keywords =     "genetic algorithms, genetic programming, Darwinian
                 principle, GP-based M amp, R decision model, Taiwan
                 highway bureau, evolutionary computation technique,
                 pavement distress surveys, pavement knowledge
                 extraction process, pavement maintenance, pavement
                 performance evaluation, problem-solving method,
                 rehabilitation decisions, stochastic search method,
                 maintenance engineering, road building, search
                 problems, stochastic processes",
  DOI =          "doi:10.1109/ICNC.2010.5583502",
  notes =        "Dept. of Civil Eng. & Environ. Inf., MingHsin Univ. of
                 Sci. & Technol., Hsinchu, Taiwan Also known as
                 \cite{5583502}",
}

Genetic Programming entries for Jia-Ruey Chang Sao-Jeng Chao

Citations