2000
DOI: 10.3141/1699-14
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Solution of Pavement Deterioration Equations by Genetic Algorithms

Abstract: Pavement deterioration models are indispensable for many purposes; as a result, a number of models are in use. Models with simple equation forms are easier to use, but frequently such models may not suffice for many purposes. Consequently, complex nonlinear forms of models are to be considered. However, determination of the solution to a complex model form is not an easy task. There are various methods of obtaining solutions to such models, with each method having its own advantages and disadvantages. The use … Show more

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Cited by 13 publications
(5 citation statements)
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“…Shekharan [18] used genetic algorithms to evaluate five pavement deterioration models from synthetic databases created from multiple regression models. Deterioration models are essential in the pms to estimate the evolution of pavement conditions in the analysis period and trigger the warrants for prescribing treatments or rehabilitation procedures.…”
Section: Reference Descriptionmentioning
confidence: 99%
“…Shekharan [18] used genetic algorithms to evaluate five pavement deterioration models from synthetic databases created from multiple regression models. Deterioration models are essential in the pms to estimate the evolution of pavement conditions in the analysis period and trigger the warrants for prescribing treatments or rehabilitation procedures.…”
Section: Reference Descriptionmentioning
confidence: 99%
“…The simulation results pointed out that as the costs of maintenance activities increase concerning rehabilitation, it is more economical to rehabilitate more pavement segments. Shekharan (2000) used genetic algorithms to evaluate five pavement deterioration models from synthetic databases created from multiple regression models. Deterioration models are essential in the pavement management system to estimate pavement condition evolution and trigger the warrants for prescribing treatments or rehabilitation procedures in the analysis period.…”
Section: Optimization With Genetic Algorithmsmentioning
confidence: 99%
“…These functions can be broadly categorized into four major functional families: exponential, logistic, polynomial, and power families. See, for example, Patterson and Attoh-Okine (1992), Labi and Sinha (2002), Lamptey (2004) and George et al (1989) for the exponential family, Johnson and Cation (1992) for the polynomial family, and George et al (1989), Sharaf et al (1988), Chan et al (1997), and Shekaran (1997) for the power family.…”
Section: Literature Reviewmentioning
confidence: 99%