Determining future maintenance costs of low-volume highway rehabilitation projects for incorporation into life-cycle cost analysis

Kunhee Choi, Yoo Hyun Kim, Junseo Bae, Hyun Woo Lee

Research output: Contribution to journalArticle

Abstract

Life-cycle cost analysis (LCCA) has grown in importance, yet it is also regarded as a daunting task, because of the lack of reliable analytical models specifically aimed at quantifying future maintenance costs (FMC). The key objectives of this study are to identify the most critical factors that affect pavement performance, create a FMC predictive model that accounts for such factors, and validate the model’s robustness in quantifying a reliable FMC for low-volume highway rehabilitation projects. This study combines regression analysis techniques with a cluster analysis by using a large quantity of real-world data obtained from the pavement management information system. The clustering analysis revealed that traffic loading would be the most crucial affecting factor for pavement performance. A series of sensitivity analyses were performed to investigate the effect of individual critical performance-driven factors on FMC. A prediction error analysis validated the model’s robustness, proving that reliable FMC can be estimated by analyzing how it interacts with the affecting factors. This study assists industry practitioners and researchers in quickly and reliably determining long-term FMC for incorporation into LCCA. Knowing a conceptual estimate of FMC in the very early project scoping stage could also help them develop a sounder strategic plan within project constraints and peculiarities from the LCCA standpoint.

Original languageEnglish
JournalJournal of Computing in Civil Engineering
Volume30
Issue number4
Early online date11 Sep 2015
DOIs
Publication statusPublished - 1 Jul 2016
Externally publishedYes

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Patient rehabilitation
Life cycle
Costs
Pavements
Management information systems
Cluster analysis
Regression analysis
Error analysis
Analytical models
Acoustic waves

Cite this

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title = "Determining future maintenance costs of low-volume highway rehabilitation projects for incorporation into life-cycle cost analysis",
abstract = "Life-cycle cost analysis (LCCA) has grown in importance, yet it is also regarded as a daunting task, because of the lack of reliable analytical models specifically aimed at quantifying future maintenance costs (FMC). The key objectives of this study are to identify the most critical factors that affect pavement performance, create a FMC predictive model that accounts for such factors, and validate the model’s robustness in quantifying a reliable FMC for low-volume highway rehabilitation projects. This study combines regression analysis techniques with a cluster analysis by using a large quantity of real-world data obtained from the pavement management information system. The clustering analysis revealed that traffic loading would be the most crucial affecting factor for pavement performance. A series of sensitivity analyses were performed to investigate the effect of individual critical performance-driven factors on FMC. A prediction error analysis validated the model’s robustness, proving that reliable FMC can be estimated by analyzing how it interacts with the affecting factors. This study assists industry practitioners and researchers in quickly and reliably determining long-term FMC for incorporation into LCCA. Knowing a conceptual estimate of FMC in the very early project scoping stage could also help them develop a sounder strategic plan within project constraints and peculiarities from the LCCA standpoint.",
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Determining future maintenance costs of low-volume highway rehabilitation projects for incorporation into life-cycle cost analysis. / Choi, Kunhee; Kim, Yoo Hyun; Bae, Junseo; Lee, Hyun Woo.

In: Journal of Computing in Civil Engineering, Vol. 30, No. 4, 01.07.2016.

Research output: Contribution to journalArticle

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