Operations research and data mining applications in pavement management
dc.contributor.advisor | Smadi, Omar | |
dc.contributor.advisor | Dong, Jing | |
dc.contributor.advisor | Wood, Jonathan | |
dc.contributor.advisor | Madson, Katherine | |
dc.contributor.advisor | Dixon, Philip | |
dc.contributor.author | Abukhalil, Yazan | |
dc.contributor.department | Department of Civil, Construction and Environmental Engineering | |
dc.date.accessioned | 2022-11-08T23:56:23Z | |
dc.date.available | 2022-11-08T23:56:23Z | |
dc.date.issued | 2021-08 | |
dc.date.updated | 2022-11-08T23:56:23Z | |
dc.description.abstract | Transportation agencies manage and operate large pavement networks with the goal of maintaining high performance levels through cost-effective decision-making. To be able to achieve this goal, transportation agencies use decision support tools (DSTs) to evaluate networks’ current condition, predict future condition, and find optimum maintenance and rehabilitation (M&R) budget allocation strategies. DSTs have drawbacks including subjectivity and inefficiency. Given the drawbacks of the currently used DSTs, and since transportation agencies started to move toward more efficient and objective data-driven decision-making approaches with the continuous pavement network data collection, this dissertation aims to develop data-driven DSTs for pavement management systems (PMS) that transportation agencies can use to improve the efficiency of their decision-making process. To help achieve this goal, three objectives are addressed in this dissertation. A bootstrap-based binary integer programming algorithm is designed to allocate the budget over an extensive pavement network under budget and performance constraints. This algorithm succeeded in significantly reducing the computational cost required for budget allocation in PMS. Furthermore, data-driven decision trees (DTs) are developed for M&R selection in PMS using the Classification and Regression Trees algorithm. These trees were able to capture patterns from historical M&R actions with relatively high accuracy levels. The last component of this dissertation addresses a data quality issue, the underreporting of M&R projects. A logistic regression-based approach for detecting unreported pavement treatments from historical condition trends is suggested. High validation accuracies were achieved for the three logistic models developed for the interstate, U.S., and state route systems in Iowa. | |
dc.format.mimetype | ||
dc.identifier.doi | https://doi.org/10.31274/td-20240329-563 | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/2vaZ4Ygr | |
dc.language.iso | en | |
dc.language.rfc3066 | en | |
dc.subject.disciplines | Civil engineering | en_US |
dc.subject.disciplines | Transportation | en_US |
dc.subject.disciplines | Management | en_US |
dc.subject.keywords | Budget allocation | en_US |
dc.subject.keywords | data mining | en_US |
dc.subject.keywords | machine learning | en_US |
dc.subject.keywords | optimization | en_US |
dc.subject.keywords | pavement management | en_US |
dc.title | Operations research and data mining applications in pavement management | |
dc.type | dissertation | en_US |
dc.type.genre | dissertation | en_US |
dspace.entity.type | Publication | |
relation.isOrgUnitOfPublication | 933e9c94-323c-4da9-9e8e-861692825f91 | |
thesis.degree.discipline | Civil engineering | en_US |
thesis.degree.discipline | Transportation | en_US |
thesis.degree.discipline | Management | en_US |
thesis.degree.grantor | Iowa State University | en_US |
thesis.degree.level | dissertation | $ |
thesis.degree.name | Doctor of Philosophy | en_US |
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