Bayesian estimation of finite mixture roughness model
| dc.contributor.advisor | Prozzi, Jorge Alberto | |
| dc.contributor.committeeMember | Zhang, Zhanmin | |
| dc.contributor.committeeMember | Gilbert, Robert B | |
| dc.contributor.committeeMember | Müller, Peter | |
| dc.contributor.committeeMember | Mikhail, Magdy | |
| dc.creator | Serigos, Pedro A. (Pedro Antonio) | |
| dc.date.accessioned | 2017-02-10T15:40:23Z | |
| dc.date.accessioned | 2018-01-22T22:31:37Z | |
| dc.date.available | 2017-02-10T15:40:23Z | |
| dc.date.available | 2018-01-22T22:31:37Z | |
| dc.date.issued | 2016-12 | |
| dc.date.submitted | December 2016 | |
| dc.date.updated | 2017-02-10T15:40:23Z | |
| dc.description.abstract | Highway infrastructure systems provide a crucial service to society and constitute a major asset with a significant maintenance and rehabilitation cost, highway pavements comprising a major component of the total cost. The increasing need for greater capital investment, in the face of ever-decreasing federal funding to maintain highway infrastructure, highlights the importance of developing and implementing effective methods for managing pavement assets. A key for the success of pavement management is to accurately predict the future condition of the pavements in the network. This dissertation proposes a mixture of regression models to capture the systematic differences in pavement performance not explained by variables typically available in pavement management systems. This approach assumes that the heterogeneous pavement performance, which results from the combined effect of the several unobserved factors and interactions, is manifested through a finite number of latent groups. The estimation of the proposed model allows for defining the parameters of the group-specific models while clustering the observations into the latent groups. The insights provided by the model-based clustering of performance data can also be incorporated into the design of maintenance and rehabilitation strategies, as clustering of sections according to their deterioration rate allows for identifying pavements in the network with structural deficiencies and tailoring actions in response. The gain in model fit, along with the insights provided by the proposed methodology for the unsupervised model-based clustering of pavement performance was demonstrated using experimental data. In addition, the proposed mixture model was applied to develop a Bayesian pavement roughness model specified with variables from an existing pavement management system, plus climatic and preventive maintenance variables, and estimated using nationwide field data from the Long-Term Pavement Performance program. Lastly, the developed roughness mixture model was calibrated for Texas pavement conditions by combining both the nationwide data and data extracted from the processing and merging of various Texas Department of Transportation databases. The proposed methodology produces accurate predictions of the progression of roughness as well as robust estimates of the factor effects driving the deterioration of pavements, which, ultimately, lead to a more efficient management of highway assets. | |
| dc.description.department | Civil, Architectural, and Environmental Engineering | |
| dc.format.mimetype | application/pdf | |
| dc.identifier | doi:10.15781/T2445HH6D | |
| dc.identifier.uri | http://hdl.handle.net/2152/45628 | |
| dc.language.iso | en | |
| dc.subject | Pavements | |
| dc.subject | Roughness | |
| dc.subject | Mixture model | |
| dc.subject | Heterogeneity | |
| dc.subject | Performance model | |
| dc.subject | Infrastructure management system | |
| dc.subject | Preventive maintenance | |
| dc.title | Bayesian estimation of finite mixture roughness model | |
| dc.type | Thesis | |
| dc.type.material | text |