Graduation Year
2022
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Industrial and Management Systems Engineering
Major Professor
Mingyang Li, Ph.D.
Committee Member
Tapas K. Das, Ph.D.
Committee Member
Changhyun Kwon, Ph.D.
Committee Member
Qiong Zhang, Ph.D.
Committee Member
Qing Lu, Ph.D.
Committee Member
He Zhang, Ph.D.
Keywords
Co-location, Degradation, Heterogeneity, Interdependent Infrastructures, Joint Maintenance
Abstract
Serving as the backbone of the nation’s economy, a large and growing number of deteriorating critical infrastructures, such as transportation and water infrastructures, are underperforming, aging, becoming structurally deficient, and must be repaired or replaced. Due to the influence of a variety of factors (e.g., material structure, design, operation, and environmental conditions) at different phases of lifecycle and the costly data acquisition process, field degradation of deteriorating infrastructures is highly uncertain with limited degradation data. The co-location and spatial proximity between many infrastructures, such as road and water infrastructures, further makes them physically and operationally interdependent. The sheer deterioration of critical infrastructures with field degradation uncertainty, observational data scarcity, and complex interdependencies/dependencies calls for the development of advanced analytics methods to improve degradation modeling and maintenance optimization of in-service deteriorating infrastructures. In this dissertation, a series of predictive and prescriptive analytics methods are developed to improve the degradation performance modeling and cost-effective maintenance planning of the deteriorating critical infrastructures by addressing various data-driven modeling and decision analytics challenges. First, a Bayesian degradation performance modeling approach is developed to characterize population heterogeneity and individual sparsity of degradation data for a heterogeneous population of deteriorating road sections. The proposed model relaxes the homogeneity assumption of the conventional degradation modeling approach and further improves the prediction accuracy of individual roads with sparse data observations via data fusion and sequential learning. Second, a component-level joint maintenance planning model is developed for the co-located deteriorating road and pipe under their degradation uncertainties. The proposed model minimizes the long-term maintenance planning costs by simultaneously capturing both the physical and operational dependencies between road and pipe as well as the multiple competing failure modes of the pipe. Third, a network-wide maintenance optimization model is developed to achieve the proactive and cost-effective maintenance planning for the co-located deteriorating road and water infrastructure systems subjected to the limited maintenance budget. The proposed model jointly determines the budget prioritization and maintenance planning decisions under network-wide budget constraints by considering the spatial heterogeneity of multiple co-located roads and pipes with varied physical, environmental, and socioeconomic characteristics. Case studies are provided to illustrate the proposed predictive and prescriptive analytics methods and further demonstrate their improved performance (e.g., prediction accuracy/precision, cost reduction) over existing analytics-based models.
Scholar Commons Citation
Nguyen, Hung Q., "Degradation Performance Modeling and Optimal Maintenance Planning of Deteriorating Critical Infrastructures" (2022). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10336
