ORCID
0000-0002-4435-9002
Date of Award
2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Civil and Environmental Engineering
First Advisor
Eric Hernandez
Abstract
When uncertainties in civil engineering systems are relatively small, conservative estimates may be determined from typical behaviors of systems and structures, taken from samples or historical performance. However, when uncertainties are large and the consequences of disruption or failure are high, probabilistic measures can act as a bridge between observed data and mathematical models that measure risk. Massive allocations of public spending rely on methods of dealing with these types of uncertainties in civil engineering. Methods of dealing with uncertainty that capitalize on the increasing availability of current and historical data can help reduce these factors of safety and substantially reduce the cost of our built environment. The research described in this dissertation exhibits the use of probabilistic methods to support broader applications of reliability analysis that capitalize on the use of frequency distributions to address the uncertainties and stochasticity involved when we have a relatively small number of samples to define the governing mechanisms of a system whose failure has high consequences. Frequency distributions are used to represent highway capacities in a transportation network for network criticality analyses, household demographics for critical accessibility measurement, and design targets for in-place concrete compressive strength (CCS) for structures under construction. The first approach, in Chapter 2 of this dissertation, uses empirical speed-flow and surface-condition data to calibrate link capacity reliability (LCR) models and empirical roadway damage data to develop a new 2-stage LCR model that addresses the effects of extreme weather. A case study demonstrates the contribution of the new LCR model to network criticality analysis, as the new 2-stage LCR model shows greater impacts across 5,000 simulations of available capacity. The calibrated LCR model is useful for any analysis which requires measurement of highway capacity, including traditional travel demand modeling. The new 2-stage LCR model advances criticality modeling so that project prioritization can focus on mitigating risk. The second approach, in Chapter 3, is a population-synthesis method for evaluating critical accessibility. The resulting critical accessibility measure is demonstrated via a case study that examines food accessibility by vulnerable rural populations to support the expansion of a fixed-route public transit service. The synthesis of household demographics for the purpose of identifying and weighting the vulnerability of rural households, allows for improved transportation investments that address the needs of specific sub-populations, but it also more broadly useful is any measurement of critical accessibility, or any application of agent-based travel demand modeling where disaggregated household-level attributes are needed. The third approach, in Chapter 4, is a novel quality measure, the percent-within-distribution, or PWD, for acceptance and payment of in-place concrete. The new quality measure is demonstrated for a quality control / quality assurance performance specification (QC/QA PS) with upper and lower bounds on 28-day CCS. The PWD can have widespread influence on public infrastructure projects, as it is effective for developing an initial pay factor schedule but also for enforcing payment in a QC/QA performance specification.
Language
en
Number of Pages
150 p.
Recommended Citation
Sullivan, James L., "Probabilistic Measures To Support Performance-Based Decision-Making In Transportation: Networks And Asset Management" (2025). Graduate College Dissertations and Theses. 2095.
https://scholarworks.uvm.edu/graddis/2095