The inherent uncertainty in travel forecasting models -- arising from errors in input data, parameter estimation, or model formulation -- is receiving increasing attention from the scholarly and practicing community. In this research, we investigate the variance in forecasted traffic volumes resulting from varying the mode and destination choice parameters in an advanced trip-based travel demand model. Using Latin hypercube sampling to construct several hundred combinations of parameters across the plausible parameter space, we introduce substantial changes to mode and destination choice logsums and probabilities. However, the aggregate effects of of these changes on forecasted traffic volumes is small, with a variance of approximately 1 percent on high-volume facilities. Thus, parameter uncertainty does not appear to be a significant factor in forecasting traffic volumes using transportation demand models.

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Pile-supported ports allow seawater to run below the deck, and thus may suffer structural damages during extreme coastal events such as hurricanes. These structural damages, in turn, may result to port closures that can cause significant economic losses. Risk analysis can predict the post-hazard functionality of ports though the structural damage assessment of these structures prior to coastal events. However, assumptions on the selected demand estimates may affect the estimated probability of structural damage. This research aims to shed light on the sensitivity of the wave model selection for the risk assessment of pile-supported ports when subjected to storm surge and waves. The examined structural damage is the uplift of the deck, and the risk assessment is conducted through the development of fragility curves for a typical deck-pile connection, for which fragility curves are developed for different wave models. Uncertainties are also considered in parameters affecting the demand and capacity of the examined deck-pile connection and are propagated through the Monte Carlo simulation using the Latin Hypercube Sampling. The results indicate changes to the uplift probability as a result of the selected wave model. Thus, wave model selection can alter the uplift failure probability. In addition, the study proposes parameterized fragility models to enable the uplift risk assessment across a region. The presented results aim to throw light on the proper model selection to produce more realistic risk assessment estimates towards the resilience of coastal infrastructure. / Thesis / Master of Applied Science (MASc)

The efficient evaluation of reliability index is of considerable importance in the assessment of component reliability and reliability-based structural optimization. In this thesis, the structural reliabiltiy analysis is performed using the random sampling techniques such as traditional Monte Carlo simulation and the analytical techniques such as first-order reliability method. The feasibility of Gauss quadrature points as means of target sampling of design space and generating accurate first- and second-order response surface models of failure functions is examined. Parametric uncertainty is considered by probabilistic modeling of design parameters. Various alternative approaches for estimation of component reliability index are examined with application to two structural problems: ply failure in a multidirectional composite laminate and axial buckling of a composite circular cylinder. The probabilistic sensitivity analysis is performed to measure the influence of each random variable on the estimated reliability index. The advantages and disadvantages of each approach are discussed and the approach considered the most efficient in terms of accuracy and computational requirements is identified. Furthermore, the most efficient approach is applied in reliability-based structural optimization of a composite circular cylinder with ply failure and axial buckling constraints. The optimization problem is solved using sequential quadratic programming based on sequential local response surface approximations of failure functions. The optimization results are presented for different geometric properties, laminate configurations, and coefficients of variation.

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