Due to increases in congestion, transportation costs, and associated environmental impacts, a variety of new enhanced transit strategies are being investigated worldwide. The transit-oriented development (TOD) concept is a key area where several enhanced transit strategies can be implemented. TODs integrate transit, residential, retail and/or commercial entities into a compact, pedestrian-friendly community, thereby reducing private car usage and increasing transit use. This research report addresses two enhanced strategies within the TOD framework: 1) using Bus Lanes with Intermittent Priorities (BLIPs) to enhance bus transit; and 2) addressing how and what Intelligent Transportation System (ITS) technology can be used within TOD system architectures. With respect to 1), it has been shown that the implementation of BLIPs for bus rapid transit can greatly increase system efficiencies without compromising the level of service for other facility users. The basic analysis in this report shows that both conservative and liberal approaches have similar impacts to traffic and identical benefits. The macroscopic analysis illustrates that traffic disturbances caused by BLIP activation will not slow down subsequent buses, and that roads with medium traffic demand can easily support a BLIP implementation. The microscopic analysis provides some quantitative equations that can help decision makers determine whether a given intersection can be outfitted with a BLIP implementation within predefined parameters. A framework for cost-benefit analysis was provided for BLIP implementation. With respect to 2), it has been shown that transportation efficiency and effectiveness within a TOD can certainly be enhanced with the integration of ITS technology. This project report has identified technology bundles and architectures that have the greatest potential for increasing mobility. Further, it has demonstrated that ITS technologies implemented in a well-integrated fashion will promote transit efficiency and convenience and lead to transit usage beyond levels currently observed.

Over the life cycle of large civil infrastructure, there is often significant degradation in capability and design performance due to extend usage and, in many cases, deferred maintenance. The ultimate limit states in this case can result in unexpected failure, with potentially large economic or life-safety consequences. Structural health monitoring (SHM) is a framework for monitoring the structure over its life cycle, and this field has expanded rapidly in the past two to three decades due to the urgency of infrastructure aging beyond its design life. One of the critical challenges in a monitoring process is obtaining the most valuable information from the structure responses in the field under a continuous monitoring paradigm. This dissertation will present a new optimal sensor design framework, using Machine Learning (ML) techniques, including Gaussian Process Regression (GPR), Bayesian inference, uncertainty quantification, and Bayesian optimization, that guides risk-informed SHM system design. In particular, this dissertation: (1) demonstrates a framework for optimal sensor design using Bayes risk as the objective function; (2) further explores the framework using risk-weighted f-divergence functions and implements it in a real miter gate problem as a case study; (3) investigates the effects of sensor reliability over a life cycle monitoring of the miter gate for informing optimal sensor arrangement.

Corrosion, particularly pitting corrosion, stands as one of the most prevalent forms of deterioration in large infrastructure, known for its slow, continuous, capacity-degrading nature. With the aging of structures, there is an increasing demand for the assessment and monitoring of corrosion status to facilitate system life-cycle management and optimal maintenance. However, due to the intricacies involved in the corrosion process, several challenges impede effective diagnosis and prognosis. These challenges include the inadequate coupling of mechanical stress into existing corrosion simulations, the computational intensity of most physics-based corrosion simulations limiting probabilistic studies and real-time predictions, and the difficulty in detecting pitting corrosion due to its localized nature, making it impractical to measure individual pits. This dissertation presents a comprehensive prognosis and diagnosis framework applied to a miter gate case study. The framework integrates multi-scale simulation including mesoscale Phase-Field (PF) simulation and macroscale structural analysis with Machine Learning (ML) methods to enable real-time corrosion diagnosis and prognosis. It enables for effective structural health monitoring (SHM) with digital twin (DT) for corrosion damage. The key components of this framework encompass the development of a multi-scale simulation for simulating pitting corrosion in large structures, the construction of ML-based surrogate models to expedite simulations, the implementation of uncertainty quantification (UQ), and the integration of pitting corrosion diagnosis and prognosis.

This paper aims to use structural health monitoring (SHM) on fiberglass reinforced plastic (FRP) panels subject to fatigue loading. The testing procedure will assess the structural integrity and predict the remaining serviceability of a panel. Fiber optic sensors embedded into the specimens collect readings after discrete fatigue cycle damage level increments which are then transformed into multidimensional damage sensitive features. The features are then placed in a mahalanobis distance space where they are examined under a positive feedback relationship. Constraints on the model, linearize the method, and finally, a data driven approach produces probabilistic time of failure predictions at each damage phase.