Vehicular emissions contribute significantly to poor air quality in urban areas. An integrated modelling approach is adopted to estimate microscale urban traffic emissions. The modelling framework consists of a traffic microsimulation model, a microscopic emissions model, and two dispersion models. This framework is applied to a traffic network in downtown Toronto to evaluate summer time morning peak traffic emissions during weekdays for carbon monoxide and nitrogen oxide. The model predicted results are validated against sensor observations with a reasonably good fit. Availability of local estimates of ambient concentration is useful for accurate comparisons of total predicted concentrations with observed concentrations. Both predicted and observed concentrations are significantly smaller than the National Ambient Air Quality Objectives established by Environment Canada. Sensitivity analysis is performed on a set of input parameters and horizontal wind speed is found to be the most influential factor in pollutant dispersion.

Infrastructure deterioration models are an integral part of asset management. Deterioration models are used to predict future asset condition and to estimate funding requirements. The purpose of this research is to develop a framework to create infrastructure deterioration models. An overview of the various types of deterioration models is included, presenting the advantages and disadvantages of each type. Existing deterioration model frameworks are also considered. A deterioration modelling framework is then proposed. The selection of the model type, calibration and validation is presented. The framework is then applied to two case studies. The first case study involves a comparison of three pavement deterioration models, created for the City of Oshawa for use in their asset management system. The second case study involves modelling sewer deterioration. This model has been developed to explore the relationship between age, material and deterioration in trunk sewers.

The focus of this project is highway construction zones and the impact of lane closures on traffic. By measuring throughput (the number of vehicles that can flow through a work zone in a given amount of time), analysis was performed to determine the factors that affect this value. Successful data collection uncovered a paradox. Often work zones would see no queuing, indicating the lane could have been closed for longer hours. Alternatively sites with forced flow allowed fewer vehicles through the site than expected, indicating that the design value is too high, requiring shorter lane closure hours. A generic model for throughput was developed as well as a better functioning highway specific model. In addition to these models, this project developed a spreadsheet-based form of analysis named SZUDA (Simplified work Zone User Delay Analysis) that determines queue length and approximate costs associated with road user delay.

Pore pressure exists ubiquitously in the Earth’s subsurface and very often exhibits a cyclic loading on pre-existing faults due to seasonal and tidal changes, as well as the impoundment and discharge of surface reservoirs. The effect of oscillating pore pressure on induced seismicity is not fully understood. This effect exhibits a dynamic variation in effective stresses in space and time. The redistribution of pore pressure as a result of fluid flow and pressure oscillations can cause spatial and temporal changes in the shear strength of fault zones, which may result in delayed and protracted slips on pre-existing fractures. This research uses an experimental approach to investigate the effects of oscillating pore pressure on induced seismicity. With the aid of geophysical techniques, the spatial and temporal distribution of seismic events was reconstructed and analysed. Triaxial experiments were conducted on two types of sandstone, one with low permeability (Fontainebleau sandstone) and the other with high permeability (Darley Dale sandstone). Cyclic pore pressures were applied to the naturally-fractured samples to activate and reactivate the existing faults. The results indicate that the mechanical properties of the sample and the heterogeneity of the fault zone can influence the seismic response. Initial seismicity was induced by applying pore pressures that exceeded the previous maximum attained during the experiment. The reactivation of faults and foreshock sequences was found in the Fontainebleau sandstone experiment, a finding which indicates that oscillating pore pressure can induce seismicity for a longer period of time than a single-step increase in pore pressure. The corresponding strain change due to cyclic pore pressure changes suggests that progressive shearing occurred during the pore pressure cycles. This shearing progressively damaged the existing fault through the wearing of asperities, which in turn reduced the friction coefficient and, hence, reduced the shear strength of the fault. This ‘slow’ seismic mechanism contributed to the prolonged period of seismicity. This study also applied a material forecast model for the estimation of time-to-failure or peak seismicity in reservoir-induced seismicity, which may provide some general guidelines for short-term field case estimations.

In this thesis, seven multi-sensor data fusion based estimation techniques are investigated. All methods are compared in terms of their ability to fuse data from loop detectors and Bluetooth tracked probe vehicles to accurately estimate freeway traffic speed. In the first case study, data generated from a microsimulation model are used to assess how data fusion might perform with present day conditions, having few probe vehicles, and what sort of improvement might result from an increased proportion of vehicles carrying Bluetooth-enabled devices in the future. In the second case study, data collected from the real-world Bluetooth traffic monitoring system are fused with corresponding loop detector data and the results are compared against GPS collected probe vehicle data, demonstrating the feasibility of implementing data fusion for real-time traffic monitoring today. This research constitutes the most comprehensive evaluation of data fusion techniques for traffic speed estimation known to the author.

Insulating historic masonry buildings will improve thermal performance. However, heritage requirements often limit the addition of insulation to the interior surface. This can lead to colder and wetter walls. Freezing temperatures coupled with high moisture levels in the brick leave the walls susceptible to frost damage. Current retrofit designs attempt to control condensation of interior moisture. However, these designs do not consider exterior moisture sources, nor do they allow for interstitial moisture to be easily removed. This thesis presents an innovative, ventilated masonry retrofit that utilizes drainage and drying to address moisture accumulation issues. Computer simulations are used to assess and compare the hygrothermal performance of typical and ventilated masonry retrofits. The results show masonry moisture contents are reduced when ventilation drying is provided. Further, these simulations show it may be possible to increase thermal insulation levels in historic masonry buildings without damaging the very façade to be preserved.

In this thesis, seven multi-sensor data fusion based estimation techniques are investigated. All methods are compared in terms of their ability to fuse data from loop detectors and Bluetooth tracked probe vehicles to accurately estimate freeway traffic speed. In the first case study, data generated from a microsimulation model are used to assess how data fusion might perform with present day conditions, having few probe vehicles, and what sort of improvement might result from an increased proportion of vehicles carrying Bluetooth-enabled devices in the future. In the second case study, data collected from the real-world Bluetooth traffic monitoring system are fused with corresponding loop detector data and the results are compared against GPS collected probe vehicle data, demonstrating the feasibility of implementing data fusion for real-time traffic monitoring today. This research constitutes the most comprehensive evaluation of data fusion techniques for traffic speed estimation known to the author.

The main objective of this study was to quantitatively explore the connection between network structure and network effect and its impact on transit usage as seen through the real-world experience of the Toronto and Melbourne transit systems. In this study, the comparison of ridership/capita and mode split data showed that Toronto’s TTC has better performance for the annual data of 1999/2001 and 2006. After systematically investigating travel behaviour, mode choice factors and the various evidence of the network effect, it was found that certain socio-economic, demographic, trip and other design factors in combination with the network effect influence the better transit patronage in Toronto over Melbourne. Overall, this comparative study identified differences that are possible explanatory variables for Toronto’s better transit usage as well as areas where these two cities and their transit systems could learn from one another for both short and long term transit planning and design.

The analysis of reinforced concrete structures under blast and impact loads is an area of research that has become increasingly relevant in recent years. Complex hydrocodes are typically used for impact analyses, although single-degree-of-freedom methods have also been developed. There are a number of disadvantages associated with both methods, and the Canadian Nuclear Safety Commission (CNSC) is looking for a tool that can be used in conjunction with hydrocodes to analyze hard and soft missile impacts, with target damage ranging from flexural cracking to perforation. The VecTor programs, a suite of nonlinear finite element programs developed at the University of Toronto for the analysis of reinforced concrete structures, can potentially be developed into such tools. The analytical work done in this study serves to investigate the current impact and impulse loading analysis capabilities in VecTor2 and VecTor3, and to identify areas where work should be focused in the future.

The main objective of this study was to quantitatively explore the connection between network structure and network effect and its impact on transit usage as seen through the real-world experience of the Toronto and Melbourne transit systems. In this study, the comparison of ridership/capita and mode split data showed that Toronto’s TTC has better performance for the annual data of 1999/2001 and 2006. After systematically investigating travel behaviour, mode choice factors and the various evidence of the network effect, it was found that certain socio-economic, demographic, trip and other design factors in combination with the network effect influence the better transit patronage in Toronto over Melbourne. Overall, this comparative study identified differences that are possible explanatory variables for Toronto’s better transit usage as well as areas where these two cities and their transit systems could learn from one another for both short and long term transit planning and design.