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Infrastructure failure causes the loss of human lives and high socio-financial costs. Due to the continuous aging of infrastructure, a proper structural health monitoring (SHM) system is required to ensure the safety of structures and reduce repair costs through the early detection of structural damage. Existing visual inspection methods are not reliable due to the low frequency of inspection, subjective evaluation of structural damage, and vulnerability of inspectors’ safety, along with high costs. Traditional damage detection methods have similar limitations, since they require a large number of sensors to monitor large-scale infrastructure and involve high levels of uncertainty due to environmental noises and sensor malfunctions. Computer vison techniques have been implemented to overcome the limitations mentioned above, relying on image processing algorithms to extract damage-sensitive features. However, it is very difficult to extract a robust damage-sensitive feature. To resolve this limitation, I developed two deep learning-based damage detection methods using computer vision. The first method is a hybrid pixel-level crack segmentation and quantification method for complex cracks on rough scenes. The developed hybrid method provides robust damage detection for images, which addresses the uncertainties of traditional approaches. The second method is a real-time semantic transformer representation network (STRNet) for crack segmentation. The proposed STRNet can process 49 images per second with a mean intersection over union score of 92.6, which represents state-of-the-art performance in this area when it comes to accuracy. Using advanced deep learning methods and computer vision for damage detection still requires a great number of cameras to monitor large-scale infrastructure, which can be expensive. Consequently, another achievement of this thesis is that I developed an autonomous flight method using unmanned aerial vehicles (UAVs) for SHM purposes. Some cri

This research involved investigating the effects of a First Nations school’s indoor environmental quality on health and human performance. This research project was con-ducted in partnership with the Sagkeeng First Nations community in Manitoba. The in-door environment of a school building is a complex system involving many parameters that may have an impact on indoor air quality and thermal comfort. Air quality in schools depends strongly, on one hand, on the interaction between the building and its outdoor environment, and, on the other hand, on the way a building is used, operated, and maintained. School buildings should use a properly designed heating, ventilation, and air conditioning system that maintains an adequate supply of cleaner air and sets up optimal heating. This study provided empirical evidence to the claim that First Nations schools can be exposed to adverse indoor environmental conditions based on the school studied in this research. The evidence was based on objective measurements of indoor environmental quality and a subjective survey conducted among the school’s teachers. In addition, a series of statistical analyses in Statistical Package for Social Science 25 and Excel were performed to examine the associations between building characteristics, indoor environmental parameters, and teacher's health. Finally, thermal imaging was conducted to check the thermal performance of the building envelope. The physical measurement campaign consisted of measuring the indoor environmental parameters: indoor air temperature, relative humidity, and carbon dioxide concentrations in 9 classrooms. Consequently, along with the comprehensive physical measurements, fundamental knowledge, of the building and its systems was also needed to identify principal factors that adversely affect indoor school environments. On-site continuous records of air temperature showed underheating and overheating of the school during the year. Air temperatures as high as 35.1°C (wint

Hempcrete is a particularly promising lightweight, porous, and breathable biocomposite material that has the potential to significantly reduce the embodied energy related to the construction of buildings while improving their indoor air quality. However, hempcrete’s properties and performance depend on various parameters such as ingredient amounts, binder type, hurd characteristics (e.g., particle size and porosity), and the amount of water. Therefore, there is a great need for research that will focus on the development and production of hempcrete mixes and materials using local, Canadian resources. This research study characterizes the physical, microstructural, and mechanical properties of nine hempcrete mixes developed using lime and eco-friendly pozzolans such as metakaolin, crushed brick, and natural hydraulic lime in varying relative proportions. Furthermore, it compares three different types of hempcrete walls against the conventionally insulated walls of a single-story house located in Winnipeg, using S-Timber 2019 software. The microstructure analysis of hurd particles showed their porous nature. The dry densities of all design mix range from 294.59 kg/m3 to 399.68 kg/m3, with the majority (⁓73%) falling between 320 kg/m3 and 370 kg/m3. The average compression strength of the developed samples ranged between 0.11 MPa and 0.51 MPa, whereas the average splitting tensile strength ranged between 0.010 MPa and 0.0348 MPa. The results show a positive correlation between the hempcrete’s mechanical properties and density, mainly, in the case of compressive strength. The microstructure analysis of all the design mixes exhibit adequate adhesion at the interface, and present high carbonation with some hydrates. The main findings suggest that locally sourced metakaolin and crushed brick are excellent alternatives to the expensive, imported hydraulic lime. The modeling analysis indicates an increase in the dead loads and foundation sizes due to hempcrete infill compare

Hempcrete is a carbon-negative, non-toxic, breathable, and biodegradable building mate-rial. Nevertheless, the utilization of hempcrete in the construction industry remains low, mainly due to the high-variability of hemp-lime composites. Therefore, the development of locally available products, standards, and best practice guidelines requires comprehen-sive experimental tests and analysis to obtain reliable information about the material’s thermophysical performance. The aim of this study was twofold: (1) development of hempcrete infill “wall” formula with excellent thermal properties using locally sourced materials; and (2) numerical investigation of the long-term hygro-thermal performance of hempcrete wall types that satisfy current National Energy Code of Canada for Buildings. The thermal properties of the hempcrete were obtained with heat flow meter Fox314, whereas the hygrothermal analysis of wall assemblies was performed using WUFI soft-ware. The densities of hempcrete samples produced in this study show excellent con-sistency, ranging from 298.55 kg/m3 to 318.05 kg/m3, with the average density of all sam-ples is 306.13 kg/m³. Furthermore, the average thermal conductivities of all the samples range from 0.081 W/mK to 0.089 W/mK, with a standard deviation of 0.004–0.007 indi-cating consistency in the results. The results of the modeling analysis show that the aver-age water contents in the mass percent of both wall assemblies under all four cases are significantly below the 20 mass-percent. Nevertheless, on average, the base wall has 36% to 54% higher water content than the multilayer wall throughout the simulation period. Moreover, RH profiles of both walls have regular patterns of seasonal fluctuation that gradually decrease over time, and especially of the multilayer wall, which under all sce-narios, has lower RH compared to the base wall. The multilayer wall performs better and exhibits lower annual heat flow than the base wall under all cases, and in parti

Extracting heat from a sewage pipe through a typical horizontal ground heat exchanger has recently been introduced as a renewable energy alternative to reduce fossil fuel usage. This paper presents a novel design for a ground heat exchanger that extracts heat from the surrounding soil and sewage within the pipe while simultaneously being carried to a wastewater treatment plant. This research focuses on the long-term efficiency of the system under transient conditions in a cold climate. A numerical model using COMSOL Multiphysics was developed to verify the sustainability of the system for over 25 years. The model used constant inlet fluid temperatures to evaluate heat by conductive and convective heat transfer mechanisms within the pipe and surrounding soils by considering phase change of pore water. The results showed, by adopting the operation strategy proposed in this study, a maximum temperature change in the surrounding soil adjacent to the heat extraction system over 25 years was 0.10°C during the heating season in Winnipeg, Manitoba. The horizontal distance at which the heat extraction system did not show an impact on temperature change of adjacent soil was determined at 4 meters. Critical parameters in this evaluation were system depth, sewage level, and the high-density polyethylene pipe thermal properties. The sustainability of the system was not affected by the system depth due to thermal balancing between climatic, subsurface and sewage heat fluxes. Sustainable behavior was achieved at 50% and 75% of sewage pipe capacity. The effect on thermal performance from the high-density polyethylene pipe thermal properties was deemed insignificant.

Velocity and turbulence measurement uncertainty using acoustic methods in highly energetic river flows are quantified by performing measurements to isolate specific error terms, and by applying analytical methods and statistical analysis to recorded data. A MATLAB code is developed for motion compensation and data filtering. The methodology adopted is to perform acoustic measurements in a controlled laboratory setting and in an energetic river test site at Reynolds number from 0.05x10^7 to 3x10^7. As the performance of hydrokinetic turbines operating in highly energetic flows depends on localized velocity and turbulence parameters, uncertainty analysis contributes to improving river site characterization for the marine industry. The uncertainty analysis applies to acoustic instruments deployed from a stationary platform, a moving vessel, or alternatively, suspended in the water column via a cabling system in energetic river flows, which are typical configurations required by this industry. A methodology to conduct measurement and estimate the uncertainty is presented for these three configurations. The uncertainty analysis results are dependent on multiple inputs which are processed using algorithms made accessible online. A typical result of the error analysis at a velocity of 1.10 m/s a maximum deviation of 23% is observed between the measured and expected streamwise velocity at an instrument pitch angle of 45 degree. After implementing angle compensation the uncertainty reduces to 4%. Furthermore, by applying an IMU correction algorithm, the maximum velocity error is reduced by 29.3% and the turbulence by 77.0% after applying the motion compensation to river surface measurements. Processed flow results from an energetic river test site show the ADV overestimate streamwise mean time-averaged velocity and underestimate turbulence intensity measurements with an average of 4.6% and 27.4%, respectively.

There is little empirical evidence in literature on the performance of green residential buildings, even more limited is the indoor environmental quality (IEQ) performance of green low-income residential buildings. This is particularly interesting because residents of low-income housing are exposed to higher levels of indoor pollutants. To address the lack of empirical evidence on the indoor environmental quality of green low-income housing, this study utilised a mixed-method approach to evaluate IEQ of 17 green low-income single attached family houses in Brandon, Manitoba, Canada. Snapshot physical measurements took place in 17 single family attached low-income housing clustered into four blocks over two seasons: the fall of 2016 and winter of 2017. The indoor physical environment was monitored with sensors in three sampling spaces per apartment; while a paper-based questionnaire was used to assess occupants’ satisfaction with their indoor environment. Moreover, long-term evaluation of two selected apartments was carried out to elucidate the hourly variation of thermal comfort and indoor air quality. The long-term data showed that concentration levels peaked in the mornings and evenings during weekdays for the most pollutants. The comparison of the snapshot fall to winter data revealed that indoor air quality levels in the fall season were lower compared to the winter except particulate matter (PM). Same result was reported for the long-term evaluation. The Wilcoxon signed rank test showed that there were statistically significant differences in relative humidity (RH), temperature, carbon dioxide, carbon monoxide, particles smaller than 2.5 µm (PM2.5), total volatile organic compound (TVOC) and background noise between the two seasons. Further, pertaining to the long-term evaluation, statistical significant differences were observed in concentration levels (i.e. CO, PM, and RH) between weekdays and weekend during the fall period. During the winter period, statistic

Lightweight construction gained popularity due to many advantages, such as faster construction, higher design flexibility, and cost-efficiency. However, lightweight façade systems raise concerns over indoor thermal comfort and increased space energy use due to the lack of thermal storage properties. Phase Change Materials (PCMs) can enable better control of thermal performance by storing excessive heat during the melting phase and releasing this heat during the freezing phase. Nevertheless, PCMs are emerg-ing technology at a high cost. Therefore, an efficient and cost-effective application of PCMs typically requires customized solutions based on detailed numerical analysis. Building energy modeling (BPS) is a robust technique that provides a pathway to test, analyze, and optimize various energy efficiency measures and technologies. The Stanley Pauley Engineering (SPEB) building used as a case study was still under the construction when this research study was conducted. Therefore, to have more confidence in the predictions of the models developed in EnergyPlus its predictions were compared against the models developed in IES by the company Stantec. Approximately 2.1% and 1.7% discrepancy between the predictions of the total energy consumption and energy use intensity, respectively demonstrate good agreement between the two models. There is a limited number of BPS tools capable of simulating the phase change phenomena and in particular those able to model the hysteresis effect. As of version 8.9, EnergyPlus can model the hysteresis effect. The results show a discrepancy between the hysteresis and enthalpy method that may vary considerably with the intensity and duration of the solar radiation received by the location with PCM. Additionally, The PCM improved energy and thermal performance of the investigated spaces by achieving heat-ing and cooling energy savings ranging from 1.9% to 11.4% and 5.5% to 37.4%, respectively as well as reducing the discomfort hours from 1

Building Information Modeling (BIM) is a process that provides architecture, engineering, and construction (AEC) professionals the digital twin of physical and functional characteristics of the actual facility that paves the way for more efficiently design, construct and operate. The data volume achieved with the BIM process has the means to coalesce, become information, and opens the doors for the analyzing as never before for the built environment. Although creating a 3D visual representation of the facility is the manifestation of changing ways of working, BIM is the management and analysis of information through the life cycle of the facility. The BIM process can be used to facilitate decisions at the early design stage that have a direct effect on the energy efficiency of buildings. The impact of the two main factors, shape and orientation of the buildings on energy consumption was analyzed and discussed to determine the optimal solution in cold climates. The results for the energy consumption have been presented with the combination of six building shapes with eight orientations, and the study showed the possibility to reduce the energy intensity by approximately 20%. The fourth dimension of the BIM process is the intelligent linking of model elements with time and scheduling related information for monitoring progress of construction activities and improves project management. A data center building has been used as a case study to present the custom-developed add-in for the construction progress visualization method. The method developed made it possible to monitor the progress of the individual model elements for the various states of progress. Beyond the use of BIM during design and construction, there is much to gain in the long run throughout the facilities lifecycle, and the impact of the accumulated information with BIM transcends these phases. Three approaches for the integration of the sensor database with the BIM process have been demonstrated by us

Passive energy systems hold the potential to achieve high heating and cooling energy savings when integrated with building systems. Proper integration of these systems can contribute to the efficiency of heating and cooling energy uses as well as improvement of daylight in nearly any buildings, and in particular in highly-glazed and lightweight buildings, which are becoming popular worldwide. This study aims to investigate the optimal application of Phase Change Materials (PCMs), natural ventilation, and solar shading systems in maximizing energy conservation and comfort level in a highly-glazed study room located in a cold climate. For this purpose, a whole-building energy model of the Stanley Pauley Engineering Building (SPEB) is developed using a building performance simulation (BPS) tool, EnergyPlus. In order to gain confidence in the SPEB model, its outputs are compared against the SPEB model developed in an IES tool. The discrepancies of around 2.1% and 1.7% between the predictions of the total energy consumption and energy use intensity, respectively, suggest excellent agreement between the two models. Afterward, the SPEB model is used to examine, test, and analyze different design strategies for optimal application and use of PCM, shading, and natural ventilation. This is accomplished by using sophisticated Energy Management System (EMS) within the EnergyPlus as well as coupling the SPEB model with the programing language MATLAB. A case study in this research was a graduate study room located on the top floor of the SPEB. The study concluded that the integration of PCM28 with the hydronic radiant floor system significantly reduces the annual heating and total energy demand by around 24% and 19%, respectively. Also, the economic analysis showed that the application of PCM-enhanced radiant floor, when incorporated with a thin layer of insulation, could recover the initial investment within 5 to 10 years, which indicates a high economic value and appears to be

The aging of infrastructure in North America has pushed the investigations of new structural health monitoring (SHM) solutions. Visual inspections are commonly performed for SHM, but they can be extensive and often rely on the inspector’s experience. Complex, expensive sensor setups are also used for SHM. Computer vision provides efficient alternatives to these procedures, allowing low-cost, remote data acquisition. In this study, a Faster Region-based Convolutional Neural Network (Faster R- CNN)-based damage detection method coupled with an inexpensive depth sensor is proposed. A database composed of 1091 images with resolution of 853 1440 pixels, labeled for volumetric damage is developed and the deep learning network is modified, trained, and validated using the proposed database. The output from the Faster R-CNN is utilized as a starting point to identify the surface of the member, segment and quantify damage. The methodology is validated using a polystyrene test rig with damage of known volumes, as well as reinforced concrete members. The trained Faster R-CNN presented average precision (AP) of 90.79%. Volume quantifications show mean precision error (MPE) of 9.45% when considering distances from 100 cm to 250 cm between the element and the sensor. Also, MPE of 3.24% was obtained for maximum damage depth measurements across the same distance range. Damages are detected, segmented, and quantified regardless of the distance between the member and the sensor, which allows the system to be implemented in unmanned vehicles for safe data acquisition in hazardous scenarios.

Energy performance of basement structure of Stanley Pauley Engineering Building of the University of Manitoba is studied. A total of eighteen soil samples were collected at different locations and depths based on the geological profile of the ground. Thermal properties of the collected soil samples were obtained in the Geotechnical Engineering Lab using a KD2-Pro device by Decagon. Heat losses are predicted for the below-grade envelope by considering thermal properties of soil and building materials. For that purpose, a numerical model was created based on construction drawings using COMSOL Multiphysics software. The applied approach includes the calculation of heat loss due to heat transfer through conductive mechanism, also it considers phase change of water during freezing/thawing cycles. Different alternatives for heat preservation are suggested and compared in terms of economic effectiveness.

In this research, the piezoelectric nanocomposites gum sensors are fabricated by direct mixing of constituent materials (ZnO and CNTs) with bubble gum. SWCNTs and MWCNTs are used to compare the effect of their addition to the gum sensor. The mixing process is achieved by multiple folding and stretching of the gum after the addition of the constituent materials .The constituent materials properly adhered to the surface of the gum. The piezoelectric calibration shows that the addition of SWCNTs gives higher piezoelectric coefficient as compared to the MWCNTs addition. The effects of two parameters (temperature and strain) are considered on the sensor resistance to further evaluate the piezoelectric performance of the gum sensors. ZnO-MWCNTs gum sensor has higher resistance as compared to ZnO-SWCNTs gum sensor, which implies that ZnO-SWCNTs gum sensor has a better piezoelectric performance. The mechanical strengths results show that ZnO-SWCNTs gum sensor has the best mechanical strength.

Based on the Wolff’s law, mechanical stimuli affect bone strength and therefore affect its bone mineral density (BMD) distribution. There are a number of mechanical stimuli, for example, von-Mises stress, tensile stress, compressive stress, and strain energy density (SED). It is not clear which is the dominant stimulus. The objective of this study was to determine the dominant stimulus that regulates femur BMD by iterative finite element simulations of Wolff’s law. Four finite element models of the same femur initially had the same uniform BMD and were affected by the same loading. In the iterative simulations, BMD in each model was ‘remodeled’ by one of the four stimuli. The results showed BMD distribution in the finite element model regulated by SED was closest to QCT measured BMD, followed by von-Mises stress, then tensile and compressive stress. It is thus concluded that SED was the dominant stimulus in regulating femur BMD.

Detailed domestic stock energy models can be used to help formulate optimum energy reduction strategies. However, there will always be considerable uncertainty related to their predictions due to the complexity of the housing stock and the many assumptions required to implement the models. This paper presents a simple Monte Carlo (MC) model that can be easily extended and/or transformed in relation to data available for investigating and quantifying uncertainties in both the housing stock models predictions and scenario assumptions. While 90% of the MC model predictions fell within a range which is +/- 19% the mean value, 50% of them were within +/- 8% of the mean. The findings suggest that the uncertainties associated with the model predictions and scenario assumptions need to be acknowledged fully and - where possible - quantified as even fairly small variability in the influential variables may result in rather large uncertainty in the aggregated models prediction.

This thesis introduces an approach to study the effect of battery parameters on the stability and the response dynamics of a grid-connected battery energy storage systems (BESS). In this study, averaged-value modeling technique is used to formulate a grid-connected battery energy storage system. State equations are used to study the impact of battery parameters on system’s performance. Furthermore, a fully-detailed transient model of a grid-connected battery energy storage system is developed in an electromagnetic transient simulation software (PSCAD/EMTDC). This model includes BESS components such as batteries, power-electronic converters, voltage source converters (VSCs) and BESS’ controllers of all levels. This research investigates the impact of battery parameters such as the internal resistance and the state of charge on the stability of BESSs and determines a stability region in which BESSs perform normal operation. It is shown that in an unstable region, the BESS is incapable of delivering the desired power to the grid. This research is also focused on improvement of BESS dc-link voltage regulation upon a change in the BESS power set-point and also BESS response dynamics in the grid. To analyse the stability region and improve the response dynamic of a BESS, an alternative configuration of battery cells in a battery bank is proposed and evaluated. Control systems of a BESS are also investigated as key contributors to the response dynamics of the system. A feed-back/feed-forward control strategy is proposed and evaluated to improve the response dynamic of a BESS. The outcomes of this research are useful in the design stages of a BESS where response dynamics of an energy storage system is important. In addition, since the internal resistance of a battery increases by aging of battery cells, studying the effect of battery aging on BESS stability and operation is useful in annual maintenance of a commissioned BESS in the grid.