Browsing by Department "Department of Civil Engineering"
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Item A comparison of 4th and 5th generation thermal networks with energy hub(Energy, 2025) Lédée, François; Evins, RalphState-of-the-art thermal networks are key to address decarbonization of the heating and cooling in buildings. Energy Hub considers synergy between elements and allows rapid comparison between district energy systems incorporating 4th generation (4G) and 5th generation (5G) thermal networks at early-stage designs. To better understand and quantify the differences between systems based on 4G and 5G, a mixed-integer linear model distinguishing specific features of both technologies is implemented. We find systems relying on 5G to perform environmentally and financially better than with 4G generation over a wide set of scenarios. This is due to the warm/cold coupling characterizing 5G technologies. Systems based on 5G can also reduce its carbon emissions more than those with 4G. However, performances with both technologies appear sensitive to the topology and location of central energy station.Item A Machine learning approach to surrogate development for Canadian power system toward decarbonization(2025) Jahangiri, Zahra; McPherson, MadeleineAs Canada works toward a net-zero emissions economy by 2050, understanding optimal strategies for power sector expansion and decarbonization is crucial. To address this challenge, this thesis uses machine learning, specifically neural networks, to conduct a detailed sensitivity analysis, uncertainty analysis and provincial analysis. We developed a supervised learning surrogate model for a capacity expansion model, reducing computation costs by five orders of magnitude. Using this model, we perform sensitivity analysis to evaluate how changes in input variables, such as generation technology capital costs, electricity demand, and carbon taxation, impact model outputs. Additionally, we perform an uncertainty analysis to explore the behavior of the model’s outputs in response to variability, uncertainty, and potential fluctuations in these inputs. This approach allows for a more advanced exploration of the design options for Canadian national and provincial power systems. This model reduces computational time from 11–72 hours to milliseconds with minimal resource requirements. The computational efficiency enables integration into various platforms and tools for decision-making. It’s essential because it makes the model accessible to users who may not have technical expertise, such as stakeholders and decision-makers. By reducing the need for extensive technical resources, these users can leverage the model's outputs to inform real-time decisions without relying on advanced computing power. The study in chapter 4, uses unsupervised machine learning and statistical techniques to identify key factors influencing system outcomes. These include the increasing importance of gas combined cycles in a low-carbon system and the strong potential of wind energy in Canada's decarbonization. Our methodology identifies key patterns in power system outcomes. For example, it uncovers critical correlations like that between variable renewable energy capacity factors and transmission expansion. The results in chapter 5, underscore the importance of flexible grid systems and offering a province-specific roadmap. This thesis introduces the use of machine learning for large-scale energy system planning. It contributes by developing analytical frameworks for model usage and offering a detailed discussion of the results. These insights provide a foundation for strategic planning and policy formulation, particularly in supporting Canada’s transition to a sustainable energy future.Item A meta-analysis of the schematic design process of deep retrofit projects(Energy & Buildings, 2024) Gutland, Michael; Munro, Katelyn; Cant, Kevin; Kotha, Rajeev; Evins, RalphDeep retrofits of the existing building stock will be necessary to meet global emissions reductions targets. One building archetype, low-rise MURBs have been neglected in terms of research and funding for deep retrofits. A meta-analysis was conducted that compares and contrasts the schematic design approach taken for six such buildings in British Columbia which are scheduled to undergo deep retrofits with the goal of reducing GHG emissions by 80%. The analysis showed that design teams had converged toward common solutions for each building while achieving the GHG reduction target. The recommended measures include electrification of space and domestic hot water heating, adding insulation through overcladding, air sealing, ventilators for each unit, and double pane windows. A life cycle cost analysis showed that the economic viability of deep retrofits were dependent on energy price forecasts, capital cost reductions through market forces and transformation, or incentives cover the non-monetizable co-benefits of deep retrofits such as improved resiliency to climate-change or reducing overheating and air quality risks. The meta-analysis can help to streamline the early-stage and schematic design process for such buildings, which is critical to increasing the retrofit rate. This process could be replicated for other building types and construction archetypes.Item A novel approach to life cycle assessment for early-stage design of low-carbon buildings(2024) Torabi, Mahsa S.; Evins, Ralph; Bristow, DavidBuilding design processes are dynamic and complex. The context of a building project is manifold and depends on the context, climatic conditions and personal design preferences. Many stakeholders may be involved in deciding between a number of possible designs defined by a set of influential design parameters. Building LCA is the state-of-the-art way to provide estimates of the building carbon content and environmental performance of various design alternatives. However, setting up a simulation model can be labour intensive and evaluating it can be computationally unfeasible. As a result, building simulations often occur at the end of the design process instead of being an influential factor in making early design decisions. Given this, the growing availability of machine learning algorithms as a potential method of exploring analytical problems has lead to the development of surrogate models in recent years. The idea of surrogate models is to learn from physics-based models, here a building LCA model, by emulating the simulation outputs given the simulation inputs. The key advantage is their computational efficiency in terms of accuracy and time. They can produce performance estimates for any desired building design within seconds, while in physics based modeling hours maybe needed to run the analysis. This shows the great potential of surrogate modelling to innovate the field. Instead of only being able to assess a few specific designs, entire regions of the design space can be explored, or instant feedback on the sustainability metrics of building can be given to architects during design sessions. This PhD thesis aims to advance the young field of building LCA surrogate models. It contributes by: (a) developing a parametric model capable of whole design space exploration, to solve the issue of lack of building LCA data and (b) deriving surrogate models that can process dataset of building carbon results and estimate the associated impact on building performance. The result of this study can assist architects, engineers, researchers and policy makers both by provided results and also the proposed methodology to integrated LCA in strategic and early-stage decision making in the design process.Item A numerical modelling framework for vibration assessment of timber composite floors in mass timber buildings(Journal of Building Engineering, 2025) Cheraghi-shirazi, Najmeh; Creagh, Ariel; Setiawan, Fendy; Parra, Roger; Khoshkbari, Parham; Malek, SardarTimber composite floors are vulnerable to human-induced vibrations due to their low weight and long spans used in office buildings. Introducing concrete into timber panels is a common approach to enhance the vibration performance of long-span timber floors. While the effects of certain parameters on the vibration performance of timber composite floors have been extensively studied in laboratory settings, and some numerical models have been proposed, predictions are often sensitive to variations in input parameters. Many of these numerical models are "calibrated" using test data from specific experiments (e.g., connection or 4-point bending tests) conducted on specific laboratory floors and may not be applicable to real building floors. This paper presents a comprehensive physics-based finite element (FE) modelling framework aimed at accurately predicting the vibration characteristics (i.e. frequency and acceleration) of long-span Timber Concrete Composite (TCC) floors and understanding the vibration response of composite floors. The accuracy of the approach is examined by comparing modelling predictions against test data for a 9 m (?30 ft) composite floor within a real office building. The application of analytical equations for predicting floor static stiffness, and frequency, and limitations of simple approaches suggested in some standards are discussed. The developed framework is shown to be a valuable tool for benchmarking the impact of various boundary conditions and input parameters recommended in design guides. Specifically, the effects of key parameters, including the dynamic modulus of concrete, shear stiffness of glulam beam-to-CLT and CLT-to-concrete connectors, and the stiffness of beam-to-beam connections are demonstrated and discussed.Item A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction(Renewable and Sustainable Energy Reviews, 2024) Hou, D.; Evins, RalphBecause of their low computational costs, surrogate models (SMs), also known as meta-models, have attracted attention as simplified approximations of detailed simulations. Besides conventional statistical approaches, machine-learning techniques, such as neural networks (NNs), have been used to develop surrogate models. However, surrogate models based on NNs are currently not developed in a consistent manner. The development process of the models is not adequately described in most studies. There may be some doubt regarding the abilities of such models due to a lack of documented validation. In order to address these issues, this paper presents a protocol for the systematic development of NN-based surrogate models and how the procedure should be reported and justified. The protocol covers the model development procedure sample generation, data processing, SM training and validation, how to report the implementation, and how to justify the modeling choices. The protocol is used to critically review the quality of NN-based SMs in the prediction of building energy consumption. Sixty-eight papers are reviewed, and details of the developed surrogate models are summarized. The reported developing procedures were evaluated using the criteria proposed in the protocol. The results show that the selection of the number of neurons is the best-implemented step with a justification, followed by the determination of model architecture, mostly justified in a discussion way. While greater focus should be given to sample dataset generation, especially input variables selection, considering independence check and clear report of model validation on training and test data. Also, data preprocessing is strongly recommended.Item Access to drinking water in low-and middle-income countries: monitoring and assessment(2020-09-02) Cassivi, Alexandra; Dorea, Caetano; Tilley, Elizabeth; Waygood, OwenLack of access to drinking water remains widespread as 2.1 billion people live without safely managed service that includes improved water sources located on premises, available when needed, and free from contamination. Monitoring global access to drinking water is complex, yet essential, particularly in settings where households need to fetch water to meet their basic needs, as multiple factors that relate to accessibility, quantity and quality ought to be considered. The overall objective of this observational study is to increase knowledge surrounding monitoring and assessment of access to drinking water supply in low-and middle-income countries. The dissertation was comprised of five manuscripts which address the objective using various approaches including systematic review (manuscript 1), secondary data analysis (manuscript 2), and primary data analysis (manuscripts 3-5) to gather evidence towards improving access to drinking water. Primary data were collected through a seasonal cohort study conducted in Southern Malawi that included 375 households randomly selected in three different urban and rural sites. Methods used included structured questionnaires, observations, GPS-based measurements, and water quality testing. Findings from this study highlight the importance of conducting appropriate assessment of household behaviours in accessing drinking water in view of improving reliability of the indicators and methods used to monitor access to water. Seasonal variations that may affect water sources' reliability and household’s needs should be put forward to improve benefits of improving access to water and sustainable health outcomes. Further to target reliable and continuous availability from an improved water source at proximity to the household, interventions should aim to ensure safe quality of water at the point of use for mitigating the effect of post-collection contamination, and ensure sufficient quantities of water to allocate for personal and domestic hygiene. Focusing on the benefits of improving access to water at the point of consumption is essential to generate more realistic estimations, suitable interventions and appropriate responses to need.Item Achieving safe free residual chlorination at point-of-use in emergencies: a modelling approach(2020-05-06) Wu, Hongjian; Dorea, CaetanoWhile free (breakpoint) chlorination is widely utilized in humanitarian water treatment, a main challenge limiting its effective application is in determining the initial dose to satisfy both health requirements and aesthetic considerations (i.e. taste and odour). International guidelines and studies showed varying recommendations for the initial chlorine dose and many did not consider chlorine decay during water transportation and storage for up to 24 hours. The main objective of this thesis is to develop a tool for humanitarian staff to accurately determine the initial chlorine dose for achieving free chlorine residual (FCR) objectives with the limited instrumentation and information in the field. The first manuscript included in the thesis gathered and evaluated seven basic chlorine decay models’ applicability in humanitarian treatment contexts. All seven models were found able to accurately describe chlorine decay in water representative of humanitarian treatment contexts with more than half of the regression resulted in R2 over 0.95. However, each model had its own limitations, which were discussed. The second manuscript involved conducting extensive chlorine decay tests in water with different characteristics, explored the relationships between the estimated chlorine decay constant and several water parameters including pH, turbidity, ultraviolet absorption at 254 nm wavelength (UVA254), temperature and 30-minute chlorine demand. It was found that the UVA254 of water followed linear and exponential relationships with the decay constant in Feben and Taras’s empirical model and that in the first order model respectively. Arrhenius-type relations were verified between the decay constant and water’s temperature. A model developed to predict FCR decay in water with known 30-minute chlorine demand accurately predicted FCR level in synthetic water (with humic acid being the main constituent) but underpredicted FCR decay in water with additional chlorine consuming matter. Further research on additional chlorine decay mechanisms are needed to expand the applicability of the model.Item Addressing technical barriers for reliable, safe removal of fluoride from drinking water using minimally processed bauxite ores(Development Engineering, 2018) Buckley, Heather L.; Molla, Nusrat J.; Cherukumilli, Katya; Boden, Kathryn S.; Gadgil, Ashok J.Throughout the developing world, over 200 million people drink groundwater containing fluoride concentrations surpassing the World Health Organization's maximum recommended contaminant level (WHO-MCL) of 1.5 mg F−/L, resulting in adverse health effects ranging from mottled tooth enamel to debilitating skeletal fluorosis. Existing technologies to remove fluoride from water, such as reverse osmosis and filtration with activated alumina, are expensive and are not accessible for low-income communities. Our group and others have demonstrated that minimally-processed bauxite ores can remove fluoride to safe levels at a fraction of the cost of activated alumina. We report results from testing for some technical challenges that may arise in field deployment of this technology at large scale, particularly in a sufficiently robust manner for application in development contexts. Anticipating possible modes of failure and addressing these challenges in advance in the laboratory is particularly important for technologies for vulnerable communities where the opportunity to re-launch pilot projects is limited and small failures can keep solutions from the people that need them most. This work addresses three potential technical barriers to reliable removal of fluoride from drinking water with bauxite ore from Visakhapatnam, Andhra Pradesh, India. We evaluate competition from co-occurring ions, adsorption reversibility, and potability of the product water with regards to leaching of undesirable ions during treatment with various adsorbent materials including raw and thermally activated bauxite, and synthetic gibbsite (a simple model system). Under the conditions tested, the presence of phosphate significantly impacts fluoride adsorption capacity on all adsorbents. Sulfate impacts fluoride adsorption on gibbsite, but not on either bauxite adsorbent. Nitrate and silicate (as silicic acid), tested only with gibbsite, do not affect fluoride adsorption capacity. Both thermally activated bauxite and gibbsite show non-reversible adsorption of fluoride at a pH of 6. Raw bauxite leached arsenic and manganese in a TCLP leaching test at levels indicating the need for ongoing monitoring of treated water, but not precluding safe deployment of bauxite as a fluoride remediation technology. Understanding these phenomena is crucial to ensure field deployment over large diverse geographical areas with aquifers varying in groundwater composition, and for ensuring that the appropriate engineering processes are designed for field implementation of this innovation.Item Advanced Cementitious Materials: Mechanical Behavior, Durability, and Volume Stability(Advances in Materials Science and Engineering, 2017-08) Yoo, Doo-Yeol; Banthia, Nemkumar; Fujikake, Kazunori; Borges, Paulo H.R.; Gupta, RishiItem Advances in reverse osmosis membrane engineering and alginate recycling: Covalent surface modifications for membrane performance and analysis of molecular changes and calcium crosslinker removal for alginate sustainable practices(2024) Rahmati, Negar; Buckley, Heather; Basu, OnitaThe increasing demand for potable water and the environmental impact of conventional plastics present significant global challenges that require innovative solutions. Addressing these issues effectively involves advancements in both water purification technologies and material sustainability. This thesis explores these challenges through two complementary projects: enhancing reverse osmosis (RO) membrane technology and improving the sustainability of alginate-based bioplastics. Reverse osmosis is a critical water purification technology used to produce fresh water from diverse sources, including seawater and brackish water. Central to this technology are RO membranes, which are predominantly made of polyamide. Despite their effectiveness, these membranes face performance limitations due to biofouling and chemical degradation. Biofouling, caused by microorganisms forming biofilms on the membrane surface, reduces water flux, increases operational pressure, and raises energy consumption. Chemical degradation affects membrane longevity and performance, leading to frequent cleaning and replacement. These issues contribute to significant economic costs and environmental waste. Chapter 1 explores the attachment of PEI-diazirine onto PET surfaces, with the hypothesis that successful cross-linking in these trials could also be applied to polyamide-based RO due to the reactivity between polyamides and carbenes. The objective was to develop a method for covalent modification of RO membranes using diazirine moieties, which could serve as a foundation for further functionalization. Covalent bonding offers advantages such as improved stability, durability, and compatibility, making it a favorable approach for RO membrane surface modification. In conclusion, the study introduced an innovative approach to enhancing the surface properties of RO polyamide-based membranes by incorporating covalently bonded diazirine-containing molecules. While direct evidence of covalent attachment was not confirmed, indirect observations—such as results from dye and water angle tests, DSC, and FTIR—support the presence of activated diazirine on the surface. These tests disproved the idea that diazirine molecules react exclusively with each other rather than with the surface. This groundwork sets the stage for future functionalization processes to impart foul-release properties to RO membranes. Chapter 2 focuses on advancing the sustainability of alginate-based bioplastics, particularly those derived from kelp. The study investigates the recycling potential of alginate, aiming to enhance sustainable practices. Recycling alginate helps preserve resources by reintegrating used materials into the production cycle, reducing the need for fresh raw materials. Additionally, recycling alginate-based products reduces waste volume, supports waste reduction goals, and minimizes the environmental impact of landfill disposal. The research in Chapter 2 builds on optimized sodium alginate extraction methods and evaluates the recycling potential of alginate films produced by these methods. It proposes and assesses a recycling protocol for its effectiveness in terms of yield and purity, focusing on calcium crosslinker removal and structural changes in alginate films. This study provides valuable insights into sustainable alginate recycling, promoting a circular economy by extending the life cycle of materials and reducing waste generation. Although each chapter addresses different material types and applications, they share a common theme: enhancing material performance while mitigating environmental impact. Improving RO membrane fouling resistance and chemical stability directly contributes to reducing waste and energy consumption in water treatment. For alginate bioplastics, optimizing recycling processes ensures effective material reuse, decreasing plastic waste and the demand for new raw materials. These projects reflect a broader commitment to sustainability by tackling critical issues in material performance and environmental responsibility. The outcomes from these chapters offer practical solutions that align with global efforts to conserve resources and minimize ecological footprints. Through the development of advanced membrane technologies and sustainable bioplastics, this thesis contributes to a more sustainable future, demonstrating that innovation in material science can drive significant improvements in both industrial applications and environmental stewardship. In conclusion, this thesis bridges the gap between technological advancement and environmental sustainability. By addressing the challenges of water purification and plastic waste management, it provides valuable insights and practical solutions that enhance material performance and promote a more sustainable approach to resource use.Item Advancing risk assessment of climate change and the resiliency of cities(2023-07-20) Viseh, Hiva; Bristow, David N.Climate change is widely acknowledged to have significant impacts on socio-ecological systems, affecting different regions to varying extents through shocks and stresses related to extreme weather events, sea level rise, and other climate-related changes. This highlights the critical role of adaptation measures in reducing the imposed cost of climate change by increasing urban resilience under a variety of likely climate change-induced scenarios while capitalising on the opportunities presented by a changing climate. The initial step in comprehending the importance of investing in adaptation measures, and subsequently implementing effective strategies in urban areas, is to disseminate information regarding the risks posed by climate change and cultivate awareness and understanding of these risks. While the magnitude of climate change impacts and their resulting socioeconomic consequences still remain uncertain, the increasing complexity and interconnection of diverse social and environmental systems have dramatically impacted our ability to foresee future-imposed threats from climate change. This PhD thesis aims to foster advances in risk perceptions of climate change by progressing risk assessment of potential hazards and changes imposed by climate change on urban areas, as well as by proposing new methods to assess the vulnerability and reliability of complex systems and networks that cities rely on under stresses and shocks, while using all available data and sources, and communicating the complex and multifaceted aspects of climate change in such a way that the data can be used practically in resilience planning and resource allocation. The proposed new methods are: using Euclidean distance in conjunction with the modified Mann-Kendall test to capture both the direction and magnitude of changes in climate data derived from a large number of models; using the Epps-Singleton test to compare climate change in neighbouring cities to see the degree to which it may be possible for adaptation plans to be similar; combining damage functions and probability bound analysis to estimate potential flood damage caused by different climate change-driven flood scenarios at a regional scale by measuring a fraction of the buildings while addressing often-missing uncertainty quantification in damage estimates; and estimating time to failure and repair time of complex systems and networks when dealing with uncertainty in input parameters as well as indeterminant functional dependency using imprecise probability analysis in a probability box approach. In addition, the introduced methods were used to assess the potential variations in diverse climate variables across Canadian cities; to estimate the impact of climate change on flood damage to residential buildings in Metro Vancouver, Canada; to quantify time to failure and repair time of power, water, and wastewater networks, and to calculate the time to failure that an average utility customer of a two-story office building may experience for different types of internal and external functional dependencies.Item Advancing risk assessment of climate change for the resiliency of the built environment: A multi-physical risk analysis(2025) Ryan, Bona; Froese, ThomasThe built-environment sectors in Canada are highly vulnerable to a wide range of climate-related risks through varying extent of stresses and shocks linked to extreme weather events and other climate-related changes. The impacts on assets are significant, with inflation-adjusted insured losses from environmental perils showing a rising trend, totaling $30 billion (2023 C$) over the past decade, not including the socioeconomic impacts of the resulting functional disruptions. These impacts underscore the key role of adaptation measures to reduce the costs of climate risks by enhancing the resilience of built assets under a variety of climate change scenarios. However, accelerating the adaptation of built assets to the mounting effects of climate change is complex and presents significant challenges for decision-makers. It requires extensive local data, involves uncertainty, and often relies on expensive, one-off contracts, if at all. This has impacted on our ability to foresee future-imposed climate risks in built-environment. This PhD thesis aims to enhance advances in risk analysis by proposing new methods in quantifying the vulnerability and reliability of building systems under stresses and shocks at asset level of resolution, as well as forecasting the potential changes in energy demand imposed by climate change on urban areas. The thesis presents four risk modeling approaches to assess the physical climate risks on the Canadian built assets and to provide reliable risk forecasting to improve the decision-making in asset operations. The first study presents a runtime-based degradation model using stochastic processes with random effects to assess climate change risks on HVAC systems. The proposed method captures the correlation between climate parameters and degradation rate of the units by leveraging runtime data and future climate projections. It quantified non-stationary changes in degradation rates over asset lifecycles and the functional degradation of filtration effectiveness in varying climates. The second study presents a meta-modeling approach using Response Surface Methodology (RSM) to assess moisture-related degradation risk of building envelopes in different ASHRAE climate zones. From this method, the resulting analytical functions can be used to compare the moisture performance of different enclosure solutions across various climate zones. The third study presents a degradation model using dynamic Bayesian approach that integrates condition-based degraded failure and faulty failure of building components under climate stress and shock. The method extends reliability analysis through an economic-based assessment to evaluate value-at-risk and optimal maintenance strategies for building assets. The fourth study applies a Monte-Carlo regression approach to explore the impact of climate change on energy demand in Victoria, Canada. This study adopted the established response function from literature and applied it to future climate projections in the city of Victoria to estimate the log electricity demand. The results can be used, at building level, for adaptation strategies and resource allocation, such as retrofitting action plan and building energy management. The thesis findings culminated in the development of the Resiliency Opportunity Assessment and Response (ROAR) Tool, funded by the Greater Victoria 2030 District Program. This high-level IT solution enables users to quickly assess climate-related risks and identify opportunities to improve resilience and to know where to prioritize responses by identifying low-cost, low-carbon options, as well as opportunities that warrant further investigation through detailed audits or studies. The tool features three modules—“stress” risks, “shock” risks, and “energy” risks—and contributes to the Canadian Sustainability Disclosure Standards (CSDS), as an enabler for ESG requirements to disclose climate risks under securities regulations. Overall, this research provides new insights, methods, and tools for minimizing climate risks and supporting evidence-based decision-making in the built environment.Item Advancing surrogate modelling for sustainable building design.(2020-09-14) Westermann, Paul W.; Evins, RalphBuilding design processes are dynamic and complex. The context of a building pro- ject is manifold and depends on the cultural context, climatic conditions and personal design preferences. Many stakeholders may be involved in deciding between a large space of possible designs defined by a set of influential design parameters. Building performance simulation is the state-of-the-art way to provide estimates of the energy and environmental performance of various design alternatives. However, setting up a simulation model can be labour intensive and evaluating it can be com- putationally costly. As a consequence, building simulations often occur towards the end of the design process instead of being an active component in design processes. This observation and the growing availability of machine learning algorithms as an aid to exploring analytical problems has lead to the development of surrogate mo- dels. The idea of surrogate models is to learn from a high-fidelity counterpart, here a building simulation model, by emulating the simulation outputs given the simula- tion inputs. The key advantage is their computational efficiency. They can produce performance estimates for hundreds of thousands of building designs within seconds. This has great potential to innovate the field. Instead of only being able to assess a few specific designs, entire regions of the design space can be explored, or instan- taneous feedback on the sustainability of building can be given to architects during design sessions. This PhD thesis aims to advance the young field of building energy simulation surrogate models. It contributes by: (a) deriving Bayesian surrogate models that are aware of their uncertainties and can warn of large approximation errors; (b) deriving surrogate models that can process large weather data (≈150’000 inputs) and estimate the associated impact on building performance; (c) calibrating a simulation model via fast iterations of surrogate models, and (d) benchmarking the use of surrogate-based calibration against other approaches.Item The all-you-can-eat economy: How never-ending economic growth affects our happiness and our chances for a sustainable future(World, 2020) Wilson, Eric; Mukhopadhyaya, PhalguniThis paper explores the relationship between energy consumption, economic growth, and life satisfaction and makes the case that economic growth as usual is no longer a desirable or sustainable policy goal. Historically, economic and social development go along with energy sector transformation and total energy use. As a country develops, its use of energy increases, resource consumption increases, population booms, life expectancy rises, and overall socio-economic outcomes are improved. One might deduce then, that life satisfaction is also tightly correlated to economic development and energy consumption, but is this the case? To answer this question, current academic literature and data on the relationship between energy consumption, GDP, and quality of life were explored. The review showed a weak relationship between GDP and quality of life, a saturation relationship between energy use and social returns (social returns increase with increasing energy use to a point), and a strong relationship between GDP and energy use. There have been high hopes that improvements in energy-efficient technology will reduce global aggregate resource consumption, however, there is a growing body of research to suggest the opposite is likely to occur due to ”rebound effects”. The major environmental issues of our time have been seen predominantly as issues to be solved through advancements in technology; however, it is the argument of this paper that they cannot be addressed from a purely technological standpoint. Of course, improving energy efficiency is an important factor, but we must not forget the equally important subject of human behavior and our addiction to continual economic growth. We must first address the human desire to consume resources in the pursuit of happiness and socio-economic status, and shift towards a mentality of sufficiency. Future research must demonstrate concrete examples of sustainable development and consumption, advance the discourse on how the individual can be part of the solution, and empower the implementation of sustainable government policy.Item An agent-based framework for prioritizing building retrofits(Journal of Building Engineering, 2025) Lari, Khosro; Cant, Kevin; Evins, RalphAs the urgency to address climate change grows, municipalities face the challenge of lowering carbon emissions from buildings, which account for a large portion of total emissions. However, many cities lack the tools and data required to develop effective policies. This study proposes a practical framework for solving this by creating a user-friendly dashboard tailored to the needs of decision-makers in municipalities. The framework analyses current energy consumption, carbon emissions and building characteristics by leveraging existing datasets such as energy assessment databases and the property tax databases. Decision-makers can visualize the potential impact of various retrofit alternatives using scenario analysis and policy simulation, anticipate future construction rates and analyze the embodied carbon impact. The framework provides insights into the current carbon status and targets and enables municipalities to effectively identify and prioritize climate solutions. This paper applies the framework to single-family houses in the City of Victoria, British Columbia, Canada, however its flexibility enables adaption to other contexts around the world. This study adds to the expanding discussion about municipal climate action by proposing a practical, comprehensive approach to inform policy decisions and expedite progress towards carbon reduction targets.Item Antimicrobial photodynamic inactivation of planktonic and biofilm cells by covalently immobilized porphyrin on polyethylene terephthalate surface(SSRN, 2022) Shatila, Fatima; Tieman, Grace M. O.; Musolino, Stefania F.; Wulff, Jeremy E.; Buckley, Heather L.The appearance of resistant strains and the persistence of biofilms on different surfaces in a wide range of settings represent serious public health threats. Antimicrobial photodynamic inactivation (aPDI) is a promising alternative technology to overcome these challenges. The current study assessed the antimicrobial effect of polyethylene terephthalate (PET) discs covalently functionalized with a cationic porphyrin, against E. coli and P. aeruginosa growth. Irradiation with white LED light for 6 h resulted in 1.51 ± 0.03 and 3.26 ± 0.24 log reduction of planktonic P. aeruginosa and E. coli, respectively. The study also assessed the effect of the functionalized discs on biofilm formation by E. coli, P. aeruginosa, and S. aureus. The biovolumes of S. aureus, P. aeruginosa, and E. coli biofilms were decreased by 0.6 ± 0.1, 0.56 ± 0.13 and 0.74 ± 0.06 log reduction, respectively. These results emphasize the ability of porphyrin-functionalized photoactive surfaces to kill bacterial cells and consequently prevent biofilm formation.Item Applicability of GPR and a rebar detector to obtain rebar information of existing concrete structures(Case Studies in Construction Materials, 2019) Rathod, Harsh; Debeck, Scott; Chow, BrianMuch of Canada’s existing infrastructure was constructed during 1950s, 1960s and 1970s. These include all transportation infrastructures such as bridges, highways, tunnels, etc. It is important to know the condition of these aging infrastructures in terms of their load carrying capacity to ensure their safety and serviceability. There are several old reinforced concrete slab bridges within the network of Ministry of Forests in B.C. Canada that have unknown rebar spacing, cover and diameter. This research paper discusses the application of Ground Penetrating Radar (GPR) and a Rebar detector in obtaining valuable information about rebar diameter, spacing and cover depth required to determine the structural capacity (load rating) of bridge decks. For this, GPR and the rebar detector have been applied on an existing bridge deck, a precast bridge girder and a reinforced concrete test slab panel available in the materials lab at the Facility for Innovative Materials and Infrastructure Monitoring (FIMIM) at the University of Victoria (UVic). To assess the applicability of GPR and Profoscope (Rebar Detector) in obtaining rebar information, the results obtained using both the techniques were compared in terms of their errors in determining all three parameters of rebar; diameter, spacing and cover depth. The results were validated by measuring the actual diameter, spacing and cover depth of the rebar in the reinforced concrete test slab available in the lab at UVic.Item Applicability of the Dutch ‘Energiesprong’ model to British Columbia(2019-08-06) Tripathi, Ishan; Froese, Thomas M.Item Application of Non-Oxidizing Biocides to Mitigate Biofouling in Reverse Osmosis Membrane Systems(2023-03-17) Gamm, NicoleReverse Osmosis (RO) is a water treatment process that can turn seawater, wastewater and brackish water into freshwater by applying pressure and forcing water molecules through a semi-permeable membrane. For this reason, RO is an ideal process for communities with limited access to potable water; however, RO is limited by biofouling. Biofouling is the accumulation of microorganisms on the membrane surface. Research was conducted on the applicability of six different non-oxidizing biocides to mitigate biofouling. To determine the efficacy, the minimum biofilm inhibitory concentration (MBIC) and minimum biofilm eradication concentration (MBEC) were determined for each biocide using 96-well plates. These tests evaluated each biocide’s ability to prevent and remove biofilms. The results found that Ethyl Lauroyl arginate (LAE) was the most promising candidate for mitigating biofouling in RO systems.