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Sustainable Construction Research Group (SCRG)
University of Alberta, Department of Civil & Environmental Engineering
Building a Greener Future, One Phase at a Time
The principal focus of the team is to find sustainable, innovative, practical, and cost-effective solutions to decrease GHG emissions in the six major phases of construction projects to meet Canada’s ambitious GHG reduction goals.
The main objectives of our team are:
Enhance interdisciplinary collaboration among academic researchers, industry practitioners, and not-for-profit associations to leverage methods and models, fast-track data acquisition and insights, and enhance industry engagement and uptake of developed systems.
Conduct socio-economic analysis to assess the GHG emissions in current feasibility, engineering, procurement, construction and operation & maintenance phases of AEC projects to benchmark.
Develop a data acquisition model to capture the collected data in a structured database format using work breakdown structures (WBS) and production breakdown structures (PBS) concepts.
Analyze data for key emissions predictors using statistical and machine learning techniques.
Use this knowledge, expert elicitation, decision-making tools, and optimization techniques to develop a set of knowledge-based decision support systems (KBDSS) for industry practitioners and policymakers to select the optimum alternatives to reduce GHG emissions generated by the AEC industry.
Integrate this research into actionable decision-making supports to achieve net-zero GHG emissions targets in construction.
As sustainable PPS problems involve multiple and often conflicting incompatible objectives, the concepts of multi-objective optimization will be utilized in this research project. In the first step, we will interview subject matter experts from our construction owners partners, to define the decision-making criteria, such as GHG emissions, energy consumption, return on investment, and social impacts. Subject matter experts’ input will be statistically analyzed, classified, and then ranked using Fuzzy techniques. After that, the obtained knowledge will be combined with a multi-objective optimization model, subject to predefined constraints to develop a KBDSS to select the optimal portfolio that minimizes GHG emissions while maximizing added-value. Our partners will validate the inputs and outputs of the developed model for use in their organizations to continuously improve our analysis and outputs.
Subject matter experts from our consultant partners will help us summarize the major innovations to significantly improve architectural and engineering designs to reduce GHG emissions. Our team will perform a comprehensive literature review of the major available Green Building Rating Systems (GBRS) to include the most cost-effective GHG emissions reduction innovations. These ideas will be grouped in our ontology, based on project category and other specific project characteristics. Based on this generated list, we will identify the major decision points during the engineering phase, which have the most significant impact on minimizing GHG emissions in all project phases. Design decisions made early during the engineering phase have the least cost and greatest impact on reducing project emissions. The knowledge obtained from the operation and maintenance phase of existing facilities (as shown in phase five of this research project) will be used as a major feedback loop/input to the developed ontology. The obtained knowledge will be merged with subject matter experts’ opinions on efficacy, to develop a generic KBDSS, for decision–making criteria and rank-ordered alternatives, to optimize designs.
We will test the assumption that a combination of Integrated Project Delivery (IPD), Qualification Based Selection (QBS) and Early Construction Involvement (ECI) works best to reduce GHG emissions from construction projects. We will use life cycle sustainability analysis (LCSA) in a comparative study to compare the outcomes of projects delivered with traditional procurement versus projects that used IPD, QBS, and ECI. The LCSA is an interdisciplinary framework that simultaneously evaluates the impacts associated with products and processes from an environmental, social, and economic perspective. The concept of Externality (assessing the indirect benefits & harms arising in contracts) will be also utilized in the developed study. We will perform a comprehensive literature review and semi-structured interviews with our partners to determine the critical success factors (CSF) and key performance indicators (KPI) to be utilized in our comparative study.
We will develop a KBDSS to select the optimum construction execution scenario to reduce GHG emissions. The research will analyze new construction practices such as prefabrication, module sub-assembly, digital project delivery (DPD), and lean construction to achieve this objective. The proposed model will start by developing a tool to identify the critical construction work packages (CCWP), that are significant enough to be used for optimizing the four objectives: GHG emissions, time, cost, and labor hours. The proposed model utilizes Building Information Modeling (BIM) to represent each CCWP to calculate the total GHG emissions for various scenarios of execution. Then Genetic Algorithms (GA) will be used to detect the optimal construction execution scenario.
We plan to generate a continuous feedback loop that will capture the GHG emissions, energy utilization, and end-user satisfaction data during the operation and maintenance phase of completed projects as input to the feasibility and engineering phases of new projects. This search will examine the utilization of installing data sensors in existing facilities to capture and store the necessary data in our data acquisition model. Our PhD student will perform a comprehensive literature review and work with our owner partners to decide what data needs to be collected and in which format. Stroulia will lead the efforts to define the appropriate sensors and design the comprehensive data acquisition model. Our developed model will utilize digital construction tools such as Building Information Modeling (BIM) and Virtual Design and Construction (VDC) to capture the necessary data. As explained in phase 2, ML will be used to transfer the captured data to useful knowledge to improve the designs of new projects.
We focus on identifying strategies to maximize the diversion of Construction and Demolition (C&D) waste from landfills. We explore techniques such as waste prevention through design for deconstruction, as well as reduction, reuse, repurposing, and recycling of C&D materials to enhance sustainability in the built environment.
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