Application of knowledge-based decision support systems (KBDSS) to reduce Greenhouse Gas (GHG) emissions in the major phases of construction projects

建筑、工程和建筑（AEC）行业占全球国内生产总值（GDP）的13%，雇用约7%的劳动力。然而，该行业产生了全球25%的温室气体（GHG）排放，消耗了60%的原材料和36%的全球能源。该研究计划的目标是减少人为温室气体排放和隐含碳在设计，制造，模块化，运输，施工和运营阶段的AEC项目。第二个目标是使用生命周期评估（LCA）最大限度地提高长期环境影响。第三个目标是加强学术研究人员、行业从业者和政策制定者之间的跨学科合作，帮助加拿大实现净零愿景和联合国可持续发展目标（SDG）9（工业、创新和基础设施）和13（气候行动）。研究目标将通过开发一套基于知识的决策支持系统（KBDSS）来实现，为施工过程中的利益相关者（业主、建筑师、专业工程师、主承包商、分包商、其他供应商、监管机构）提供支持，以增强设计和施工阶段的实时LCA决策。拟议的KBDSS将结合联合收割机从行业主题专家的定性输入，有用的知识挖掘定量数据使用机器学习（ML），多标准决策（MCDM）技术和优化算法，以实现我们的目标。我们将致力于选择最具可持续性和成本效益的解决方案;建筑材料，设计流程和施工方法。以下概念：将利用基于资格的选择（QBS）、早期施工参与（ECI）、施工数字化、综合项目交付（IPD）和外部性来减少温室气体排放。我们得到了加拿大主要公共、私人和非营利AEC组织的支持，如附信中所述。

The Architectural, Engineering and Construction (AEC) industry contributes 13% to the world's gross domestic product (GDP) and employs around 7% of the workforce. However, the industry produces 25% of global greenhouse gas (GHG) emissions, consumes 60% of the raw materials, and 36% of global energy. The objective of this research program is to reduce anthropogenic GHG emissions and embodied carbon during the design, manufacturing, modularization, transportation, construction, and operational phases of AEC projects. The second objective is to maximize the long-term environmental impact using life cycle assessment (LCA). The third objective is to enhance interdisciplinary collaboration among academic researchers, industry practitioners and policymakers to help Canada achieve Net-zero vision and the UN Sustainable Development Goals (SDG) 9 (Industry, Innovation, and Infrastructure) and 13 (Climate Action).The research objectives will be achieved via developing a set of knowledge-based decision support systems (KBDSS), for stakeholders in the construction process (owners, architects, discipline-specific engineers, prime contractors, subcontractors, other suppliers, regulators), to enhance real-time LCA decision-making during the design and construction phases. The proposed KBDSS will combine qualitative input from industry subject matter experts, useful knowledge mined from quantitative data using machine learning (ML), multi-criteria decision-making (MCDM) techniques, and optimization algorithms to achieve our goals. We will work on topics including selection of the most sustainable, cost-effective solutions; construction materials, design processes and construction methods. The concepts of: Qualification Based Selection (QBS), early construction involvement (ECI), construction digitization, integrated project delivery (IPD) and externality will be utilized to reduce GHG emissions. We have the support of major public, private and not-for-profit AEC organizations in Canada as explained in the cover letter.