FNR - Attenuating the Environmental Impact of our Buildings through Semantic-based Dynamic Life cycle Assessment (SemanticLCA)

FNR - 通过基于语义的动态生命周期评估 (SemanticLCA) 减轻建筑物的环境影响

• 批准号: EP/T019514/1
• 负责人: Yacine Rezgui
• 金额: $ 81.37万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT019514%2F1
• 关键词: FNR; Attenuating; Environmental; Impact; our

Our vision is that humans can attenuate and control positively the impact of their buildings on the environment and mitigate the effects of climate change. This can be achieved by a new generation of life cycle assessment methods and tools that are model-based, continuously learn from real-time data, while informing effective operation and management strategies of buildings and districts.In that respect, current LCA methods present important limitations and gaps, including:(a) Lack of reasoning and decision support capabilities, such as exploring "what if" scenarios for the evaluation of alternative design options and devising adapted strategies, thus promoting active control of buildings and districts.(b) Lack of alignment with domain models, e.g. BIM (Building Information Modelling), GIS (Geographical Information Systems), and LCA data structures. (c) Lack of support of temporal information. There is a need to factor in temporal information in the life cycle inventory (LCI) and Impact Assessment (LCIA) phases to address maintenance, operation, deconstruction, disposal and recycling stages. The proposed research addresses the challenge of leveraging digital built environment resources by using semantic web technologies to deliver life cycle assessment solutions to our built assets. Our hypothesis is that: life cycle assessment underpinned by semantics and informed by dynamic data paves the way to more accurate life cycle impact assessment while supporting life cycle decision making and active control of buildings and districts. In a nutshell, the aim of SemanticLCA is the development of a (near) real-time semantic capability that exploits a wide range of digital data sources and leverages artificial intelligence to assess the whole-life cycle environmental impacts of built assets. The following research questions are posited: RQ1: Can the use of semantics, including BIM (IFC) and GIS (CityGML), to integrate and contextualise existing life cycle inventory databases, provide a sound basis to streamline the life cycle assessment process of buildings and districts? RQ2: Can access to dynamic data, managed in a BIM and GIS friendly time series database, provide more accurate accounts of environmental impacts during the construction and operation stages? RQ3: Can the resulting SemanticLCA environment assist in decision making by non-experts by exploring a wide range of options and scenarios with the least environmental impact, while also advising on corrective plans?Our work programme involves three Work-Packages (WP), each addressing one of our posited research questions, and a fourth cross-cutting WP addressing demonstration and validation activities. The evaluation will be carried out in two demonstration sites: Cardiff (UK) and Belval (Luxembourg). The Cardiff demonstration will be carried out in the Queen's building (School of Engineering) and scaled up to the 130 buildings owned and managed by Cardiff university, majority of which are located in the city centre. The LIST demonstration will be carried out in the Maison de l'Innovation in Belval and scaled up to the entire district of Belval (managed by Fonds Belval). Given the complexity of LCA at district level, validation will utilise a simulation based approach with a subset of use cases demonstrated and validated in real operation conditions. The validation work will leverage ongoing developments of city platforms for Cardiff and Belval, as illustrated on the CUSP website: www.cuspplatform.com.SemanticLCA is supported by 10 partners and an experienced team of investigators from Cardiff University and LIST bringing together complementary expertise in: a) AI applications in the built environment, b) semantic contextualisation of multi-scale built environment data, c) intelligent cloud/edge computing, d) Life cycle assessment methods and tools, e) Building Information Modelling for asset modelling and energy efficiency.

我们的愿景是，人类可以减轻和积极控制其建筑物对环境的影响，并减轻气候变化的影响。这可以通过新一代的生命周期评估方法和工具来实现，这些方法和工具基于模型，不断从实时数据中学习，同时为建筑物和地区的有效运营和管理策略提供信息。在这方面，当前的生命周期评估方法存在重要的局限性和差距，包括：（a）缺乏推理和决策支持能力，例如探讨“假设”情景，以评价备选设计方案和制定适应性战略，从而促进对建筑物和区域的主动控制。(b)缺乏与领域模型的一致性，例如BIM（建筑信息建模），GIS（地理信息系统）和LCA数据结构。(c)缺乏时间信息的支持。有必要在生命周期清单和影响评估阶段考虑时间信息，以处理维护、操作、拆除、处置和回收阶段。拟议的研究解决了利用数字建筑环境资源的挑战，通过使用语义网技术，提供生命周期评估解决方案，我们的建筑资产。我们的假设是：生命周期评估以语义学为基础，并以动态数据为依据，为更准确的生命周期影响评估铺平了道路，同时支持生命周期决策和建筑物和地区的主动控制。SemanticLCA的目标是开发（近）实时语义能力，利用广泛的数字数据源，并利用人工智能来评估建筑资产的全生命周期环境影响。提出了以下研究问题：RQ 1：可以使用语义，包括BIM（IFC）和GIS（CityGML），整合现有的生命周期库存数据库，并将其置于上下文，提供一个良好的基础，以简化建筑物和地区的生命周期评估过程？第二个问题：在BIM和GIS友好的时间序列数据库中管理的动态数据能否提供施工和运营阶段更准确的环境影响报告？第三个问题：由此产生的SemanticLCA环境是否可以通过探索各种对环境影响最小的选项和场景来帮助非专家进行决策，同时还可以为纠正计划提供建议？我们的工作计划涉及三个工作包（WP），每个解决我们的一个假设的研究问题，第四个跨领域的WP解决演示和验证活动。评价将在两个示范点进行：卡迪夫（联合王国）和贝尔瓦尔（卢森堡）。卡迪夫示范将在女王大楼（工程学院）进行，并扩大到卡迪夫大学拥有和管理的130栋建筑，其中大部分位于市中心。LIST示范将在Belval的Maison de l 'Innovation进行，并扩大到整个Belval区（由Fonds Belval管理）。鉴于地区层面LCA的复杂性，验证将采用基于模拟的方法，并在真实的运行条件下演示和验证一部分用例。验证工作将利用卡迪夫和贝尔瓦尔城市平台的持续发展，如CUSP网站：www.cuspplatform.com所示。SemanticLCA由10个合作伙伴和来自卡迪夫大学和LIST的经验丰富的研究人员团队提供支持，汇集了互补的专业知识：a）建筑环境中的AI应用，B）多尺度建筑环境数据的语义情境化，c）智能云/边缘计算，（d）生命周期评估方法和工具，（e）用于资产建模和能源效率的建筑信息建模。

项目成果

Decarbonisation of seaports: A review and directions for future research

• DOI: 10.1016/j.esr.2021.100727
• 发表时间: 2021-11
• 期刊: Energy Strategy Reviews
• 影响因子: 8.2
• 作者: Ateyah Alzahrani;I. Petri;Y. Rezgui;Ali Ghoroghi

A proposed roadmap for delivering zero carbon fishery ports

实现零碳渔港的拟议路线图

• DOI: 10.1016/j.egyr.2022.01.083
• 发表时间: 2022
• 期刊: Energy Reports
• 影响因子: 5.2
• 作者: Alzahrani A

A framework using BIM and digital twins in facilitating LCSA for buildings

• DOI: 10.1016/j.jobe.2023.107232
• 发表时间: 2023-07
• 期刊: Journal of Building Engineering
• 影响因子: 6.4
• 作者: Calin Boje;Álvaro José Hahn Menacho;A. Marvuglia;E. Benetto;S. Kubicki;T. Schaubroeck;Tomás Navarrete Gutiérrez

Using mixed methods around a digital twin to study the prevalence of Sick Building Syndrome symptoms among University students

使用围绕数字孪生的混合方法来研究大学生中病态建筑综合症症状的患病率

• DOI: 10.1109/ice/itmc52061.2021.9570228
• 发表时间: 2021
• 作者: Brown J

Impact of ventilation and avoidance measures on SARS-CoV-2 risk of infection in public indoor environments.

• DOI: 10.1016/j.scitotenv.2022.156518
• 发表时间: 2022-09-10
• 期刊: SCIENCE OF THE TOTAL ENVIRONMENT
• 影响因子: 9.8
• 作者: Ghoroghi, Ali;Rezgui, Yacine;Wallace, Ruth
• 通讯作者: Wallace, Ruth