Real-Time Data in Architectural Practice; Embracing A.I. in the design for energy efficiency

建筑实践中的实时数据；

• 批准号: 1944417
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1944417
• 关键词: Real; Time; Data; Architectural; Practice

The project aims to address methods for creating a computational system for design efficiency of the Higher Education (HE) campuses in the North-West. With new developments in technology, like generative design processes, autonomous systems and self-aware bots, the AEC industry is bound to be affected at a global scale. But how would Artificial Intelligence (AI) augment the architectural design and what are the possibilities for architects working with these technological advancements?The case study will run as a collaboration with an experienced industrial partner that has been involved in designing and building school campuses in the UK. More specifically, the project's main focus is to identify improvement strategies for energy consumption in university campus buildings. A university campus usually occupies large areas in a city's fabric. The urge to create additional campuses or to further develop the existing ones continues as contracts that reach £2 billion are planned between 2017 and 2020, according to Barbour ABI, the construction analysts. Similarly, the Financial Times report that, in 2016, British universities have increased their budget on new buildings by 43 per cent in six months. However a campus is a complex structure that hosts various activities. These activities change continuously over time. Thus, the development or expansion of a campus is characterized by complexity. This complexity requires an approach different from the terraced houses construction style that was applied during the UK's rapid urbanization. The mere repetitive construction approach wouldn't respond efficiently since the overall behavior of a campus changes and it happens on a procedural level. Therefore, the approach should consider the flexibility of an adaptive system in the early stages of their design. During this project, the benefits of combining Human and Artificial Intelligence will be explored. Through the use of data from the campus, the goal is to realize which factor is the biggest contributor for the energy consumption. The data will relate to the design of the campus, as well as its' energy performance, and post-occupancy information & insights. By embracing this type of data with computation in the early stages of the design process, the project will investigate improvements in the workflow and output of the architectural practice for HE campuses. Based on The Clean Growth Strategy paper, the majority of buildings including campuses, heating creates around 32 per cent of total UK emissions. However the design of an efficient HE campus in energy consumption may not only reduce the emissions but help to drive growth. According to Terence Fox, a director in the finance department at Edinburgh University, new buildings can prove decisive. "If a student [comes] from the US, Australia or China, they need to come here because we've got not just the best students, but the best facilities. If the choice is Manchester or Edinburgh, and Manchester has new buildings, they'll probably go to Manchester."From a research aspect, the applications of the respective methodology could extend beyond the energy planning of higher education campuses. While the energy factor is becoming ever more critical within the AEC practice, further equally important design variables could be revisited and explored. People's flow in a building or the exterior view maximization are examples of such variables. In this manner, the architect's professional boundaries could be transcended. This application of Artificial Intelligence for design decision-making however is its' infancy even for large international firms. As more and more practices create specialist groups to embrace this technological development the benefits will become more tangible. And the AEC industry will be enhanced by embedding new ways of tackling design challenges; ways that would be impossible without the implementation of computational modelling systems.

该项目旨在解决创建西北地区高等教育(HE)校区设计效率计算系统的方法。随着创生式设计过程、自主系统和自我感知机器人等技术的新发展，AEC行业肯定会在全球范围内受到影响。但人工智能(AI)将如何增强建筑设计？建筑师利用这些技术进步的可能性是什么？该案例研究将作为与英国参与设计和建设学校校园的经验丰富的工业合作伙伴的合作。更具体地说，该项目的主要重点是确定大学校园建筑能耗的改善策略。大学校园通常在城市的结构中占据很大的面积。建筑业分析师Barbour ABI表示，随着2017年至2020年计划签订20亿GB的合同，新建或进一步开发现有校区的冲动仍在继续。同样，英国《金融时报》报道称，2016年，英国大学在新建建筑上的预算在6个月内增加了43%。然而，校园是一个复杂的结构，举办各种活动。这些活动随着时间的推移不断变化。因此，校园的发展或扩建具有复杂性的特点。这种复杂性需要一种不同于英国快速城市化期间应用的梯形房屋建造风格的方法。单纯的重复建设方法不会有效地应对，因为校园的整体行为发生了变化，而且是在程序层面上发生的。因此，该方法应该在设计的早期阶段考虑自适应系统的灵活性。在这个项目中，将探索人类和人工智能相结合的好处。通过使用来自校园的数据，目标是了解哪个因素对能源消耗的贡献最大。这些数据将涉及校园的设计，以及它的能源表现，以及入驻后的信息和见解。通过在设计过程的早期阶段将这种类型的数据与计算相结合，该项目将调查高等教育校园建筑实践的工作流程和产出方面的改进。根据清洁增长战略文件，包括校园在内的大多数建筑产生的排放量约占英国总排放量的32%。然而，设计一个高效的高等教育校园在能源消耗方面不仅可以减少排放，而且有助于推动增长。根据爱丁堡大学金融系主任特伦斯·福克斯的说法，新的建筑可以被证明是决定性的。“如果一名学生来自美国、澳大利亚或中国，他们需要来这里，因为我们不仅有最好的学生，还有最好的设施。如果选择曼彻斯特或爱丁堡，而曼彻斯特有新的建筑，他们很可能会去曼彻斯特。”从研究的角度来看，各自方法的应用可能超出了高等教育校园的能源规划。虽然能源因素在AEC实践中变得越来越重要，但同样重要的设计变量也可以重新考虑和探索。建筑物内的人流或外部景观的最大化就是这些变量的例子。通过这种方式，架构师的职业界限可以被超越。然而，人工智能在设计决策中的应用即使对大型国际公司来说也是“起步”。随着越来越多的实践创建专家小组来拥抱这一技术发展，好处将变得更加切实。通过嵌入应对设计挑战的新方法，AEC行业将得到加强；如果没有计算建模系统的实施，这些方法是不可能实现的。