metis II - Artificial intelligence methods for auto-completion of designs based on semantic building information (BIM) for supporting architects in early design phases.

metis II - 基于语义建筑信息 (BIM) 自动完成设计的人工智能方法，用于在早期设计阶段为建筑师提供支持。

• 批准号: 419390235
• 负责人: Professor Dr. Andreas Dengel
• 金额: --
• 依托单位: Bereich Intelligente Informationssysteme (IIS)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/419390235?language=en
• 关键词: metis; II; Artificial; intelligence; methods

The aim of the "metis II" project is to develop methods for auto-completion of building designs. Using artificial intelligence approaches, (partial) information is extracted from reference designs and proposed to the architect as additions to his own design. Thus, methods are developed to apply useful information from a semantic building model (BIM) in the design context. Methods are examined to suggest e.g. kitchens, corridors or bathrooms and e.g. their location to a given formalized spatial configuration of e.g. a living room and a bedroom. The methods to be developed must be able to recognize both the context of the building design and the user-specific context. For this purpose, methods of artificial intelligence (AI), especially case-based reasoning (CBR) and artificial neural networks (KNN), are applied, expanded and developed. The explainability of the system to be developed (XAI - Explainable AI) is a further focus of the project, since the automatically generated solution parts as well as the learned knowledge must be explained to the user - in addition to the usual search results.For the integration of (partial) information from digital semantic building information models (BIM) as semantic and topological design specifications (spatial arrangement), the steps of the CBR cycle are fully integrated into the design process and new methods for the domain-specific adaptation of CBR knowledge containers (case basis, similarity measure, vocabulary and adaptation knowledge) are developed. The current state of (CBR)-research results in acquisition and administrative deficits: A special challenge of CBR, especially of retrieve and retain steps, is the size and quality of the case base, since the largest and most high-quality database possible is required. However, the data must be acquired, processed and at the same time the quality of the cases must be ensured. In order to increase the quality of the database, deep-learning methods for targeted "forgetting" are examined in the project "metis II".The project "metis II" is based on the results of metis I ("metis - Knowledge-based search and query methods for the development of semantic information models (BIM) for use in early design phases" funded by the DFG from 2013-2017. In "metis I", approaches for drawing a retrieval for semantic building models (BIM) were investigated and methods were developed to process the information. The concept of semantic building fingerprints used for this has proven to be robust enough and the methodological approaches have been confirmed.

“ METIS II”项目的目的是开发建筑设计自动完成的方法。使用人工智能方法，（部分）信息是从参考设计中提取的，并向建筑师提出了自己的设计。因此，开发了在设计环境中应用语义构建模型（BIM）的有用信息的方法。检查方法以提出例如厨房，走廊或浴室，例如它们位于给定的形式化的空间配置，例如客厅和卧室。要开发的方法必须能够识别建筑物设计的上下文和特定于用户的上下文。为此，应用，扩展和开发了人工智能方法（AI），尤其是基于病例的推理（CBR）和人工神经网络（KNN）。要开发的系统（XAI-可解释的AI）的解释性是该项目的进一步重点，因为除了通常的搜索结果外，还必须向用户解释自动生成的解决方案零件以及学习的知识。将（部分）信息集成（部分）信息（BIM）信息（BIM）的（BIM），该方法（BIM）为“语义和拓扑”（BIM）进行了整合，该方法是在语义和拓扑设计的过程中，该方法是c的（spatial nead），该方法是C.开发了CBR知识容器（案例基础，相似性度量，词汇和适应知识）的特定领域适应。 （CBR）研究的当前状态会导致收购和管理缺陷：CBR的特殊挑战，尤其是检索和保留步骤，是案例基础的大小和质量，因为可能需要最大，最高质量的数据库。但是，必须获取，处理数据，同时必须确保案例的质量。为了提高数据库的质量，在项目“ Metis II”中检查了针对目标“忘记”的深度学习方法。项目“ METIS II”基于METIS I（“基于METIS-基于知识的搜索和基于知识的搜索和查询方法）用于开发语义信息模型（BIM）在早期设计阶段中使用dfg for dfg for dfg for dfg for dfg for 201 i i。研究了语义构建模型（BIM），并开发了处理信息的概念。