Scholarlink

In the multidisciplinary building design process, early design decisions have a significant influence on the sustainability of the building design. The early integration of the structural engineering expertise in the process allows an efficient support of the involved highly complex decision-making. Regarding the structural design, this mainly concerns the structural concept, possible structural specifications and the preliminary dimensioning of structural members. Through the early provision of design knowledge that arises from structural engineering experience, a design support is enabled by offering structural variants, design options and qualified estimates. Regarding computer-aided design, the use of artificial intelligence methods is considered to be highly beneficial. For this purpose, an information concept is proposed that comprises determined levels of structural design in early design phases. Thus, an inclusion of suitable material-specific structural engineering knowledge is enabled. The representation of such design-level-dependent knowledge is accomplished with the aid of crisp and fuzzy knowledge bases. Additionally, fuzzy possibility theory allows the formalization of experience values for qualified structural design assessments. An imitation of the human decision-making behavior is achieved by the use of an easily understandable knowledge formulation in the form of rules following the Modus Ponens and Fuzzy Logic inference mechanisms. Usable methods for knowledge acquisition are presented briefly. To process the resulting knowledge network as well as for the provision and the usage of the involved structural engineering expertise, a knowledge-based system is developed. This performs an evaluation of bearing structures and the proposal of design options through the application of the design-level-related fuzzy knowledge bases and associated inference systems. The resulting system enables a valuable support of design decisions to achieve an efficiency increase in the early design process and a promotion of sustainable building designs.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-Based Integration of Structural Engineering Knowledge in Early Design Phases

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Martina Schnellenbach-Held,
  • Daniel Steiner

摘要

In the multidisciplinary building design process, early design decisions have a significant influence on the sustainability of the building design. The early integration of the structural engineering expertise in the process allows an efficient support of the involved highly complex decision-making. Regarding the structural design, this mainly concerns the structural concept, possible structural specifications and the preliminary dimensioning of structural members. Through the early provision of design knowledge that arises from structural engineering experience, a design support is enabled by offering structural variants, design options and qualified estimates. Regarding computer-aided design, the use of artificial intelligence methods is considered to be highly beneficial. For this purpose, an information concept is proposed that comprises determined levels of structural design in early design phases. Thus, an inclusion of suitable material-specific structural engineering knowledge is enabled. The representation of such design-level-dependent knowledge is accomplished with the aid of crisp and fuzzy knowledge bases. Additionally, fuzzy possibility theory allows the formalization of experience values for qualified structural design assessments. An imitation of the human decision-making behavior is achieved by the use of an easily understandable knowledge formulation in the form of rules following the Modus Ponens and Fuzzy Logic inference mechanisms. Usable methods for knowledge acquisition are presented briefly. To process the resulting knowledge network as well as for the provision and the usage of the involved structural engineering expertise, a knowledge-based system is developed. This performs an evaluation of bearing structures and the proposal of design options through the application of the design-level-related fuzzy knowledge bases and associated inference systems. The resulting system enables a valuable support of design decisions to achieve an efficiency increase in the early design process and a promotion of sustainable building designs.