Additive manufacturing (AM) is no longer a new technology and is already being used profitably in many sectors of the economy. AM is also becoming increasingly popular in the construction industry, and more and more research is focused on unlocking new building materials for AM. As a digital fabrication method, AM provides many new opportunities for the design of innovative and complex architecture and also has the potential to increase the productivity of the construction industry. However, the planning effort can increase accordingly and only experts in this field are able to apply this technology to construction projects. A methodology to improve planning efficiency has already been developed for the construction industry in the form of Building Information Modeling. In BIM, however, only conventional manufacturing processes have been taken into account so far, meaning that computer-aided manufacturing processes such as AM are still considered separately. Even more importantly, the granularity of product and process information is normally not sufficient for automated manufacturing. For this reason, this study proposes a framework, Fabrication Information Modeling, which can be used to generate BIMsupported fabrication information for the use of AM in the context of construction projects. Additionally to an expected reduction in planning effort, FIM would also provide the means of realizing an end-to-end digital chain from the first draft to the production of a construction project.

Digital manufacturing methods have been successfully used in different industries for years and have since had a positive effect on the development of their productivity. These methods offer significantly greater design freedom and make it possible to develop shape-optimized and function-activated components. In the construction industry, however, these technologies are only being used reluctantly, even though additive methods could make resource-efficient construction possible. The possibly decisive disadvantage of these methods is that a significantly higher granularity of product and process information is required, thus significantly increasing the planning effort. A circumstance that the framework described in this study, fabrication information modeling (FIM), could significantly mitigate by linking digital fabrication and BIM-based digital building design via a digital chain. For this purpose, FIM provides a methodology with which the information of a digital building model can be detailed, component by component, in a fabrication-aware manner. Based on the open exchange data format IFC, the FIM framework integrates seamlessly into the BIM context and enables automated detailing of the design information.

No abstract

Exact data in the form of technical drawings and plans of built assets are a significant requirement for the successful operation and reconstruction of such assets. When the consistency between this data and the real world situation cannot be assured, the data is not reliable and needs to be updated by comparing plans and reality. Depending on the size and number of assets this may involve an enormous amount of manual effort. In the scope of this research, an approach for supporting and automating such a process by utilizing concepts developed in the field of machine learning was developed. This paper focuses on the interpretation of technical drawings in terms of detecting and classifying plan symbols as this is a time intensive and error prone process when done manually. It is described how the capabilities of Convolutional Neural Networks are employed in analyzing images to automatically detect important plan symbols in the field of Train Traffic Control and Supervision Systems and how those networks are trained without the need for a time consuming-manual labeling process.