Christoph Langenhan

Bio: Christoph Langenhan is an academic researcher from Technische Universität München. The author has contributed to research in topics: Architecture & Computer science. The author has an hindex of 8, co-authored 27 publications receiving 243 citations. Previous affiliations of Christoph Langenhan include Ludwig Maximilian University of Munich.

a.SCatch: semantic structure for architectural floor plan retrieval

Abstract: Architects' daily routine involves working with drawings. They use either a pen or a computer to sketch out their ideas or to do a drawing to scale. We therefore propose the use of a sketch-based approach when using the floor plan repository for queries. This enables the user of the system to sketch a schematic abstraction of a floor plan and search for floor plans that are structurally similar. We also propose the use of a visual query language, and a semantic structure as put forward by Langenhan. An algorithm extracts the semantic structure sketched by the architect on DFKI's Touch& Write table and compares the structure of the sketch with that of those from the floor plan repository. The a.SCatch system enables the user to access knowledge from past projects easily. Based on CBR strategies and shape detection technologies, a sketch-based retrieval gives access to a semantic floor plan repository. Furthermore, details of a prototypical application which allows semantic structure to be extracted from image data and put into the repository semi-automatically are provided.

Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs

Abstract: To provide the retrieval of information, that can be considered useful during the design conceptualization process, with advantages of distributed artificial knowledge, an approach, that distributes retrieval-related and knowledge maintaining tasks among autonomously working and case-based self-learning agents and agent groups, can be used. In this work we present the distributed retrieval system MetisCBR for the architectural design domain, where agents work in groups (containers) on resolving of user queries built with a semantic description model Semantic Fingerprint. The main aim of our approach is to carry out a basis for a considerable retrieval tool for architects, where the combination of case-based reasoning and multi-agent methods helps to achieve valuable and helpful search results in a comprehensive building design collection.

Migrating the Classical Pen-and-Paper Based Conceptual Sketching of Architecture Plans Towards Computer Tools - Prototype Design and Evaluation

Abstract: While computer-based design tools are widely used in architecure during late design phases for creating final floor plans, early design phases usually still take place in a traditional manner, using pen, paper and scissors. At the beginning of these phases, there is often only a rough idea of how a building should look like. Viewing existing floorplans of similar buildings can help an architect in his/her creative work, but searching for those plans manually is very time-consuming. Automated tools for searching similar floor plans could help to lower the amount of time needed for such investigations tremendously. In order to employ such search mechanisms, proper user interfaces are needed that fit to the architect’s working process. These interfaces should be useable easily and naturally, requiring less initial training. They should be capable of creating search requests that can be processed by the attached search mechanism. In this article, we describe two different user interfaces to serve this purpose. We describe their structures and interaction principles. Afterwards we show their general usability and user acceptance by the means of a users study.

Building Information Modelling (BIM) uptake: Clear benefits, understanding its implementation, risks and challenges

Abstract: Rapid advancement of technology continues to leverage change and innovation in the construction industry. Continued digitization of the industry offers the opportunity to totally reinvent contemporary construction design and delivery practice for future development. Building Information Modelling (BIM) within the context of Architecture, Engineering & Construction (AEC) has been developing since the early 2000s and is considered to be a key technology. Despite major technical advancements in BIM, it has not been fully adopted and its definitive benefits have not been fully capitalized upon by industry stakeholders. The lack of widespread uptake of BIM appears to be linked to the risks and challenges that are potentially impeding its effectiveness. This paper aims to discuss the reality of BIM, its widespread benefits and current level of uptake. The risks and challenges associated with the adoption of BIM, as well as recommendations regarding how future BIM adoption could be developed are also highlighted.

A Natural-Language-Based Approach to Intelligent Data Retrieval and Representation for Cloud BIM

Abstract: As the information from diverse disciplines continues to integrate during the whole life cycle of an Architecture, Engineering, and Construction AEC project, the BIM Building Information Model/Modeling becomes increasingly large. This condition will cause users difficulty in acquiring the information they truly desire on a mobile device with limited space for interaction. The situation will be even worse for personnel without extensive knowledge of Industry Foundation Classes IFC or for nonexperts of the BIM software. To improve the value of the big data of BIM, an approach to intelligent data retrieval and representation for cloud BIM applications based on natural language processing was proposed. First, strategies for data storage and query acceleration based on the popular cloud-based database were explored to handle the large amount of BIM data. Then, the concepts "keyword" and "constraint" were proposed to capture the key objects and their specifications in a natural-language-based sentence that expresses the requirements of the user. Keywords and constraints can be mapped to IFC entities or properties through the International Framework for Dictionaries IFD. The relationship between the user's requirement and the IFC-based data model was established by path finding in a graph generated from the IFC schema, enabling data retrieval and analysis. Finally, the analyzed and summarized results of BIM data were represented based on the structure of the retrieved data. A prototype application was developed to validate the proposed approach on the data collected during the construction of the terminal of Kunming Airport, the largest single building in China. The case study illustrated the following: 1 relationships between the user requirements and the data users concerned are established, 2 user-concerned data can be automatically retrieved and aggregated based on the cloud for BIM, and 3 the data are represented in a proper form for a visual view and a comprehensive report. With this approach, users can significantly benefit from requesting for information and the value of BIM will be enhanced.

Statistical segmentation and structural recognition for floor plan interpretation

Abstract: A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.