Advanced computing for the built environment

Creating a human-friendly, economically and ecologically sustainable built environment is among the main global challenges of the 21st century. The development of advanced computational methods for the design, engineering, construction and maintenance of buildings and infrastructure facilities is recognized as one key enabler to achieve this goal. In the last years, new and innovative computational methods and technologies evolved, which have the potential for a substantial improvement in supporting the decision making processes. This includes improvements in the performance and the accuracy of computational methods, as well as innovations in the field of data modeling, monitoring and management, sensor technologies and design assistance by knowledge representation and optimization. The annual workshop of the European Group for Intelligent Computing in Engineering (EG-ICE) brings together leading experts in the field to provide a platform for exchanging ideas and presenting the latest results of their research. In 2012, Technische Universität München hosted the workshop with more than 60 participants in Herrsching, a beautiful small town close to Munich, Germany. This special issue compiles the most important workshop contributions, which were selected based on their outstanding scientific quality and have been subsequently extended to full-scale journal publications. Doing so, this Special Issue follows the successful series of Special Issues presenting the major outcomes of previous EG-ICE workshops [1–4]. The articles selected for this special issue cover a broad range of advanced computational methods for the built environment, starting from the support of the early phases of building design using structural optimization and graph-based searches for reference solutions, over the as-built data collection for the digital fabrication of building components, to the monitoring of energy consumption in the building’s operation phase by means of innovative sensor technology. A significant part of the papers focuses on the concept of Building Information Modeling, a comprehensive approach for the rigorous use of digital information throughout the building’s lifecycle. This concept attains growing adoption in industrial practice, resulting in new and challenging research questions. Another emphasis of this special issue is on computational methods, which help to capture engineering knowledge. These intelligent methods are meant to assist in designing and monitoring buildings and infrastructure, to manage information, to assess the performance and to develop and propose innovative solutions. This reflects a current and important research demand in the field of computational design assistance. The paper ‘‘Graph-based Retrieval of Building Information Models for Supporting the Early Design Stages’’ [5] introduces a novel method for retrieving design solutions from a Building Information

[1]  Carlos Martinez-Ortiz,et al.  Building simulation approaches for the training of automated data analysis tools in building energy management , 2013, Adv. Eng. Informatics.

[2]  Jakob Beetz,et al.  BIMQL - An open query language for building information models , 2013, Adv. Eng. Informatics.

[3]  John Miles Editorial for special issue - Civil engineering informatics , 2009, Adv. Eng. Informatics.

[4]  Wolfgang Huhnt Special issue on construction informatics , 2010, Adv. Eng. Informatics.

[5]  Ian F. C. Smith,et al.  Intelligent computing in engineering and architecture , 2008, Adv. Eng. Informatics.

[6]  Timo Hartmann Advances in architectural, engineering and construction informatics , 2012, Adv. Eng. Informatics.

[7]  Ioannis K. Brilakis,et al.  A videogrammetric as-built data collection method for digital fabrication of sheet metal roof panels , 2013, Adv. Eng. Informatics.

[8]  Marcus Liwicki,et al.  Graph-based retrieval of building information models for supporting the early design stages , 2013, Adv. Eng. Informatics.

[9]  Anthony Rowe,et al.  Towards automated appliance recognition using an EMF sensor in NILM platforms , 2013, Adv. Eng. Informatics.

[10]  Prakash Kripakaran,et al.  Support vector regression for anomaly detection from measurement histories , 2013, Adv. Eng. Informatics.

[11]  Juan Manuel Davila Delgado,et al.  Automated design studies: Topology versus One-Step Evolutionary Structural Optimisation , 2013, Adv. Eng. Informatics.