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Automated Generation of BIM Models from 2D CAD Drawings

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Existing buildings are often lacking BIM models. This paper proposes a method to semi-automate the generation of BIM models from 2D CAD drawings. The method has two parts: the first part, 2D CAD drawing preparation, involves cleaning the drawings to obtain simplified 2D input geometry and the second, 3D BIM model generation, involves generating and extracting parameters to generate 3D BIM components. This research focuses on the semi-automation of the second part. The the model is generated storey by storey, with each building element type being processed. A demonstration was carried out for a case-study building. The main modelling strategies used by the method are described and key challenges are discussed.
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... RAD can bring several benefits, including: (1) the less error-prone and time-consuming generation of AD programs (when compared to manual AD processes) and, consequently, the faster exploration and optimization of complex designs; (2) the correction of mistakes/inconsistencies in a CAD/BIM model that are harder to detect manually; (3) the semiautomatic translation of designs from CAD to BIM [12]; and (4) the exploration of different parametric interpretations of a static model. ...
Chapter
Algorithmic Design (AD) is an approach that uses algorithms to represent designs. AD allows for a flexible exploration of complex designs, which helps not only the designer but also optimization methods that autonomously search for better-performing solutions. Despite its advantages, AD is still not widely used. This is owed in part to the large amount of time, effort, and expertise required for the development of an AD program, a problem that grows with the complexity of the design. To overcome this issue, this paper proposes Reverse Algorithmic Design (RAD), which infers AD programs from existing CAD or BIM models. RAD comprises two main steps: the automatic generation of an initial low-level AD program from a CAD/BIM model, followed by a semi-autonomous refactoring step that improves the generated program. The benefits of the RAD approach are demonstrated with its application in two use-case scenarios.
... Note that, this study adopted the semi-automated modeler module to guarantee the generated structural analysis model reliable. Moreover, the StructGAN modeler module is ready to be improved by adopting the existing method of 3D model generation from 2D drawings [52]. ...
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Artificial intelligence is reshaping building design processes to be smarter and automated. Considering the increasingly wide application of shear wall systems in high-rise buildings and envisioning the massive benefit of automated structural design, this paper proposes a generative adversarial network (GAN)-based shear wall design method, which learns from existing shear wall design documents and then performs structural design intelligently and swiftly. To this end, structural design datasets were prepared via abstraction, semanticization, classification, and parameterization in terms of building height and seismic design category. The GAN model improved its shear wall design proficiency through adversarial training supported by data and hyper-parametric analytics. The performance of the trained GAN model was appraised against the metrics based on the confusion matrix and the intersection-over-union approach. Finally, case studies were conducted to evaluate the applicability , effectiveness, and appropriateness of the innovative GAN-based structural design method, indicating significant speed-up and comparable quality.
... Note that, this study adopted the semi-automated modeler module to guarantee the generated structural analysis model reliable. Moreover, the StructGAN modeler module is ready to be improved by adopting the existing method of 3D model generation from 2D drawings [52]. ...
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The published version is available. Please see & cite: Liao WJ, Lu XZ, Huang YL, Zheng Z, Lin YQ, Automated structural design of shear wall residential buildings using generative adversarial networks, Automation in Construction, 2021, 132, 103931. https://doi.org/10.1016/j.autcon.2021.103931 【Abstract】Artificial intelligence is reshaping building design processes to be smarter and automated. Considering the increasingly wide application of shear wall systems in high-rise buildings and envisioning the massive benefit of automated structural design, this paper proposes a generative adversarial network (GAN)-based shear wall design method, which learns from existing shear wall design documents and then performs structural design intelligently and swiftly. To this end, structural design datasets were prepared via abstraction, semanticization, classification, and parameterization in terms of building height and seismic design category. The GAN model improved its shear wall design proficiency through adversarial training supported by data and hyper-parametric analytics. The performance of the trained GAN model was appraised against the metrics based on the confusion matrix and the intersection-over-union approach. Finally, case studies were conducted to evaluate the applicability, effectiveness, and appropriateness of the innovative GAN-based structural design method, indicating significant speed-up and comparable quality.
... To solve this problem, the idea of automatically generating BIM models was proposed. Nowadays there are several methods to generate As-planned BIM model through 2D drawings (Cho and Liu 2017;Lim et al. 2018). However, some challenges exist, e.g. ...
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Building maintenance to get boost from technology
  • W L Toh
Toh, W.L.: 2017, "Building maintenance to get boost from technology". Available from <http://www.straitstimes.com/singapore/housing/building-maintenance-to-get-boostfrom-technology> (accessed 20th December 2017).