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Four sample conceptual sketches. (a) Drawn using plane canvas. (b) Drawn using a cylinder as canvas. (c) and (d) are both drawn using a sphere canvas. Sketches drawn by Ingrid Erb.
Source publication
State-of-the-art workflows within Architecture, Engineering, and Construction (AEC) are still caught in sequential planning processes. Digital design tools in this domain often lack proper communication between different stages of design and relevant domain knowledge. Furthermore, decisions made in the early stages of design, where sketching is use...
Contexts in source publication
Context 1
... and start drawing on the canvas from their viewpoint. In this setting, as the designer starts drawing strokes on the tablet's surface, the ray originating from the camera view is intersected with the canvas and the resulting 3D point is stored for the continuation of the stroke polyline. The sequence of such points forms a 3D stroke, see Fig. 2. The brush strokes are rendered as ruled surfaces or triangle strips of user-specified colour and width centred around the captured stroke polyline positions lying on the canvas. Furthermore, the designer can switch between sketch mode and select mode. Via select mode, previously drawn strokes can be selected at any stage of drawing to ...
Context 2
... timestamp, position, normal, tilt, twist, pressure, and materialInfo are recorded. Once the sketching is finished, all this info can be exported into a single JSON file to be further used for sketch analysis. Moreover, besides the JSON file, the designer can export the whole sketch to a single OBJ file comprising drawn strokes as triangle strips. Fig. 2 conceptual sketches of stage walls in a theatre setting. Fig. 3 shows four additional sketches, which take four actually built structures as a visual reference. All the sketch images are rendered using Polyscope ...
Context 3
... the following we demonstrate two conceivable feedback loops for a material-informed design process. Two of the sketched structures shown in Fig. 2(c) and 3(a) were selected for this, as they represent two different design intents. Use Case 1 is a curved wall intended to be used in a theatre stage design scenario. The structure should be lightweight and should visibly deform at both ends when an actor is hanging from the top ends of the wall, supported by ropes. Use Case 2 is a ...
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Citations
... Ampanavos et al. [126] carried out similar work using a convolution neural network for the development of structural floor layouts in the initial design phase. Rasoulzadeh et al. [127] sought to fully integrate early design stage workflows between architectural, engineering, and construction teams with a 4D sketching interface that comprises geometric modeling, material modeling, and structural analysis modules. The three modules create a framework for reconstructing architectural forms from sketches, predicting the mechanical behavior of materials, and assessing the form and materials based on finite element simulations. ...
Modeling and simulation have been extensively used to solve a wide range of problems in structural engineering. However, many simulations require significant computational resources, resulting in exponentially increasing computational time as the spatial and temporal scales of the models increase. This is particularly relevant as the demand for higher fidelity models and simulations increases. Recently, the rapid developments in artificial intelligence technologies, coupled with the wide availability of computational resources and data, have driven the extensive adoption of machine learning techniques to improve the computational accuracy and precision of simulations, which enhances their practicality and potential. In this paper, we present a comprehensive survey of the methodologies and techniques used in this context to solve computationally demanding problems, such as structural system identification, structural design, and prediction applications. Specialized deep neural network algorithms, such as the enhanced probabilistic neural network, have been the subject of numerous articles. However, other machine learning algorithms, including neural dynamic classification and dynamic ensemble learning, have shown significant potential for major advancements in specific applications of structural engineering. Our objective in this paper is to provide a state-of-the-art review of machine learning-based modeling in structural engineering, along with its applications in the following areas: (i) computational mechanics, (ii) structural health monitoring, (iii) structural design and manufacturing, (iv) stress analysis, (v) failure analysis, (vi) material modeling and design, and (vii) optimization problems. We aim to offer a comprehensive overview and provide perspectives on these powerful techniques, which have the potential to become alternatives to conventional modeling methods.
