Approach to Digital Twins of Woody Vegetation (Trees and Shrubs)

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DC I/O 2022 Paper and slides by Andreas Luka and Yong Guo. https://doi.org/10.47330/DCIO.2022.MUJM9015 | Watch Left | Left | Left


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Abstract

There has been a growing interest in integrating plants into building information systems (BIM) and city information systems (CIM) to analyze and optimize vegetation's positive impact on the built environment. In this paper, we summarize our progress in research over the last few years on developing digital twins for woody plants representing them in different modeling environments. Efficient integration of individual plants is still one of the major obstacles to successful BIM implementation in landscape design and the correct representation of urban vegetation in CIM [Barth, 2019]. Vegetation is a very dynamic element considering the plant's constant changes in size and shape during the whole life cycle. The seasonal cycle with the aspects of spring, summer, autumn, and winter is from a fewer interest in BIM but a significant stake in visualization and eco-services performance estimations. Over detailed 3D representation of new and existing vegetation is irregular and complex in shape and can lead to serious performance problems. Often information about roots is missing and not modeled as 3D objects. Therefore we cannot perform essential BIM tasks like clash detection. The change of size and shape also significantly impacts eco service performance (i.e., cooling effects and carbon sequestration). Our research focuses on three main topics. First, the 3D representation of the shape and size of new trees and shrubs over their whole life cycle (dynamic), including roots (complete), trunk with major branches (adequate Level of Detail), specific for species and cultivars (correct) as solids or meshes (BIM orientated) [Luka and Guo 2021]. We developed models based on a set of Chapman-Richard growth functions [Richard, 1959 and Chapman 1961] (total height, height of the trunk, height/diameter of the largest crown radius, and root depth) and Cescatti hull models [Cescatti 1997]. The development of each individual starts with the size and shape delivered at planting time. We transfer models between different systems only by parameter sets or subsets (i.e., position, age, growth, and hull parameters) and recreate the geometry in the targeted system, avoiding poor performance by inefficient geometry conversation. Second, integration of existing trees with 3D objects (meshes) derived from terrestrial laser scanning [Guo et al. 2022]. We adjusted some and added new algorithms to the CompuTree platform with the SimpleForest plugin [Hackenberg, 2020] to create Quantitative Structural Models (QSM) and Cescatti hull models for trees in urban environments on different Levels of Detail (LoD). We can simulate future growth based on the QSM and hull for further analysis. Third, tools to integrate plants into generative design workflows. [Luka 2022] We developed particular nodes to manipulate vegetation distribution on terrains with Dynamo in Revit (i.e., quasirandom, individual/agent-based models, and Boids). Combined with our dynamic tree models, we can simulate further development and explore each simulation by exporting it to programs like ENVI-met or querying a service (i.e., carboncalc) via a web API with JSON. All research is part of an effort to create a digital twin of woody plants, covering the whole life cycle, linking information from designers, suppliers, construction companies, and maintainers over different systems like BIM, LIM, GIS, FM, and CIM.

Keywords

Planting Design, BIM, CIM, Generative Design.

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