A Data Integration and Simplification Framework for Improving Site Planning and Building Design

IEEE Access, 2021

Recommended citation: Leng, S., Lin, J.R., Li, S.W., Hu, Z.Z.* (2021). A Data Integration and Simplification Framework for Improving Site Planning and Building Design. IEEE Access, 9, 148845-148861. doi: 10.1109/ACCESS.2021.3124010 http://doi.org/10.1109/ACCESS.2021.3124010 cited by count

Abstract

Site planning and building design results are generally managed in Geographic Information System (GIS) and Building Information Modeling/Model (BIM) separately. The incompatibility of data has brought potential challenges for the assessment and delivery of the results. A data integration and simplification framework for improving site planning and building design is proposed in this paper. A BIM-GIS integrated model with a multi-scale data structure is developed to link the results of site planning and building design together. Geometric optimization algorithms are then designed to generate simplified building models with different levels of details (LODs) based on the information required at each scale. This paper provides a feasible way to integrate planning and design data from different sources to enhance the evaluation and delivery of the results. The proposed approach is validated by a village construction project in east China, and results show that the method is capable to integrate site planning and building design results from different platforms and support seamless visualization of multi-scale geometric data. It is also found that a seamless database facilitates understanding of planning and design results and improves communication efficiency. Currently, the main limitation of this paper is the limited access to 3D real-world data, and data collection techniques like point cloud are expected to solve the problem.

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This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFD1100900, and in part by the National Natural Science Foundation of China under Grant 51778336 and Grant 72091512.

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