A demand-oriented Building Information Model (BIM) model built using high-fidelity point cloud data can better protect architectural heritage. The multi-level detail (mutli-LoD) parametric model emphasizes the differe...
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A demand-oriented Building Information Model (BIM) model built using high-fidelity point cloud data can better protect architectural heritage. The multi-level detail (mutli-LoD) parametric model emphasizes the different protection requirements of typical components and the automaticextraction of corresponding parameters of high-fidelity point clouds, which are two related key issues. Taking the typical Chinese wooden architectural heritage as an example, according to different requirements, the multi-LoD principle of typical components is proposed. On this basis, the automatic extraction method of the above parameters is developed, and the key parameters of the method are recommended. In order to solve the above problems, taking the three typical Dou-Gong used in Liao Dynasty and Song Dynasty, including Zhutou Puzuo, Bujian Puzuo and Zhuanjiao Puzuo, as an example, briefly introduced the standardization characteristics of the typical components of the "Yingzao Fashi". Subsequently, the corresponding multiple LoD principles are recommended according to different requirements. Based on this and high-fidelity point cloud data, an automatic extraction method for multi-LoD BIM model parameters for typical components of wooden architectural heritage is proposed.
A building-information-modeling (BIM) model, which is established based on high-fidelity point-cloud data, can be well used to preserve architectural heritage. Two related critical issues for this conservation are mul...
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A building-information-modeling (BIM) model, which is established based on high-fidelity point-cloud data, can be well used to preserve architectural heritage. Two related critical issues for this conservation are multiplelevel-of-detail (multi-LoD) parametric models that emphasize different protection requirements for typical components, and a method for automatically extracting the corresponding parameters from a high-fidelity point cloud. Taking typical Chinese wooden architectural-heritage structures as an example, multi-LoD principles for typical components without damage are proposed according to the different requirements. Then, a framework of multi-LoD parametric models was developed and implemented in BIM. Based on this, a method for automatically extracting the abovementioned parameters is developed and the critical parameters of this method are recommended. To validate the reliability and efficiency of this method, the parameters of multi-LoD models of typical components are extracted. The results indicate that the relative and absolute errors of values of such parameters are mostly less than 2% and 0.5 mm, respectively. Moreover, this method is capable of extracting parameters from millions of point-cloud data within 7 min, thus validating the high efficiency and reliability of the proposed method.
When dealing with problems using Fuzzy Rule Based Classification Systems it is difficult to know in advance whether the model will perform well or badly. In this work we present an automatic extraction method to deter...
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ISBN:
(纸本)9781424469208
When dealing with problems using Fuzzy Rule Based Classification Systems it is difficult to know in advance whether the model will perform well or badly. In this work we present an automatic extraction method to determine the domains of competence of Fuzzy Rule Based Classification Systems As a case of study we use the Fuzzy Hybrid Genetic Based Machine Learning method. We consider twelve metrics of data complexity in order to analyze the behavior patterns of this method, obtaining intervals of such data complexity measures with good or bad performance of it. Combining these intervals we obtain rules that describe both good or bad behaviors of the Fuzzy Rule Based Classification System mentioned. These rules allow describe both good or bad behaviors of the Fuzzy Rule Based Classification Systems mentioned, allowing us to characterize the response quality of the methods from the data set complexity metrics of a given data set. Thus, we can establish the domains of competence of the Fuzzy Rule Based Classification Systems considered, making it possible to establish when the method will perform well or badly prior to its application.
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