首页> 外文OA文献 >BIM AUTOMATION: ADVANCED MODELING GENERATIVE PROCESS for COMPLEX STRUCTURES
【2h】

BIM AUTOMATION: ADVANCED MODELING GENERATIVE PROCESS for COMPLEX STRUCTURES

机译:BIM自动化:复杂结构的高级建模生成过程

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

The new paradigm of the complexity of modern and historic structures, which are characterised by complex forms, morphological and typological variables, is one of the greatest challenges for building information modelling (BIM). Generation of complex parametric models needs new scientific knowledge concerning new digital technologies. These elements are helpful to store a vast quantity of information during the life cycle of buildings (LCB). The latest developments of parametric applications do not provide advanced tools, resulting in time-consuming work for the generation of models. This paper presents a method capable of processing and creating complex parametric Building Information Models (BIM) with Non-Uniform to NURBS) with multiple levels of details (Mixed and ReverseLoD) based on accurate 3D photogrammetric and laser scanning surveys. Complex 3D elements are converted into parametric BIM software and finite element applications (BIM to FEA) using specific exchange formats and new modelling tools. The proposed approach has been applied to different case studies: the BIM of modern structure for the courtyard of West Block on Parliament Hill in Ottawa (Ontario) and the BIM of Masegra Castel in Sondrio (Italy), encouraging the dissemination and interaction of scientific results without losing information during the generative process.
机译:具有复杂形式,形态和类型变量的现代和历史结构复杂性的新范例,是建筑信息模型(BIM)面临的最大挑战之一。复杂参数模型的生成需要有关新数字技术的新科学知识。这些元素有助于在建筑物(LCB)的生命周期中存储大量信息。参数应用程序的最新开发没有提供高级工具,从而导致模型生成过程耗时。本文提出了一种基于3D摄影测量和激光扫描测量的方法,能够处理和创建具有多级细节(混合和ReverseLoD)且与NURBS不一致的复杂参数化建筑信息模型(BIM)。使用特定的交换格式和新的建模工具,可以将复杂的3D元素转换为参数BIM软件和有限元应用程序(从BIM到FEA)。提议的方法已应用于不同的案例研究:渥太华(安大略省)国会山西街区庭院的现代结构的BIM和索纳德里奥(意大利)的Masegra Castel的BIM,这鼓励了科学成果的传播和互动在生成过程中不会丢失任何信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号