Collaborative Research: Living Building Information Model (BIM): A Layered Approach for Automatic and Continuous Built Environment Model Update

协作研究：生活建筑信息模型（BIM）：自动连续建筑环境模型更新的分层方法

• 批准号: 1562515
• 负责人: Liang Chung Lo
• 金额: $ 19.99万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1562515&HistoricalAwards=false
• 关键词: Collaborative; Research; Living; Building; Information

Infrastructure and buildings are designed to have long life cycles - on the order of decades. Many buildings in the world are still in operation after centuries amid numerous renovation efforts. This long operational phase represents the majority of a building's lifecycle, yet the information regarding maintenance and renovation is rarely kept up to date. Building Information Models (BIMs) can alleviate this data shortage by centrally storing this data. However, even with a BIM, building updates are not kept due to the difficulty of continuous manual updates over a building's lifetime. This project will create a method to automatically update a BIM by exploiting recent advancements in machine vision. This automation process can systematically and continuously analyze the built environment, detecting changes from a previous assessment. It can distill and update the critical building information with minimal human error and effort. This research will result in a fundamental change in construction record keeping and benefit building operators by enabling maintenance and renovation activities to more easily be planned throughout a building's lifetime. The findings of this work will be integrated into undergraduate and graduate educational modules. Videos created for YouTube will also be developed to attract high school and underrepresented persons to a career in civil engineering.The project will generate contextual-data relationships for use by an active illumination range camera-based, machine-vision system for automatically updating a BIM database with construction changes and leveraging metadata distilled from BIM object-oriented database models, the Computer-Aided Facilities Management (CAFM) database, and expert knowledge input by human operators. The project will tackle several intellectual challenges. A primary challenge resides in the machine vision component of the work, which will need to provide additional meta-data beyond a 3D geometric model of an object, pushing the boundary of machine vision for the context of civil engineering systems. Another challenge is to extend data modeling capabilities for the built environment. Specifically, capabilities to translate meta-data from machine vision to identify and obtain meaningful contextual data that is specific for the objects will be created. A specific logical component, the Contextual Decision Maker (CDM), will also be developed to merge meta-data from multiple data sources. Finally, the entire system will be tested in an indoor renovation project to provide a realistic machine-learning process that will grow more robust over time.

基础设施和建筑物旨在在数十年的时间内拥有长寿周期。几个世纪以来，由于许多翻新工作，世界上许多建筑物仍在运营。这个漫长的操作阶段代表着建筑物的生命周期的大多数，但是有关维护和翻新的信息很少保持最新状态。建筑信息模型（BIMS）可以通过集中存储此数据来减轻此数据短缺。但是，即使使用BIM，由于建筑物一生中连续的手动更新的困难，建筑物的更新也无法保留。该项目将通过利用机器视觉的最新进步来创建一种自动更新BIM的方法。这种自动化过程可以系统，连续地分析建筑环境，从而从先前的评估中检测到变化。它可以以最小的人为错误和精力来提炼和更新关键的建筑信息。这项研究将通过在建筑物的一生中更容易地计划维护和翻新活动，从而导致建筑记录保存和利益建筑运营商的根本变化。这项工作的发现将集成到本科和研究生教育模块中。 Videos created for YouTube will also be developed to attract high school and underrepresented persons to a career in civil engineering.The project will generate contextual-data relationships for use by an active illumination range camera-based, machine-vision system for automatically updating a BIM database with construction changes and leveraging metadata distilled from BIM object-oriented database models, the Computer-Aided Facilities Management (CAFM) database, and expert knowledge人类操作员的投入。该项目将应对几个智力挑战。 主要的挑战位于机器视觉部分中，该作品将需要在物体的3D几何模型之外提供其他元数据，从而为土木工程系统的背景推动机器视觉的边界。另一个挑战是扩展为构建环境的数据建模功能。具体而言，将创建对对象特定的有意义的上下文数据，将元数据转换为机器视觉的能力。还将开发一个特定的逻辑组件，即上下文决策者（CDM），以合并多个数据源的元数据。最后，将在一个室内翻新项目中对整个系统进行测试，以提供一个逼真的机器学习过程，随着时间的流逝，它将变得更加健壮。