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

基础设施和建筑物的设计具有很长的生命周期-几十年的数量级。世界上许多建筑物在经过几个世纪的翻新之后仍然在运行。这一漫长的运营阶段代表了建筑物生命周期的大部分时间，但有关维护和翻新的信息很少保持最新。建筑信息模型（BIM）可以通过集中存储这些数据来缓解这种数据短缺。然而，即使使用BIM，由于在建筑物的生命周期内连续手动更新的难度，建筑更新也不会被保留。该项目将通过利用机器视觉的最新进展来创建一种自动更新BIM的方法。这个自动化过程可以系统地、持续地分析建筑环境，检测先前评估的变化。它可以以最小的人为错误和努力提取和更新关键的建筑信息。这项研究将导致建筑记录保存的根本变化，并使维护和翻新活动更容易在建筑物的整个生命周期中进行规划，从而使建筑运营商受益。这项工作的结果将纳入本科和研究生教育模块。该项目还将为YouTube制作视频，以吸引高中生和代表性不足的人从事土木工程职业。该项目将生成上下文数据关系，供基于主动照明范围相机的机器视觉系统使用，用于根据施工变化自动更新BIM数据库，并利用从BIM面向对象的数据库模型、计算机辅助设施管理（CAFM）数据库、以及由人类操作员输入的专家知识。该项目将解决几个智力挑战。 一个主要的挑战在于机器视觉部分的工作，这将需要提供额外的元数据以外的3D几何模型的对象，推动机器视觉的边界，为土木工程系统的背景。另一个挑战是扩展构建环境的数据建模功能。具体来说，将创建从机器视觉转换元数据的能力，以识别和获得特定于对象的有意义的上下文数据。还将开发一个特定的逻辑组件，即上下文决策器（CDM），以合并来自多个数据源的元数据。最后，整个系统将在室内装修项目中进行测试，以提供一个逼真的机器学习过程，随着时间的推移，这个过程将变得更加强大。

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.