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

Drexel University, Philadelphia PA

Investigators

Abstract

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.