EAGER/Collaborative Research:Science-Based Exploration of Invariant Signatures of Architecture/Engineering/Construction Objects to Enable Interoperability of Building Info Modeling

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Building Information Modeling integrates 3D models with physical and functional characteristics of an infrastructure project and has the potential to facilitate the exchange of information between different parties involved in the same project throughout its lifecycle, ranging from design and construction to maintenance and operation, and beyond. However, the fundamental problem -- lack of interoperability (ie. the inability to exchange information between different platforms) due to model inconsistency and missing information -- prevents the exchange of such information. Existing research efforts to address this lack of interoperability have been heavily focused on standardization and semantic modeling. These standard methods do not address the underlying problem and still depend on the computer models involved. This EArly-concept Grant for Exploratory Research (EAGER) project aims to both scientifically and empirically study the intrinsic properties and discover invariant signatures of architecture, engineering, and construction (AEC) objects, such as footings, slabs, walls, beams, and columns, for supporting seamless and universal interoperability of Building Information Modeling. Invariant signatures of an AEC object are defined as a set of intrinsic properties (e.g., geometry, location, material) of the object that distinguish it from other objects, and they do not change with software implementation, modeling decisions, and/or language and cultural contexts. An interdisciplinary approach involving geometry theorems, computer algorithms, and material mechanics will be employed to explore and quantify these intrinsic properties. If successful, the approach is expected to open the door to full automation of building information modeling analysis, which will significantly improve the project performance in all respects.The underlying hypothesis of the project is that invariant signatures of an AEC object collectively defined by the Cartesian points-based geometric, relative location and orientation, and material mechanical properties will enable seamless and universal interoperability of building information modeling (BIM) software in various analysis phases from architectural design and preliminary structural design to detailed structural analysis and construction cost estimation. The project is divided into two thrusts: 1) test the hypothesis particularly on the kinds of geometric, locational and material signatures that can be identified as inherent signatures from a wide range of AEC objects; 2) test the ability of discovered signatures to support BIM interoperability in the automated quantity takeoff and structural analysis scenarios. Publicly available BIM data will be used to support the exploration of the invariant signatures and the testing of these signatures. This project is the first systematic effort designated to test the idea of leveraging the intrinsic properties of AEC objects to support BIM interoperability, which is radically different from the existing efforts that are focused on data schema standardization and/or term-based semantics of AEC objects. If the underlying hypothesis is supported, this research has the potential to transform the way future BIM standards are developed and used to support seamless and universal interoperability of BIM models among all modeling and engineering analysis tasks. The results of this research could be widely applicable in construction engineering and beyond, and could ultimately lead to: (1) seamless and universal interoperability of BIM models; (2) full automation of BIM analysis; and (3) optimized specifications of material selections for future construction in different environments. The methods and results of this project will be integrated into university coursework at both collaborating institutions. This project will also broaden the participation of underrepresented groups by giving priority to women/minority students when recruiting the research assistants.