<div> <div> <p>Building Information Modeling (BIM) serves as an important media in supporting automation in the architecture, engineering, and construction (AEC) domain. However, with its fast development by different software companies in different applications, data exchange became labor-intensive, costly, and error-prone, which is known as the problem of interoperability. Industry foundation classes (IFC) are widely accepted to be the future of BIM in solving the challenge of BIM interoperability. However, there are practical limitations of the IFC standards, e.g., IFC’s flexibility creates space for misuses of IFC entities. This incorrect semantic information of an object can cause severe problems to downstream uses. To address this problem, the author proposed to use the concept of invariant signatures, which are a new set of features that capture the essence of an AEC object. Based on invariant signatures, the author proposed a rule-based method and a machine learning method for BIM-based AEC object classification, which can be used to detect potential misuses automatically. Detailed categories for beams were tested to have error-free performance. The best performing algorithm developed by the methods achieved 99.6% precision and 99.6% recall in the general building object classification. To promote automation and further improve the interoperability of BIM tasks, the author adopted invariant signature-based object classification in quantity takeoff (QTO), structural analysis, and model validation for automated building code compliance checking (ACC). Automation in such BIM tasks was enabled with high accuracy.</p><p><br></p><p><br></p> </div> </div>

<p dir="ltr">Building Information Modeling (BIM) supports engineering and performance analysis for buildings and civil infrastructure from the initial design stage. BIM offers engineers access to building and infrastructure objects, along with their associated data, which can be utilized across various platforms to develop analytical models. However, the interoperability between BIM and analytical models is still limited and challenging. One such limitation and challenge is in the interoperability between BIM and Building Energy Modeling (BEM). Despite the fact that interoperability of geometry and material information between BIM and BEM has been extensively investigated, the interoperability of heating, ventilation, and air conditioning (HVAC) information, which is a crucial part in BEM, was underinvestigated. Another limitation is that the shared objects frequently lose their identification across different models during the processes of their creation, design iterations, and model transformation. In addition, current building and civil infrastructure projects mainly rely on Portable Document Format (PDF) plans as the official deliverables and documents to be stored, communicated, and transferred among different stakeholders. The transition from 2D PDF plans to 3D BIM remains challenging because manually creating a BIM instance model from 2D drawings can be laborious, time-intensive, and susceptible to errors.</p><p dir="ltr">To address these gaps, this dissertational research introduces new Industry Foundation Classes (IFC)-based algorithmic methods that utilize the state-of-the-art Data-driven Reverse Engineering Algorithm Development (D-READ) method and the invariant signatures of architecture, engineering, and construction (AEC) objects to (1) develop algorithms that can extract the information from 2D PDF drawings and reconstruct the 3D semantically segmented and enriched BIM instance models, (2) develop object mapping algorithms for interfacing BIM and analytical models (e.g., BEM, structural analysis models, etc.) by automatically mapping building objects, and (3) iteratively develop the HVAC information transformation algorithm between BIM and BEM. Following the proposed methods, algorithms were developed to (1) semi-automate the creation of semantically segmented and enriched 3D IFC-based bridge BIM instance models using 2D PDF bridge plans, (2) map space objects between BIM instance models and BEM (OpenStudio model) based on their invariant signatures, and (3) transform HVAC objects from IFC-based BIM instance models to BEM with all the necessary information for energy simulation, using (1) PDF drawings for 12 bridges located in various parts of Indiana, (2) a 2-story duplex apartment building, and (3) a 2-story office building model and a 2-story residential building model, respectively.</p><p dir="ltr">The developed algorithms were tested on three cases: (1) the PDF information extraction algorithm was tested on six bridges, which achieved 97.7% precision and 94.4% recall. In addition, it decreased the time required to create bridge BIM instance models by 94.9% compared to the manual approach; (2) the object mapping algorithm was evaluated using a 4-story office building model containing 82 spaces. The results demonstrated that the algorithm attained 90% precision and 90% recall in mapping space objects. Additionally, a 4.88% improvement in the accuracy of energy simulation results was observed when compared to simulations without space mapping; (3) the HVAC transformation algorithm was tested on two models with distinct HVAC systems: a 4-story office building model featuring a boiler radiator system and a 2-story clinic building featuring a VAV system. The algorithm achieved transformation accuracies of 97.5% and 98.7%, respectively, compared to manually created evaluation models in OpenStudio. Additionally, the algorithm-generated models demonstrated satisfactory performance with regard to precision, with less than 9.6% error in total annual energy consumption compared to the evaluation models.</p><p dir="ltr">This dissertational research introduces a new IFC-based approach to fill the forementioned research gaps in BIM interoperability for buildings and civil infrastructures. It facilitates improved accessibility compared to a proprietary workflow and will contribute to filling informational gaps (1) between 3D BIM and 2D PDF drawings, and (2) between BIM and analytical models. It builds a solid foundation for achieving (1) automated BIM reconstruction using 2D plans, and (2) smooth, accurate, and fully-automated HVAC objects transformation between BIM and BEM, for complete BIM-BEM interoperability. The proposed approach can also be leveraged to further expand BIM interoperability support by providing a novel data-driven approach for building and civil infrastructure projects.</p>