Bonsang Koo

As the digitalization of construction standards accelerates, the integration of structural analysis results into Building Information Modeling (BIM) environments has become a critical prerequisite for effective BIM-based Automated Code Checking (ACC), particularly for structural code compliance. In current practice, structural analysis results generated by Computer-Aided Engineering (CAE) tools are often manually transferred into IFC-based BIM models, leading to inefficiencies and increased risk of human error. To address this limitation, this study proposes an extended IFC-based representation, termed IFC-KR-Structure, designed to systematically organize and manage section-wise and load combination-dependent structural analysis results required for code compliance within the IFC environment. Based on the proposed schema, an automated CAE-to-BIM integration module was implemented within the IFC-KR Toolkit to enable direct integration of analysis results generated by a commercial CAE tool (midas Civil NX) into IFC models. The approach establishes consistent element correspondence between structural and BIM models through coordinate alignment and spatial mapping procedures and represents multidimensional analysis results using a schema-compliant, tabular data structure embedded within IFC models. The applicability of the proposed framework was validated using a prestressed concrete girder bridge case, confirming that structural analysis results were accurately mapped, stored, visualized, and subsequently utilized within a BIM-based ACC workflow. The results demonstrate that the proposed approach enables systematic reintegration of CAE-generated analysis results into BIM models and significantly improves the efficiency, consistency, and reliability of BIM-based code compliance processes.

As the digitalization of construction standards accelerates, the integration of structural analysis results into Building Information Modeling (BIM) environments has become a critical prerequisite for effective BIM-based Automated Code Checking (ACC), particularly for structural code compliance. In current practice, structural analysis results generated by Computer-Aided Engineering (CAE) tools are often manually transferred into IFC-based BIM models, leading to inefficiencies and increased risk of human error. To address this limitation, this study proposes an extended IFC-based representation, termed IFC-KR-Structure, designed to systematically organize and manage section-wise and load combination–dependent structural analysis results required for code compliance within the IFC environment. Based on the proposed schema, an automated CAE-to-BIM integration module was implemented within the IFC-KR Toolkit to enable direct integration of analysis results generated by a commercial CAE tool (midas Civil NX) into IFC models. The approach establishes consistent element correspondence between structural and BIM models through coordinate alignment and spatial mapping procedures and represents multidimensional analysis results using a schema-compliant, tabular data structure embedded within IFC models. The applicability of the proposed framework was validated using a prestressed concrete girder bridge case, confirming that structural analysis results were accurately mapped, stored, visualized, and subsequently utilized within a BIM-based ACC workflow. The results demonstrate that the proposed approach enables systematic reintegration of CAE-generated analysis results into BIM models and significantly improves the efficiency, consistency, and reliability of BIM-based code compliance processes.

In Korea, research initiatives have commenced to digitize engineering and construction codes, and automate their compliance using BIM models for mandatory submission in public projects. The initiatives require employing IFC, a neutral and standard data format, to associate individual codes with relevant BIM elements. However, the IFC does not provide explicit extensions to represent the varied codes, which include the Korean Design Standards (KDS) and the Korean Construction Specifications (KCS). To address this limitation, this study developed an extended IFC schema called 'IFC-KR', focusing initially on bridges. The extensions define subtypes to represent various types of bridges, structures, and elements, along with property sets that capture the relevant attributes and properties of 1,437 KDS codes. Moreover, a specialized extension module named 'IFC-KR Toolkit' has been developed to automate the addition of KDS data relevant to each element within the IFC Physical File. To validate the functionality of the module, KDS items were applied to an Extradosed (ED) cablestayed bridge model. The results confirmed that all data were accurately integrated, and the extended data was consistently verified using a third-party IFC viewer, demonstrating its accuracy and interoperability.

The South Korean government has mandated the use of Building Information Modeling (BIM) as the main medium for design deliverables in public projects. Such stipulations require detailed BIM models to be submitted in the detailed design phases, which places a burden especially on small- to medium-sized design firms. The requirement means that both the interior and exterior finishes of a building need to be explicitly modeled, which when performed manually is labor intensive and error prone. The goal of this study was to apply parametric modeling techniques to automatically model such details, focusing on the exterior finishes. Through interviews with design experts, four elements were selected: parapets, external wall finishes, downspouts, and inspection ladders. Parametric processes and rules for each finish were developed and subsequently programmed as add-ins in Autodesk Revit. Validation through case studies conducted using three different BIM models resulted in the average scores of 92.28% and 92.44% in terms of accuracy and efficiency, respectively. In particular, downspouts achieved a perfect accuracy score, while external wall finishes achieved a 94.42% reduction in modeling time. Modeling errors, which occurred due to inconsistencies in the test models, also demonstrate the need for specifying uniform modeling conventions of BIM model submissions in the detailed design phases.

Numerous studies have developed extensions to the IFC schema to meet the needs of specialized domains or represent nascent technologies, and in turn have expanded the scope of interoperability for BIM data exchanges. However, these studies used varying approaches for IFC extensions and validation, making it difficult to identify research gaps and agree on legitimate extension protocols. This study collected 64 studies of IFC schema extensions spanning over two decades, from 2001 to 2022. The analysis first focused on categorizing these cases with respect to their target domains and sectors, their purpose and extension approaches, as well as their methods for implementation and validation. Timeline analyses were also conducted to track the temporal trends over the specified period. The results revealed that architectural cases have recently shifted from process to product representations due to new technology adoptions, while infrastructure cases, initially centered on major sector elements, have transitioned towards operation and maintenance processes. The findings also showed the need for a more holistic and organized approach for extensions, as current ad hoc developments were limited to products and processes only applicable for specific sectors.

The utilization of Building Information Modeling (BIM) on-site has been limited due to the lack of an appropriate medium for visualizing and accessing models and project data intuitively in the field. AROS, an Augmented Reality-based site management system, was developed to allow the visual projection of BIM models and relevant data directly to field personnel. Detailed field experiments with inspection experts revealed specific issues with the stability and accurate placement of model projections in AROS. Projection stability was improved by reducing the number of triangle meshes of the model to relieve the need for processing power. Investigations revealed that simplification rates of 40% and 20% were optimal for rectilinear and curvilinear components, respectively. Projection placement was improved by implementing a hybrid of target anchoring methods. Specifically, ARWorldMap was used to add additional anchor points, which were identified from the topologies of the structure and surrounding planes. Post-evaluations demonstrated increased stability and reductions in displacement errors. The formalizations provide measures for using AR and BIM models when applying these technologies to large-scale civil infrastructure projects.

Recent studies in the domain of semantic enrichment have employed artificial intelligence (AI) approaches to distinguish and classify building information modeling (BIM) elements to check their conformance with open standard data formats. Training AI algorithms requires the development of well-balanced and robust datasets of BIM elements. However, collection is difficult as sources are limited to existing models and sample libraries. This study developed a parametric augmentation approach to create synthetic copies of BIM elements, and thus rapidly supplement manually collected samples. The approach was used to create ArchShapesNet, a dataset consisting of 11 common architectural elements with an equal size of 4,000 samples per class. Two multi-view convolutional neural networks (CNN), a geometric deep learning algorithm, were trained and tested separately on ArchShapesNet and an initial dataset with sample imbalances. Results showed significant improvement in the accuracy and F₁ scores, providing evidence of the utility of ArchShapesNet. The size and scope of the dataset are considered to be the first of their kind and provide a benchmark for testing the semantic integrity of BIM models. The augmentation approach also provides a general framework to create custom datasets for different specialties in the Architectural Engineering and Construction industry.

“High risk high return” is a general rule in the overall industry; however, high-risk projects in the construction industry frequently fail to yield a high return. In order to achieve a sustainable business in the international construction market, contractors require an average to high return yield under high-risk conditions. This study aims to reveal what risk factors and risk management performance enables high-risk projects to yield high returns. The study investigated 124 international construction projects by Korean contractors and classified them into four groups: high-risk high-return (HH), high-risk low-return (HL), low-risk high-return (LH), and low-risk low-return (LL). The study found that risk assessment accuracy was the most important trigger in discriminating between high return projects (HH, LH) and low return projects (HL, LL), whereas risk mitigation performance showed little difference between high return and low return projects. In addition, the contingency amount did not significantly affect project return in HL, LH, and LL projects, but HH projects showed a positive relation between contingency and predicted risk amount. This article contributes to recognizing the differences between high return and low return projects and provides insights for practitioners into the relation between risk management performance and high returns in different risk conditions.