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Research Article
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Barriers to BIM Adoption and Strategies for Accelerating BIM Implementation in Developing Countries: A Case Study of Myanmar’s Construction Industry
개발도상국의 BIM 도입 장벽과 BIM 구현 가속화 전략: 미얀마 건설 산업 사례 연구
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Yin Nyein Pann, Leenseok Kang
판옌 니인, 강인석
- Building Information Modeling (BIM) holds considerable promise for enhancing the efficiency of construction processes, including design, construction, and maintenance. Nevertheless, the adoption …
- Building Information Modeling (BIM) holds considerable promise for enhancing the efficiency of construction processes, including design, construction, and maintenance. Nevertheless, the adoption and utilization of BIM remains insufficiently advanced within the Architecture, Engineering, and Construction (AEC) industries of both developed and developing nations. This study investigates the barriers to BIM adoption and strategies to fast-track its implementation among construction firms in Myanmar. Through a literature review, 28 barriers and 16 strategies to overcome them were identified and refined through a questionnaire survey with 56 construction professionals in Myanmar. The findings reveal that inadequate government policies and legislation, the absence of mandatory requirements, a lack of support from the government, inadequate BIM project experience, and a lack of BIM awareness and understanding constitute the most significant barriers to BIM adoption. The recommended strategies to facilitate adoption include enhancing BIM awareness and understanding, developing BIM guidelines, providing BIM-related IT training, offering additional training opportunities, and providing government support for the use of BIM. These findings highlight the necessity for a comprehensive strategy encompassing education, standardization, upskilling, and policy advocacy in order to promote BIM adoption and integration within the Myanmar construction industry. This study helps filling the gap of research on BIM adoption issues in developing countries and provides a rich information source for stakeholders who plan BIM implementation in analogous contexts. This study acknowledges the limitation of sample size which included 56 respondents that did not adequately represent the broader context of the Myanmar construction industry. Therefore caution should be taken when generalizing the finding. Future studies should include a larger and more diverse sample in order to improve validity. - COLLAPSE
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Barriers to BIM Adoption and Strategies for Accelerating BIM Implementation in Developing Countries: A Case Study of Myanmar’s Construction Industry
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Research Article
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Formal Grammar-Based Inspection Rule Framework for Automated BIM Data Quality Management
BIM데이터 품질검토 자동화를 위한 정형 문법 기반 검사 규칙 프레임워크 연구
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Taewook Kang, Suwon Song
강태욱, 송수원
- This study proposes a format grammar-based quality inspection framework to overcome the inefficiencies caused by the heterogeneity and tool dependency of various …
- This study proposes a format grammar-based quality inspection framework to overcome the inefficiencies caused by the heterogeneity and tool dependency of various BIM data formats, such as IFC and COBie. Existing quality inspection methods rely on format-specific scripts, which prevent the creation of an independent, consistent rule set for quality management. To solve this, the research introduces a structured, key-value-based rule grammar that functions independently of the BIM data model. The framework supports multiple condition types (e.g., range, list, equation) and uniquely integrates a Large Language Model (LLM) to interpret rules requiring contextual or semantic judgment. This allows for the automation of complex, text-based regulation checks that are difficult for traditional rule-based systems. A rule interpretation engine processes these rules, and users can upload BIM files, visually review the inspection results, and export reports in PDF, Excel, or BCF formats. Experimental results confirm that a single ruleset can be applied effectively across different BIM formats. The LLM demonstrated high potential for automating semantic-based inspections, proving more effective than existing methods. This framework serves as a foundational technology for systematically and reusably managing the quality of diverse BIM models throughout the design, construction, and maintenance phases. - COLLAPSE
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Formal Grammar-Based Inspection Rule Framework for Automated BIM Data Quality Management
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Research Article
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Recommendations for Urban Planning Policies for Seoul's Universities based on a Comparison of Seoul's Campus Towns with Overseas Cases
서울 캠퍼스타운과 해외사례 비교를 통한 서울시 대학 중심 도시계획 정책 제언
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Hyun-Nam Sim, In-Su Na
심현남, 나인수
- This study aims to analyze Seoul's university-linked urban planning model and propose future development directions. Seoul has been expanding university-linked urban planning …
- This study aims to analyze Seoul's university-linked urban planning model and propose future development directions. Seoul has been expanding university-linked urban planning by establishing Campus Towns at 39 universities since 2016. In the context of this project expansion, this study examines the definition and characteristics of university-linked urban planning, analyzes overseas cases and Seoul's Campus Town project cases, and proposes future development directions. The research methodology involved comparative analysis of overseas cases including China's Zhongguancun, the United States' Kendall Square, Sweden's Malmö, Finland's Startup Sauna, and Germany's EUREF, alongside Seoul's Campus Town projects in terms of actors, methods, and content. The analysis revealed that improving university-public cooperation structures, diversifying project methods, and strengthening connectivity between startups and regional development were identified as key tasks for enhancing the effectiveness of Campus Town projects. While this study has limitations in addressing a restricted number of overseas cases, it holds significance in providing new policy perspectives for domestic university-linked urban planning. - COLLAPSE
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Recommendations for Urban Planning Policies for Seoul's Universities based on a Comparison of Seoul's Campus Towns with Overseas Cases
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Research Article
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Semantic Knowledge Graph Construction and Utilization for Clash Type and Severity Classification in Federated BIM Models
시멘틱 지식그래프 구축 및 활용을 통한 BIM 모델 내 간섭 유형 및 심각도 분류 방안
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Dong-Uk Shin, Young-Su Yu, Won-Bok Lee, Hyun-Woo Lee, Bon-Sang Koo
신동욱, 유영수, 이원복, 이현우, 구본상
- Clash detection in BIM is a critical process for identifying potential interferences during the design phase and preventing design errors. However, conventional …
- Clash detection in BIM is a critical process for identifying potential interferences during the design phase and preventing design errors. However, conventional clash detection approaches are limited to simple geometric overlap checks and fail to consider contextual information such as clash types and severity, reducing their effectiveness in supporting design adjustments. To address these limitations, this study proposes the ‘BIMClash’ framework, which leverages a semantic knowledge graph to represent clash information and classifies both clash types and severity levels using Cypher-based automated queries. A domain-specific ontology schema was developed, and object and relational data were extracted from IFC-based BIM models to construct the semantic graph in Neo4j. The framework incorporates spatial adjacency, attribute data, and predefined classification criteria to enable automated clash classification. Experimental results demonstrated that BIMClash achieved over 90% accuracy in clash type and severity classification compared to expert annotations, while reducing classification time by approximately 84% relative to manual processes. These findings highlight the practical value of the proposed framework in enhancing both the accuracy and efficiency of clash detection during the design stage. - COLLAPSE
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Semantic Knowledge Graph Construction and Utilization for Clash Type and Severity Classification in Federated BIM Models
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Research Article
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Empirical Validation of wise‑BIM: A Large‑Language‑Model- Driven Framework for BIM Modeling Evaluation and Feedback
LLM 기반 BIM 모델링 평가 및 피드백 프레임워크의 설계와 실증적 효과
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Do Young Kim, Sun Joo Cho
김도영, 조선주
- This study proposes wise-BIM, a BIM modeling evaluation and feedback framework that embeds large language models (LLMs), and empirically validates its effectiveness …
- This study proposes wise-BIM, a BIM modeling evaluation and feedback framework that embeds large language models (LLMs), and empirically validates its effectiveness in civil-engineering design and checking scenarios. The framework comprises three stages: (A) structuring decision nodes, (B) defining purpose-centered elements, and (C) a RAG-prompt conversational feedback loop. By integrating static resources with dynamic reasoning, wise-BIM provides contextualized guidance to modelers without training on project files. In a minimum-viable expert study across representative tasks, the LLM-RAG condition, compared with manual work, improved goal refinement, reduced error-detection time, decreased rework cycles, lowered cognitive workload (NASA-TLX), reduced clarification requests (Clarify count), and increased usability (SUS). We also observed model-specific trade-offs between communication efficiency and the depth of goal elaboration, informing practical model-selection strategies. These findings indicate that conversational feedback by LLMs can mitigate the adaptability and explainability limitations of rule-based automation in BIM and provide a human-in-the-loop pathway for quality control during modeling. - COLLAPSE
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Empirical Validation of wise‑BIM: A Large‑Language‑Model- Driven Framework for BIM Modeling Evaluation and Feedback
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Research Article
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A Research on the Utilization of BIM Libraries for CPTED Certification Assessment
BIM 기반 CPTED 인증평가를 위한 라이브러리 활용방안에 대한 연구
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Eun-Sang Yu, Yong-Han Ahn, Jung-Sik Choi
유은상, 안용한, 최중식
- This Research proposes a BIM-based methodology to support automated evaluation for CPTED (Crime Prevention Through Environmental Design) certification. Traditional certification methods rely …
- This Research proposes a BIM-based methodology to support automated evaluation for CPTED (Crime Prevention Through Environmental Design) certification. Traditional certification methods rely heavily on manual reviews of 2D drawings, which often result in subjective judgments and a lack of design-stage feedback. To address these limitations, this research develops a structured evaluation framework that maps CPTED criteria to specific BIM object attributes using a predefined library. Key evaluation factors—including territoriality, surveillance, access control, and lighting—are linked to BIM elements such as walls, fences, doors, lighting fixtures, cameras, and signage. Each object’s parameters, including material, height, visibility, and placement, are used to automate the assessment process based on attribute rules and spatial data. The study also defines a library structure for these elements, enabling consistent use across various building types. Through this structured connection between BIM data and CPTED principles, the system can provide designers with immediate feedback and minimize the need for post-design modifications. Furthermore, the framework facilitates compatibility with other certification systems and enables scenario-based simulation in early design stages. This research demonstrates that BIM libraries, when structured with evaluation-relevant properties, can significantly improve the efficiency, reliability, and objectivity of CPTED certification. The proposed methodology forms a foundation for future development of fully automated certification support systems in the architectural domain. - COLLAPSE
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A Research on the Utilization of BIM Libraries for CPTED Certification Assessment
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Research Article
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Enhancing Construction Equipment Detection with Generative AI-Based Synthetic Image Data
건설장비 객체인식 모델 성능 향상을 위한 생성형 AI 기반 합성 이미지 데이터 활용
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SeongHyun Moon, Ahreum Lee, Yong-Ju Lee, Man-Woo Park
문성현, 이아름, 이용주, 박만우
- Dynamic and complex construction environments require effective monitoring to enhance productivity and ensure safety. While recent advancements in computer vision and deep …
- Dynamic and complex construction environments require effective monitoring to enhance productivity and ensure safety. While recent advancements in computer vision and deep learning have enabled the application of object detection models for this purpose, their performance is often constrained by the scarcity and diversity of training data. The process of collecting and annotating sufficient real-world construction images is labor-intensive and frequently limited by specific site conditions. This study addresses this challenge by investigating whether synthetic images, generated via a Text-to-Image model, can effectively supplement real datasets. For our experiments, we extracted roller and dozer classes from two publicly available datasets, ACID and MOCS, and generated corresponding synthetic images. We trained YOLOv11n models under three distinct scenarios: using synthetic images only, real images only, and a combination of both. Models trained with real data augmented by synthetic images showed consistent performance gains in both mAP50 and mAP50-95. This performance improvement was most significant with the smaller MOCS dataset, suggesting that synthetic data has a stronger supplementary effect when real-world data is limited. - COLLAPSE
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Enhancing Construction Equipment Detection with Generative AI-Based Synthetic Image Data
Journal of KIBIM
Journal of KIBIM
한국BIM학회
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Tel: +82-2-567-4478 / Fax: +82-2-552-4729 / E-mail: kibim@kibim.or.kr Copyright© Journal of KIBIM. Powered by APUB