BIM log mining framework using deep learning for productivity assessment in construction facilities
Assessing construction productivity objectively and in real-time remains challenging. This study proposes an automated framework leveraging Building Information Modeling (BIM) interaction logs and predictive modeling. The methodology involves systematic log data processing and feature engineering, g...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-07-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2025.2508442 |
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| _version_ | 1849393201100619776 |
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| author | Ali Akbar Younghee Chang Jinwoo Song Seojoon Lee Sanghyeon Park Jinhyun Bae Soonwook Kwon |
| author_facet | Ali Akbar Younghee Chang Jinwoo Song Seojoon Lee Sanghyeon Park Jinhyun Bae Soonwook Kwon |
| author_sort | Ali Akbar |
| collection | DOAJ |
| description | Assessing construction productivity objectively and in real-time remains challenging. This study proposes an automated framework leveraging Building Information Modeling (BIM) interaction logs and predictive modeling. The methodology involves systematic log data processing and feature engineering, generation of pseudo-label productivity scores derived from an initial expert-informed model, and a rigorous comparative evaluation of predictive architectures. Sixteen diverse models were evaluated using 10-fold cross-validation on a 500-instance dataset derived from construction logs. The cross-validation identified XGBoost as the top-performing architecture (R2 = 0.97 ± 0.01), demonstrating the effectiveness of gradient boosting on the engineered tabular features. The framework incorporates an integrated interface with visualization and natural language processing for enhanced insight generation and accessibility. While acknowledging limitations concerning pseudo-label usage and initial data processing steps, this research presents a robust, validated methodology for data-driven productivity assessment, offering a scalable alternative to traditional methods in construction project management. |
| format | Article |
| id | doaj-art-db43ee25bcf44a55b8732a20c2a2ba75 |
| institution | Kabale University |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-db43ee25bcf44a55b8732a20c2a2ba752025-08-20T03:40:30ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-07-010011610.1080/13467581.2025.25084422508442BIM log mining framework using deep learning for productivity assessment in construction facilitiesAli Akbar0Younghee Chang1Jinwoo Song2Seojoon Lee3Sanghyeon Park4Jinhyun Bae5Soonwook Kwon6Sungkyunkwan UniversitySungkyunkwan UniversitySungkyunkwan UniversitySungkyunkwan UniversitySungkyunkwan UniversitySungkyunkwan UniversitySungkyunkwan UniversityAssessing construction productivity objectively and in real-time remains challenging. This study proposes an automated framework leveraging Building Information Modeling (BIM) interaction logs and predictive modeling. The methodology involves systematic log data processing and feature engineering, generation of pseudo-label productivity scores derived from an initial expert-informed model, and a rigorous comparative evaluation of predictive architectures. Sixteen diverse models were evaluated using 10-fold cross-validation on a 500-instance dataset derived from construction logs. The cross-validation identified XGBoost as the top-performing architecture (R2 = 0.97 ± 0.01), demonstrating the effectiveness of gradient boosting on the engineered tabular features. The framework incorporates an integrated interface with visualization and natural language processing for enhanced insight generation and accessibility. While acknowledging limitations concerning pseudo-label usage and initial data processing steps, this research presents a robust, validated methodology for data-driven productivity assessment, offering a scalable alternative to traditional methods in construction project management.http://dx.doi.org/10.1080/13467581.2025.2508442building information modeling (bim)deep learning architecturesconstruction productivitybim log mininglstm autoencoder |
| spellingShingle | Ali Akbar Younghee Chang Jinwoo Song Seojoon Lee Sanghyeon Park Jinhyun Bae Soonwook Kwon BIM log mining framework using deep learning for productivity assessment in construction facilities Journal of Asian Architecture and Building Engineering building information modeling (bim) deep learning architectures construction productivity bim log mining lstm autoencoder |
| title | BIM log mining framework using deep learning for productivity assessment in construction facilities |
| title_full | BIM log mining framework using deep learning for productivity assessment in construction facilities |
| title_fullStr | BIM log mining framework using deep learning for productivity assessment in construction facilities |
| title_full_unstemmed | BIM log mining framework using deep learning for productivity assessment in construction facilities |
| title_short | BIM log mining framework using deep learning for productivity assessment in construction facilities |
| title_sort | bim log mining framework using deep learning for productivity assessment in construction facilities |
| topic | building information modeling (bim) deep learning architectures construction productivity bim log mining lstm autoencoder |
| url | http://dx.doi.org/10.1080/13467581.2025.2508442 |
| work_keys_str_mv | AT aliakbar bimlogminingframeworkusingdeeplearningforproductivityassessmentinconstructionfacilities AT youngheechang bimlogminingframeworkusingdeeplearningforproductivityassessmentinconstructionfacilities AT jinwoosong bimlogminingframeworkusingdeeplearningforproductivityassessmentinconstructionfacilities AT seojoonlee bimlogminingframeworkusingdeeplearningforproductivityassessmentinconstructionfacilities AT sanghyeonpark bimlogminingframeworkusingdeeplearningforproductivityassessmentinconstructionfacilities AT jinhyunbae bimlogminingframeworkusingdeeplearningforproductivityassessmentinconstructionfacilities AT soonwookkwon bimlogminingframeworkusingdeeplearningforproductivityassessmentinconstructionfacilities |