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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 謝尚賢 | zh_TW |
| dc.contributor.advisor | Shang-Hsien HSIEH | en |
| dc.contributor.author | Akarsth Kumar Singh | zh_TW |
| dc.contributor.author | Akarsth Kumar Singh | en |
| dc.date.accessioned | 2023-09-22T16:25:43Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-12 | - |
| dc.identifier.citation | References
[1] F. Amer and M. Golparvar-Fard. Formalizing Construction Sequencing Knowledge and Mining Company-Specific Best Practices from Past Project Schedules. In Proceeding of the ASCE International Conference on Computing in Civil Engineering 2019, pages 215–223. American Society of Civil Engineers (ASCE), 2019. [2] F. Amer and M. Golparvar-Fard. Modeling Dynamic Construction Work Template from Existing Scheduling Records via Sequential Machine Learning. Advanced Engineering Informatics, 47:101198, 2021. [3] F. Amer, J. Hockenmaier, and M. Golparvar-Fard. Learning and Critiquing Pairwise Activity Relationships for Schedule Quality Control via Deep Learning-Based Natural Language Processing. Automation in Construction, 134:104036, 2022. [4] F. Amer, Y. Jung, and M. Golparvar-Fard. Transformer Machine Learning Language Model for Auto-alignment of Long-term and Short-term Plans in Construction. Automation in Construction, 132, 2021. [5] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5:135–146, 2017. [6] S. Changali, A. Mohammad, and M. Van Nieuwland. The Construction Productivity Imperative. McKinsey Quarterly, pages 1–10, 2015. [7] A. Darwiche, R. E. Levitt, and B. Hayes-Roth. OARPLAN: Generating Project Plans by Reasoning about Objects, Actions and Resources. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing, 2(3):169–181, 1988. [8] H. Doloi. Cost Overruns and Failure in Project Management: Understanding the Roles of Key Stakeholders in Construction Projects. Journal of Construction Engineering and Management, 139(3):267–279, 2013. [9] T. Dong, M. Fischer, D. Ge, R. Levitt, and S. U. C. . E. E. Department. Automated Look-ahead Schedule Generation and Optimization for the Finishing Phase of Complex Construction Projects. Stanford University, 2012. [10] B. D. Echeverry, S. Member, C. W. Ibbs, and S. Kim. Sequencing Knowledge for Construction Scheduling. Journal of Construction Engineering and Management, 117(1):118–130, 1991. [11] T. E. El-Diraby. Domain Ontology for Construction Knowledge. Journal of Construction Engineering and Management, 139(7):768–784, 2013. [12] M. ElMenshawy and M. Marzouk. Automated BIM schedule generation approach for solving time–cost trade-off problems. Engineering, Construction and Architectural Management, 28(10):3346–3367, 2021. [13] V. Faghihi, A. Nejat, K. F. Reinschmidt, and J. H. Kang. Automation in Construction Scheduling: a Review of the Literature. International Journal of Advanced Manufacturing Technology, 81(9-12):1845–1856, 2015. [14] A. Fazeli. Automated 4D BIM development : The Resource Specification and Optimization Approach. Engineering, Construction and Architectural Management, 2022. [15] M. A. Fischer, A. Member, and F. A. Asce. Scheduling with Computer- Interpretable Construction MethodModels. Journal of Construction Engineering and Management, 122(4):337–347, 1996. [16] J. P. Fitzsimmons, R. Lu, Y. Hong, and I. Brilakis. Construction Schedule Risk Analysis - a Hybrid Machine Learning Approach. Journal of Information Technology in Construction, 27(February 2021):70–93, 2022. [17] H. Hamledari, B. McCabe, S. Davari, and A. Shahi. Automated Schedule and Progress Updating of IFC-Based 4D BIMs. Journal of Computing in Civil Engineering, 31(4):1–16, 2017. [18] K. K. Han, D. Cline, and M. Golparvar-fard. Formalized knowledge of construction sequencing for visual monitoring of work-in-progress via incomplete point clouds and low-LoD 4D BIMs. Advanced Engineering Informatics, 29(4):889–901, 2015. [19] Y. Hong, H. Xie, E. Agapaki, and I. Brilakis. Graph-Based Automated Construction Scheduling without the Use of BIM. Journal of Construction Engineering and Management, 149(2), 2023. [20] Y. Hong, H. Xie, V. Hovhannisyan, and I. Brilakis. A Graph-based Approach for Unpacking Construction Sequence Analysis to Evaluate Schedules. Advanced Engineering Informatics, 52:101625, 2022. [21] C. D. M. Jeffrey Pennington, Richard Socher. GloVe: Global Vectors for Word Representation. Proceedings of EMNLP, pages 1532–1543, Doha, Qatar, 31(6):682– 687, 2017. [22] H. Kim, K. Anderson, S. Lee, and J. Hildreth. Generating Construction Schedules through Automatic Data Extraction Using Open BIM (Building Information Modeling) Technology. Automation in Construction, 35:285–295, 2013. [23] B. Koo, M. Fischer, and J. Kunz. Formalization of Construction Sequencing Rationale and Classification Mechanism to Support Rapid Generation of Sequencing Alternatives. 21(6):423–433, 2008. [24] S.-k. Lee, K.-r. Kim, and J.-h. Yu. Automation in Construction BIM and ontology based approach for building cost estimation. Automation in Construction, 41:96– 105, 2014. [25] X. Li, C. Wu, Z. Yang, Y. Guo, and R. Jiang. Knowledge graph-enabled adaptive work packaging approach in modular construction. Knowledge-Based Systems, 260, 2023. [26] H. Liu, M. Al-Hussein, and M. Lu. BIM-based integrated approach for detailed construction scheduling under resource constraints. Automation in Construction, 53:29–43, 2015. [27] T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, pages 1–12, 2013. [28] E. Mikulakova, M. König, E. Tauscher, and K. Beucke. Advanced Engineering Informatics Knowledge-based schedule generation and evaluation. Advanced Engineering Informatics, 24(4):389–403, 2010. [29] A. Pal, J. J. Lin, and S.-H. Hsieh. Automated Construction Progress Monitoring of Partially Completed Building Elements Leveraging Geometry Modeling and Appearance Detection with Deep Learning. In Proceedings of the Construction Research Congress 2022, pages 708–717, 2022. [30] A. Pal, J. J. Lin, and S.-H. Hsieh. Schedule-driven Analytics of 3D Point Clouds for Automated Construction Progress Monitoring. In International Conference on Computing in Civil Engineering 2023, pages 1–8, 2023. [31] PMI. A Guide to the Project Management Body of Knowledge (PMBOK Guide), volume 6. 2016. [32] R. Sacks, I. Brilakis, E. Pikas, H. S. Xie, and M. Girolami. Construction with Digital Twin Information Systems. Data-Centric Engineering, 1(6), 2020. [33] S. M. Sepasgozar, R. Karimi, S. Shirowzhan, M. Mojtahedi, S. Ebrahimzadeh, and D. McCarthy. Delay Causes and Emerging Digital Tools: A Novel Model of Delay Analysis, Including Integrated Project Delivery and PMBOK. Buildings, 9(9), 2019. [34] Q. Shen, S. Wu, Y. Deng, H. Deng, and J. C. Cheng. BIM‐Based Dynamic Construction Safety Rule Checking Using Ontology and Natural Language Processing. Buildings, 12(5), 2022. [35] K. Sigalov and M. König. Recognition of Process Patterns for BIM-based Construction Schedules. Advanced Engineering Informatics, 33:456–472, 2017. [36] J. Singh and C. J. Anumba. Real-Time Pipe System Installation Schedule Generation and Optimization Using Artificial Intelligence and Heuristric Techniques. Journal of Information Technology in Construction (ITcon), 27:173–190, 2022. [37] S. Staub-french, A. M. Asce, M. Fischer, A. M. Asce, J. Kunz, B. Paulson, and M. Asce. An Ontology for Relating Features with Activities to Calculate Costs. 17(October):243–254, 2003. [38] A. J. P. Tixier, M. Vazirgiannis, and M. R. Hallowell. Word Embeddings for the Construction Domain, 2016. [39] H. W. Wang, J. R. Lin, and J. P. Zhang. Work package-based information modeling for resource-constrained scheduling of construction projects. Automation in Construction, 109:102958, 2020. [40] Z. Wang and E. Rezazadeh Azar. BIM-Based Draft Schedule Generation in Reinforced Concrete-Framed Buildings. Construction Innovation, 19(2):280–294, 2019. [41] K. Woestenenk. Implementing the lexicon for practical use. 2000. [42] S. Wu, Q. Shen, Y. Deng, and J. Cheng. Natural-Language-Based Intelligent Retrieval Engine for BIM Object Database. Computers in Industry, 108:73–88, 2019. [43] Q. Xie, X. Zhou, J. Wang, X. Gao, X. Chen, and L. Chun. Matching Real-World Facilities to Building Information Modeling Data Using Natural Language Processing. IEEE Access, 7:119465–119475, 2019. [44] A. Zahedi, J. Abualdenien, F. Petzold, and A. Borrmann. Bim-Based Design Decisions Documentation Using Design Episodes, Explanation Tags, and Constraints. Journal of Information Technology in Construction, 27:756–780, 2022. [45] X. Zhao, K. W. Yeoh, and D. K. H. Chua. Extracting Construction Knowledge from Project Schedules Using Natural Language Processing. In Lecture Notes in Mechanical Engineering, pages 197–211, 2020. [46] Y. J. Zidane and B. Andersen. The Top 10 Universal Delay Factors in Construction Projects. International Journal of Managing Projects in Business, 11(3):650–672, 2018. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89859 | - |
| dc.description.abstract | 幾十年來，建築、工程和施工(AEC) 行業一直遭受延誤和成本超支的困擾。為了按時並在預算範圍內完成項目，AEC 行業在很大程度上依賴於施工項目進度。然而，這些進度表仍然是手動創建和更新的，這會花費大量時間，導致錯誤，並導致規劃和調度不善，這是當今項目延誤的主要原因之一。因此，克服這些挑戰需要一種自動化的施工進度管理方法，該方法從現有和以前的數據庫中提取資訊和知識，並學習施工過程以改進施工規劃和進度安排。本研究旨在開發一種基於學習的施工進度模板，自動從歷史專案規劃和進度記錄中學習施工知識以生成進度模板。在最近的研究中，自然語言處理(NLP) 技術可以通過提供從專案文檔、模型和過去的專案知識中提取和解釋數據的能力，徹底改變施工規劃和調度。具體來說，我們提出了基於構建活動的向量和實體表示的基於自動學習的進度開發，其中使用三種不同的語言模型（基於BERT 的uncased、DistilBERT 和RoBERTa）對排序和邏輯依賴知識進行建模。我們從各個建築項目獲得的16 個施工進度表的多樣化數據集對本研究所提出的基於學習的進度建構方法進行了詳盡的測試和驗證。結果表明，我們的方法可以有效地組織施工進度活動並準確預測活動的順序及其在不同工作包中的邏輯聯繫。它支持規劃人員建立利用歷史數據的知識管理系統，從而改進未來專案中重複情境的決策。 | zh_TW |
| dc.description.abstract | The Architecture, Engineering, and Construction (AEC) industry has been suffering from delays and cost overruns for decades. To complete projects on time and under budget the AEC industry heavily relies on construction project planning and scheduling. However, these schedules are still created and updated manually, which takes a lot of time, causes mistakes, and leads to poor planning and scheduling, one of the main reasons for project delays today. Consequently, overcoming these challenges requires an automated construction plan schedule management method that extracts information and knowledge from existing and previous databases as well as learns the construction process to improve construction planning and scheduling. This study developed a learning-based construction planning which, automatically learns the construction knowledge from historical project planning and scheduling records. In recent studies, Natural Language Processing (NLP) technologies that can revolutionise construction planning and scheduling by providing the ability to extract and interpret data from project documents, models, and past project knowledge-based. Specifically, we present Automatic Learning-Based Construction Planning based on a vector and entity representation for construction activities where the sequencing and logic dependency knowledge is modeled with three different language model (BERT-based uncased, DistilBERT and RoBERTa). Our learning-based language models are exhaustively tested and validated on a diverse dataset of 16 construction schedules obtained from various building projects. The results indicated that our method is effective in formalizing construction planning activities and accurately predicting the sequences of activities and their logic dependency in different work items. It supports the planners to establish knowledge management systems that leverage historical data, leading to improved decision making for repetitive scenarios in future projects. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T16:25:43Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T16:25:43Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Contents
Page Verification Letter from the Oral Examination Committee i 摘要iii Abstract v Contents vii List of Figures ix List of Tables xi Chapter 1 Introduction 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Scope of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 2 Literature Review 9 2.1 Automation in Planning and Scheduling . . . . . . . . . . . . . . . . 9 2.2 Model-Based in Construction Scheduling . . . . . . . . . . . . . . . 11 2.3 Learning-Based in Construction Scheduling . . . . . . . . . . . . . . 14 2.4 Formalization of Activities And Sequences in Construction Scheduling 17 Chapter 3 Methodology 21 3.1 Framework and Workflow Overview . . . . . . . . . . . . . . . . . 21 3.2 Embedding Techniques for Construction Activities . . . . . . . . . . 24 3.3 Activity Entity Recognition for Construction Activities . . . . . . . . 27 3.4 Sequence Learning and Logic Dependency Approaches for Construction Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Chapter 4 Result and Discussion 35 4.1 Data Collection and Annotation . . . . . . . . . . . . . . . . . . . . 35 4.2 Evaluation of Word and Activity Embedding . . . . . . . . . . . . . 38 4.3 Performance of Part of Activity . . . . . . . . . . . . . . . . . . . . 43 4.4 Activity Sequence Learning with Language Model . . . . . . . . . . 45 4.5 Evaluation of Logic Dependency with Language Model . . . . . . . 50 Chapter 5 Conclusion and Future Work 55 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 References 59 | - |
| dc.language.iso | en | - |
| dc.subject | 工作包形式化 | zh_TW |
| dc.subject | 自動化施工規劃與排程 | zh_TW |
| dc.subject | 從過去的排程學習 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | Learning from Past Schedule | en |
| dc.subject | Formalizing Work Items | en |
| dc.subject | Automated Construction Planning | en |
| dc.subject | Natural Language Processing (NLP) | en |
| dc.title | 利用自然語言處理技術學習過去施工排程以達成自動施工規劃 | zh_TW |
| dc.title | Automated Construction Planning using NLP to Learn from Past Schedules | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林之謙;林祐正 | zh_TW |
| dc.contributor.oralexamcommittee | Jacob Je-Chian LIN;Yu-Cheng Lin | en |
| dc.subject.keyword | 自動化施工規劃與排程,從過去的排程學習,工作包形式化,自然語言處理, | zh_TW |
| dc.subject.keyword | Automated Construction Planning,Learning from Past Schedule,Formalizing Work Items,Natural Language Processing (NLP), | en |
| dc.relation.page | 65 | - |
| dc.identifier.doi | 10.6342/NTU202303805 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-12 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| 顯示於系所單位： | 土木工程學系 | |
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