Przyszłość modelowania informacji o budynku - systematyczny przegląd literatury z wykorzystaniem sztucznej inteligencji

• Autorzy: Borkowski Andrzej Szymon

Identyfikatory

DOI

10.5604/01.3001.0054.3484

Warianty tytułu

EN

The future of building information modeling - a systematic literature review using artificial intelligence

• Języki publikacji: PL

Abstrakty

PL

BIM jest podstawą do zrealizowania idei cyfrowego bliźniaka, co z kolei przybliża branżę budowlaną do koncepcji gospodarki o obiegu zamkniętym. BIM łączy się z GIS, IoT, ML i wykorzystuje metody sztucznej inteligencji. Dotychczasowe badania wielokrotnie mapowały BIM w różnych kontekstach. W systematycznych przeglądach naukowcy przeanalizowali obecny stan wiedzy i/lub techniki. Zidentyfikowaną luką badawczą jest brak systematycznego przeglądu, który mapowałby publikacje dotyczące przyszłości BIM. Stąd celem niniejszego artykułu było przeprowadzenie szerokiego systematycznego przeglądu dostępnych prognoz rozwoju BIM i kierunków przyszłych badań nad BIM. W tym celu wykorzystano narzędzia sztucznej inteligencji - dimension.ai i ChatGPT4. Do mapowania wykorzystano narzędzie VosViewer. W artykule zaprezentowano obecne i przyszłe wyzwania stojące przed branżą AECOO. Przedstawiono również prognozy dotyczące rozwoju BIM w nadchodzących latach.

EN

BIM is the basis for arriving at the idea of the digital twin, which in turn brings the construction industry closer to the concept of the circular economy. BIM enters into fusion with GIS, loT, ML and uses artificial intelligence methods. Research to date has repeatedly mapped BIM in different contexts. In systematic reviews, researchers have analyzed the current state of knowledge and/or techniques. An identified research gap is the lack of a systematic review that maps publications on the future of BIM. Hence, the aim of this thesis was to conduct a broad systematic review of available predictions of BIM development and directions for future BIM research. Artificial intelligence tools - dimension.ai and ChatGPT4 - were used to support this aim. The VosViewer tool was used in the mapping. The paper presents the current and future challenges facing the AECOO industry. A prediction was also made about the development of BIM in the coming years.

Słowa kluczowe

PL

modelowanie informacji o budynku; BIM; prognoza rozwoju; kierunek rozwoju; kierunek badań; sztuczna inteligencja; przegląd literatury

EN

building information modelling; BIM; development forecast; development direction; research direction; artificial intelligence; literature review

• Wydawca: Fundacja Polskiego Związku Inżynierów i Techników Budownictwa
• Czasopismo: Inżynieria i Budownictwo
• Rocznik: 2024
• Tom: R. 80, nr 1-2
• Strony: 2--7
• Opis fizyczny: Bibliogr. 35 poz., il., tab.

Twórcy

autor

Borkowski Andrzej Szymon

• Politechnika Warszawska, Wydział Geodezji i Kartografii

• https://orcid.org/0000-0002-7013-670X

Bibliografia

• [1] Xu J., & Lu W.: (2021). The iron triangle of BIM adoption in construction project organizations. In Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate (pp. 1363-1377). Springer Singapore.
• [2] Borkowski A.S.: (2023). Evolution of BIM: epistemology, genesis and division into periods. Journal of Information Technology in Construction (ITcon), 28(34), 646-661.
• [3] Sepasgozar S.M., Khan A.A., Smith K., Romero J.G., Shen X., Shirowzhan S. et al.: (2023). BIM and Digital Twin for Developing Convergence Technologies as Future of Digital Construction. Buildings, 13(2), 441.
• [4] Mêda P., Calvetli D., Hjelseth E., Sousa H.: (2021). Incremental digital twin conceptualizations targeting data-driven circular construction. Buildings, 11(11), 554.
• [5] Hu Z.Z., Leng S., Lin J.R., Li S.W., Xiao Y.Q.: (2021). Knowledge extraction and discovery based on BIM: a critical review and future directions. Archives of Computational Methods in Engineering, 1-22.
• [6] Pan Y., Zhang L.: (2023). Integrating BIM and Al for smart construction management: Current status and future directions. Archives of Computational Methods in Engineering, 30(2),1081-1110.
• [7] Potrč Obrecht T., Röck M., Hoxha E., Passer A.: (2020). BIM and LCA integration: A systematic literature review. Sustainability, 12(14), 5534.
• [8] Zabin A., González V.A., Zou Y., Amor R.: (2022). Applications of machine learning to BIM: A systematic literature review. Advanced Engineering Informatics, 51, 101474.
• [9] Singh V.K., Singh P., Karmakar M., Leta J., Mayr P.: (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics 126(6): 5113-5142.
• [10] Martin-Martin A., Thelwall M., Orduna-Malea E., Delgado López-Cózar E.: (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations' COCI: a multidisciplinary comparison of coverage via citations. Scientometrics 126(1): 871-906.
• [11] Soltani S., Maxwell D., Rashidi A.: (2023). The State of Industry 4.0 in the Australian Construction Industry: An Examination of Industry and Academic Point of View. Buildings, 13(9), 2324.
• [12] Jiang F., Ma L., Broyd T., Chen K.: (2021). Digital twin and its implementations in the civil engineering sector. Automation in Construction, 130, 103838.
• [13] Na S., Heo S., Choi W., Han S., Kim C.: (2023). Firm Size and Artificial Intelligence (AI)-Based Technology Adoption: The Role of Corporate Size in South Korean Construction Companies. Buildings, 13(4), 1066.
• [14] Ali K.N., Alhajlah H.H., Kassem M.A.: (2022). Collaboration and Risk in Building Information ModelIing (BIM): A Systematic Literature Review. Buildings, 12(5), 571.
• [15] Lin C., Hu Z.Z., Yang C., Deng Y.C., Zheng W., Lin J.R.: (2022). Maturity Assessment of Intelligent Construction Management. Buildings, 12(10), 1742.
• [16] Lu Q., Xie X., Parlikad A.K., Schooling J.M.: (2020). Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118, 103277.
• [17] Begić H., Galić M.: (2021). A Systematic Review of Construction 4.0 in the Context of the BIM 4.0 Premise. Buildings 2021, 11, 337.
• [18] Turk Ž.: (2020). Interoperability in construction-Mission impossible? Developments in the Built Environment, 4, 100018.
• [19] Waqar A., Othman I., Hayat S., Radu D., Khan M.B., Galatanu T.F., et al.: (2023). Building Information Modeling-Empowering Construction Projects with End-to-End Life Cycle Management. Buildings, 13(8), 2041.
• [20] Korus K., Czerniawski T., Salamak M.: (2023). Visual programming simulator for producing realistic labeled point clouds from digital infrastructure models. Automation in Construction, 156, 105126.
• [21] Marinelli M.: (2022). Human-robot collaboration and lean waste elimination: Conceptual analogies and practical synergies in industrialized construction. Buildings, 12(12), 2057.
• [22] Chen Y., Wang X., Liu Z., Cui J., Osmani M., Demian P.: (2023). Exploring Building Information Modeling (BIM) and Internet of Things (IoT) Integration for Sustainable Building. Buildings, 13(2), 288.
• [23] Liu Z., Gong S., Tan Z., Demian P.: (2023). Immersive Technologies-Driven Building Information Modeling (BIM) in the Context of Metaverse. Buildings, 13(6), 1559.
• [24] Vahdatikhaki F., El Ammari K., Langroodi A.K., Miller S., Hammad A., Doree A.: (2019). Beyond data visualization: A context-realistic construction equipment training simulator. Automation in construction, 106, 102853.
• [25] Yan J., Kensek K., Konis K., Noble D.: (2020). Cfd visualization in a virtual reality environment using building information modeling tools. Buildings, 10(12), 229.
• [26] Alizadehsalehi S., Hadavi A., Huang J.C.: (2019, June). BIM/MR-Lean construction project delivery management system. In 2019 IEEE Technology & Engineering Management Conference (TEMSCON) (pp. 1-6). IEEE.
• [27] El Ammari K., Hammad A.: (2019). Remote interactive collaboration in facilities management using BIM-based mixed reality. Automation in Construction, 107, 102940.
• [28] Zhao J., Feng H., Chen Q., de Soto B.G.: (2022). Developing a conceptual framework for the application of digital twin technologies to revamp building operation and maintenance processes. Journal of Building Engineering, 49, 104028.
• [29] Wu J., Sadraddin H.L., Ren R., Zhang J., Shao X. (2021). Invariant signatures of architecture, engineering, and construction objects to support BIM interoperability between architectural design and structural analysis. Journal of Construction Engineering and Management, 147(1), 04020148.
• [30] Li H., Zhang J.: (2021). Interoperability belween BIM and BEM using IFC. In Computing in Civil Engineering 2021 (pp. 630-637).
• [31] Lee YC., Eastman C.M., Solihin W.: (2021). Rules and validation processes for interoperable BIM data exchange. Journal of Computational Design and Engineering, 8(1), pp. 97-114.
• [32] Tuhaise V.V., Tah J.H.M., Abanda F.H.: (2023). Technologies for digital twin applications in construction. Automation in Construction, 152,104931.
• [33] Pedral Sampaio R., Aguiar Costa A., Flores-Colen I.: (2022). A Systematic Review of Artificial Intelligence Applied to Facility Management in the Building Information Modeling Context and Future Research Directions. Buildings, 12(11), 1939.
• [34] Borkowski A.S.: (2019). File Hygiene and BIM Models Restrictions. Trends in Civil Engineering and its Architecture, 3(3), 430-435.zeg
• [35] Christenson M.: (2023). Problematizing the model-building duality: Examining the New Sacristy at S. Lorenzo, Florence, Italy. Frontiers of Architectural Research, 12(4),651-663.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).