Research Prospect of BIM-Based Information Technologies for Engineering Management

HU Zhenzhong; LIU Yi; LIN Chao

doi:10.13204/j.gyjzG22073009

Volume 52 Issue 10

Oct. 2022

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > INDUSTRIAL CONSTRUCTION > 2022 > 52(10): 195-203

HU Zhenzhong, LIU Yi, LIN Chao. Research Prospect of BIM-Based Information Technologies for Engineering Management[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 195-203. doi: 10.13204/j.gyjzG22073009

Citation:

HU Zhenzhong, LIU Yi, LIN Chao. Research Prospect of BIM-Based Information Technologies for Engineering Management[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 195-203. doi: 10.13204/j.gyjzG22073009

HU Zhenzhong, LIU Yi, LIN Chao. Research Prospect of BIM-Based Information Technologies for Engineering Management[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 195-203. doi: 10.13204/j.gyjzG22073009

Citation:

HU Zhenzhong, LIU Yi, LIN Chao. Research Prospect of BIM-Based Information Technologies for Engineering Management[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 195-203. doi: 10.13204/j.gyjzG22073009

PDF( 5777 KB)

Research Prospect of BIM-Based Information Technologies for Engineering Management

doi: 10.13204/j.gyjzG22073009

Tsinghua Shenzhen International Graduate School, Shenzhen 518071, China

Received Date: 2022-07-30
Available Online: 2023-03-22

Abstract

Abstract

As the process of social informatization accelerates, information technologies have gradually become an important means for construction practitioners to improve the quality of engineering management. As a key component of the digital development of the construction industry, building information modeling (BIM) has been widely applied in engineering management. How to further develop information technologies for engineering management on the basis of existing theories and applications has thus become the focus of researchers' attention. This paper summarized BIM-related research from two aspects:the organization and processing of engineering data as well as the mining and analysis of such data. The research investigated involved the Industry Foundation Classes (IFC) standard, multi-scale information models, model lightweighting technology, and the key steps and typical methods of data mining in the engineering industry. On this basis, the bottleneck problems faced by BIM and information technologies for engineering were analyzed, and three main future development directions were then proposed and discussed:digital twins in the engineering industry, city information models, and knowledge graphs in the engineering industry. This paper is expected to provide a reference for further research and technology application.
- BIM,
- engineering management,
- information technology,
- digital twin,
- CIM,
- knowledge graph

FullText(HTML)

References(28)

References

[1]	LASI H, FETTKE P, KEMPER H, et al. Industry 4.0[J]. Business & Information Systems Engineering, 2014, 6(4):239-242.
[2]	ABDELKHALEK H, REFAIE H, AZIZ R. Optimization of time and cost through learning curve analysis[J]. Ain Shams Engineering Journal, 2020, 11(4):1069-1082.
[3]	CHANG L J, WANG Y N, FENG G H, et al. Research on problems and solutions in construction quality supervision of construction projects[J]. Smart Construction Research, 2018, 2(3). DOI: 10.18063/scr.v2i3.588.
[4]	PENG R L, ZHANG M, LIU T Y. Analysis of key points in safety supervision and management of construction site[J].Earth and Environmental Science, 2021.DOI: 10.1088/1755-1315/760/1/012035.
[5]	ZHAI X M, CHENG X. Environmental protection requirements of power transmission and transformation project construction based on internet of things technology and its change trend analysis[J].Materials Science and Engineering, 2020. DOI: 10.1088/1757-899X/740/1/012144.
[6]	马智亮,李松阳."互联网+"环境下项目管理新模式[J].同济大学学报(自然科学版),2018,46(7):991-995.
[7]	KANG T, CHOI H. BIM-based data mining method considering data integration and function extension[J]. KSCE Journal of Civil Engineering, 2018, 22(5):1523-1534.
[8]	HABIBI S. The promise of BIM for improving building performance[J]. Energy and Buildings, 2017, 153:525-548.
[9]	VALINEJADSHOUBI M, BAGCHI A, MOSELHI O. Development of a BIM-based data management system for structural health monitoring with application to modular buildings:case study[J]. Journal of Computing in Civil Engineering, 2019, 33(3):05019003.
[10]	ISMAIL S, BANDI S, MAAZ Z. An appraisal into the potential application of big data in the construction industry[J]. International Journal of Built Environment and Sustainability, 2018, 5(2):145-154.
[11]	ZHANG J P, YU F Q, LI D, et al. Development and implementation of an industry foundation classes-based graphic information model for virtual construction[J]. Computer-Aided Civil and Infrastructure Engineering, 2014, 29(1):60-74.
[12]	FAI S, RAFEIRO J. Establishing an appropriate level of detail (LoD) for a building information model (BIM)-West Block, Parliament Hill, Ottawa, Canada[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, 2(5):123-130.
[13]	HU Z Z, ZHANG J P, YU F Q, et al. Construction and facility management of large MEP projects using a multi-scale building information model[J]. Advances in Engineering Software, 2016, 100:215-230.
[14]	THOMAS D, MILLER C, KÄMPF J, et al. Multiscale co-simulation of EnergyPlus and CitySim models derived from a building information model[C]//Bausim 2014:Fifth German-Austrian IBPSA Conference. Aachen:RWTH Aachen University, 2014:469-476.
[15]	DELVAL T, GEFFROY B, REZOUG M, et al. BIM to develop integrated, incremental and multiscale methods to assess comfort and quality of public spaces[C]//International Conference on Computing in Civil and Building Engineering. Springer:2020:160-179.
[16]	付沙, 周航军. 关联规则挖掘Apriori算法的研究与改进[J]. 微电子学与计算机, 2013, 30(9):110-114.
[17]	WEN Q, ZHANG J P, HU Z Z, et al. A data-driven approach to improve the operation and maintenance management of large public buildings[J]. IEEE Access, 2019(7):176127-176140.
[18]	LENG S, LIN J R, HU Z Z, et al. A hybrid data mining method for tunnel engineering based on real-time monitoring data from tunnel boring machines[J]. IEEE Access, 2020(8):90430-90449.
[19]	PENG Y, LI S W, HU Z Z. A self-learning dynamic path planning method for evacuation in large public buildings based on neural networks[J]. Neurocomputing, 2019, 365:71-85.
[20]	马智亮,蔡诗瑶.基于BIM的建筑施工智能化[J].施工技术,2018,47(6):70-72 ,83.
[21]	陶飞,张贺,戚庆林,等.数字孪生十问:分析与思考[J].计算机集成制造系统,2020,26(1):1-17.
[22]	QIAN Y C, LENG J W. CIM-based modeling and simulating technology roadmap for maintaining and managing Chinese rural traditional residential dwellings[J]. Journal of Building Engineering, 2021, 44.DOI: 10.1016/j.jobe.2021.103248.
[23]	任萍. 智慧城市背景下的智慧地铁建设[J]. 经济学, 2022, 5(2):25-29.
[24]	傅连珍,胡道功,张绪教,等. 基于GIS空间分析模型的祁连山多年冻土研究[J]. 地质力学学报, 2022, 21(3):371-377.
[25]	LENG S, LI S W, HU Z Z, et al. Development of a micro-in-meso-scale framework for simulating pollutant dispersion and wind environment in building groups[J]. Journal of Cleaner Production, 2022, 364.DOI: 10.1016/j.jclepro.2022.132661.
[26]	刘毅, 吴浪韬, 梁雄, 等. 知识图谱在BIM模型审查中的应用研究[C]//第六届全国BIM学术会议论文集.2020:122-126.
[27]	王莉,王建平,许娜,等.基于知识图谱的地铁工程事故知识建模与分析[J].土木工程与管理学报,2019,36(5):109-114 ,122.
[28]	李新琴, 史天运, 李平,等. 基于文本的高速铁路信号设备故障知识抽取方法研究[J]. 铁道学报, 2021, 43(3):92-100.