A Risk Management System for Deep Excavation Based on BIM-3DGIS Framework and Optimized Grey Verhulst Model

KSCE Journal of Civil Engineering volume 24, pages715–726(2020)Cite this article

Abstract

Risk management of deep excavation is always an important issue. One of the core problems is to accurately simulate and predict the time series of displacement collected from the site sensors to monitor the risk variation. Meanwhile, the applications of building information modelling (BIM) and geographic information system (GIS) can integrate the construction structures into the surrounding environment, visualizing various information and supporting decision making for risk treatment. Therefore, this paper proposes a risk management system to monitor the risk variation for deep excavation based on optimized grey Verhulst model (GVMm), BIM-3DGIS framework and risk monitoring. The grey Verhulst model (GVM) has demonstrated well performance on saturation curve, such as displacement of deep excavation. This paper establishes the GVMm by improving GVM to predict the displacement more precisely. BIM-3DGIS framework is also built by integrating BIM and 3DGIS in the application level for the efficiency of system operation and the interaction with the risk management platform. BIM-3DGIS framework, working with the risk management platform, can monitor the risk variation of deep excavation effectively and provide visual decision-making supports. A real case of deep excavation is used as an illustrative example to verify the practicability. The results show that the prediction precision of GVMm is better than that of GVM. The application scenarios also demonstrate the effectiveness of the risk management system.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

References

  1. Aien A, Rajabifard A, Kalantari M, Shojaei D (2015) Integrating legal and physical dimensions of urban environments. ISPRS International Journal of Geo-Information 4(3):1442–1479, DOI: https://doi.org/10.3390/ijgi4031442

    Google Scholar 

  2. Amirebrahimi S, Rajabifard A, Mendis P, Ngo T (2015) A framework for a microscale flood damage assessment and visualization for a building using BIM-GIS integration. International Journal of Digital Earth 9(4):363–386, DOI: https://doi.org/10.1080/17538947.2015.1034201

    Google Scholar 

  3. Amirebrahimi S, Rajabifard A, Mendis P, Ngo T (2016) A BIM-GIS integration method in support of the assessment and 3D visualisation of flood damage to a building. Journal of Spatial Science 61(2):317–350, DOI: https://doi.org/10.1080/14498596.2016.1189365

    Google Scholar 

  4. Chen Z, Wang T, Feng K, Gou HJ (2014) Application of Verhulst model in predicting the horizontal displacement of the soil around the deep foundation pit. Journal of North China University of Water Resources and Electric Power 1:42–45, DOI: https://doi.org/10.3969/j.issn.1002-5634.2014.01.010

    Google Scholar 

  5. Cheng MY, Chiu KC, Hsieh YM, Yang IT, Chou JS, Wu YW (2017) BIM integrated smart monitoring technique for building fire prevention and disaster relief. Automation in Construction 84:14–30, DOI: https://doi.org/10.1016/j.autcon.2017.08.027

    Google Scholar 

  6. Ding S, Dang Y, Xu N, Chen D, Cui J (2015) The optimization of grey Verhulst model and its application. Journal of Grey System 27(2):1–12

    Google Scholar 

  7. Ding LY, Zhong BT, Wu S, Luo HB (2016) Construction risk knowledge management in BIM using ontology and semantic web technology. Safety Science 87:202–213, DOI: https://doi.org/10.1016/j.ssci.2016.04.008

    Google Scholar 

  8. Donkers S, Ledoux H, Zhao J, Stoter JE (2016) Automatic conversion of IFC datasets to geometrically and semantically correct CityGML LOD3 buildings. Transactions in GIS 20(4):547–569, DOI: https://doi.org/10.1111/tgis.12162

    Google Scholar 

  9. Doumbouya L, Guan C, Deng S, Gao G (2016) Collinear force and frequency analysis of a viaduct pile foundation construction project: A case study on the application of BIM technology. Proceedings of 15th international scientific conference engineering for rural development, May 25–27, Jelgava, Latvia

  10. Evans M (2014) An alternative approach to estimating the parameters of a generalised grey Verhulst model: An application to steel intensity of use in the UK. Expert Systems with Applications 41(4):1236–1244, DOI: https://doi.org/10.1016/j.eswa.2013.08.006

    Google Scholar 

  11. Hashem-Nazari M, Esfahanipour A, Fatemi Ghomi SMT (2017) Non-equidistant “basic form”-focused grey Verhulst models (NBFGVMs) for ill-structured socio-economic forecasting problems. Journal of Business Economics and Management 18(4):676–694, DOI: https://doi.org/10.3846/16111699.2017.1337045

    Google Scholar 

  12. Isikdag U, Underwood J, Aouad G (2008) An investigation into the applicability of building information models in geospatial environment in support of site selection and fire response management processes. Advanced Engineering Informatics 22(4):504–519, DOI: https://doi.org/10.1016/j.aei.2008.06.001

    Google Scholar 

  13. Kang TW, Hong CH (2018) IFC-CityGML LOD mapping automation using multiprocessing-based screen-buffer scanning including mapping rule. KSCE Journal of Civil Engineering 22(2):373–383, DOI: https://doi.org/10.1007/s12205-017-0595-9

    Google Scholar 

  14. Kiviniemi M, Sulankivi K, Kähkönen K, Mäkelä T, Merivirta ML (2011) BIM-based safety management and communication for building construction. VTT Research Notes 22(2597):1–123

    Google Scholar 

  15. Lee PC, Wang Y, Lo TP, Long D (2018) An integrated system framework of building information modelling and geographical information system for utility tunnel maintenance management. Tunneling and Underground Space Technology 79:263–273, DOI: https://doi.org/10.1016/j.tust.2018.05.010

    Google Scholar 

  16. Li J, Hua Z (2012) An object library approach for managing construction safety components based on BIM. Proceedings of the International Symposium on Automation and Robotics in Construction 29:1–6. DOI: https://doi.org/10.4017/gt.2012.11.02.314.745

    Google Scholar 

  17. Lin JR, Hu ZZ, Zhang JP, Yu FQ (2016) A natural-language-based approach to intelligent data retrieval and representation for cloud BIM. Computer-Aided Civil and Infrastructure Engineering 31(1): 18–33, DOI: https://doi.org/10.1111/mice.12151

    Google Scholar 

  18. Liu X, Wang X, Wright G, Cheng J, Li X, Liu R (2017) A state-of-the-art review on the integration of building information modeling (BIM) and geographic information system (GIS). ISPRS International Journal of Geo-Information 6(2):53, DOI: https://doi.org/10.3390/ijgi6020053

    Google Scholar 

  19. Luo H, Gong P (2014) A BIM-based code compliance checking process of deep foundation construction plans. Journal of Intelligent & Robotic Systems 79(3–4):549–576, DOI: https://doi.org/10.1007/s10846-014-0120-z

    Google Scholar 

  20. Ma Z, Ren Y (2017) Integrated application of BIM and GIS: An overview. Procedia Engineering 196:1072–1079, DOI: https://doi.org/10.1016/j.proeng.2017.08.064

    Google Scholar 

  21. Madhi MH, Mohamed N (2017a) An initial condition optimization approach for improving the prediction precision of a GM(1,1) Model. Mathematical and Computational Applications 22(1):21–28, DOI: https://doi.org/10.3390/mca22010021

    Google Scholar 

  22. Madhi MH, Mohamed N (2017b) Optimized GM(1,1) model based on the modified initial condition. Journal of Applied Sciences 17:90–96, DOI: https://doi.org/10.3923/jas.2017.90.96

    Google Scholar 

  23. Miao S, Hao X, Guo X, Wang Z, Liang M (2017) Displacement and landslide forecast based on an improved version of Saito’s method together with the Verhulst-Grey model. Arabian Journal of Geosciences 10(3):1–10, DOI: https://doi.org/10.1007/s12517-017-2838-y

    Google Scholar 

  24. Pärn EA, Edwards DJ, Sing MCP (2017) The building information modelling trajectory in facilities management: A review. Automation in Construction 75:45–55, DOI: https://doi.org/10.1016/j.autcon.2016.12.003

    Google Scholar 

  25. Pezeshki Z, Ivari SAS (2018) Applications of BIM: A brief review and future outline. Archives of Computational Methods in Engineering 25(2):273–312, DOI: https://doi.org/10.1007/s11831-016-9204-1

    Google Scholar 

  26. Qiu D, Liang Q, Li Q, Wu R (2009) Safety monitoring information system of urban subway. 2009 WASE international conference on information engineering, July 10–11, Chanxi, China, DOI: https://doi.org/10.1109/ICIE.2009.189

  27. Rafiee A, Dias E, Fruijtier S, Scholten H (2014) From BIM to geoanalysis: View coverage and shadow analysis by BIM/GIS integration. Procedia Environmental Sciences 22:397–402, DOI: https://doi.org/10.1016/j.proenv.2014.11.037

    Google Scholar 

  28. Ran L, Ye XW, Zhu HH (2011) Long-term monitoring and safety evaluation of a metro station during deep excavation. Procedia Engineering 14:785–792, DOI: https://doi.org/10.1016/j.proeng.2011.07.099

    Google Scholar 

  29. Shao Y, Macari EJ (2008) Information feedback analysis in deep excavations. International Journal of Geomechanics 8(1):91–103, DOI: https://doi.org/10.1061/(asce)1532-3641(2008)8:1(91)

    Google Scholar 

  30. Shu H, Wang WP, Xiong PP (2015) A novel modeling method of grey Verhulst model based on optimizing initial condition. 2015 IEEE international conference on grey systems and intelligent services (GSIS), August 18–20, Leicester, UK, DOI: https://doi.org/10.1109/GSIS.2015.7301859

  31. Vilgertshofer S, Amann J, Willenborg B, Borrmann A, Kolbe TH (2017) Linking BIM and GIS models in infrastructure by example of IFC and CityGML. ASCE international workshop on computing in civil engineering 2017, June 25–27, Seattle, WA, USA, DOI: https://doi.org/10.1061/9780784480823.017

  32. Wang QK, Li P, Xiao YP, Liu ZG (2014) Integration of GIS and BIM in metro construction. Applied Mechanics and Materials 608–609:698–702, DOI: https://doi.org/10.4028/www.scientific.net/amm.608-609.698

    Google Scholar 

  33. Wang N, Xu CS, Du XL, Zhang MJ (2018) A risk assessment method of deep excavation based on Bayesian analysis and expert elicitation. International Journal of System Assurance Engineering and Management 9(2):452–466, DOI: https://doi.org/10.1007/s13198-017-0689-2

    Google Scholar 

  34. Wu IC, Lu SR, Hsiung BC (2015) A BIM-based monitoring system for urban deep excavation projects. Visualization in Engineering 3(1):1–11, DOI: https://doi.org/10.1186/s40327-014-0015-x

    Google Scholar 

  35. Xie N, Wang R, Chen N (2018) Measurement of shock effect following change of one-child policy based on grey forecasting approach. Kybernetes 47(3):559–586, DOI: https://doi.org/10.1108/k-05-2017-0159

    Google Scholar 

  36. Xiong PP, Dang YG, Yiao TX (2012) The research on the modeling method of background value optimization in grey Verhulst model. Chinese Journal of Management Science 20(6):154–159

    Google Scholar 

  37. Zhang JF, Chen JJ, Wang JH, Zhu YF (2013) Prediction of tunnel displacement induced by adjacent excavation in soft soil. Tunnelling and Underground Space Technology 36:24–33, DOI: https://doi.org/10.1016/j.tust.2013.01.011

    Google Scholar 

  38. Zhang C, Peng Z (2016) Application of the optimized Verhulst model to prediction of foundation settlement. Journal of Railway Science & Engineering 13(8):1535–1542, DOI: https://doi.org/10.3969/j.issn.1672-7029.2016.08.013

    Google Scholar 

  39. Zhang B, Xi GQ (2013) GM (1,1) model optimization based on the background value and boundary value correction. Systems Engineering-Theory and Practice 33(3):682–688, DOI: https://doi.org/10.12011/1000-6788(2013)3-682

    Google Scholar 

  40. Zhang Q, Xiao J (2014) Comparison in applicability of GM(1,1) and Verhulst models in prediction of foundation settlement. Journal of Shenyang University of Technology 36(6):716–720, DOI: https://doi.org/10.7688/j.issn.1000-1646.2014.06.22

    Google Scholar 

  41. Zhao X (2017) A scientometric review of global BIM research: Analysis and visualization. Automation in Construction 80:37–47, DOI: https://doi.org/10.1016/j.autcon.2017.04.002

    Google Scholar 

  42. Zheng J, Chen B (2018) Initial condition optimization of GM(1,1) model based on time weighted sequence. Control and Decision 33(3):529–534, DOI: https://doi.org/10.13195/j.kzyjc.2017.0033

    MATH  Google Scholar 

  43. Zhou W, He JM (2013) Generalized GM (1,1) model and its application in forecasting of fuel production. Applied Mathematical Modelling 37:6234–6243, DOI: https://doi.org/10.1016/j.apm.2013.01.002

    MathSciNet  MATH  Google Scholar 

  44. Zhou HB, Zhang H (2011) Risk assessment methodology for a deep foundation pit construction project in Shanghai, China. Journal of Construction Engineering and Management 137(12):1185–1194, DOI: https://doi.org/10.1061/(asce)co.1943-7862.0000391

    Google Scholar 

  45. Zhou Y, Su W, Ding L, Luo H, Love PED (2017) Predicting safety risks in deep foundation pits in subway infrastructure projects: Support vector machine approach. Journal of Computing in Civil Engineering 31(5):04017052, DOI: https://doi.org/10.1061/(asce)cp.1943-5487.0000700

    Google Scholar 

  46. Zou Y, Kiviniemi A, Jones SW (2017) A review of risk management through BIM and BIM-related technologies. Safety Science 97:88–98, DOI: https://doi.org/10.1016/j.ssci.2015.12.027

    Google Scholar 

  47. Zverovich V, Mahdjoubi L, Boguslawski P, Fadli F, Barki H (2016) Emergency response in complex buildings: Automated selection of safest and balanced routes. Computer-Aided Civil and Infrastructure Engineering 31(8):617–632, DOI: https://doi.org/10.1111/mice.12197

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 51808455) and the Fundamental Research Funds for the Central Universities (Grant No. 2682018CX03).

Author information

Affiliations

  1. School of Civil Engineering, Chongqing University, Chongqing, 400044, China

    Pin-Chan Lee

  2. Huadong Engineering Corporation Ltd., Hangzhou, 311122, China

    Li-Long Zheng

  3. Chengdu Liangzi Intelligent Technology Ltd., Chengdu, 610041, China

    Tzu-Ping Lo

  4. School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Dan-Bing Long

Authors
  1. Pin-Chan Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li-Long Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tzu-Ping Lo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan-Bing Long
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tzu-Ping Lo.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lee, PC., Zheng, LL., Lo, TP. et al. A Risk Management System for Deep Excavation Based on BIM-3DGIS Framework and Optimized Grey Verhulst Model. KSCE J Civ Eng 24, 715–726 (2020). https://doi.org/10.1007/s12205-020-1462-7

Download citation

Keywords

  • Building information modelling
  • Deep excavation
  • Geographic information system
  • Grey Verhulst model
  • Risk management