Integration of BIM and GIS for Construction Automation, a Systematic Literature Review (SLR) Combining Bibliometric and Qualitative Analysis


For several decades now, the construction industry is suffering from low productivity, especially in comparison to manufacturing industries which have succeeded to benefit from digitalization of their processes. Furthermore, scarceness of qualified workforce is expected in the near future. Construction automation is introduced as a solution to these challenges. The capabilities of construction robots are improving at an accelerated pace. They are starting to be used in non-laboratory contexts for automating processes ranging from infrastructure inspection to digital fabrication. One fundamental requirement of employing robots in construction is their autonomous positioning. Building information modelling (BIM) and geographic information system (GIS) are now a necessity for the construction projects. Integration between BIM and GIS provides holistic digital representation of the built environment that robots could potentially utilize for positioning purposes. Preceding this research, a number of reviews have been conducted on BIM–GIS integration, but none studied it from automation perspective. This research addresses this deficiency through a systematic literature review of the state-of-the-art on BIM–GIS integration with the purpose of robot positioning and navigation on construction sites. Using software tools and “science-mapping” methods, 236 papers were explored. Trends, challenges, potentials, and deficiencies identified and mapped. Citation patterns of journal articles along with the analysis of studies; visualized and analyzed. Bibliometric analysis is followed by a thorough qualitative analysis of the articles identified by the systematic methodology indicating limitations of current studies such as vertical navigation, inaccuracy, dynamics of construction sites, indoor-outdoor navigation. Requirements for robot positioning using BIM–GIS integration are defined.

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  1. 1.

    Chen Q, García de Soto B, Adey BT (2018) Construction automation: research areas, industry concerns and suggestions for advancement. Autom Constr 94:22–38.

    Article  Google Scholar 

  2. 2.

    The Scape Group (2016) Sustainability in the supply chain. Accessed 02 Dec 2019

  3. 3.

    Bock T (2015) The future of construction automation: technological disruption and the upcoming ubiquity of robotics. Autom Constr 59:113–121.

    Article  Google Scholar 

  4. 4.

    García de Soto B, Agustí-Juan I, Hunhevicz J et al (2018) Productivity of digital fabrication in construction: cost and time analysis of a robotically built wall. Autom Constr 92:297–311.

    Article  Google Scholar 

  5. 5.

    Barbosa F, Woetzel J, Mischke J et al (2017) Reinventing construction: a route to higher productivity. McKinsey Global Institute

  6. 6.

    Streule T, Miserini N, Bartlomé O et al (2016) Implementation of scrum in the construction industry. Proc Eng 164:269–276.

    Article  Google Scholar 

  7. 7.

    Agarwal R, Chandrasekaran S, Sridhar M (2016) Imagining construction’s digital future. Accessed 19 Sept 2019

  8. 8.

    Rich BD (2014) Principles of future proofing: a broader understanding of resiliency in the historic built environment. Preserv Educ Res 7:31–49

    Google Scholar 

  9. 9.

    Bowmaster J, Rankin J (2019) A research roadmap for off-site construction: automation and robotics. In: Modular and offsite construction (MOC) summit proceedings, pp 173–180.

  10. 10.

    Froese TM, Rankin J (2009) Strategic roadmaps for construction innovation: assessing the state of research. J Inf Technol Constr 14:400–411.

    Article  Google Scholar 

  11. 11.

    Ma Z, Ren Y (2017) Integrated application of BIM and GIS: an overview. Proc Eng 196:1072–1079

    Article  Google Scholar 

  12. 12.

    Wang H, Pan Y, Luo X (2019) Integration of BIM and GIS in sustainable built environment: a review and bibliometric analysis. Autom Constr 103:41–52.

    Article  Google Scholar 

  13. 13.

    Doumbouya L, Guan CS, Gao G, Pan Y (2017) Application of BIM technology in design and construction: a case study of pharmaceutical industrial base of amino acid building project. In: 16th international scientific conference on engineering for rural development, Latvia University of Agriculture, Faculty of Engineering, Jelgava, Latvia, pp 1495–1502

  14. 14.

    Longley PA, Goodchild MF, Maguire DJ, Rhind DW (2005) Geographic information systems and science. Wiley, London

    Google Scholar 

  15. 15.

    Li Z, Quan SJ, Yang PP-J (2016) Energy performance simulation for planning a low carbon neighborhood urban district: a case study in the city of Macau. Habit Int 53:206–214.

    Article  Google Scholar 

  16. 16.

    Yamamura S, Fan L, Suzuki Y (2017) Assessment of urban energy performance through integration of BIM and GIS for smart city planning. Proc Eng 180:1462–1472.

    Article  Google Scholar 

  17. 17.

    Tashakkori H, Rajabifard A, Kalantari M (2015) A new 3D indoor/outdoor spatial model for indoor emergency response facilitation. Build Environ 89:170–182.

    Article  Google Scholar 

  18. 18.

    Brundu FG, Patti E, Osello A et al (2017) IoT software infrastructure for energy management and simulation in smart cities. IEEE Trans Ind Inf 13:832–840.

    Article  Google Scholar 

  19. 19.

    Deng Y, Cheng JCP, Anumba C (2016) A framework for 3D traffic noise mapping using data from BIM and GIS integration. Struct Infrastruct Eng 12:1267–1280.

    Article  Google Scholar 

  20. 20.

    Afkhamiaghda M, Mahdaviparsa A, Afsari K, McCuen T (2019) Occupants behavior-based design study using BIM–GIS integration: an alternative design approach for architects. In: Mutis I, Hartmann T (eds) Advances in informatics and computing in civil and construction engineering. Springer, Cham, pp 765–772

    Google Scholar 

  21. 21.

    Amirebrahimi S, Rajabifard A, Mendis P, Ngo T (2016) A framework for a microscale flood damage assessment and visualization for a building using BIM–GIS integration. Int J Digital Earth 9:363–386.

    Article  Google Scholar 

  22. 22.

    Morris B (2003) The components of the wired spanning forest are recurrent. Probab Theory Relat Fields 125:259–265.

    MathSciNet  Article  Google Scholar 

  23. 23.

    Kitchenham B (2004) Procedures for performing systematic reviews. Keele UK Keele Univ 33:1–26

    Google Scholar 

  24. 24.

    Moher D, Shamseer L, Clarke M et al (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 4:1

    Article  Google Scholar 

  25. 25.

    van Eck NJ, Waltman L (2014) Visualizing Bibliometric Networks. In: Ding Y, Rousseau R, Wolfram D (eds) Measuring scholarly impact: methods and practice. Springer, Cham, pp 285–320

    Google Scholar 

  26. 26.

    Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F (2011) Science mapping software tools: review, analysis, and cooperative study among tools. J Am Soc Inform Sci Technol 62:1382–1402.

    Article  Google Scholar 

  27. 27.

    De Nooy W, Mrvar A, Batagelj V, Granovetter M (2005) Exploratory social network analysis with Pajek Cambridge University Press, Cambridge, p 334

  28. 28.

    Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: 3rd international AAAI conference on weblogs and social media

  29. 29.

    van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538

    Article  Google Scholar 

  30. 30.

    Cherven K (2015) Mastering Gephi network visualization. Packt Publishing Ltd, London

  31. 31.

    Chaomei C (2014) The CiteSpace manual.

  32. 32.

    ISARC Proceedings—The international association for automation and robotics in construction. Accessed 19 Nov 2019

  33. 33.

    Lapierre A, Cote P (2007) Using open web services for urban data management: a testbed resulting from an OGC initiative for offering standard CAD/GIS/BIM services. In: Urban and regional data management. Annual Symposium of the Urban Data Management Society, pp 381–393

  34. 34.

    Su H-N, Lee P-C (2010) Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight. Scientometrics 85:65–79.

    Article  Google Scholar 

  35. 35.

    Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: generalizing degree and shortest paths. Soc Net 32:245–251.

    Article  Google Scholar 

  36. 36. Accessed 22 Aug 2019

  37. 37.

    Chen C, Ibekwe-SanJuan F, Hou J (2010) The structure and dynamics of cocitation clusters: a multiple-perspective cocitation analysis. J Am Soc Inform Sci Technol 61:1386–1409.

    Article  Google Scholar 

  38. 38.

    Hosseini MR, Martek I, Zavadskas EK et al (2018) Critical evaluation of off-site construction research: a Scientometric analysis. Autom Constr 87:235–247.

    Article  Google Scholar 

  39. 39.

    Dunning T (1993) Accurate methods for the statistics of surprise and coincidence. Comput Linguist 19:61–74

    Google Scholar 

  40. 40.

    Shibata N, Kajikawa Y, Takeda Y, Matsushima K (2008) Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation 28:758–775.

    Article  Google Scholar 

  41. 41.

    Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65.

    Article  Google Scholar 

  42. 42.

    Hicks D (1999) The difficulty of achieving full coverage of international social science literature and the bibliometric consequences. Scientometrics 44:193–215.

    Article  Google Scholar 

  43. 43.

    Guidry JA, Guidry Hollier BN, Johnson L et al (2004) Surveying the cites: a ranking of marketing journals using citation analysis. Mark Educ Rev 14:45–59.

    Article  Google Scholar 

  44. 44.

    gephi/gephi. In: GitHub. Accessed 24 Oct 2019

  45. 45.

    Khokhar D (2015) Gephi cookbook. Packt Publishing Ltd, London

  46. 46.

    Ding Y (2011) Scientific collaboration and endorsement: network analysis of coauthorship and citation networks. J Inf 5:187–203.

    Article  Google Scholar 

  47. 47.

    Luwel M (2005) The use of input data in the performance analysis of R&D systems. In: Moed HF, Glänzel W, Schmoch U (eds) Handbook of quantitative science and technology research: the use of publication and patent statistics in studies of S&T systems. Springer, Dordrecht, pp 315–338

    Google Scholar 

  48. 48.

    Lu H, Feng Y (2009) A measure of authors’ centrality in co-authorship networks based on the distribution of collaborative relationships. Scientometrics 81:499.

    Article  Google Scholar 

  49. 49.

    Delbrügger T, Lenz LT, Losch D, Roßmann J (2017) A navigation framework for digital twins of factories based on building information modeling. In: 2017 22nd IEEE international conference on emerging technologies and factory automation (ETFA). IEEE, New York, pp 1–4

  50. 50.

    Arkin RC (1987) Path planning for a vision-based autonomous robot. In: Mobile robots I. International Society for Optics and Photonics, pp 240–250

  51. 51.

    Geraerts R, Overmars MH (2007) The corridor map method: a general framework for real-time high-quality path planning. Comput Anim Virt Worlds 18:107–119

    Article  Google Scholar 

  52. 52. Accessed 27 Nov 2019

  53. 53.

    3D Simulation Software. In: VEROSIM Solutions. Accessed 27 Nov 2019

  54. 54.

    Ibrahim A, Roberts D, Golparvar-Fard M, Bretl T (2017) An interactive model-driven path planning and data capture system for camera-equipped aerial robots on construction sites. Comput Civ Eng 2017:117–124

    Google Scholar 

  55. 55.

    Darwish W, Li W, Tang S et al (2019) An RGB-D Data processing framework based on environment constraints for mapping indoor environments. In: Vosselman G, Oude Elberink SJ, Yang MY (eds) ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. Copernicus GmbH, London, pp 263–270

  56. 56.

    Endres F, Hess J, Sturm J et al (2014) 3-D mapping with an RGB-D camera. IEEE Trans Robot 30:177–187.

    Article  Google Scholar 

  57. 57.

    Tsai G-J, Chiang K-W, Chu C-H et al (2015) The performance analysis of an indoor mobile mapping system with RGB-D Sensor. In: ISPRS—international archives of the photogrammetry, remote sensing and spatial information sciences XL-1/W4:183–188.

  58. 58.

    Nahangi M, Heins A, McCabe B, Schoellig A (2018) Automated localization of UAVs in GPS-denied indoor construction environments using fiducial markers. In: ISARC—Int. Symp. Autom. Robot. in Constr. Int. AEC/FM Hackathon: the future of build. Things. International Association for Automation and Robotics in Construction I.A.A.R.C)

  59. 59.

    Lin WY, Lin PH, Tserng HP (2017) Automating the generation of indoor space topology for 3D route planning using BIM and 3D-GIS techniques. In: ISARC—Proc. Int. Symp. Autom. Robot. Constr. International Association for Automation and Robotics in Construction I.A.A.R.C), pp 437–444

  60. 60.

    Siemiątkowska B, Harasymowicz-Boggio B, Przybylski M et al (2013) BIM based indoor navigation system of Hermes mobile robot. In: Padois V, Bidaud P, Khatib O (eds) Romansy 19—robot design, dynamics and control. Springer, Vienna, pp 375–382

    Google Scholar 

  61. 61.

    Hamieh A, Deneux D, Tahon C (2017) BiMov: BIM-based indoor path planning. In: Eynard B, Nigrelli V, Oliveri SM et al (eds) Advances on mechanics, design engineering and manufacturing: proceedings of the international joint conference on mechanics, design engineering and advanced manufacturing (JCM 2016), 14–16 September, 2016, Catania, Italy. Springer, Cham, pp 889–899

  62. 62.

    Quintana B, Prieto SA, Adán A, Bosché F (2018) Door detection in 3D coloured point clouds of indoor environments. Autom Constr 85:146–166.

    Article  Google Scholar 

  63. 63.

    Kayhani N, Heins A, Zhao WD et al (2019) Improved tag-based indoor localization of UAVs using extended Kalman filter. In: Al-Hussein M (ed) Proc. Int. Symp. Autom. Robot. Constr., ISARC. International Association for Automation and Robotics in Construction I.A.A.R.C), pp 624–631

  64. 64.

    Neges M, Wolf M, Propach M et al (2017) Improving indoor location tracking quality for construction and facility management. In: ISARC—Proc. Int. Symp. Autom. Robot. Constr. International Association for Automation and Robotics in Construction I.A.A.R.C), pp 88–95

  65. 65.

    Palacz W, Ślusarczyk G, Strug B, Grabska E (2019) Indoor robot navigation using graph models based on BIM/IFC. In: Rutkowski L, Scherer R, Korytkowski M et al (eds) Artificial intelligence and soft computing. Springer, Cham, pp 654–665

    Google Scholar 

  66. 66.

    Kim P, Chen J, Kim J, Cho YK (2018) Slam-driven intelligent autonomous mobile robot navigation for construction applications. In: Workshop of the European group for intelligent computing in engineering. Springer, London, pp 254–269

  67. 67.

    Ibrahima M, Moselhib O (2015) IMU-based indoor localization for construction applications. In: ISARC. Proceedings of the international symposium on automation and robotics in construction. IAARC Publications, London, p 1

  68. 68.

    Caldas CH, Torrent DG, Haas CT (2006) Using global positioning system to improve materials-locating processes on industrial projects. J Constr Eng Manag 132:741–749

    Article  Google Scholar 

  69. 69.

    Goodrum PM, McLaren MA, Durfee A (2006) The application of active radio frequency identification technology for tool tracking on construction job sites. Autom Constr 15:292–302

    Article  Google Scholar 

  70. 70.

    Jang W-S, Skibniewski MJ (2008) A wireless network system for automated tracking of construction materials on project sites. J Civ Eng Manag 14:11–19

    Article  Google Scholar 

  71. 71.

    Taneja S, Akinci B, Garrett JH Jr, Soibelman L (2016) Algorithms for automated generation of navigation models from building information models to support indoor map-matching. Autom Constr 61:24–41

    Article  Google Scholar 

  72. 72.

    Mangiameli M, Muscato G, Mussumeci G, Milazzo C (2013) A GIS application for UAV flight planning. IFAC Proc Vol 46:147–151.

    Article  Google Scholar 

  73. 73.

    Zaki O, Dunnigan M (2017) A navigation strategy for an autonomous patrol vehicle based on multi-fusion planning algorithms and multi-paradigm representation schemes. Robot Auton Syst 96:133–142.

    Article  Google Scholar 

  74. 74.

    Yang Q, Wang M, Kwan M-P, Yang Y (2015) A novel GIS platform for UGV application in the unknown environment. In: 2015 23rd international conference on geoinformatics, pp 1–6

  75. 75.

    Fernández-Caramés C, Serrano FJ, Moreno V et al (2016) A real-time indoor localization approach integrated with a geographic information system (GIS). Robot Auton Syst 75:475–489.

    Article  Google Scholar 

  76. 76.

    Mirats Tur JM, Zinggerling C, Corominas Murtra A (2009) Geographical information systems for map based navigation in urban environments. Robot Auton Syst 57:922–930.

    Article  Google Scholar 

  77. 77.

    Sun M, Yang S, Liu H (2018) GLANS: GIS based large-scale autonomous navigation system. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. Springer, Cham, pp 142–150

    Google Scholar 

  78. 78.

    Park W-I, Kim D-J, Lee H-J (2013) Terrain trafficability analysis for autonomous navigation: a GIS-based approach. Int J Control Autom Syst 11:354–361

    Article  Google Scholar 

  79. 79.

    Rackliffe N, Yanco HA, Casper J (2011) Using geographic information systems (GIS) for UAV landings and UGV navigation. In: 2011 IEEE conference on technologies for practical robot applications. IEEE, London, pp 145–150

  80. 80.

    Hwang J-R, Hong C-H, Choi H-S (2013) Implementation of prototype for interoperability between BIM and GIS: Demonstration paper. In: IEEE 7th international conference on research challenges in information science (RCIS). IEEE, London, pp 1–2

  81. 81.

    Liu L, Li B, Zlatanova S, Liu H (2018) The path from BIM to A 3D indoor framework—a requirement analysis. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII–4:373–378.

  82. 82.

    Irizary J, Karan E (2012) Optimizing location of tower cranes on construction sites through GIS and BIM integration. Electron J Inf Technol Constr 17:351–366

    Google Scholar 

  83. 83.

    Zhu J, Wang X, Wang P et al (2019) Integration of BIM and GIS: Geometry from IFC to shapefile using open-source technology. Autom Constr 102:105–119.

    Article  Google Scholar 

  84. 84.

    Zhu J, Wang X, Chen M et al (2019) Integration of BIM and GIS: IFC geometry transformation to shapefile using enhanced open-source approach. Autom Constr 106:102859.

    Article  Google Scholar 

  85. 85.

    Hong C-H, Hwang J-R, Kang H-Y (2012) A study on the correlation analysis for connection between IFC and CityGML. In: Proceedings of the 4th ACM SIGSPATIAL international workshop on indoor spatial awareness. ACM, New York, pp 9–12

  86. 86.

    Adouane K, Stouffs R, Janssen P, Domer B (2019) A model-based approach to convert a building BIM-IFC data set model into CityGML. J Spat Sci 2019:1–24

    Google Scholar 

  87. 87.

    Zhu J, Wright G, Wang J, Wang X (2018) A critical review of the integration of geographic information system and building information modelling at the data level. ISPRS Int J Geoinf 7:66

    Article  Google Scholar 

  88. 88.

    Isikdag U, Zlatanova S, Underwood J (2013) A BIM-oriented model for supporting indoor navigation requirements. Comput Environ Urban Syst 41:112–123.

    Article  Google Scholar 

  89. 89.

    Irizarry J, Karan EP, Jalaei F (2013) Integrating BIM and GIS to improve the visual monitoring of construction supply chain management. Autom Constr 31:241–254.

    Article  Google Scholar 

  90. 90.

    Kang TW, Hong CH (2015) A study on software architecture for effective BIM/GIS-based facility management data integration. Autom Constr 54:25–38.

    Article  Google Scholar 

  91. 91.

    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. J Spat Sci 61:317–350.

    Article  Google Scholar 

  92. 92.

    Mignard C, Nicolle C (2014) Merging BIM and GIS using ontologies application to urban facility management in ACTIVe3D. Comput Ind 65:1276–1290.

    Article  Google Scholar 

  93. 93.

    Donkers S, Ledoux H, Zhao J, Stoter J (2016) Automatic conversion of IFC datasets to geometrically and semantically correct CityGML LOD3 buildings. Trans GIS 20:547–569.

    Article  Google Scholar 

  94. 94.

    Wu I, Hsieh S (2007) Transformation from IFC data model to GML data model: methodology and tool development. J Chin Inst Eng 30:1085–1090.

    Article  Google Scholar 

  95. 95.

    Wyvill B, Guy A, Galin E (1999) Extending the CSG Tree. Warping, blending and boolean operations in an implicit surface modeling system. Comput Graph Forum 18:149–158.

    Article  Google Scholar 

  96. 96.

    Deng Y, Cheng JCP, Anumba C (2016) Mapping between BIM and 3D GIS in different levels of detail using schema mediation and instance comparison. Autom Constr 67:1–21.

    Article  Google Scholar 

  97. 97.

    Environmental Systems Research Institute, Inc (1997) ESRI Shapefile technical description. Accessed 26 Nov 2019

  98. 98.

    Environmental Systems Research Institute, Inc (2008) The Multipatch geometry type. Accessed 26 Nov 2019

  99. 99.

    Gröger G, Kolbe TH, Nagel C, Häfele K-H (2012) OGC city geography markup language (CityGML) encoding standard version 2.0; OGC Doc; Open Geospatial Consortium: Wayland, MA, USA

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The authors are grateful to Natural Sciences and Engineering Research Council of Canada for the financial support through its CRD program 543867-2019 as well as the industrial partners of the ETS Industrial Chair on the Integration of Digital Technology in Construction.

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Karimi, S., Iordanova, I. Integration of BIM and GIS for Construction Automation, a Systematic Literature Review (SLR) Combining Bibliometric and Qualitative Analysis. Arch Computat Methods Eng (2021).

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