BIM-based prototype of a mathematical model of construction planning
Abstract
This article tackles the problem of rational and effective planning of the entire construction site, including the planning of mechanisms, equipment, warehouse space, temporary buildings, temporary engineering networks, etc. The authors propose the principles of creating a mathematical model to calculate the needs of construction objects, using the photogrammetry model. The problems raised can be solved with the use of BIM in the preparation for construction planning stage. The prototype mathematical model presented in this article addresses these issues: identify current situation, using photogrammetry model, define optimal number and location of construction site objects, avoid conflicts between cranes, detect possible hoisting problem, avoid overload of cranes, and of course construction site planning. Therefore, it becomes possible to perform a multicriteria decision-making analysis. Extensive analysis in the pre-construction stage is often abandoned due to the lack of data on the current situation, difficult calculations of the need for mechanisms, equipment and simply due to the lack of time to analyze all possible rational solutions. The data received from the created mathematical prototype could also be used in further construction stages for planning human and material resources, the project schedule and cost estimate.
Keyword : BIM, photogrammetry, construction equipment selection, building information modelling, construction site planning
How to Cite
Kontrimovičius, R., & Ustinovichius, L. (2023). BIM-based prototype of a mathematical model of construction planning. Journal of Civil Engineering and Management, 29(1), 1–14. https://doi.org/10.3846/jcem.2023.18313
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Rojek, I., Mikołajewski, D., Kotlarz, M., Macko, M., & Kopowski, J. (2021). Intelligent system supporting technological process planning for machining and 3D printing. Bulletin of the Polish Academy of Sciences. Technical Sciences, 69(2), e136722. https://doi.org/10.24425/bpasts.2021.136722
Sadeghpour, F., Moselhi, O., & Alkass, S. (2004). A CAD-based model for site planning. Automation in Construction, 13(6), 701–715. https://doi.org/10.1016/j.autcon.2004.02.004
Sanad, H. M., Ammar, M. A., & Ibrahim, M. E. (2008). Optimal construction site layout considering safety and environmental aspects. Journal of Construction Engineering and Management, 134(7), 536–544. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:7(536)
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Skrzypczak, I., Oleniacz, G., Leśniak, A., Zima, K., Mrówczyńska, M., & Kazak, J. (2022) Scan-to-BIM method in construction: assessment of the 3D buildings model accuracy in terms inventory measurements. Building Research & Information, 50(8), 859–880. https://doi.org/10.1080/09613218.2021.2011703
Ustinovichius, L., Zavadskas, E. K., & Podvezko, V. (2007). The application of a quantitative multiple criteria decision making (MCDM-1) approach to the analysis of investments in construction. Control and Cybernetics, 36(1), 251–268.
Ustinovichius, L., & Simanaviciene, R. (2008). The application of stochastic dominance to sensitivity analysis in quantitative multiple criteria decision making (MCDM-1). In Y. Luo (Ed.), Lecture notes in computer science: Vol. 5220. Cooperative design, visualization, and engineering. CDVE 2008 (pp. 184–191). Springer. https://doi.org/10.1007/978-3-540-88011-0_25
Zhang, H., & Yu, L. (2021). Site layout planning for prefabricated components subject to dynamic and interactive constraints. Automation in Construction, 126, 103693. https://doi.org/10.1016/j.autcon.2021.103693
Zhu, X., Meng, X., & Zhang, M. (2021). Application of multiple criteria decision making methods in construction: a systematic literature review. Journal of Civil Engineering and Management, 27(6), 372–403. https://doi.org/10.3846/jcem.2021.15260
Zouein, P. P., & Kattan, S. (2022). An improved construction approach using ant colony optimization for solving the dynamic facility layout problem. Journal of the Operational Research Society, 73(7), 1517–1531. https://doi.org/10.1080/01605682.2021.1920345
Briskorn, D., & Dienstknecht, M. (2019). Mixed-integer programming models for tower crane selection and positioning with respect to mutual interference. European Journal of Operational Research, 273(1), 160–174. https://doi.org/10.1016/j.ejor.2018.07.033
Briskorn, D., & Dienstknecht, M. (2020). Covering polygons with discs: Problem of crane selection and location on construction sites. Omega, 97, 102114. https://doi.org/10.1016/j.omega.2019.102114
Cekus, D., Kwiatoń, P., Šofer, M., & Šofer, P. (2022). Application of heuristic methods to identification of the parameters of discrete-continuous models. Bulletin of the Polish Academy of Sciences. Technical Sciences, 70(1), e140150. https://doi.org/10.24425/bpasts.2022.140150
Easa, S. M., & Hossain, K. M. A. (2008). New mathematical optimization model for construction site layout. Journal of Construction Engineering and Management, 134(8), 653–662. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:8(653)
El-Rayes, K., & Khalafallah, A. (2005). Trade-off between safety and cost in planning construction site layouts. Journal of Construction Engineering and Management, 131(11), 1186–1195. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:11(1186)
Fadoul, A., Tizani, W., & Koch, C. (2018). A BIM-based model for constructability of conceptual design. Advances in Computational Design, 3(4), 367–384. https://doi.org/10.12989/acd.2018.3.4.367
Gbadamosi, A. Q., Mahamadu, A. M., Oyedele, L. O., Akinade, O. O., Manu, P., Mahdjoubi, L., & Aigbavboa, C. (2019). Offsite construction: Developing a BIM-Based optimizer for assembly. Journal of Cleaner Production, 215, 1180–1190. https://doi.org/10.1016/j.jclepro.2019.01.113
Guo, H. L., Yu, Y. T., & Skitmore, M. (2016). Visualization technology-based construction safety management: A review. Automation in Construction, 73, 135–144. https://doi.org/10.1016/j.autcon.2016.10.004
Huang, C., Li, R., Fu Y., & Ireland, V. (2019). Optimal selection and location of tower crane for the construction of prefabricated buildings with different prefabrication ratios. Journal of Engineering Science and Technology Review, 12(6), 173–181. https://doi.org/10.25103/jestr.126.22
Ilce, A. C., & Ozkaya, K. (2018). An integrated intelligent system for construction industry: a case study of raised floor material. Technological and Economic Development of Economy, 24(5), 1866–1884. https://doi.org/10.3846/20294913.2017.1334242
Ji, Y. S., & Leite, F. (2018). Automated tower crane planning: leveraging 4-dimensional BIM and rule-based checking. Automation in Construction, 93, 78–90. https://doi.org/10.1016/j.autcon.2018.05.003
Ji, Y. S., Sankaran, B., Choi, J. Y., & Leite, F. (2017). Integrating BIM and optimization techniques for enhanced tower crane planning. In ASCE International Workshop on Computing in Civil Engineering (pp. 67–74). Seattle, Washington, USA. http://dx.doi.org/10.1061/9780784480823.009
Jin, R. Y., Zhong, B. T., Ma, L., Hashemi, A., & Ding, L. Y. (2019). Integrating BIM with building performance analysis in project lifecycle. Automation in Construction, 106, 102861. https://doi.org/10.1016/j.autcon.2019.102861
Kulkarni, R. H., & Padmanabham, P. (2017). Integration of artificial intelligence activities in software development processes and measuring effectiveness of integration. IET Software, 11(1), 18–26. https://doi.org/10.1049/iet-sen.2016.0095
Liu, H. X., Al-Hussein, M., & Lu, M. (2015). BIM-based integrated approach for detailed construction scheduling under resource constraints. Automation in Construction, 53, 29–43. https://doi.org/10.1016/j.autcon.2015.03.008
Leśniak, A., & Zima, K. (2018). Cost calculation of construction projects including sustainability factors using the case based reasoning (CBR) method. Sustainability, 10(5), 1608. https://doi.org/10.3390/su10051608
Lucarelli, M., Laurini, E., & De Berardinis, P. (2019). 3D and 4D modelling in building site working control. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42-2/W9, 441–446. https://doi.org/10.5194/isprs-archives-XLII-2-W9-441-2019
Osman, H. M., Georgy, M. E., Ibrahim, M. E. (2003). A hybrid cad-based construction site layout planning system using genetic algorithms. Automation in Construction, 12(6), 749–764. https://doi.org/10.1016/S0926-5805(03)00058-X
Riga, K., Jahr, K., Thielen, C., & Borrmann, A. (2020). Mixed integer programming for dynamic tower crane and storage area optimization on construction sites. Automation in Construction, 120, 103259. https://doi.org/10.1016/j.autcon.2020.103259
Rojek, I., Mikołajewski, D., Kotlarz, M., Macko, M., & Kopowski, J. (2021). Intelligent system supporting technological process planning for machining and 3D printing. Bulletin of the Polish Academy of Sciences. Technical Sciences, 69(2), e136722. https://doi.org/10.24425/bpasts.2021.136722
Sadeghpour, F., Moselhi, O., & Alkass, S. (2004). A CAD-based model for site planning. Automation in Construction, 13(6), 701–715. https://doi.org/10.1016/j.autcon.2004.02.004
Sanad, H. M., Ammar, M. A., & Ibrahim, M. E. (2008). Optimal construction site layout considering safety and environmental aspects. Journal of Construction Engineering and Management, 134(7), 536–544. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:7(536)
Sacks, R., Eastman, C., Lee, G., & Teicholz, P. (2018). A guide to Building Information Modeling for owners, managers, architects, engineers, contractors, and fabricators. John Wiley and Sons. https://doi.org/10.1002/9781119287568
Skrzypczak, I., Oleniacz, G., Leśniak, A., Zima, K., Mrówczyńska, M., & Kazak, J. (2022) Scan-to-BIM method in construction: assessment of the 3D buildings model accuracy in terms inventory measurements. Building Research & Information, 50(8), 859–880. https://doi.org/10.1080/09613218.2021.2011703
Ustinovichius, L., Zavadskas, E. K., & Podvezko, V. (2007). The application of a quantitative multiple criteria decision making (MCDM-1) approach to the analysis of investments in construction. Control and Cybernetics, 36(1), 251–268.
Ustinovichius, L., & Simanaviciene, R. (2008). The application of stochastic dominance to sensitivity analysis in quantitative multiple criteria decision making (MCDM-1). In Y. Luo (Ed.), Lecture notes in computer science: Vol. 5220. Cooperative design, visualization, and engineering. CDVE 2008 (pp. 184–191). Springer. https://doi.org/10.1007/978-3-540-88011-0_25
Zhang, H., & Yu, L. (2021). Site layout planning for prefabricated components subject to dynamic and interactive constraints. Automation in Construction, 126, 103693. https://doi.org/10.1016/j.autcon.2021.103693
Zhu, X., Meng, X., & Zhang, M. (2021). Application of multiple criteria decision making methods in construction: a systematic literature review. Journal of Civil Engineering and Management, 27(6), 372–403. https://doi.org/10.3846/jcem.2021.15260
Zouein, P. P., & Kattan, S. (2022). An improved construction approach using ant colony optimization for solving the dynamic facility layout problem. Journal of the Operational Research Society, 73(7), 1517–1531. https://doi.org/10.1080/01605682.2021.1920345