Abstract
The layout design and spatial coordination of multiple pipe systems is major challenge in the construction industry. The purpose of multiple pipe layout design is to find out an optimal layout for numerous individual pipes to route from a different start locations to different end locations in a 3D environment with no clashes under various kinds of constraints. Currently, pipe layout design is conducted manually by consultants, which is tedious, labor intensive, error-prone, and time-consuming. This paper proposes a BIM-based approach for layout design of multiple pipes using heuristic search methods. Algorithms are developed based on a directed weighted graph according to the physical, design, economical and installation requirements of pipe layout design. Clashes between pipes and with building components are considered and subsequently avoided in the layout optimization. Based on the developed algorithms, simulated annealing (SA) algorithm is used to approximate global optimization in a large search space for multiple pipe layout optimization. As for layout design, Dijkstra algorithm and two heuristic algorithms namely 3D A* and fruit fly optimization algorithm (FOA) are implemented and compared to obtain the multiple pipe system layout design. An example of a typical plant room with nine pipe routes is used to illustrate the developed approach on multiple pipe layout design. The result shows that the developed approach can generate optimal and clash-free multiple pipe system layout. Compared with the conventional method, the developed approach significantly reduces the time and cost for designing multiple pipe layout.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Singh, J., Deng, M., Cheng, J.C.P.: Implementation of mass customization for MEP layout design to reduce manufacturing cost in one-off projects. In: Proceeding of the 26th Annual Conference of the International Group for Lean Construction, Chennai, India, pp. 625–635 (2018). http://iglc.net/Papers/Details/1587
Guirardello, R., Swaney, R.E.: Optimization of process plant layout with pipe routing. Comput. Chem. Eng. 30, 99–114 (2005). https://doi.org/10.1016/j.compchemeng.2005.08.009
Qu, Y., Jiang, D., Gao, G., Huo, Y.: Pipe routing approach for aircraft engines based on ant colony optimization, J. Aerosp. Eng. 29(3) (2016). https://doi.org/10.1061/(ASCE)AS.1943-5525.0000543
Ito, T.: A genetic algorithm approach to piping route path planning. J. Intell. Manuf. 10, 103–114 (1999). https://doi.org/10.1023/A:1008924832167
Park, J.-H., Storch, R.L.: Pipe-routing algorithm development: case study of a ship engine room design. Expert Syst. Appl. 23(3), 299–309 (2002)
Kang, S., Myung, S., Han, S.: A design expert system for auto-routing of ship pipes. J. Ship Prod. 15(1), 1–9 (1999)
Blaschke, J.C., Jatzek Jr., H.A.: Electronic engine ‘MockUp’ shortens design time. Aerosp. Am. 23, 98–100 (1985)
Bile, W., Ruxin, N., Jianhua, L.: Architecture of cable harness and tube assembly planning system in virtual environment. Comput. Integr. Manuf. Syst. 13, 1579–1585 (2007)
Jebamalai, J.M., Marlein, K., Laverge, J., Vandevelde, L., van den Broek, M.: An automated GIS-based planning and design tool for district heating: scenarios for a Dutch city. Energy 183, 487–496 (2019)
Qiqi, L., Wong, Y.-H.: A BIM-based approach to automate the design and coordination process of mechanical, electrical, and plumbing systems. HKIE Trans. 25(4), 273–280 (2018). https://doi.org/10.1080/1023697x.2018.1537813
Eleftheriadis, S., Duffour, P., Stephenson, B., Mumovic, D.: Automated specification of steel reinforcement to support the optimization of RC floors. Autom. Constr. 96, 366–377 (2018). https://doi.org/10.1016/j.autcon.2018.10.005
Wong, J.K.W., Zhou, J.: Enhancing environmental sustainability over building life cycles through green BIM: a review. Autom. Constr. 57, 156–165 (2015). https://doi.org/10.1016/j.autcon.2015.06.003
Wang, C., Sun, X., Sun, L., Yuan, T.: A method based on PSO for pipe routing design. In: Proceeding of the 6th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, Chengdu, China (2016). https://doi.org/10.1109/CYBER.2016.7574862
Jiang, W.-Y., Chen, Y.L.M., Yu, Y.-Y.: A co-evolutionary improved multi-ant colony optimization for ship multiple and branch pipe route design. Ocean Eng. 102, 63–70 (2015). https://doi.org/10.1016/j.oceaneng.2015.04.028
Qu, Y.-F., Jiang, D., Zhang, X.-L.: A new pipe routing approach for aero-engines by octree modeling and modified max-min ant system optimization algorithm. J. Mech. 34(1), 11–19 (2018). https://doi.org/10.1017/jmech.2016.86
Sui, H., Niu, W.: Branch-pipe-routing approach for ships using improved genetic algorithm. Front. Mech. Eng. 11(3), 316–323 (2016). https://doi.org/10.1007/s11465-016-0384-z
Xu, S., Ho, E.S.L., Shum, H.P.H.: A hybrid metaheuristic navigation algorithm for robot path rolling planning in an unknown environment. Mechatron. Syst. Control 47(4), 216–224 (2019). https://doi.org/10.2316/J.2019.201-3000
Cook, S.A.: An overview of computational complexity. Commun. ACM 26, 401–408 (1983)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)
Padhy, C.P., Sen, D., Bhaskaran, P.K.: Application of wave model for weather routing of ships in the North Indian Ocean. Nat. Hazards 44(3), 373–385 (2008)
Wang, H., Mao, W., Eriksson, L.: A three-dimensional Dijkstra’s algorithm for multi-objective ship voyage optimization. Ocean Eng. 186, 106131 (2019)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agent. IEEE Trans. Syst. Man Cybern.-Part B Cybern. 26(1), 29–41 (1996)
He, Y., Zeng, Q., Liu, J., Xu, G.: Path planning for indoor UAV based on ant colony optimization. In: Proceedings of the 25th Chinese Control and Decision Conference (CCDC), Guiyang, China, pp. 2919–2923 (2013)
Jiang, W.-Y., Lin, Y., Chen, M., Yu, Y.-Y.: An optimization approach based on particle swarm optimization and ant colony optimization for arrangement of marine engine room. J. Shanghai Jiaotong Univ. 48(4), 502–507 (2014)
Jin, R., Hou, P., Yang, G., Qi, Y., Chen, C., Chen, Z.: Cable routing optimization for offshore wind power plants via wind scenarios considering power loss cost model. Appl. Energy 254 (2019). Article no. 113719. https://doi.org/10.1016/j.apenergy.2019.113719
Kumar, S.S., Manimegalai, P., Karthik, S.: An energy - competent routing protocol for MANETs: a particle swarm optimization approach. In: Proceedings of IEEE International Conference on Soft-Computing and Network Security (2018). Article no. 8573677. https://doi.org/10.1109/icsns.2018.8573677
Liu, Q., Mao, L.: Multi-objective layout optimization for branch pipe of aero-engine based on MOPSO. J. Mech. Eng. 54(19), 197–203 (2018). https://doi.org/10.3901/JME.2018.19.197
Zhu, J.-L., Li, W., Li, H., Wu, Q., Zhang, L.: A novel swarm intelligence algorithm for the evacuation routing optimization problem. Int. Arab J. Inf. Technol. 14(6), 880–889 (2017)
Liu, Q., Wang, C.: A discrete particle swarm optimization algorithm for rectilinear branch pipe routing. Assembly Autom. 31(4), 363–368 (2011). https://doi.org/10.1108/01445151111172952
Darwish, S.M., Elmasry, A., Ibrahim, S.H.: Optimal shortest path in mobile ad-hoc network based on fruit fly optimization algorithm. In: The International Conference on Advanced Machine Learning Technologies and Application, pp. 91–101 (2019)
Jiang, Z.-B., Yang, Q.: A discrete fruit fly optimization algorithm for the traveling salesman problem. PLoS ONE 11(11), e0165804 (2016). https://doi.org/10.1371/journal.pone.0165804
Iscan, H., Gunduz, M.: An application of fruit fly optimization algorithm for traveling salesman problem. Procedia Comput. Sci. 111, 58–63 (2017). https://doi.org/10.1016/j.procs.2017.06.010
Shitagh, N.A., Jalal, L.D.: Path planning of intelligent mobile robot using modified genetic algorithm. Int. J. Soft Comput. Eng. (IJSCE) 3(2), 31–36 (2013)
Mathias, H.D., Foley, S.S.: Improving a genetic algorithm for route planning using parallelism with speculative execution. In: ACM International Conference Proceeding Series (2019). Article no. 3333251. https://doi.org/10.1145/3332186.3333251
Xin, J., Zhong, J., Yang, F., Cui, Y., Sheng, J.: An improved genetic algorithm for path-planning of unmanned surface vehicle. Sensors (Switzerland) 19(11) (2019). Article no. 2640. https://doi.org/10.3390/s19112640
Alves, R.M.F., Lopes, C.R.: Using genetic algorithms to minimize the distance and balance the routes for the multiple traveling salesman problem. In: IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, pp. 3171–3178 (2015). Article no. 7257285. https://doi.org/10.1109/cec.2015.7257285
Hasdemir, S., Yilmaz, S., Sen, S.: A novel multi-featured metric for adaptive routing in mobile ad hoc networks. Appl. Intell. 49(8), 2823–2841 (2019). https://doi.org/10.1007/s10489-018-01401-4
Kumar, S.S., Cheng, J.C.P.: A BIM-based automated site layout planning framework for congested construction sites. Autom. Constr. 59, 24–37 (2015). https://doi.org/10.1016/j.autcon.2015.07.008
Tan, Y., Song, Y., Liu, Y., Wang, X., Cheng, J.C.P.: A BIM-based framework for lift planning in topsides disassembly of offshore oil and gas platforms. Autom. Constr. 79, 19–30 (2017). https://doi.org/10.1016/j.autcon.2017.02.008
Hajad, M., Tangwarodomnukun, V., Jaturanonda, C., Dumkum, C.: Laser cutting path optimization using simulated annealing with an adaptive large neighborhood search. Int. J. Adv. Manuf. Technol. 103(1–4), 781–792 (2019). https://doi.org/10.1007/s00170-019-03569-6
Daryanavard, H., Harifi, A.: UAV path planning for data gathering of IoT nodes: ant colony or simulated annealing optimization. In: Proceedings of 3rd International Conference on Internet of Things and Applications, IoT 2019 (2019). Article no. 8808834. https://doi.org/10.1109/iicita.2019.8808834
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983). https://doi.org/10.1126/science.220.4598.671
Nayyar, M.L.: Piping Handbook. Mcgraw-Hill, New York (2002). ISBN 9780070471061
Black, P.E., Vreda, P.: Manhattan distance. In: Dictionary of Algorithms and Data Structures, pp. 16–54 (2006)
Hart, P.E.G., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4, 100–107 (1968)
Autodesk Inc., Dynamo. http://dynamobim.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Singh, J., Cheng, J.C.P. (2021). Automating the Generation of 3D Multiple Pipe Layout Design Using BIM and Heuristic Search Methods. In: Toledo Santos, E., Scheer, S. (eds) Proceedings of the 18th International Conference on Computing in Civil and Building Engineering. ICCCBE 2020. Lecture Notes in Civil Engineering, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-030-51295-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-51295-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51294-1
Online ISBN: 978-3-030-51295-8
eBook Packages: EngineeringEngineering (R0)