Baynes, T. M. et al. The Australian industrial ecology virtual laboratory and multi-scale assessment of buildings and construction. Energy Build. 164, 14–20. https://doi.org/10.1016/j.enbuild.2017.12.056 (2018).
Ibn-Mohammed, T. et al. Operational vs. embodied emissions in buildings: A review of current trends. Energy Build. 66, 232–245. https://doi.org/10.1016/j.enbuild.2013.07.026 (2013).
Dixit, M. K. Life cycle embodied energy analysis of residential buildings: A review of literature to investigate embodied energy parameters. Renew. Sustain. Energy Rev. 79, 390–413. https://doi.org/10.1016/j.rser.2017.05.051 (2017).
Cabeza, L. F. et al. Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review. Renew. Sustain. Energy Rev. 29, 394–416 (2014).
Ortiz, O., Castells, F. & Sonnemann, G. Sustainability in the construction industry: A review of recent developments based on LCA. Constr. Build. Mater. 23, 28–39 (2009).
Sanchez, B., Rausch, C., Haas, C. & Hartmann, T. A framework for BIM-based disassembly models to support reuse of building components. Resour. Conserv. Recycl. 175 https://doi.org/10.1016/j.resconrec.2021.105825 (2021).
Fonseca Arenas, N. & Shafique, M. Recent progress on BIM-based sustainable buildings: State of the art review. Developments Built Environ. 15, 100176. https://doi.org/10.1016/j.dibe.2023.100176 (2023).
Muller, M. F. et al. A systematic literature review of interoperability in the green building information modeling lifecycle. J. Clean. Prod. 223, 397–412. https://doi.org/10.1016/j.jclepro.2019.03.114 (2019).
Manzoor, B., Othman, I., Gardezi, S. S. S. & Harirchian, E. Strategies for adopting building information modeling (Bim) in sustainable building projects: A case of Malaysia. Buildings 11. https://doi.org/10.3390/buildings11060249 (2021).
Wang, G. & Song, J. The relation of perceived benefits and organizational supports to user satisfaction with building information model (BIM). Comput. Hum. Behav. 68, 493–500. https://doi.org/10.1016/j.chb.2016.12.002 (2017).
Cortez-Lara, P. & Sanchez, B. A digital integrated methodology for semi-automated analysis of water efficiency in buildings. Buildings 13, 2911. https://doi.org/10.3390/buildings13122911 (2023).
Sajjad, M. et al. BIM-driven energy simulation and optimization for net-zero tall buildings: sustainable construction management. Front. Built Environ. 10 https://doi.org/10.3389/fbuil.2024.1296817 (2024).
Cortez-Lara, P., Hernández Gress, E. S., Ballinas-Gonzalez, R. & Sanchez, B. A methodology to simulate peak water demand for water supply systems using semi-direct methods: A case study for a residential building in Mexico. Heliyon 10, e25104. https://doi.org/10.1016/j.heliyon.2024.e25104 (2024).
International Association of Plumbing and Mechanical Officials. Uniform Plumbing Code (International Association of Plumbing and Mechanical Officials, 2024).
British Standard Institution. BS EN 806-3:2006. Specifications for installations inside buildings conveying water for human consumption – Pipe sizing. Simplified method (2006).
Deutsches Institut für Normung. DIN 1988 – 300:2012-05, Technische Regeln für Trinkwasser-Installationen_- Teil_300 (Ermittlung der Rohrdurchmesser; Technische Regel des DVGW, 2012).
Shoen, L. J. Preventive Maintenance Guidebook: Best Practices to Maintain Efficient and Sustainable Buildings, Third (Building Owners and Managers Association International, 2010).
Jong Tan, A. & Nutter, D. W. CO2e emissions from HVAC equipment and lifetime operation for common U.S. building types. Int. J. Energy Environ. 2, 415–426 (2011).
Jusselme, T., Rey, E. & Andersen, M. Surveying the environmental life-cycle performance assessments: Practice and context at early building design stages. Sustain. Cities Soc. 52, 101879. https://doi.org/10.1016/j.scs.2019.101879 (2020).
Medas, M. et al. Towards BIM-integrated, resource-efficient building services. In Braithwaite N, Moreno M, Salvia G (eds) Product Lifetimes and The Environment Conference (eds Cooper, T. et al.) 236–242 (Nottingham Trent University: CADBE, 2015).
Chang, Y. T. & Hsieh, S. H. A review of building information modeling research for green building design through building performance analysis. J. Inform. Technol. Constr. 25, 1–40. https://doi.org/10.36680/j.itcon.2020.001 (2020).
Ciribini, A. L. C., Mastrolembo Ventura, S. & Paneroni, M. Implementation of an interoperable process to optimise design and construction phases of a residential building: A BIM Pilot Project. Autom. Constr. 71, 62–73. https://doi.org/10.1016/j.autcon.2016.03.005 (2016).
Ciribini, A. L. C., Ventura, S. M. & Paneroni, M. Implementation of an open and interoperable process to optimise design and construction phases of a residential building project: A case study using BIM in a public procurement. In Proceedings of the 32nd International Symposium on Automation and Robotics in Construction and Mining (ISARC 2015) (International Association for Automation and Robotics in Construction (IAARC), 2017).
Rajabi, M., Bigga, T. & Bartl, M. A. Optimization of the Quantity Take-off (QTO) Process for Mechanical, Electrical and Plumbing (MEP) Trades in Tender Estimation Phase of the Construction Projects (2015).
Hu, Z. Z. et al. Geometric optimization of building information models in MEP projects: Algorithms and techniques for improving storage, transmission and display. Autom. Constr. 107. https://doi.org/10.1016/j.autcon.2019.102941 (2019).
Hsu, H. C., Chang, S., Chen, C. C. & Wu, I. C. Knowledge-based system for resolving design clashes in building information models. Autom. Constr. 110 https://doi.org/10.1016/j.autcon.2019.103001 (2020).
Zhang, N., Wang, J., Al-Hussein, M. & Yin, X. BIM-based automated design of drainage systems for panelized residential buildings. Int. J. Constr. Manage. https://doi.org/10.1080/15623599.2022.2085853 (2022).
Rodriguez, B. X. et al. Mechanical, electrical, plumbing and tenant improvements over the building lifetime: Estimating material quantities and embodied carbon for climate change mitigation. Energy Build. 226 https://doi.org/10.1016/j.enbuild.2020.110324 (2020).
Marocco, M. & Garofolo, I. Integrating disruptive technologies with facilities management: A literature review and future research directions. Autom. Constr. 131, 103917. https://doi.org/10.1016/j.autcon.2021.103917 (2021).
Chen, Z-S. et al. Optimization-based probabilistic decision support for assessing building information modelling (BIM) maturity considering multiple objectives. Inform. Fusion. 102, 102026. https://doi.org/10.1016/j.inffus.2023.102026 (2024).
Baek, F., Ha, I. & Kim, H. Augmented reality system for facility management using image-based indoor localization. Autom. Constr. 99, 18–26. https://doi.org/10.1016/j.autcon.2018.11.034 (2019).
Heaton, J., Parlikad, A. K. & Schooling, J. Design and development of BIM models to support operations and maintenance. Comput. Ind. 111, 172–186. https://doi.org/10.1016/j.compind.2019.08.001 (2019).
Zhan, J. et al. Improvement of the inspection-repair process with building information modelling and image classification. Facilities 37, 395–414. https://doi.org/10.1108/F-01-2018-0005 (2019).
Halmetoja, E. The conditions data model supporting building information models in facility management. Facilities 37, 484–501. https://doi.org/10.1108/F-11-2017-0112 (2019).
Du, J., Zou, Z., Shi, Y. & Zhao, D. Zero latency: Real-time synchronization of BIM data in virtual reality for collaborative decision-making. Autom. Constr. 85, 51–64. https://doi.org/10.1016/j.autcon.2017.10.009 (2018).
Neuville, R., Pouliot, J. & Billen, R. Identification of the best 3D viewpoint within the BIM Model: Application to visual tasks related to facility management. Buildings 9, 167. https://doi.org/10.3390/buildings9070167 (2019).
El Ammari, K. & Hammad, A. Remote interactive collaboration in facilities management using BIM-based mixed reality. Autom. Constr. 107, 102940. https://doi.org/10.1016/j.autcon.2019.102940 (2019).
Cheng, J. C. P., Chen, W., Chen, K. & Wang, Q. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Autom. Constr. 112, 103087. https://doi.org/10.1016/j.autcon.2020.103087 (2020).
Diao, P-H. & Shih, N-J. BIM-Based AR Maintenance System (BARMS) as an Intelligent Instruction Platform for Complex Plumbing Facilities. Appl. Sci. 9, 1592. https://doi.org/10.3390/app9081592 (2019).
Chen, Y-J., Lai, Y-S. & Lin, Y-H. BIM-based augmented reality inspection and maintenance of fire safety equipment. Autom. Constr. 110, 103041. https://doi.org/10.1016/j.autcon.2019.103041 (2020).
GhaffarianHoseini, A. et al. ND BIM-integrated knowledge-based building management: Inspecting post-construction energy efficiency. Autom. Constr. 97, 13–28. https://doi.org/10.1016/j.autcon.2018.10.003 (2019).
Ma, Z., Ren, Y., Xiang, X. & Turk, Z. Data-driven decision-making for equipment maintenance. Autom. Constr. 112, 103103. https://doi.org/10.1016/j.autcon.2020.103103 (2020).
Chen, W. et al. BIM-based framework for automatic scheduling of facility maintenance work orders. Autom. Constr. 91, 15–30. https://doi.org/10.1016/j.autcon.2018.03.007 (2018).
Zhong, B., Gan, C., Luo, H. & Xing, X. Ontology-based framework for building environmental monitoring and compliance checking under BIM environment. Build. Environ. 141, 127–142. https://doi.org/10.1016/j.buildenv.2018.05.046 (2018).
Bonci, A. et al. A cyber-physical system approach for building efficiency monitoring. Autom. Constr. 102, 68–85. https://doi.org/10.1016/j.autcon.2019.02.010 (2019).
Petri, I. et al. Optimizing energy efficiency in operating built environment assets through building information modeling: A case study. Energies (Basel). 10, 1167. https://doi.org/10.3390/en10081167 (2017).
Wu, I-C. & Liu, C-C. A visual and persuasive energy conservation system based on BIM and IoT technology. Sensors 20, 139. https://doi.org/10.3390/s20010139 (2019).
Dave, B., Buda, A., Nurminen, A. & Främling, K. A framework for integrating BIM and IoT through open standards. Autom. Constr. 95, 35–45. https://doi.org/10.1016/j.autcon.2018.07.022 (2018).
Zhang, Y-Y. et al. Building information modeling–based cyber-physical platform for building performance monitoring. Int. J. Distrib. Sens. Netw. 16, 155014772090817. https://doi.org/10.1177/1550147720908170 (2020).
D.R. M, I.C. G Optimal urban water distribution design. Water Resour. Res. 21, 642–652 (1985).
Samani, H. M. V. & Mottaghi, A. Optimization of water distribution networks using integer linear programming. J. Hydraul. Eng. 132, 501–509. https://doi.org/10.1061/(asce)0733-9429(2006)132 (2006).
Sarbu, I. Optimisation models of looped urban water supply networks. Heat pump systems for sustainable heating and cooling View project Numerical modelling in building services engineering View project optimisation models of looped urban water supply networks (2020).
Awwalu, H. B., Abdullahi, N. & Hussaini, M. Conceptual model of mixed-integer linear programming water distribution system. Math. Appl. Sci. Eng. 37–49. https://doi.org/10.5206/mase/15591 (2023).
Mahmoodabadi, M. J. & Nemati, A. R. A novel adaptive genetic algorithm for global optimization of mathematical test functions and real-world problems. Eng. Sci. Technol. Int. J. 19, 2002–2021. https://doi.org/10.1016/j.jestch.2016.10.012 (2016).
Goldberg, D. E. & Holland, J. H. Genetic Algorithms and Machine Learning. Mach. Learn. 3, 95–99. https://doi.org/10.1023/A:1022602019183 (1988).
Kennedy, J. & Eberhart, R. Particle swarm optimization. In Proceedings of ICNN’95 – International Conference on Neural Networks. 1942–1948. IEEE.
He, S., Prempain, E. & Wu, Q. H. An improved particle swarm optimizer for mechanical design optimization problems. Eng. Optim. 36, 585–605. https://doi.org/10.1080/03052150410001704854 (2004).
Dorigo, M. & Stützle, T. Ant Colony Optimization (The MIT Press, 2004).
Tadaros, M. & Kyriakakis, N. A. A Hybrid clustered ant colony optimization approach for the hierarchical multi-switch multi-echelon vehicle routing problem with service times. Comput. Ind. Eng. 190, 110040. https://doi.org/10.1016/j.cie.2024.110040 (2024).
Deb, K. Multi-objective Optimization Using Evolutionary Algorithms (Wiley, 2001).
Deb, K. Recent Developments in Evolutionary Multi-objective Optimization 339–368 (2010).
Siddique, N. & Adeli, H. Simulated annealing, its variants and engineering applications. Int. J. Artif. Intell. Tools. 25, 1630001. https://doi.org/10.1142/S0218213016300015 (2016).
Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671–680. https://doi.org/10.1126/science.220.4598.671 (1983).
Juan, A. A. et al. A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics. Ann. Oper. Res. 320 https://doi.org/10.1007/s10479-021-04142-9 (2023).
Sadeeq, H. T. & Abdulazeez, A. M. Metaheuristics: A Review of Algorithms. Int. J. Online Biomedical Eng. 19 (2023).
Giri, J. M. Simulated Annealing and Its Applications to Mechanical Engineering (A Review, 2023).
Da Cunha, M. & Sousa, J. Hydraulic infrastructures design using simulated annealing. J. Infrastruct. Syst. (2001).
Torii, A. J. & Pereira, J. T. Pipe network design using mixed simulated annealing and Tabu Search-MSATS. Proceedings of COBEM 2009 (2009).
Karovic, O. & Mays, L. W. Sewer system design using simulated annealing in excel. Water Resour. Manage. 28, 4551–4565. https://doi.org/10.1007/s11269-014-0750-8 (2014).
Park, M., Chung, G., Yoo, C. & Kim, J. H. Optimal design of stormwater detention basin using the genetic algorithm. KSCE J. Civ. Eng. 16, 660–666. https://doi.org/10.1007/s12205-012-0991-0 (2012).
Sangroula, U. et al. Optimization of water distribution networks using genetic algorithm based SOP–WDN program. Water 14. https://doi.org/10.3390/w14060851 (2022).
Ahmed, A. O. M., Osman, S. M. Y., Yousif, T. E. H. & Kheiri, A. A reinforcement learning hyper-heuristic for water distribution network optimisation. In 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) 1–4 (IEEE, 2021).
Jung, B. S. Water distribution system optimization accounting for worst-case transient loadings. J. Environ. Inf. Lett. 7, 20–29. https://doi.org/10.3808/jeil.202200077 (2022).
American Society for Testing and Materials. Specification for Seamless Copper Water Tube (2020).
American Society for Testing and Materials. Specification for Poly(Vinyl Chloride) (PVC) Plastic Pipe, Schedules 40, 80, and 120 (2021).
American Society for Testing and Materials. Specification for Pressure-rated Polypropylene (PP) Piping Systems, 2021).
Hammond Geoffrey, J. et al. Embodied carbon : the Inventory of Carbon and Energy (ICE) (BSRIA, 2011).
American Society of Plumbing Engineers. Plumbing Engineering Design Handbook A Plumbing Engineer’s Guide to System Design and Specifications Plumbing Components and Equipment (2020).
Haktanir, T. & Ardiçlioǧlu, M. Numerical modeling of Darcy-Weisbach friction factor and branching pipes problem. Adv. Eng. Softw. 35 https://doi.org/10.1016/j.advengsoft.2004.07.005 (2004).
American Society of Plumbing Engineers. Plumbing Engineering Design Handbook A Plumbing Engineer’s Guide to System Design and Specifications Plumbing Systems (2018).
Ingber, L. Simulated annealing: Practice versus theory. Math. Comput. Model. 18, 29–57. https://doi.org/10.1016/0895-7177(93)90204-C (1993).
Cortez-Lara, P. BIM-SA-optimization-framework. Zenodo. (2025). https://doi.org/10.5281/zenodo.14963422
Autodesk Revit Sample Project Files. (2024). https://help.autodesk.com/view/RVT/2024/ENU/?guid=GUID-61EF2F22-3A1F-4317-B925-1E85F138BE88. Accessed 5 May 2025.
Hunter, R. Building Materials and Structures: Methods of Estimating Loads in Plumbing Systems (1940).
Reca, J., Martínez, J., Gil, C. & Baños, R. Application of several meta-heuristic techniques to the optimization of real looped water distribution networks. Water Resour. Manage. 22, 1367–1379. https://doi.org/10.1007/s11269-007-9230-8 (2008).
Carlson, K. M., Boczek, L. A., Chae, S. & Ryu, H. Legionellosis and recent advances in technologies for Legionella control in premise plumbing systems: A review. Water (Switzerland) 12, 1–22 (2020).
International Association of Plumbing and Mechanical Officials. Water Demand Calculator Study (2020).
