Digital Twin-Enhanced MCDM Framework for Circular Construction Dynamic Lifecycle Optimization of Hybrid Concrete Mix Design

Hongling Jiang

doi:10.31181/dmame8220251476

Authors

Hongling Jiang Senior Engineer,Water Resources Research Institute of Anhui Province and Huaihe River Commission, Ministry of Water Resources of the People’s Republic of China, Hefei, China,230088

DOI:

https://doi.org/10.31181/dmame8220251476

Keywords:

Hybrid Concrete Mix Design, Circular Construction, Digital Twin, Multi-Criteria Decision-Making (MCDM), Particle Swarm Optimization (PSO).

Abstract

This study introduces an integrated, intelligent framework aimed at optimising hybrid concrete mix design by employing Digital Twin (DT) modelling, dynamic decision-making processes, and real-time optimisation. The DT model is developed to continuously mirror and update the changing physical characteristics of hybrid concrete materials, thereby facilitating predictive simulations and lifecycle assessments. Data quality is ensured through pre-processing and feature extraction procedures, which involve cleaning, normalisation, and parameter selection focused on compressive strength, recyclability, embodied carbon, and degradation indicators. A retained carbon emission accounting model is utilised to dynamically monitor environmental impacts throughout the construction lifecycle, thereby enabling the formulation of low-carbon mix design strategies. The framework incorporates recycled aggregates, biochar, and industrial waste materials, aligning with sustainability objectives and circular economy principles. A dynamic multi-criteria decision-making (MCDM) process, based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is employed to assess mix design alternatives. This process evaluates various criteria including cost factors, performance indicators, and environmental impacts, with adaptable weight adjustment mechanisms tailored to the specific phase of the project. Optimisation of mix designs is conducted using PSO, which dynamically integrates data derived from MCDM procedures and Decision Tables (DT). A real-time feedback and decision-making interface allows operators to actively oversee operations, enabling proactive adjustments and informed decisions. The integration of these components results in an adaptive and resilient system for sustainable hybrid concrete construction, balancing structural performance with environmentally conscious practices. Future investigations may expand the framework by incorporating AI-based predictive maintenance and adaptive learning algorithms for real-time modelling of material behaviour. Furthermore, extending the system’s scalability to accommodate large-scale infrastructure projects may enhance its applicability within smart construction environments.

Downloads

Download data is not yet available.

References

[1] Abdullahi, I., Longo, S., & Samie, M. (2024). Towards a distributed digital twin framework for predictive maintenance in industrial internet of things (IIoT). Sensors, 24(8), 2663. https://doi.org/10.3390/s24082663

[2] Al-Sehrawy, R., & Kumar, B. (2021). Digital twins in architecture, engineering, construction and operations. A brief review and analysis. Proceedings of the 18th International Conference on Computing in Civil and Building Engineering: ICCCBE 2020, 924-939. https://doi.org/10.1007/978-3-030-51295-8_64

[3] Al-Sehrawy, R., Kumar, B., & Watson, R. (2021). A digital twin uses classification system for urban planning & city infrastructure management. Journal of Information Technology in Construction, 26, 832-362. https://pureportal.strath.ac.uk/en/publications/a-digital-twin-uses-classification-system-for-urban-planning-amp-

[4] AlBalkhy, W., Karmaoui, D., Ducoulombier, L., Lafhaj, Z., & Linner, T. (2024). Digital twins in the built environment: Definition, applications, and challenges. Automation in Construction, 162, 105368. https://doi.org/10.1016/j.autcon.2024.105368

[5] Ammar, A., Nassereddine, H., AbdulBaky, N., AbouKansour, A., Tannoury, J., Urban, H., & Schranz, C. (2022). Digital twins in the construction industry: a perspective of practitioners and building authority. Frontiers in Built Environment, 8, 834671. https://doi.org/10.3389/fbuil.2022.834671

[6] Arvindhan, M. (2021). Possibilities of industrial trends and business benefits with industry 4.0 technology: A survey. In Artificial Intelligence for a Sustainable Industry 4.0 (pp. 1-18). Springer. https://doi.org/10.1007/978-3-030-77070-9_1

[7] Asare, K. A., Liu, R., Anumba, C. J., & Issa, R. R. (2024). Real-world prototyping and evaluation of digital twins for predictive facility maintenance. Journal of Building Engineering, 97, 110890. https://doi.org/10.1016/j.jobe.2024.110890

[8] Caballero-Peña, J., Osma-Pinto, G., Rey, J. M., Nagarsheth, S., Henao, N., & Agbossou, K. (2024). Analysis of the building occupancy estimation and prediction process: A systematic review. Energy and Buildings, 114230. https://doi.org/10.1016/j.enbuild.2024.114230

[9] Gardas, B. B., Gunasekaran, A., & Narwane, V. S. (2024). Unlocking factors of digital twins for smart manufacturing: a case of emerging economy. International Journal of Computer Integrated Manufacturing, 37(10-11), 1463-1493. https://doi.org/10.1080/0951192X.2023.2257655

[10] Ghansah, F. A. (2024). Digital twins for smart building at the facility management stage: a systematic review of enablers, applications and challenges. Smart and Sustainable Built Environment. https://doi.org/10.1108/SASBE-10-2023-0298

[11] Gordo-Gregorio, P., Alavi, H., Edwards, D. J., Forcada, N., & Guéna, F. (2025). An occupant-centric approach on digital twins for building management. Building Research & Information, 1-20. https://doi.org/10.1080/09613218.2025.2463676

[12] Haverkamp, P., Anantheswar, A., May, M., Wadlig, G., Hildebrandt, J., Thiessat, K. A., ... & Lehner, W. (2025). Digital Twin Road: value and implications involving data and application. Discover Applied Sciences, 7(6), 1-21. https://doi.org/10.1007/s42452-025-07018-w

[13] Hu, W., & Cai, Y. (2024). A semi-supervised method for digital twin-enabled predictive maintenance in the building industry. Neural Computing and Applications, 36(25), 15759-15775. https://doi.org/10.1007/s00521-024-09926-1

[14] Jiang, F., Ma, L., Broyd, T., & Chen, K. (2021). Digital twin and its implementations in the civil engineering sector. Automation in Construction, 130, 103838. https://doi.org/10.1016/j.autcon.2021.103838

[15] Jiang, Y., Su, S., Zhao, S., Zhong, R. Y., Qiu, W., Skibniewski, M. J., Brilakis, I., & Huang, G. Q. (2024). Digital twin-enabled synchronized construction management: a roadmap from construction 4.0 towards future prospect. Developments in the Built Environment, 100512. https://doi.org/10.1016/j.dibe.2024.100512

[16] Kineber, A. F., Singh, A. K., Fazeli, A., Mohandes, S. R., Cheung, C., Arashpour, M., Ejohwomu, O., & Zayed, T. (2023). Modelling the relationship between digital twins implementation barriers and sustainability pillars: Insights from building and construction sector. Sustainable Cities and Society, 99, 104930. https://doi.org/10.1016/j.scs.2023.104930

[17] Liu, J., Duan, L., Lin, S., Miao, J., & Zhao, J. (2025). Concept, creation, services and future directions of digital twins in the construction industry: a systematic literature review. Archives of Computational Methods in Engineering, 32(1), 319-342. https://doi.org/10.1007/s11831-024-10140-4

[18] Ma, S., Flanigan, K. A., & Bergés, M. (2024). State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies. Advanced engineering informatics, 62, 102800. https://doi.org/10.1016/j.aei.2024.102800

[19] Mêda, P., Calvetti, D., Hjelseth, E., & Sousa, H. (2021). Incremental digital twin conceptualisations targeting data-driven circular construction. Buildings, 11(11), 554. https://doi.org/10.3390/buildings11110554

[20] Miraj, P., Wang, T., Koutamanis, A., & Chan, P. (2025). Organising digital twin in the built environment: a systematic review and research directions on the missing links of use and user perspectives of digital twin in Architecture, Engineering and Construction (AEC) sector. Construction Management and Economics, 1-17. https://doi.org/10.1080/01446193.2025.2451631

[21] Mirwais, M., Adeel, M., Rahmani, A. W., & Rahmani, A. N. (2025). AI-Driven Generative Design for Next-Generation 3D Concrete Printing in Architecture: State of the Art. European Journal of Applied Science, Engineering and Technology, 3(2), 225-232. https://doi.org/10.59324/ejaset.2025.3(2).19

[22] Mohandes, S. R., Singh, A. K., Fazeli, A., Banihashemi, S., Arashpour, M., Cheung, C., Ejohwomu, O., & Zayed, T. (2024). Determining the stationary digital twins implementation barriers for sustainable construction projects. Smart and Sustainable Built Environment. https://doi.org/10.1108/SASBE-11-2023-0344

[23] Okwu, M. O., Tartibu, L. K., Maware, C., Enarevba, D. R., Afenogho, J. O., & Essien, A. (2022). Emerging technologies of industry 4.0: Challenges and opportunities. 2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), 1-13. https://doi.org/10.1109/icABCD54961.2022.9856002

[24] Omrany, H., Al-Obaidi, K. M., Husain, A., & Ghaffarianhoseini, A. (2023). Digital twins in the construction industry: a comprehensive review of current implementations, enabling technologies, and future directions. Sustainability, 15(14), 10908. https://doi.org/10.3390/su151410908

[25] Opoku, D.-G. J., Perera, S., Osei-Kyei, R., & Rashidi, M. (2021). Digital twin application in the construction industry: A literature review. Journal of Building Engineering, 40, 102726. https://doi.org/10.1016/j.jobe.2021.102726

[26] Petri, I., Rezgui, Y., Ghoroghi, A., & Alzahrani, A. (2023). Digital twins for performance management in the built environment. Journal of industrial information integration, 33, 100445. https://doi.org/10.1016/j.jii.2023.100445

[27] Radzi, A. R., Azmi, N. F., Kamaruzzaman, S. N., Rahman, R. A., & Papadonikolaki, E. (2024). Relationship between digital twin and building information modeling: A systematic review and future directions. Construction Innovation, 24(3), 811-829. https://doi.org/10.1108/CI-07-2022-0183

[28] Sindhu, V., Anitha, G., & Geetha, R. (2021). Industry 4.0-A Breakthrough in artificial Intelligence the Internet of Things and Big Data towards the next digital revolution for high business outcome and delivery. Journal of Physics: Conference Series, 012030. http://doi.org/10.1088/1742-6596/1937/1/012030

[29] Solanki, R., Sujee, S., & Dalwai, T. (2024). AI and industry 4.0: a review of applications and contributions. Information and Communication Technology in Technical and Vocational Education and Training for Sustainable and Equal Opportunity: Business Governance and Digitalization of Business Education, 93-102. https://doi.org/10.1007/978-981-99-7798-7_7

[30] Su, S., Zhong, R. Y., Jiang, Y., Song, J., Fu, Y., & Cao, H. (2023). Digital twin and its potential applications in construction industry: State-of-art review and a conceptual framework. Advanced engineering informatics, 57, 102030. https://doi.org/10.1016/j.aei.2023.102030

[31] Tyagi, A. K., Fernandez, T. F., Mishra, S., & Kumari, S. (2020). Intelligent automation systems at the core of industry 4.0. International conference on intelligent systems design and applications, 1-18. https://doi.org/10.1007/978-3-030-71187-0_1

[32] Weerapura, V., Sugathadasa, R., De Silva, M. M., Nielsen, I., & Thibbotuwawa, A. (2023). Feasibility of digital twins to manage the operational risks in the production of a ready-mix concrete plant. Buildings, 13(2), 447. https://doi.org/10.3390/buildings13020447

[33] Zhu, H., Hwang, B.-G., Tan, Y. Z., & Wei, F. (2024). Building on Digital Twin: Overcoming Barriers and Unlocking Success in the Construction Industry. Journal of Construction Engineering and Management, 150(10), 04024142. https://doi.org/10.1061/JCEMD4.COENG-13991