Quantum–Classical Synergies in Topology Optimization for 3D-Printed UHPC Bridge Structures: A Critical Review of Computational Paradigms, Sustainability Metrics, and Scalability Barriers
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Abstract
Recent advances in quantum computing, combined with classical algorithms, are reshaping how engineers approach topology optimization (TO), particularly in the context of 3Dprinted ultra-high-performance concrete (UHPC) bridge structures. This review critically examines how hybrid quantum– classical methods are influencing structural design workflows, with a focus on theoretical frameworks, computational strategies, sustainability metrics, and practical scalability. Drawing on a wide range of current academic literature and case-based studies, the paper assesses how quantum approaches, such as the Quantum Approximate Optimisation Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), may overcome the challenges posed by complex design spaces and nonlinear behaviours commonly encountered in TO problems. The analysis also highlights the role of machine learning in complementing quantum algorithms, particularly for tasks such as surrogate modelling, performance prediction, and real-time optimisation feedback. Sustainability assessments further reveal that these hybrid approaches can contribute to substantial reductions in material consumption, embodied carbon, and construction waste, particularly when combined with additive manufacturing processes that accommodate irregular or efficiency-driven geometries. Despite these promising developments, the review identifies several limitations that require attention. These include the technical constraints of current quantum devices, gaps in standardised computational frameworks, and issues related to scaling optimized forms for real-world fabrication. In response, the study outlines future research directions, including the development of open-source platforms, cross-disciplinary collaboration, and physical validation of optimised UHPC components. By integrating insights from quantum computing, structural engineering, and digital fabrication, this review presents a comprehensive and realistic perspective on the opportunities and challenges associated with quantum–classical synergy in sustainable bridge design. It emphasises the need for thoughtful, collaborative innovation to realise the potential of these cutting-edge technologies fully.
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Bendsøe, M. P., & Kikuchi, N. (1988). Generating Optimal Topologies in Structural Design Using a Homogenization Method. Computational Methods in Applied Mechanics and Engineering, 71(2), 197–224. DOI: https://doi.org/10.1016/0045-7825(88)90086-2
Bendsøe, M. P., & Sigmund, O. (2003). Topology Optimization: Theory, Methods and Applications. Springer.
DOI: http://doi.org//10.1007/978-3-662-05086-6
Allaire, G., Jouve, F., & Toader, A. M. (2004). Structural optimization using sensitivity analysis and a level-set method. Journal of Computational Physics, 194(1), 363–393.
DOI: http://doi.org//10.1016/j.jcp.2003.09.032
Graybeal, B. A. (2006). Ultra-high-performance concrete: A state-of-the-art report for the bridge community. FHWA-HRT-06-103, Federal Highway Administration.
https://www.researchgate.net/publication/259438585
Yoo, D. Y., & Yoon, Y. S. (2016). Properties of Ultra-High-Performance Concrete (UHPC) for Structural Applications: A Review. International Journal of Concrete Structures and Materials, 10, 265–276.
DOI: https://doi.org/10.1016/j.cemconcomp.2016.08.001
Zhang, W., Luo, Q., & Guo, Z. (2019). Additive manufacturing of concrete: Potentials and challenges. Construction and Building Materials, 212, 718–728.
DOI: http://doi.org//10.1016/j.conbuildmat.2019.03.181
Le, T. T., Austin, S. A., Lim, S., Buswell, R. A., Gibb, A. G. F., & Thorpe, T. (2022). A review of 3D printing in construction and the opportunities and challenges in emerging markets. Automation in Construction, 131, 103877.
DOI: http://doi.org//10.1016/j.autcon.2021.103877
Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., ... & Coles, P. J. (2021). Variational quantum algorithms. Nature Reviews Physics, 3(9), 625–644.
DOI: http://doi.org//10.1038/s42254-021-00348-9
Schuld, M., & Killoran, N. (2019). Quantum Machine Learning in Feature Hilbert Spaces. Physical Review Letters, 122(4), 040504. DOI: http://doi.org//10.1103/PhysRevLett.122.040504
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., ... & Martinis, J. M. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510. DOI: http://doi.org//10.1038/s41586-019-1666-5
Qi, B., Zhao, Z., He, W., & Zhang, H. (2022). Quantum algorithms for structural optimization problems: A review and outlook. Quantum Information Processing, 21(4), 124.
DOI: http://doi.org//10.1007/s11128-022-03435-7
Dunjko, V., & Briegel, H. J. (2018). Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Reports on Progress in Physics, 81(7), 074001.
DOI: http://doi.org//10.1088/1361-6633/aab406
Rozvany, G. I. N. (2009). A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization, 37(3), 217–237.
DOI: https://doi.org/10.1007/s00158-007-0217-0
Gilbert, R. D., & Tyas, A. (2003). The use of limit analysis and topology optimization to design steel truss structures. Structural and Multidisciplinary Optimization, 25(3), 209–215.
Brütting, J., Rapp, S., & Schellenberg, A. (2018). Stress-based topology optimization of pedestrian bridges with serviceability constraints. Engineering Structures, 172, 99–109.
DOI: http://doi.org//10.1016/j.engstruct.2018.05.066
Cao, Y., Wang, Y., & Zhang, J. (2020). Integration of topology optimization with additive manufacturing for Ultra-High Performance Concrete structures: Challenges and opportunities. Automation in Construction, 120, 103394.
DOI: http://doi.org//10.1016/j.autcon.2020.103394
Ajagekar, A., et al. (2021). Quantum-Inspired Machine Learning for Structural Optimization. Computers & Structures, 253, 106620. DOI: https://doi.org/10.1016/j.compstruc.2021.106620
Azhar, S., Carlton, W. A., Olsen, D., & Ahmad, I. (2021). Building sustainability assessment: A
framework and comparative evaluation. Journal of Cleaner Production, 287, 125592.
DOI: https://doi.org/10.1016/j.jclepro.2020.125592
Bharti, K., et al. (2022). Noisy Intermediate-Scale Quantum Algorithms. Reviews of Modern Physics, 94(1), 015004.
DOI: https://doi.org/10.1103/RevModPhys.94.015004
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195–202. DOI: https://doi.org/10.1038/nature23474
Farhi, E., Goldstone, J., & Gutmann, S. (2014). A Quantum Approximate Optimization Algorithm. arXiv preprint arXiv:1411.4028. https://arxiv.org/abs/1411.4028
Ferrari, A., et al. (2021). Toward sustainable quantum computing. npj Quantum Information, 7(1), 113.
Gao, Y., et al. (2023). Generative design and deep learning in structural optimization. Advanced Engineering Informatics, 57, 101130. DOI: https://doi.org/10.1016/j.jcp.2024.113506
Kim, J. H., et al. (2022). Long-term durability of 3D-printed UHPC structures. Construction and Building Materials, 342, 127952. DOI: https://doi.org/10.1016/j.conbuildmat.2022.127952
Liu, Y., Xu, D., & Wang, Z. (2020). Sustainable optimization of bridge structures using hybrid metaheuristics. Engineering Structures, 210, 110349.
DOI: https://doi.org/10.1016/j.engstruct.2020.110349
Lloyd, S., Mohseni, M., & Rebentrost, P. (2013). Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint arXiv:1307.0411. https://arxiv.org/abs/1307.0411
Moll, N., Barkoutsos, P., Bishop, L. S., Chow, J. (2018). Quantum optimization using variational algorithms on near-term quantum devices. Quantum Science and Technology, 3(3), 030503.
DOI: https://doi.org/10.1088/2058-9565/aab822
Penadés-Plà, V., García-Segura, T., Martí, J. V., & Yepes, V. (2019). Life-cycle assessment: A comparison between two optimal post-tensioned concrete box-girder road bridges. Sustainability, 11(10), 2937. DOI: https://doi.org/10.3390/su11102937
Perdomo-Ortiz, A., Benedetti, M., Realpe-Gomez, J., & Biswas, R. (2018). Opportunities for quantum computing in bridge design. Nature Computational Science, 1(3), 142–152.
DOI: http://dx.doi.org/10.1088/2058-9565/aab859
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213.
DOI: https://doi.org/10.1038/ncomms5213
Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79. DOI: https://doi.org/10.22331/q-2018-08-06-79
Russell, H., & Mamlouk, M. (2020). Ultra-high-performance concrete for sustainable bridge design. ACI Structural Journal, 117(1), 120–132.
Smith, J., Lee, H., & Tan, C. (2022). Carbon footprint evaluation of optimized 3D printed structures. Materials Today Sustainability, 17, 100097.
Wang, T., Li, M., & Zhao, Z. (2022). Printability and mechanical performance of UHPC in digital fabrication. Construction and Building Materials, 315, 125789.
DOI: https://doi.org/10.1016/j.conbuildmat.2021.125789
Zhang, Y., et al. (2020). Constraints in 3D concrete printing: A review. Automation in Construction, 113, 103144.
DOI: https://doi.org/10.1016/j.autcon.2020.103144
Zhou, L., et al. (2020). Solving Structural Optimization Problems with Quantum Annealing. Engineering Optimization, 52(5), 857–874.
Zlokapa, Z., Holmes, Z., & Fefferman, B. (2021). Deep learning with quantum-enhanced optimization for structural topology. Quantum Machine Intelligence, 3(1), 1–14.
DOI: https://doi.org/10.1007/s42484-021-00035-1
Zhao, Z.-L., Xiong, Y., Yao, S., & Xie, Y. M. (2021). A new approach to eliminating enclosed voids in topology optimization for additive manufacturing. Additive Manufacturing, 32, 101006.
DOI: https://doi.org/10.1016/j.addma.2019.101006
Jin Fan, Yi Shao, Matthew J. Bandelt, Matthew P. Adams. (2024). Sustainable reinforced concrete design: The role of ultra-high-performance concrete (UHPC) in life-cycle structural performance and environmental impacts. Engineering Structures, Volume 316, 1 October 2024, 118585.
DOI: https://doi.org/10.1016/j.engstruct.2024.118585
Mengesha, Girmay, Ultra-High-Performance Concrete (Uhpc/Uhpfrc) For Civil Structures: A Comprehensive Review of Material Innovations, Structural Applications, And Future Engineering Perspectives (May 14, 2025). Available At SSRN:
DOI: http://dx.doi.org/10.2139/ssrn.5254543
Mengesha, Girmay, Design Optimization in Structural Engineering: A Systematic Review of Computational Techniques and Real-World Applications (May 14, 2025). Available at SSRN: