Material Efficiency through Mechanics: A Systematic Review of Advanced Structural Modeling for Load-Optimized Building Design

Main Article Content

Girmay Mengesha Azanaw

Abstract

Material efficiency has become a central objective in contemporary building design, driven by urgent environmental imperatives and the growing need to reduce resource consumption. This systematic review examines the role of advanced structural modelling techniques in the development of load-optimised, materially efficient structures. Emphasising the synergy between structural form and force flow, the study investigates the application of computational tools, including finite element analysis (FEA), topology optimisation, parametric design, and AI-driven modelling strategies. These approaches enable designers to align structural geometry with internal stress patterns, reducing excess material use without sacrificing safety or performance. The review synthesises recent innovations in form-finding methods, geometry-informed optimisation, and performance-based design workflows that collectively support material minimisation strategies. Special attention is given to how these tools are implemented in various structural typologies, including shell structures, high-rise systems, and freeform architecture, demonstrating the practical viability and environmental benefits of computationally guided design. In addition to technical advances, the review identifies key challenges facing the broader adoption of these methods. These include limitations in computational accuracy, difficulties in scaling up optimization techniques, and the persistent divide between architectural and engineering practices. The analysis highlights the importance of interdisciplinary collaboration and robust feedback loops between digital modelling, structural analysis, and material behaviour. Ultimately, the findings advocate for a paradigm in which structural mechanics serves not only as a tool for verification but also as a generative driver of form. By leveraging emerging modelling techniques, the construction industry can move toward a more sustainable trajectory—one where resource efficiency, structural integrity, and architectural expression coexist harmoniously. This systematic review contributes to ongoing discourse on how digital technologies and structural intelligence can inform the design of buildings that are not only innovative and efficient but also environmentally responsible.

Downloads

Download data is not yet available.

Article Details

Section

Articles

How to Cite

[1]
Girmay Mengesha Azanaw , Tran., “Material Efficiency through Mechanics: A Systematic Review of Advanced Structural Modeling for Load-Optimized Building Design”, IJIES, vol. 12, no. 6, pp. 8–18, Jun. 2025, doi: 10.35940/ijies.G1106.12060625.
Share |

References

Bletzinger, K. U., Ramm, E., & Wüchner, R. (2015). Form finding of membranes and shells using finite elements. Engineering Structures, 27(12), 1788–1799.

DOI: https://doi.org/10.1016/j.engstruct.2005.05.009

Schumacher, P., Nguyen, D. H., & Grigoropoulos, C. P. (2022). A machine-learning-enhanced form-finding strategy for structural efficiency. Engineering Structures, 256, 113942.

DOI: https://doi.org/10.1016/j.engstruct.2022.113942

Nguyen, V. Q., Zhang, Y., & Gao, L. (2020). Advances in nonlinear mechanics for optimal structural design. Engineering Structures, 207, 110213.

Sigmund, O., & Maute, K. (2013). Topology optimization approaches: A comparative review. Structural and Multidisciplinary Optimization, 48(6), 1031–1055.

DOI: https://doi.org/10.1007/s00158-013-0978-6

Zhao, J., Li, H., & Chen, F. (2022). Nonlinear finite element analysis for performance-based structural design. Journal of Structural Engineering, 148(2), 04021160.

DOI: https://doi.org/10.1061/(ASCE)ST.1943-541X.0002963

Kassem, M., & Kim, J. (2019). Structural optimization of steel domes using nonlinear FEA. Engineering Structures, 198, 109506. DOI: https://doi.org/10.1016/j.engstruct.2019.109506

Ghaboussi, J., Garzon-Roca, J., & Barbosa, H. J. (2018). Multiscale modelling in structural mechanics: Advances and future directions. Structural Engineering International, 28(3), 320–329.

DOI: https://doi.org/10.1080/10168664.2018.1453954

Chen, J., Yu, Q., & Yuan, Y. (2017). Adaptive meshing strategies in nonlinear FEA for structural optimization. Computers & Structures, 180, 12–25. DOI: https://doi.org/10.1016/j.compstruc.2016.08.009

Mueller, C., Tessmann, O., & Menges, A. (2014). Material efficiency in architecture: Parametric design through adaptive structural modelling. Computer-Aided Design, 50, 40–50.

DOI: https://doi.org/10.1016/j.cad.2014.02.005

Bendsøe, M. P., & Sigmund, O. (2003). Topology Optimization: Theory, Methods, and Applications. Springer.

DOI: https://doi.org/10.1007/978-3-662-05086-6

Allaire, G. (2012). Shape Optimization by the Homogenization Method. Springer.

DOI: https://doi.org/10.1007/978-3-642-23274-4

Deaton, J. D., & Grandhi, R. V. (2014). A survey of structural and multidisciplinary continuum topology optimization: Post 2000. Structural and Multidisciplinary Optimization, 49(1), 1–38.

DOI: https://doi.org/10.1007/s00158-013-0956-z

De Temmerman, N., Brancart, S., & Mollaert, M. (2016). Shape optimization of tension structures for minimal stress and deflection. Engineering Structures, 113, 1–10.

DOI: https://doi.org/10.1016/j.engstruct.2016.01.011

Liu, J., & Ma, Y. (2016). A survey of manufacturing-oriented topology optimization methods. Structural and Multidisciplinary Optimization, 53(1), 1–22.

DOI: https://doi.org/10.1007/s00158-015-1279-1

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

Zhang, L., Huang, X., & Xie, Y. M. (2021). Topology optimization of bridge decks using dynamic loading constraints. Engineering Structures, 236, 112059.

DOI: https://doi.org/10.1016/j.engstruct.2021.112059

Alghamdi, A., Ghabraie, K., & Zulli, P. (2020). A digital twin framework for smart infrastructure using AI and edge computing. Journal of Computing in Civil Engineering, 34(6), 04020062.

DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000918

Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling technologies, challenges, and open research. IEEE Access, 8, 108952–108971.

DOI: https://doi.org/10.1109/ACCESS.2020.2998358

Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behaviour in complex systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85–113). Springer. DOI: https://doi.org/10.1007/978-3-319-38756-7_4

Li, X., Zhao, Y., & Liu, P. (2022). Smart bridge monitoring based on digital twins: Opportunities for material optimization. Structural Control and Health Monitoring, 29(3), e2892.

DOI: https://doi.org/10.1002/stc.2892

Sacks, R., Brilakis, I., Pikas, E., & Xie, H. (2022). Digital twin concepts in the AECO industry: A review of current developments and future directions. Automation in Construction, 134, 104108. DOI: https://doi.org/10.1016/j.autcon.2021.104108

Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.

DOI: https://doi.org/10.1109/TII.2018.2873186

Zhang, Y., Chen, B., & Jin, H. (2023). Real-time anomaly detection in structural digital twins using edge-AI fusion. Journal of Structural Engineering, 149(2), 04022220.

DOI: https://doi.org/10.1061/(ASCE)ST.1943-541X.0003314

Angst, U., Elsener, B., Larsen, C. K., & Vennesland, Ø. (2009). Critical chloride content in reinforced concrete—A review. Cement and Concrete Research, 39(12), 1122–1138.

DOI: https://doi.org/10.1016/j.cemconres.2009.08.006

Fortino, S., Mirianon, F., & Toratti, T. (2013). A 3D hygro-mechanical model for wood. Engineering Structures, 49, 597–608. DOI: https://doi.org/10.1016/j.engstruct.2012.11.032

Geers, M. G. D., Kouznetsova, V. G., & Brekelmans, W. A. M. (2010). Multi-scale computational homogenization: Trends and challenges. Journal of Computational and Applied Mathematics, 234(7), 2175–2182. DOI: https://doi.org/10.1016/j.cam.2009.08.077

Karihaloo, B. L., Abdalla, H. M., & Nallathambi, P. (2003). Microstructure-based modeling of high performance fiber-reinforced cementitious composites. Computers & Structures, 81(18), 1841–1851. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5278163

Kodur, V. K. R., & Dwaikat, M. (2007). Performance-based fire safety design of reinforced concrete beams. Journal of Fire Protection Engineering, 17(4), 293–320.

DOI: https://doi.org/10.1177/1042391507077198

Miehe, C., Schröder, J., & Schotte, J. (2002). Computational homogenization analysis in finite plasticity: Simulation of texture development in polycrystalline materials. Computer Methods in Applied Mechanics and Engineering, 191(46), 4971–5005.

DOI: https://doi.org/10.1016/S0045-7825(02)00391-2

Morandini, D., Sgambi, L., & De Gregorio, D. (2021). Vibroacoustic performance of lightweight footbridges using multiphysics modelling. Engineering Structures, 241, 112438.

DOI: https://doi.org/10.1016/j.engstruct.2021.112438

Pichler, B., & Hellmich, C. (2011). Upscaling quasi-brittle strength of cement paste and mortar: A multiscale engineering mechanics approach. Cement and Concrete Research, 41(5), 467–476.

DOI: https://doi.org/10.1016/j.cemconres.2011.01.010

Wang, H., Zhang, J., & Li, Y. (2021). Data-driven multiscale modelling of heterogeneous materials using machine learning. Computer Methods in Applied Mechanics and Engineering, 384, DOI: https://doi.org/10.1016/j.cma.2021.113938

Zienkiewicz, O. C., Taylor, R. L., & Zhu, J. Z. (2013). The Finite Element Method: Its Basis and Fundamentals (7th ed.). Butterworth-Heinemann.ISBN: 978-1856176330.

https://www.sciencedirect.com/book/9781856176330/the-finite-element-method-its-basis-and-fundamentals#book-info

Calderón, C. A., Alarcón, D., & Carranza, M. (2020). Generative design for structural optimization: A review. Automation in Construction, 118, 103312.

DOI: https://doi.org/10.1016/j.autcon.2020.103312

Liang, X., Lu, W., & Li, Q. S. (2000). Optimal design of steel trusses using genetic algorithms.Journal of Structural Engineering, 126(4), 506–512. DOI: https://doi.org/10.3390/buildings13061496

Mueller, C., Ochsendorf, J., & Buehler, M. J. (2016). Towards a computational design tool for folded structures based on generative design and finite element analysis.

International Journal of Architectural Computing, 14(1), 5–20. DOI: 10.1177/1478077115625603

Müller, C., Weinand, Y., & Cammarata, A. (2014). Real-time structural feedback in parametric modelling. In eCAADe 32, 285–294.

DOI: 10.52842/conf.ecaade.2014.285ecaade.org+1ecaade.org+1

Ohsaki, M. (2010). Optimization of Finite Dimensional Structures.

CRC Press. DOI: https://doi.org/10.1201/EBK1439820032

Otto, F., & Rasch, B. (2015). Finding Form: Towards an Architecture of the Minimal.

Edition Axel Menges. https://www.amazon.com/Finding-Form-Towards-Architecture-Minimal/dp/3930698668

Shea, K., Aish, R., & Gourtovaia, M. (2015). Towards integrated performance-driven generative design tools. Automation in Construction, 14(2), 253–264.

DOI: https://doi.org/10.1016/j.autcon.2004.07.002

Turrin, M., Von Buelow, P., & Stouffs, R. (2011). Design explorations of performance driven geometry in architectural design using parametric modelling and genetic algorithms. Advanced Engineering Informatics, 25(4), 656–675.

DOI: https://doi.org/10.1016/j.aei.2011.07.009

West, M., Beghini, A., & Baker, W. F. (2019). Structural optimization in tall building design. CTBUH Journal, 2019(1), 20–27.

Block, P., Dörstelmann, M., Knippers, J., & Ochsendorf, J. (2017). Designing efficient structures with geometry.Nature Reviews Materials, 2, 17082.

DOI: https://doi.org/10.1038/natrevmats.2017.82

Ghotbi, M., & Issa, A. (2016). Free-form design of shell structures using digital simulations. Automation in Construction, 63, 28–39.

DOI: https://doi.org/10.1016/j.autcon.2015.12.007

BIMForum. (2020). LOD Specification for Building Information Models.

https://bimforum.org/resource/lod-level-of-development-lod-specification/bimforum.org

Chakraborty, S., Papadopoulos, V., & Mandal, M. (2022). Physics-informed machine learning for structural response prediction. Journal of Structural Engineering, 148(4), 04022030.

DOI: https://doi.org/10.1061/(ASCE)ST.1943-541X.0003144

Kilian, A., Fischli, M., & Teuffel, P. (2021). Gradient-based form generation inspired by nature.Automation in Construction, 128, 103769.

Bhooshan, S. (2017). Parametric design thinking: A case-study of practice-embedded architectural research. Design Studies, 52, 115–143. DOI: https://doi.org/10.1016/j.destud.2017.05.003

Saksala, T., Niemelä, T., & Kantola, A. (2023). Reuse optimization of steel structural elements using parametric modeling. Sustainable Structures, 5(1), 85–102.

Mengesha, Girmay Azanaw. (2025). Design Optimization in Structural Engineering: A Systematic Review of Computational Techniques and Real-World Applications (May 14, 2025). Available at SSRN: DOI: http://dx.doi.org/10.2139/ssrn.5254589

Mengesha, Girmay Azanaw. (2025). 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

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