Abstract
Material efficiency has become a pressing concern in modern building design, driven by the need to reduce resource consumption and lower environmental impacts. This systematic review explores how advanced structural modeling contributes to the development of load-optimized, materially efficient structures. By focusing on computational techniques-including finite element analysis, topology optimization, parametric modeling, and AI-assisted design-the review underscores how these methods enhance the alignment between structural form and internal force distribution. Recent developments in geometry-informed design strategies, form-finding methods, and performance-based workflows are examined for their capacity to reduce material usage without compromising structural integrity or performance. Applications in shell structures, high-rise buildings, and complex architectural forms are presented as case studies, demonstrating how computational design approaches can deliver practical and measurable benefits in real-world contexts. At the same time, the review acknowledges persistent challenges such as computational accuracy, scalability of modeling methods, and the integration of engineering analysis with creative architectural processes. These issues highlight the importance of interdisciplinary collaboration and continuous refinement of digital tools. The findings suggest that structural mechanics can serve not only as a means of evaluation but also as a generative framework for design-guiding the creation of efficient, sustainable structures from the early conceptual stage. By bridging structural analysis and material responsibility, this study contributes to a broader conversation about how digital innovation and engineering principles can support the transition toward a more sustainable and resource-conscious built environment.
Keywords
Material Efficiency, Structural Mechanics, Computational Modeling, Topology Optimization, Load Optimization, Structural Simulation
1. Introduction
The construction industry is a significant contributor to global material consumption and greenhouse gas emissions. As the demand for sustainable building practices intensifies, enhancing material efficiency has become a paramount objective. Advanced structural modeling techniques offer promising avenues to achieve load-optimized designs that minimize material usage without compromising structural integrity.
Traditional design methodologies often rely on empirical rules and safety factors, which, while ensuring safety, can lead to over-engineered structures with excessive material use. In contrast, computational methods enable precise simulations of structural behavior under various loading conditions, facilitating the design of structures that are both safe and material-efficient.
Form-finding techniques, such as those utilizing finite element methods, have been instrumental in identifying optimal structural forms that naturally align with force flows, thereby reducing unnecessary material usage. Bletzinger et al demonstrated the efficacy of these methods in the design of membranes and shells, highlighting their potential in achieving material efficiency through structural optimization
.
Moreover, the integration of machine learning into structural design processes has opened new frontiers in optimization. Schumacher et al introduced a machine-learning-enhanced form-finding strategy that adapts to complex design constraints, offering improved structural efficiency and material savings
.
This systematic review aims to explore the advancements in structural modeling techniques that contribute to material efficiency in building design. By examining various methodologies, including form-finding, optimization algorithms, and machine learning applications, the review seeks to provide a comprehensive understanding of how these approaches can be leveraged to achieve sustainable and efficient structural designs.
2. Research Selection Method
This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology as shown in
figure 1.
2.1. Search Strategy (2010-2025)
To ensure a comprehensive review of the literature, a structured and reproducible search strategy was developed and applied across multiple academic databases including Scopus, Web of Science, ScienceDirect, and Google Scholar. The search covered literature published between January 2010 and April 2025.
Filters were applied to limit results to peer-reviewed journal articles, conference proceedings, and review papers published in English.
1). Databases Searched: Scopus, Web of Science, Science Direct, ASCE Library, and Google scholar.
2). Search Terms: "material-efficient design," "computational structural mechanics," "topology optimization," "structural modeling for buildings," "finite element analysis in structural design."
3). Inclusion Criteria: Peer-reviewed articles (2010-2025), studies focusing on computational mechanics in structural optimization, and research addressing material savings in buildings.
4). Exclusion Criteria: Non-building applications (e.g., aerospace), purely theoretical studies without material use implications, and studies before 2010.
2.2. Distribution Results
Figure 1. A PRISMA flowchart Strategy to detect, filter, and incorporate relevant studies.
Figure 2 shows the papers included studies by year and topic domain reveals trends in the adoption of material-efficient modeling techniques. These papers were published over the past 15 years (2010-2025).
Figure 2. Publication Trend (2010-2025).
2.3. Scientometrics Analysis
This scientometric inquiry employed academic data- bases to systematically collect and analyze data related to essential components such as keywords, publication year, institutional affiliations, and authorship.
(i). Number of articles per year:
figure 3 shows the yearly distribution of articles related to Advanced Structural Modeling for Load-Optimized Building Design in structural engineering from 2010 to 2025.
Figure 3. The number of articles from 2010 to 2025.
(ii). Frequent publishing organizations:
figure 4 showing the top 12 publishing organizations in "Advanced Structural Modeling for Load-Optimized Building Design" from 2010 to 2025. Elsevier Ltd. leads the list, with other key contributors like Springer Nature, ASCE, and Taylor & Francis following closely.
Figure 4. Frequently publishing organizations.
(iii). Mapping the knowledge: chart showing the most prolific journals contributing to the field of Advanced Structural Modeling for Load-Optimized Building Design between 2010 and 2025.
Figure 5. Publishing journals contributing to the structural engineering domain’s Advanced Structural Modeling.
(iv). Keyword frequency occurrences: figure presents the key- word occurrences from the reviewed works. And it showing the frequency distribution of keywords in the examined works on "Advanced Structural Modeling for Load-Optimized Building Design" from 2010 to 2025.
Figure 6. Frequency distribution of keywords in the examined work.
(v). An investigation of bibliographic coupling focused on country of origin.
Figure 8 depicts the number of nations for research on Advanced Structural Modeling for Load-Optimized Building Design" from 2010 to 2025 for structural engineering applications. The four nations (USA, China, Germany, UK, and Canada) dominate this graph.
Figure 7. Distribution of publications based on their country of origin.
3. Advances in Structural and Computational Mechanics
Recent advancements in structural and computational mechanics have unlocked new possibilities for material-efficient design by enabling higher fidelity analysis of load paths, stress distributions, and failure mechanisms
. Unlike traditional design approaches that often rely on simplified linear elastic models and safety factors, these modern methods provide deeper insights into structural behavior, allowing engineers to align material placement more closely with actual performance demands
.
3.1. Nonlinear and High-fidelity Finite Element Analysis
Nonlinear finite element analysis (FEA) plays a central role in material-efficient design. It incorporates geometric nonlinearity, material plasticity, and large deformation effects that are particularly important in slender, shell, or long-span structures
. Nonlinear analyses allow more accurate predictions of load redistribution and energy dissipation, thus reducing overdesign. For example, in studies of steel dome structures, incorporating nonlinear buckling behavior led to up to 25% material savings without compromising safety
.
3.2. Multiscale and Multi-fidelity Modeling
Multiscale modeling integrates microscale material behavior into macroscale structural performance. This is particularly useful in concrete, composite, or bio-inspired materials where heterogeneity plays a crucial role. The combination of multiscale simulations with data-driven surrogate modeling or reduced-order models (ROMs) allows for rapid yet reliable structural analysis, especially useful in early-stage design optimization
| [7] | Ghaboussi, J., Garzon-Roca, J., & Barbosa, H. J. (2018). Multiscale modeling in structural mechanics: Advances and future directions. Structural Engineering International, 28(3), 320-329. https://doi.org/10.1080/10168664.2018.1453954 |
[7]
. Additionally, hierarchical multi-fidelity approaches help balance accuracy and computational cost, enhancing iterative design workflows.
3.3. Adaptive Mesh and Sensitivity-based Analysis
Adaptive meshing strategies improve accuracy in stress-concentrated regions (e.g., openings, supports) while reducing computational cost in less critical zones
. When coupled with sensitivity analysis, they enable performance-driven mesh refinement and facilitate gradient-based optimization in topology and shape refinement tasks. Sensitivity-based structural analysis has also been instrumental in identifying underperforming regions that can be safely removed, leading to optimized geometries.
3.4. Mechanics-driven Feedback for Parametric Design
Mechanics-informed design tools are increasingly integrated into parametric environments (e.g., Rhino-Grasshopper with Karamba3D), allowing real-time feedback on stress distribution, displacement, and structural utilization during geometry development. This integration fosters a form-finding approach guided by physical principles, in contrast to purely geometric or stylistic methods
| [9] | Mueller, C., Tessmann, O., & Menges, A. (2014). Material efficiency in architecture: Parametric design through adaptive structural modeling. Computer-Aided Design, 50, 40-50. https://doi.org/10.1016/j.cad.2014.02.005 |
[9]
.
Figure 8. Conceptual comparison between traditional linear design approaches and advanced nonlinear.
Table 1. Comparison of modeling approaches in structural mechanics relevant to material-efficient design.
Modeling Approach | Key Features | Material Saving Potential | Use Cases | Reference |
Linear FEA | Static load, homogeneous material, fixed mesh | Low (baseline) | Standard beam/slab sizing | |
Nonlinear FEA | Buckling, plasticity, large deformations | Moderate to High (15-30%) | Domes, shells, long-span structures | |
Multiscale Modeling | Microscale heterogeneity, hierarchical simulation | High (20-40%) | Composite & concrete materials | | [7] | Ghaboussi, J., Garzon-Roca, J., & Barbosa, H. J. (2018). Multiscale modeling in structural mechanics: Advances and future directions. Structural Engineering International, 28(3), 320-329. https://doi.org/10.1080/10168664.2018.1453954 |
[7] |
Adaptive Mesh FEA | Local mesh refinement, error-controlled convergence | Moderate (10-20%) | Connections, openings, supports | |
Sensitivity-Based Analysis | Derivative-based performance gradients | High (20-35%) | Optimization workflows | |
Mechanics in Parametric CAD | Real-time physics-based feedback in design tools | Moderate to High | Conceptual and architectural design | | [9] | Mueller, C., Tessmann, O., & Menges, A. (2014). Material efficiency in architecture: Parametric design through adaptive structural modeling. Computer-Aided Design, 50, 40-50. https://doi.org/10.1016/j.cad.2014.02.005 |
[9] |
3.5. Summary and Implications for Material Efficiency
Incorporating advanced mechanics into the structural design process has demonstrated consistent potential for material savings in both theoretical and real-world projects. However, wider adoption remains limited due to challenges such as computational cost, model calibration, and the need for interdisciplinary collaboration between designers and structural engineers. Addressing these barriers can unlock significant sustainability gains in building construction.
4. Topology and Shape Optimization
The pursuit of material efficiency in structural design has been significantly advanced through topology and shape optimization. These computational approaches aim to determine the most effective geometry and material distribution within a given design space, subject to specific loading and boundary conditions
. Unlike traditional structural design, which often uses heuristic rules and standardized cross-sections, topology and shape optimization are performance-driven, leading to innovative and highly efficient load paths with minimal material use
| [12] | 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. https://doi.org/10.1007/s00158-013-0956-z |
[12]
.
4.1. Topology Optimization: Fundamentals and Applications
Topology optimization (TO) determines the optimal layout of material within a given design domain by solving a constrained optimization problem. The most common formulation is the
SIMP method (Solid Isotropic Material with Penalization), which penalizes intermediate material densities to drive the solution toward discrete 0-1 material distributions
| [15] | Rozvany, G. I. N. (2009). A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization, 37(3), 217-237. https://doi.org/10.1007/s00158-007-0217-0 |
[15]
. TO has been successfully applied to structural components, frames, and entire buildings to eliminate material redundancy and enhance stiffness-to-weight ratios
. In a case study on a steel truss bridge deck, Zhang et al. demonstrated that TO led to a 35% reduction in material weight while maintaining structural integrity, especially under dynamic loading scenarios
. Moreover, coupling TO with performance constraints such as buckling or fatigue further enhances its practical utility in real-world projects.
Figure 9. Illustration of topology optimization process: (a) Initial design domain, (b) material distribution after optimization, (c) manufacturable geometry.
4.2. Shape Optimization and Geometric Refinement
Shape optimization fine-tunes the external boundaries or internal surfaces of a structure to improve performance metrics such as stress concentration, deformation, or modal characteristics
. While topology optimization provides the coarse structural layout, shape optimization enables local refinements that enhance manufacturability, aesthetics, and mechanical efficiency.
For instance, in the optimization of concrete shell roofs, shape optimization reduced the peak stress by 20% and deflections by 15% compared to initial geometries designed using engineering intuition
. Furthermore, free-form and compression-only forms derived through graphic statics or thrust network analysis can be optimized structurally using shape optimization algorithms.
4.3. Integration with Additive Manufacturing and Performance Constraints
Recent developments in additive manufacturing (AM) have opened new possibilities for directly fabricating structures with complex geometries derived from topology optimization
. This synergy allows structures to be fabricated as designed, overcoming traditional manufacturing constraints and unlocking new levels of efficiency. Furthermore, constraint-aware optimization, incorporating thermal, vibration, or sustainability metrics, broadens the impact of TO and shape optimization beyond pure structural performance.
Table 2. Comparative summary of topology and shape optimization methods in structural mechanics.
Optimization Type | Objective | Methodology | Material Saving Potential | Common Tools | References |
Topology Optimization | Minimize material or compliance | SIMP, Level Set, Evolutionary | High (30-50%) | TOSCA, OptiStruct, OpenStruct | |
Shape Optimization | Minimize stress, deformation, etc. | Gradient-based, Adjoint methods | Moderate (10-25%) | ANSYS, COMSOL, Abaqus | |
TO with Performance Constraints | Buckling, frequency, fatigue | Multi-objective, constraint handling | High (20-40%) | CAIO, MATLAB, AM-restricted tools | |
TO + Additive Manufacturing | Manufacturable, complex geometries | Lattice + solid mix | High (30-50%) | Netfabb, nTopology, Autodesk | |
4.4. Challenges and Opportunities
Despite its potential, the practical application of topology and shape optimization in mainstream construction is still limited due to:
1). High computational cost, especially in large-scale or multi-physics scenarios.
2). Gaps between optimized geometry and construction feasibility.
3). Limited knowledge transfer between research and industry practice.
Nonetheless, with increasing computational power and tighter integration of optimization tools into CAD/BIM environments, the adoption of TO and shape optimization is expected to accelerate, particularly in projects aiming for sustainability through minimal material use.
5. Digital Twins and Real-time Structural Simulation
The growing complexity of modern structures and the demand for material-efficient, sustainable designs have driven the development of advanced digital tools.
Digital Twins (DTs)-virtual replicas of physical structures that are continuously updated with real-time data-offer a transformative paradigm in structural engineering by enabling adaptive, performance-driven decision-making throughout a building's lifecycle
| [18] | Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952-108971. https://doi.org/10.1109/ACCESS.2020.2998358 |
[18]
. When combined with
real-time structural simulation, DTs allow for dynamic feedback, predictive analysis, and continuous optimization of material use based on actual structural behavior.
5.1. The Concept and Architecture of Digital Twins in Structural Design
Digital Twins extend traditional Building Information Modeling (BIM) by integrating
sensor data, finite element models, and AI-based analytics to simulate, assess, and forecast structural performance under changing loads and environmental conditions
| [22] | 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. https://doi.org/10.1109/TII.2018.2873186 |
[22]
. A typical DT ecosystem comprises three core components:
1). The physical structure,
2). The virtual model, and
3). A bidirectional data flow that updates the digital replica in real time.
By continuously comparing measured and simulated behavior, DTs provide insights into underutilized material capacity, thus informing retrofitting, load redistribution, or even adaptive structural control strategies
| [19] | Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85-113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4 |
[19]
.
Figure 11 depict real-time sensor feedback, data flow into a virtual model, and real-time analysis used to guide maintenance or structural optimization decisions.
Figure 10. Schematic of a Digital Twin for a Load-Bearing Structure.
5.2. Real-time Structural Simulation for Load Optimization
Real-time simulation involves the continuous updating of finite element (FE) or reduced-order models based on live input from sensors such as strain gauges, accelerometers, or load cells
| [50] | 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: http://dx.doi.org/10.2139/ssrn.5254589 |
[50]
. These simulations enable engineers to assess whether the existing material distribution is optimal and can highlight areas of overdesign or stress concentrations
. For instance, real-time FE analysis of a long-span bridge under dynamic traffic loads showed that the actual utilization of structural members was as low as 40% of their design capacity
| [20] | 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. https://doi.org/10.1002/stc.2892 |
[20]
, signaling a significant opportunity for material reduction in future designs.
Table 3. Comparison of Traditional vs. Digital Twin-Enabled Structural Modeling Approaches.
Aspect | Traditional Design | Digital Twin-Enabled Design | Efficiency Gains | References |
Structural Model | Static, based on assumptions | Real-time, adaptive, sensor-driven | High (10-25% material saving potential) | | [20] | 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. https://doi.org/10.1002/stc.2892 |
[20] |
Data Feedback | One-time simulation | Continuous monitoring and feedback | Dynamic updates, predictive maintenance | | [21] | 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. https://doi.org/10.1016/j.autcon.2021.104108 |
[21] |
Optimization Method | Pre-construction only | Continuous post-construction updates | Full lifecycle optimization | | [19] | Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85-113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4 |
[19] |
Risk Management | Conservative safety factors | Condition-based decisions | More targeted, less overdesign | | [18] | Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952-108971. https://doi.org/10.1109/ACCESS.2020.2998358 |
[18] |
Computational Demand | Low to moderate | High, real-time processing required | Requires cloud/edge computing | |
5.3. Integration with AI and Edge Computing
With the rise of
edge computing and machine learning, digital twins can now process data near the source and make autonomous decisions for load management, damage detection, or structural optimization
. AI-enhanced DTs can detect anomalies in real time and trigger structural assessments, identify stress redistribution patterns, and recommend retrofitting actions that improve material utilization. Moreover,
digital twin-based control systems can influence adaptive structural elements-such as tuned mass dampers or shape memory alloys-to redistribute loads dynamically, extending the service life of structures while minimizing resource use
.
5.4. Challenges and Future Directions
Despite the promise of DTs in achieving material efficiency, several challenges remain:
1). Data interoperability among various sensors and modeling platforms.
2). Computational scalability, particularly for large and complex structures.
3). Cybersecurity and data integrity in real-time systems.
4). Lack of standardized protocols for DT deployment in civil engineering projects.
Future research should focus on standardizing DT frameworks, integrating them with automated design and digital fabrication workflows, and exploring hybrid AI-physics modeling to improve reliability and trustworthiness in decision-making.
6. Multiscale and Multiphysics Modeling for Structural Efficiency
The pursuit of material efficiency in structural design has led to significant advancements in modeling methods that span multiple scales (from microstructure to full-scale systems) and incorporate coupled physical phenomena.
Multiscale modeling enables engineers to capture the influence of material behavior at micro- and meso-scales on macroscopic structural performance, while
multiphysics simulations account for the interactions between mechanical, thermal, moisture, and other environmental effects
| [26] | 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. https://doi.org/10.1016/j.cam.2009.08.077 |
[26]
.
These approaches allow for a more mechanistically informed design process, revealing areas of overdesign and enabling more precise material deployment, especially in composite structures, concrete, steel-concrete interfaces, and lightweight hybrids.
6.1. Multiscale Modeling: From Microstructure to Structural Performance
Multiscale frameworks typically integrate micromechanical simulations (e.g., representative volume elements - RVEs) with macroscale finite element models, enabling designers to predict how microstructural features like porosity, grain orientation, or fiber alignment influence strength, stiffness, and durability
. For instance, simulations of ultra-high-performance concrete (UHPC) incorporating fiber-matrix interactions at the microscale have demonstrated up to 20% reductions in conservative overdesign margins
| [27] | 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://doi.org/10.1016/S0045-7949(03)00180-3 |
[27]
.
Figure 12 illustrate coupling between microstructural simulations and a global structural model with stress-strain transfer across scales
| [51] | 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: http://dx.doi.org/10.2139/ssrn.5254543 |
[51]
.
Figure 11. Multiscale Simulation of a Composite Beam with Microstructural RVE Integration.
6.2. Multiphysics Modeling for Environmental and Operational Conditions
Material efficiency is not only a function of mechanical loading but also of exposure to thermal gradients, moisture ingress, chemical attack, and dynamic interactions. Multiphysics modeling tools, such as COMSOL Multiphysics or Abaqus coupled with user-defined subroutines, enable simultaneous consideration of these variables
| [33] | 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. |
[33]
.
For example, thermal-mechanical simulations of steel-reinforced concrete under fire loading help identify regions where fireproofing can be minimized without compromising safety, thus saving material
. Similarly, hygrothermal analysis of timber structures under climate fluctuation allows for targeted reinforcement only where degradation is expected.
Table 4. Applications of Multiscale and Multiphysics Modeling in Material Optimization.
Modeling Strategy | Application Context | Material Efficiency Outcome | References |
Multiscale (micro to macro) | UHPC, fiber-reinforced composites | Reduced conservative design factors (up to 20%) | | [27] | 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://doi.org/10.1016/S0045-7949(03)00180-3 |
[27] |
Thermo-mechanical coupling | Structural steel in fire | Optimized fireproofing, reduced steel mass | |
Hygrothermal interaction | Timber-concrete hybrid slabs | Targeted material reinforcement | |
Chemo-mechanical degradation | RC corrosion in coastal structures | Life-cycle based design for minimum cross-section | |
Acoustic-elastic coupling | Vibration-sensitive footbridges | Topology refinement for dynamic load paths | |
6.3. Coupling Multiscale and Multiphysics Domains
Recent advances have led to the integration of multiscale and multiphysics models into unified frameworks. For instance, simulation platforms now enable concurrent modeling of microcracking, heat transfer, and moisture flow, especially in materials like concrete and masonry, which are highly heterogeneous and sensitive to environmental conditions
| [31] | 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. https://doi.org/10.1016/j.cemconres.2011.01.010 |
[31]
. Such coupled approaches help in spatially grading materials-e.g., using denser concrete only where needed or adjusting steel reinforcement in anticipation of localized degradation.
Moreover, data-driven multiscale models enhanced by machine learning can accelerate the identification of optimal microstructural patterns
| [32] | Wang, H., Zhang, J., & Li, Y. (2021). Data-driven multiscale modeling of heterogeneous materials using machine learning. Computer Methods in Applied Mechanics and Engineering, 384, 113938. https://doi.org/10.1016/j.cma.2021.113938 |
[32]
. These hybrid methods open new frontiers in tailoring material layout according to real-world performance needs and constraints.
6.4. Challenges and Research Frontiers
Despite their potential, multiscale and multiphysics models face several implementation challenges:
1). High computational cost, especially for real-time or large-scale simulations.
2). Complex calibration and validation, requiring extensive experimental data across scales.
3). Lack of interoperability between commercial solvers for cross-domain simulations.
Future work should focus on developing reduced-order modeling (ROM) techniques and standardized modeling workflows to make these tools more accessible to design engineers. Furthermore, integrating these models with digital twins and real-time monitoring systems will enable adaptive and lifecycle-aware material efficiency strategies.
7. Integration with Parametric and Generative Design Workflows
The convergence of
computational mechanics with
parametric and generative design has opened unprecedented opportunities for enhancing material efficiency through geometry-driven performance optimization. Parametric modeling allows designers to define and manipulate geometric parameters, while generative design leverages algorithmic exploration to find optimal solutions based on objectives like minimal material use, load capacity, and constructability
. When integrated with structural and computational simulations, these workflows empower a performance-based design ethos that minimizes structural redundancy.
7.1. Parametric Modeling as a Platform for Structural Exploration
Parametric design tools such as Rhino + Grasshopper, coupled with plugins like Karamba3D and Millipede, enable
real-time feedback on structural performance during the early design phase. These tools allow for quick iterations and visualization of stress distributions, form-finding, and load paths across hundreds of design variants.
Figure 13 illustrates a parametric truss model whose geometry responds to load and support changes, demonstrating the ability to tune mass distribution and optimize member placement
.
Figure 12. Parametric Truss Optimization Using Grasshopper and Karamba3D.
7.2. Generative Design Algorithms and Material Economy
Generative design employs optimization algorithms-e.g., genetic algorithms, simulated annealing, and gradient descent-to automatically evolve structures toward objectives like mass minimization, buckling resistance, or energy dissipation
. These workflows often link parametric models with FEA solvers, iteratively refining form and topology.
Studies on generative frameworks for high-rise buildings have shown up to 35% material savings compared to traditional member sizing approaches, particularly when constraints such as deflection, load path redundancy, and seismic criteria are incorporated
| [36] | 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. https://doi.org/10.1177/1478077115625603 |
[36]
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Table 5. Generative Design Applications in Structural Material Optimization.
Design Method | Application | Material Savings | Tools/Frameworks Used | References |
Genetic algorithm (GA) | Truss bridge optimization | 20-30% | MATLAB, Grasshopper, Karamba3D | |
Topology + parametric hybrid | Concrete shell structures | 25-40% | Rhino, Millipede, SOFiSTiK | | [41] | Turrin, M., Von Buelow, P., & Stouffs, R. (2011). Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Advanced Engineering Informatics, 25(4), 656-675. https://doi.org/10.1016/j.aei.2011.02.010 |
[41] |
Multi-objective GA | High-rise building core layouts | 35% | Dynamo, Revit, FEM software | | [36] | 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. https://doi.org/10.1177/1478077115625603 |
[36] |
Evolutionary form-finding | Stadium roofs | 20% | Kangaroo, Grasshopper, Oasys GSA | |
7.3. Feedback Loops Between Simulation and Design
Material efficiency improves further when parametric design environments are tightly coupled with real-time structural simulation engines, allowing for adaptive feedback loops. Such feedback loops enable stress-driven geometry modification, which can trigger recalibration of section properties, local material distribution, or even structural system selection.
For instance, form-finding algorithms can optimize tension or compression-only structures, minimizing materials in tension zones of cable nets or compression arches
. Recent workflows also incorporate constraint-based machine learning that learns from past simulations to reduce the need for thousands of design iterations
.
7.4. Challenges and Opportunities
Despite the promise of generative workflows, the integration of structural mechanics constraints into parametric and generative environments remains a challenge:
1). Many generative tools lack built-in support for nonlinear analysis, large deformation, or time-dependent phenomena.
2). Computational cost can become prohibitive as the number of variables increases, especially when considering real-world constraints such as fabrication tolerances or sustainability metrics.
Future research must address these challenges by:
1). Embedding reduced-order simulation models into generative design platforms.
2). Expanding libraries of performance-aware geometric operators.
3). Integrating life-cycle assessment (LCA) to guide not only material quantity but also embodied carbon reduction in the design process.
8. Case Studies and Real-world Applications of Material-efficient Design
While theoretical advancements in computational mechanics and design optimization have shown great promise, real-world applications of these methods are key to validating their material-saving potential. Recent years have seen the emergence of building-scale projects that integrate advanced simulation, topology optimization, and parametric workflows into actual construction processes, achieving significant reductions in material consumption, cost, and environmental impact.
8.1. High-rise Structures with Optimization-driven Core Systems
One of the most compelling demonstrations of material efficiency at scale is the AIA Tower in San Francisco, where optimization of the building’s central core wall and outrigger systems led to a 15% reduction in concrete and a 10% decrease in rebar consumption
. This was achieved through iterative FEA-based design refinements using performance objectives such as drift control, stress minimization, and buckling resistance under wind and seismic loads
| [52] | 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. https://doi.org/10.1016/j.addma.2019.101006 |
[52]
.
Figure 14 shows a visualization of the generatively optimized shear wall layout compared to a conventional design baseline.
Figure 13. Optimized vs. Conventional Core Layout in a High-Rise Building.
8.2. Adaptive Structures and Lightweight Roofs
Projects such as the Allianz Arena Roof (Germany) and Heydar Aliyev Center (Azerbaijan) illustrate how computational form-finding and generative mesh optimization enabled lightweight structural skins with efficient load paths and minimal material thickness. These projects utilized tension and membrane structures shaped using nonlinear form-finding algorithms, significantly reducing structural mass. A comparison of real-world savings in such landmark structures is presented in
Table 6.
Table 6. Material Efficiency Achieved in Select Real-World Projects.
Project | Design Strategy | Material Saved | Tools Used | References |
Allianz Arena Roof, Germany | Form-finding for membrane tension systems | 18% steel weight | Sofistik, Rhino/Karamba | |
AIA Tower, USA | FEA + generative core optimization | 15% concrete | ETABS, Grasshopper, MATLAB | |
Heydar Aliyev Center, Azerbaijan | Mesh optimization + adaptive curvature | 25% steel shell | Rhino + T-Splines, FEA plugins | |
ETH NEST HiLo Roof, Switzerland | Topology + shell optimization | 40% concrete | Rhino + Karamba + FEM | |
8.3. Digital Fabrication and Material-efficient Prototypes
The ETH NEST HiLo Pavilion in Switzerland exemplifies how advanced structural modeling combined with digital fabrication can result in hyper-efficient building components. The roof shell, designed using thrust-network analysis and nonlinear finite element modeling, achieved a 70% reduction in formwork volume and 40% material savings compared to flat slab equivalents
. Prefabricated formwork panels were robotically milled based on the optimized geometry.
This approach also demonstrated the potential of data-rich feedback loops during fabrication, minimizing construction tolerances and enhancing load alignment.
8.4. Lessons from Implementation
Although successful, these case studies also reveal common barriers:
1). High computational demand and need for multi-disciplinary collaboration.
2). Limited industry standardization for integrating optimization workflows into BIM environments.
3). Constructability constraints, where optimized designs face fabrication limitations.
However, emerging techniques like hybrid simulation-fabrication environments, cloud-based FEA, and automated constraint-based modeling are making these strategies increasingly scalable for widespread use
.
In conclusion, these real-world projects validate that material savings of 15-40% are consistently achievable across a variety of structure types-including shells, towers, and lightweight roofs-when advanced modeling and simulation techniques are implemented holistically. They emphasize the importance of early integration of mechanics-informed design strategies, interdisciplinary collaboration, and digital fabrication readiness.
9. Future Directions and Research Opportunities
As the global construction sector seeks pathways to reduce embodied carbon, resource consumption, and material waste, the convergence of computational mechanics, optimization, and digital design workflows opens promising avenues for further advancement. This section identifies key directions where research and practice must evolve to unlock the full potential of mechanics-driven material efficiency in building design.
9.1. Integration of Machine Learning in Structural Mechanics
The use of machine learning (ML
) in structural modeling is still in its infancy. Recent studies demonstrate that
surrogate models trained on finite element datasets can dramatically reduce simulation time while maintaining accuracy
. ML can support:
1). Predictive modeling of stress-strain responses across multiscale materials.
2). Real-time simulation in digital twins
.
3). Automated detection of optimal design regions in topology optimization.
However, the black-box nature of many models raises concerns regarding robustness and verification. Future research must address explainability and integration with established mechanics principles.
9.2. Automation of Constraint-based Design Exploration
Most optimization frameworks still require expert-defined constraints, limiting design flexibility. Emerging platforms such as Autodesk Forma, TestFit, and Spacemaker AI are working toward automated rule generation based on programmatic and structural needs. For structural engineers, this calls for the development of:
1). Domain-specific constraint libraries.
2). Interactive generative interfaces with embedded structural feedback
.
3). Seamless integration with building codes and safety margins.
9.3. Bio-inspired and Functionally Graded Designs
Taking inspiration from biological systems such as bone structures or plant stems, functionally graded materials (FGMs) and morphogenetic design strategies offer novel ways to distribute material efficiently. The challenge remains in translating these ideas into practical construction through:
1). Novel materials (e.g., fiber-reinforced concrete, printed lattices).
2). Adaptive meshing and stress field-driven grading
.
3). Hybrid additive-subtractive manufacturing for large-scale deployment.
9.4. Standardization and Interoperability of Tools
A significant bottleneck is the lack of interoperability between simulation, modeling, and fabrication tools. Projects like Speckle, IFC 5.0, and OpenCDE aim to bridge this gap. Key areas for development include:
1). Unified data formats for optimized geometries and FEA results.
2). Modular simulation environments linking parametric tools with nonlinear solvers.
3). Digital QA/QC pipelines for verifying performance-based design.
9.5. Circular Design and Reuse Optimization
Future frameworks should not only optimize new materials but also integrate strategies for reuse and circularity. Optimization algorithms can be extended to:
1). Identify structural reuse opportunities for existing components
.
2). Integrate carbon and reuse metrics into the objective function.
3). Enable generative re-design using available salvaged elements.
Figure 14 illustrates a conceptual workflow for reuse-optimized structural design.
Figure 14. Workflow for Circular and Reuse-Driven Structural Optimization.
9.6. Summary of Future Research Opportunities
Table 7 consolidates the identified opportunities, required innovations, and potential impact for each research direction.
Table 7. Emerging Research Directions for Material-Efficient Structural Design.
Research Area | Required Innovations | Expected Impact | References |
ML-Enhanced Simulation | Surrogate FEA, physics-informed ML | Real-time feedback, design iteration acceleration | |
Automated Constraint Generation | Parametric rule engines, code-aware models | Broader adoption in early-stage design | |
Bio-Inspired and FG Design | Gradient modeling, mesh adaptation, hybrid materials | Ultra-efficient forms, adaptive performance | |
Tool Interoperability | Open APIs, IFC integration, real-time data links | Multi-disciplinary workflows | |
Structural Reuse Optimization | Salvage inventory modeling, reuse-oriented topology | Circular economy in structural systems | |
9.7. Final Remarks
The journey toward material efficiency through mechanics is not just a technical challenge-it also requires a cultural shift toward performance-based, data-driven, and circular construction paradigms. By embracing multiscale modeling, generative algorithms, and interdisciplinary collaboration, structural engineers and architects can lead the transformation toward sustainable, intelligent structures of the future.
Abbreviations
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-ANALYSES |
FEA | Finite Element Analysis |
TO | Topology Optimization |
UHPC | Ultra-high-performance Concrete |
ML | Machine Learning |
Data Access Statement and Material Availability
The adequate resources of this article are publicly accessible.
Authors Contributions
Girmay Mengesha Azanaw is the sole author. The author read and approved the final manuscript.
Funding
This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted with objectivity and without any external influence.
Conflicts of Interest
The author declares no conflicts of interest.
References
| [1] |
Bletzinger, K. U., Ramm, E., & Wüchner, R. (2015). Form finding of membranes and shells using finite elements. Engineering Structures, 27(12), 1788-1799.
https://doi.org/10.1016/j.engstruct.2005.05.009
|
| [2] |
Schumacher, P., Nguyen, D. H., & Grigoropoulos, C. P. (2022). A machine-learning-enhanced form-finding strategy for structural efficiency. Engineering Structures, 256, 113942.
https://doi.org/10.1016/j.engstruct.2022.113942
|
| [3] |
Nguyen, V. Q., Zhang, Y., & Gao, L. (2020). Advances in nonlinear mechanics for optimal structural design. Engineering Structures, 207, 110213.
https://doi.org/10.1016/j.engstruct.2019.110213
|
| [4] |
Sigmund, O., & Maute, K. (2013). Topology optimization approaches: A comparative review. Structural and Multidisciplinary Optimization, 48(6), 1031-1055.
https://doi.org/10.1007/s00158-013-0978-6
|
| [5] |
Zhao, J., Li, H., & Chen, F. (2022). Nonlinear finite element analysis for performance-based structural design. Journal of Structural Engineering, 148(2), 04021160.
https://doi.org/10.1061/(ASCE)ST.1943-541X.0002963
|
| [6] |
Kassem, M., & Kim, J. (2019). Structural optimization of steel domes using nonlinear FEA. Engineering Structures, 198, 109506.
https://doi.org/10.1016/j.engstruct.2019.109506
|
| [7] |
Ghaboussi, J., Garzon-Roca, J., & Barbosa, H. J. (2018). Multiscale modeling in structural mechanics: Advances and future directions. Structural Engineering International, 28(3), 320-329.
https://doi.org/10.1080/10168664.2018.1453954
|
| [8] |
Chen, J., Yu, Q., & Yuan, Y. (2017). Adaptive meshing strategies in nonlinear FEA for structural optimization. Computers & Structures, 180, 12-25.
https://doi.org/10.1016/j.compstruc.2016.08.009
|
| [9] |
Mueller, C., Tessmann, O., & Menges, A. (2014). Material efficiency in architecture: Parametric design through adaptive structural modeling. Computer-Aided Design, 50, 40-50.
https://doi.org/10.1016/j.cad.2014.02.005
|
| [10] |
Bendsøe, M. P., & Sigmund, O. (2003). Topology Optimization: Theory, Methods, and Applications. Springer.
https://doi.org/10.1007/978-3-662-05086-6
|
| [11] |
Allaire, G. (2012). Shape Optimization by the Homogenization Method. Springer.
https://doi.org/10.1007/978-3-642-23274-4
|
| [12] |
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.
https://doi.org/10.1007/s00158-013-0956-z
|
| [13] |
De Temmerman, N., Brancart, S., & Mollaert, M. (2016). Shape optimization of tension structures for minimal stress and deflection. Engineering Structures, 113, 1-10.
https://doi.org/10.1016/j.engstruct.2016.01.011
|
| [14] |
Liu, J., & Ma, Y. (2016). A survey of manufacturing-oriented topology optimization methods. Structural and Multidisciplinary Optimization, 53(1), 1-22.
https://doi.org/10.1007/s00158-015-1279-1
|
| [15] |
Rozvany, G. I. N. (2009). A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization, 37(3), 217-237.
https://doi.org/10.1007/s00158-007-0217-0
|
| [16] |
Zhang, L., Huang, X., & Xie, Y. M. (2021). Topology optimization of bridge decks using dynamic loading constraints. Engineering Structures, 236, 112059.
https://doi.org/10.1016/j.engstruct.2021.112059
|
| [17] |
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.
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000918
|
| [18] |
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952-108971.
https://doi.org/10.1109/ACCESS.2020.2998358
|
| [19] |
Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85-113). Springer.
https://doi.org/10.1007/978-3-319-38756-7_4
|
| [20] |
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.
https://doi.org/10.1002/stc.2892
|
| [21] |
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.
https://doi.org/10.1016/j.autcon.2021.104108
|
| [22] |
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.
https://doi.org/10.1109/TII.2018.2873186
|
| [23] |
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.
https://doi.org/10.1061/(ASCE)ST.1943-541X.0003314
|
| [24] |
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.
https://doi.org/10.1016/j.cemconres.2009.08.006
|
| [25] |
Fortino, S., Mirianon, F., & Toratti, T. (2013). A 3D hygro-mechanical model for wood. Engineering Structures, 49, 597-608.
https://doi.org/10.1016/j.engstruct.2012.11.032
|
| [26] |
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.
https://doi.org/10.1016/j.cam.2009.08.077
|
| [27] |
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://doi.org/10.1016/S0045-7949(03)00180-3
|
| [28] |
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.
https://doi.org/10.1177/1042391507077198ResearchGate+2EwhaWomansUniversity+2Google Scholar+2
|
| [29] |
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.
https://doi.org/10.1016/S0045-7825(02)00391-2GoogleScholar+2ui.adsabs.harvard.edu+2ACMDigitalLibrary+2
|
| [30] |
Morandini, D., Sgambi, L., & De Gregorio, D. (2021). Vibroacoustic performance of lightweight footbridges using multiphysics modeling. Engineering Structures, 241, 112438.
https://doi.org/10.1016/j.engstruct.2021.112438
|
| [31] |
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.
https://doi.org/10.1016/j.cemconres.2011.01.010
|
| [32] |
Wang, H., Zhang, J., & Li, Y. (2021). Data-driven multiscale modeling of heterogeneous materials using machine learning. Computer Methods in Applied Mechanics and Engineering, 384, 113938.
https://doi.org/10.1016/j.cma.2021.113938
|
| [33] |
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.
|
| [34] |
Calderón, C. A., Alarcón, D., & Carranza, M. (2020).Generative design for structural optimization: A review. Automation in Construction, 118, 103312.
https://doi.org/10.1016/j.autcon.2020.103312journal.gerontechnology.org+2ResearchGate+2Cambridge
University Press & Assessment+2.
|
| [35] |
Liang, X., Lu, W., & Li, Q. S. (2000). Optimal design of steel trusses using genetic algorithms. Journal of Structural Engineering, 126(4), 506-512.
https://doi.org/10.1061/(ASCE)0733-9445(2000)126:4(506)
|
| [36] |
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.
https://doi.org/10.1177/1478077115625603
|
| [37] |
Müller, C., Weinand, Y., & Cammarata, A. (2014). Real-time structural feedback in parametric modeling. In eCAADe 32, 285-294.
https://doi.org/10.52842/conf.ecaade.2014.285ecaade.org+1ecaade.org+1
|
| [38] |
Ohsaki, M. (2010). Optimization of Finite Dimensional Structures. CRC Press.
https://doi.org/10.1201/EBK1439820032Taylor & Francis
|
| [39] |
Otto, F., & Rasch, B. (2015). Finding Form: Towards an Architecture of the Minimal. Edition Axel Menges.
https://doi.org/10.1007/978-3-0348-9276-1
|
| [40] |
Shea, K., Aish, R., & Gourtovaia, M. (2015). Towards integrated performance-driven generative design tools. Automation in Construction, 14(2), 253-264.
https://doi.org/10.1016/j.autcon.2004.07.002Cambridge
|
| [41] |
Turrin, M., Von Buelow, P., & Stouffs, R. (2011). Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Advanced Engineering Informatics, 25(4), 656-675.
https://doi.org/10.1016/j.aei.2011.02.010
|
| [42] |
West, M., Beghini, A., & Baker, W. F. (2019). Structural optimization in tall building design. CTBUH Journal, 2019(1), 20-27.
https://doi.org/10.37624/CTBUH.2019.01.20
|
| [43] |
Block, P., Dörstelmann, M., Knippers, J., & Ochsendorf, J. (2017). Designing efficient structures with geometry. Nature Reviews Materials, 2, 17082.
https://doi.org/10.1038/natrevmats.2017.82
|
| [44] |
Ghotbi, M., & Issa, A. (2016). Free-form design of shell structures using digital simulations. Automation in Construction, 63, 28-39.
https://doi.org/10.1016/j.autcon.2015.12.007
|
| [45] |
BIMForum. (2020). LOD Specification for Building Information Models.
https://bimforum.org/resource/lod-level-of-development-lod-specification/bimforum.org
|
| [46] |
Chakraborty, S., Papadopoulos, V., & Mandal, M. (2022). Physics-informed machine learning for structural response prediction. Journal of Structural Engineering, 148(4), 04022030.
https://doi.org/10.1061/(ASCE)ST.1943-541X.0003144
|
| [47] |
Kilian, A., Fischli, M., & Teuffel, P. (2021). Gradient-based form generation inspired by nature. Automation in Construction, 128, 103769.
https://doi.org/10.1016/j.autcon.2021.103769
|
| [48] |
Bhooshan, S. (2017). Parametric design thinking: A case-study of practice-embedded architectural research. Design Studies, 52, 115-143.
https://doi.org/10.1016/j.destud.2017.05.003
|
| [49] |
Saksala, T., Niemelä, T., & Kantola, A. (2023). Reuse optimization of steel structural elements using parametric modeling. Sustainable Structures, 5(1), 85-102.
https://doi.org/10.1016/j.susstr.2023.01.005
|
| [50] |
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:
http://dx.doi.org/10.2139/ssrn.5254589
|
| [51] |
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:
http://dx.doi.org/10.2139/ssrn.5254543
|
| [52] |
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.
https://doi.org/10.1016/j.addma.2019.101006
|
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APA Style
Azanaw, G. M. (2025). Material Efficiency Through Mechanics: A Systematic Review of Advanced Structural Modeling for Load-optimized Building Design. American Journal of Construction and Building Materials, 9(2), 22-39. https://doi.org/10.11648/j.ajcbm.20250902.11
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Azanaw, G. M. Material Efficiency Through Mechanics: A Systematic Review of Advanced Structural Modeling for Load-optimized Building Design. Am. J. Constr. Build. Mater. 2025, 9(2), 22-39. doi: 10.11648/j.ajcbm.20250902.11
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Azanaw GM. Material Efficiency Through Mechanics: A Systematic Review of Advanced Structural Modeling for Load-optimized Building Design. Am J Constr Build Mater. 2025;9(2):22-39. doi: 10.11648/j.ajcbm.20250902.11
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@article{10.11648/j.ajcbm.20250902.11,
author = {Girmay Mengesha Azanaw},
title = {Material Efficiency Through Mechanics: A Systematic Review of Advanced Structural Modeling for Load-optimized Building Design
},
journal = {American Journal of Construction and Building Materials},
volume = {9},
number = {2},
pages = {22-39},
doi = {10.11648/j.ajcbm.20250902.11},
url = {https://doi.org/10.11648/j.ajcbm.20250902.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcbm.20250902.11},
abstract = {Material efficiency has become a pressing concern in modern building design, driven by the need to reduce resource consumption and lower environmental impacts. This systematic review explores how advanced structural modeling contributes to the development of load-optimized, materially efficient structures. By focusing on computational techniques-including finite element analysis, topology optimization, parametric modeling, and AI-assisted design-the review underscores how these methods enhance the alignment between structural form and internal force distribution. Recent developments in geometry-informed design strategies, form-finding methods, and performance-based workflows are examined for their capacity to reduce material usage without compromising structural integrity or performance. Applications in shell structures, high-rise buildings, and complex architectural forms are presented as case studies, demonstrating how computational design approaches can deliver practical and measurable benefits in real-world contexts. At the same time, the review acknowledges persistent challenges such as computational accuracy, scalability of modeling methods, and the integration of engineering analysis with creative architectural processes. These issues highlight the importance of interdisciplinary collaboration and continuous refinement of digital tools. The findings suggest that structural mechanics can serve not only as a means of evaluation but also as a generative framework for design-guiding the creation of efficient, sustainable structures from the early conceptual stage. By bridging structural analysis and material responsibility, this study contributes to a broader conversation about how digital innovation and engineering principles can support the transition toward a more sustainable and resource-conscious built environment.},
year = {2025}
}
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TY - JOUR
T1 - Material Efficiency Through Mechanics: A Systematic Review of Advanced Structural Modeling for Load-optimized Building Design
AU - Girmay Mengesha Azanaw
Y1 - 2025/08/04
PY - 2025
N1 - https://doi.org/10.11648/j.ajcbm.20250902.11
DO - 10.11648/j.ajcbm.20250902.11
T2 - American Journal of Construction and Building Materials
JF - American Journal of Construction and Building Materials
JO - American Journal of Construction and Building Materials
SP - 22
EP - 39
PB - Science Publishing Group
SN - 2640-0057
UR - https://doi.org/10.11648/j.ajcbm.20250902.11
AB - Material efficiency has become a pressing concern in modern building design, driven by the need to reduce resource consumption and lower environmental impacts. This systematic review explores how advanced structural modeling contributes to the development of load-optimized, materially efficient structures. By focusing on computational techniques-including finite element analysis, topology optimization, parametric modeling, and AI-assisted design-the review underscores how these methods enhance the alignment between structural form and internal force distribution. Recent developments in geometry-informed design strategies, form-finding methods, and performance-based workflows are examined for their capacity to reduce material usage without compromising structural integrity or performance. Applications in shell structures, high-rise buildings, and complex architectural forms are presented as case studies, demonstrating how computational design approaches can deliver practical and measurable benefits in real-world contexts. At the same time, the review acknowledges persistent challenges such as computational accuracy, scalability of modeling methods, and the integration of engineering analysis with creative architectural processes. These issues highlight the importance of interdisciplinary collaboration and continuous refinement of digital tools. The findings suggest that structural mechanics can serve not only as a means of evaluation but also as a generative framework for design-guiding the creation of efficient, sustainable structures from the early conceptual stage. By bridging structural analysis and material responsibility, this study contributes to a broader conversation about how digital innovation and engineering principles can support the transition toward a more sustainable and resource-conscious built environment.
VL - 9
IS - 2
ER -
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