Advances in Structural Optimization: Parametric Modelling, Topology Methods, and Data-Driven Approaches in Modern Structural Engineering

Title

Advances in Structural Optimization: Parametric Modelling, Topology Methods, and Data-Driven Approaches in Modern Structural Engineering

Author

1. Thai Ngo, Student, Hanoi University, Vietnam, Vietnam
2. Linh P, Professor, Hanoi University, Vietnam, Vietnam

Abstract

Structural optimization has become one of the most transformative developments in civil, architectural, and industrial engineering. Driven by computational design tools, uncertainty modelling, and machine learning techniques, optimization approaches are reshaping how structures are conceptualized, analyzed, and built. This review synthesizes contemporary research on topology optimization, parametric modelling, multi-objective frameworks, uncertainty quantification, and machine-learning-driven strategies for structural design. Key contributions from recent studies highlight how parametric frameworks enable flexible industrial buildings, how multi-material lattices enhance robustness, and how uncertainty-aware optimization strengthens safety and performance. The review also examines holistic workflows linking material selection, conceptual design, structural analysis, and fabrication. Together, these developments demonstrate a shift toward integrated, automated, and performance-driven workflows capable of handling diverse design goals—including sustainability, lightweighting, cost efficiency, and resilience. This paper critically maps the evolution of structural optimization and identifies emerging challenges, including computational complexity, data needs, and integration into real-world design practices.

Keywords

Conclusion

Structural optimization has evolved into a highly interdisciplinary field that integrates material science, computational design, uncertainty quantification, artificial intelligence, and advanced manufacturing. Contemporary studies demonstrate that optimization workflows now span every stage of design—material proportioning, conceptual design, detailed analysis, and final fabrication. Parametric models allow flexible and adaptive structures; topology optimization uncovers highly efficient lightweight forms; uncertainty-based frameworks enhance safety and reliability; and machine learning accelerates exploration and prediction.

As engineering challenges grow—including sustainability, resilience, cost-efficiency, and manufacturing constraints—structural optimization will continue to play a transformative role. Future research will likely focus on real-time optimization, hybrid AI–physics models, and integration with digital twins for operational monitoring. This review highlights that the field is rapidly progressing toward fully automated, performance-driven structural design ecosystems.

Author Contrubution

All study-related tasks, from conception and design to data analysis and manuscript creation, were solely managed by the author.

Funding

This work did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors for its research, authorship, or publication.

Conflict of Interest

All authors declare the absence of any conflicts of interest.

Data Sharing Statement

Not applicable    

Software And Tools Use

Not applicable

Acknowledgements

I acknowledge the support and expertise of those who helped with this research and manuscript, and thank the peer reviewers for their valuable insights.