TY - M-10408
AU - Ngo, Thai
AU - P, Linh
TI - Advances in Structural Optimization: Parametric Modelling, Topology Methods, and Data-Driven Approaches in Modern Structural Engineering
T2 - Scientific Research Journal of Science, Engineering and Technology

PY - 2025
VL - 3
IS - 2
SN - 2584-0584

AB - 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.

KW - Structural optimization
KW -  Parametric modelling
KW -  Topology optimization
KW -  Uncertainty-based design
KW -  Machine learning in structural engineering
KW -  Sustainable structural design
DO -