Structural damage identification is an inverse problem, which often is formulated as an optimization problem. The design variables are degrees of damage in computational model, and the objective is the discrepancies between the computed responses and the measured responses. Conventional single-objective optimization approach defines the objective function by combining multiple error terms into a single one, which leads to a weaker constraint in solving the identification problem. An alternative approach explained in this paper is a multi-objective approach that simultaneously minimizes multiple error terms. A stronger constraint from multiple objectives promotes the solutions to converge to the correct solution. Numerical examples based on static testing are provided to illustrate the multi-objective approach. Also, conceptual explanations are given to extend the approach to include both the static testing and the dynamic testing. Expected challenges will also be explained.

(IMO) (EEDI, Energy Efficiency Design Index) (CII, Carbon Intensity Indicator) .

• Field hurricane data from nine sites benchmark wind tunnel and analytical methods. • Wind tunnel tests compare heterogeneous and homogeneous terrain representations. • Terrain-aware analytical model accurately captures roughness transition effects. • Analysis identifies when heterogeneous modeling outperforms homogeneous approach. • Results clarify trade-offs between wind tunnel methods and analytical models. Wind profile estimation is essential for predicting wind loads on structures during design. The limitations of the homogeneous-roughness assumption are increasingly recognized, prompting the development of wind tunnel experiments and analytical methods that account for roughness transitions across heterogeneous terrain. However, validation of these methods against field data remains limited. This study evaluates the ability of wind tunnel experiments and an analytical approach to replicate near-surface wind profiles over complex heterogeneous terrain. Field measurements collected at nine coastal sites during hurricane events were used for validation. Upwind terrain was modeled using high-resolution satellite imagery and physically reproduced using the Terraformer system at the University of Florida. Analytical wind profiles were generated using the extended Deaves-Harris model. Prediction performance was quantified using the coefficient of determination, mean absolute percentage error, and mean absolute error. Results show that the heterogeneous wind tunnel method outperformed the homogeneous assumption as overall roughness length increased. Conversely, the homogeneous assumption performed comparably to or better than the heterogeneous method at lower roughness levels, suggesting its continued applicability for smoother terrain where surface variability is less influential. Analytical models exhibited comparable performance to that of physical testing in several cases, highlighting their potential for efficient preliminary assessments. Through this comparative evaluation, this study critically examines the applicability and limitations of each modeling approach, and provides practical guidance for selecting appropriate wind profile modeling strategies based on terrain complexity.