Update methodology description and flowchart in the manuscript

Manuscript/RESILINK_surrogate_optimization_manuscript.tex

@@ -81,11 +81,11 @@ Data-driven approaches have mainly focused on response or property prediction. C
 
 All these works demonstrate the increasing interest in applying FEM-based and data-driven approaches, as well as in combining both, to analyse, understand and optimize seismic energy dissipation devices. However, most of these studies focus either on the prediction of the hysteretic response or on maximizing energy dissipation, leaving a critical aspect insufficiently explored: the need to control local damage while maintaining adequate dissipative capacity. In practice, excessive local damage may compromise structural integrity, reduce durability and lead to premature failure, even when global energy dissipation is improved.
 
-The present work addresses this gap through a damage-aware surrogate-assisted optimization framework in which the objective is not only to maximize distortion or energy dissipation, but to balance dissipative performance with damage indicators derived from FEM simulations. The proposed methodology combines: (i) experimentally calibrated nonlinear FEM models used as ground truth; (ii) supervised surrogate models trained to predict local damage and distortion indicators; (iii) a Differential Evolution (DE) optimizer; and (iv) an adaptive FEM validation and retraining loop. The framework remains consistent with the underlying physics, as all surrogate models are trained on FEM-generated data that capture both global response and local damage mechanisms. Figure \ref{fig:MethodologyFlowChart} illustrates the proposed framework. The methods applied in each stage of the methodology the results obtained and the conclusions drawn are described in detail in the following sections.
+The present work addresses this gap through a damage-aware surrogate-assisted optimization methodology in which the objective is not only to maximize distortion or energy dissipation, but also to balance dissipative performance with damage indicators derived from FEM simulations. The proposed approach combines: (i) experimentally calibrated nonlinear FEM models used as numerical ground truth; (ii) supervised surrogate models trained to predict local damage and distortion indicators; (iii) a Differential Evolution (DE) optimizer; and (iv) an adaptive FEM validation and retraining loop. Figure \ref{fig:MethodologyFlowChart} summarizes the proposed workflow. The different stages of the methodology, together with the surrogate modelling, optimization strategy, validation procedure, and corresponding results, are described in the following sections.
 
 \begin{figure}[!ht]
   \centering
-  \includegraphics[width=1.0\textwidth]{./Figures/MethodologyFlowChart.png}
+  \includegraphics[width=0.6\textwidth]{./Figures/MethodologyFlowChart.png}
   \caption{Flow chart of the proposed adaptive surrogate-assisted optimization framework.}
   \label{fig:MethodologyFlowChart}
 \end{figure}