Add loading pattern plotting script and update manuscript

Manuscript/Figures/LoadPatterns/LoadPatterns.csv

+Time (s),Displacement H30 (mm),Displacement H45 (mm),Displacement H60 (mm)
+0,0,0,0
+0.0625,12,16,20
+0.125,0,0,0
+0.1875,-12,-16,-16
+0.25,0,0,0
+0.3125,12,16,16
+0.375,0,0,0
+0.4375,-12,-16,-16
+0.5,0,0,0
+0.5625,25,32,32
+0.625,0,0,0
+0.6875,-25,-32,-32
+0.75,0,0,0
+0.8125,25,32,32
+0.875,0,0,0
+0.9375,-25,-32,-32
+1,0,0,0
+1.0625,38,48,48
+1.125,0,0,0
+1.1875,-38,-48,-48
+1.25,0,0,0
+1.3125,38,48,48
+1.375,0,0,0
+1.4375,-38,-48,-48
+1.5,0,0,0
+1.5625,50,64,64
+1.625,0,0,0
+1.6875,-50,-64,-64
+1.75,0,0,0
+1.8125,50,64,64
+1.875,0,0,0
+1.9375,-50,-64,-64
+2,0,0,0
+2.0625,60,75,75
+2.125,0,0,0
+2.1875,-60,-75,-75
+2.25,0,0,0
+2.3125,60,75,75
+2.375,0,0,0
+2.4375,-60,-75,-75
+2.5,0,0,0
+2.5625,70,85,85
+2.625,0,0,0
+2.6875,-70,-85,-85
+2.75,0,0,0
+2.8125,70,85,85
+2.875,0,0,0
+2.9375,-70,-85,-85
+3,0,0,0

Manuscript/Figures/LoadPatterns/plot_load_patterns.py

+"""
+Plot the loading pattern displacement histories.
+
+The script reads LoadPatterns.csv and saves a single-panel figure as PNG, SVG,
+and PDF using the same visual format as the FEM validation plots.
+"""
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.rcParams.update(
+    {
+        "font.family": "serif",
+        "font.serif": ["Times New Roman", "DejaVu Serif"],
+        "mathtext.fontset": "stix",
+        "font.size": 11,
+        "axes.labelsize": 11,
+        "axes.titlesize": 11,
+        "xtick.labelsize": 11,
+        "ytick.labelsize": 11,
+        "axes.linewidth": 0.7,
+        "axes.spines.top": False,
+        "axes.spines.right": False,
+        "figure.dpi": 300,
+        "savefig.dpi": 300,
+    }
+)
+
+COLORS = ["#1a6faf", "#E07060", "#4D9A57"]
+COLOR_GRID = "#d8d8d8"
+
+X_LABEL = "Time (s)"
+Y_LABEL = "Displacement (mm)"
+SERIES_LABELS = ["H30", "H45", "H60"]
+
+
+def load_loading_patterns(path):
+    data = np.genfromtxt(path, delimiter=",", skip_header=1)
+    if data.ndim != 2 or data.shape[1] != 4:
+        raise ValueError(f"{path.name} must contain four numeric columns.")
+    return data[:, 0], data[:, 1:]
+
+
+def style_axis(ax):
+    ax.grid(True, color=COLOR_GRID, linewidth=0.55, alpha=0.75)
+    ax.tick_params(axis="both", which="major", direction="in", length=4, width=0.7)
+    ax.tick_params(axis="both", which="minor", direction="in", length=2.5, width=0.6)
+    ax.minorticks_on()
+
+
+def save_figure_all_formats(fig, out_base):
+    fig.savefig(f"{out_base}.png", format="png", bbox_inches="tight", dpi=300)
+    fig.savefig(f"{out_base}.svg", format="svg", bbox_inches="tight")
+    fig.savefig(f"{out_base}.pdf", format="pdf", bbox_inches="tight")
+
+
+def main():
+    base_dir = Path(__file__).resolve().parent
+    time, displacements = load_loading_patterns(base_dir / "LoadPatterns.csv")
+
+    fig, ax = plt.subplots(figsize=(5.2, 3.8))
+    fig.subplots_adjust(left=0.14, right=0.985, bottom=0.28, top=0.94)
+
+    for index, label in enumerate(SERIES_LABELS):
+        ax.plot(
+            time,
+            displacements[:, index],
+            color=COLORS[index],
+            linewidth=1.6,
+            label=label,
+        )
+
+    ax.axhline(0, color="black", linewidth=0.55, alpha=0.65)
+    ax.set_xlabel(X_LABEL)
+    ax.set_ylabel(Y_LABEL)
+    ax.set_xlim(time.min(), time.max())
+    style_axis(ax)
+
+    handles, labels = ax.get_legend_handles_labels()
+    fig.legend(
+        handles,
+        labels,
+        loc="lower center",
+        ncol=3,
+        frameon=False,
+        framealpha=0.95,
+        edgecolor="#cccccc",
+        bbox_to_anchor=(0.5, 0.055),
+    )
+
+    output_base = base_dir / "LoadPatterns"
+    save_figure_all_formats(fig, output_base)
+    plt.close(fig)
+    print(f"Saved: {output_base}.png/.svg/.pdf")
+
+
+if __name__ == "__main__":
+    main()

Manuscript/Figures/PatronesCarga/PatronesCarga.csv

-Tiempo [s],Desplazamiento H30 [mm],Desplazamiento H45 [mm]
-0,0,0
-0.0625,12,16
-0.125,0,0
-0.1875,-12,-16
-0.25,0,0
-0.3125,12,16
-0.375,0,0
-0.4375,-12,-16
-0.5,0,0
-0.5625,25,32
-0.625,0,0
-0.6875,-25,-32
-0.75,0,0
-0.8125,25,32
-0.875,0,0
-0.9375,-25,-32
-1,0,0
-1.0625,38,48
-1.125,0,0
-1.1875,-38,-48
-1.25,0,0
-1.3125,38,48
-1.375,0,0
-1.4375,-38,-48
-1.5,0,0
-1.5625,50,64
-1.625,0,0
-1.6875,-50,-64
-1.75,0,0
-1.8125,50,64
-1.875,0,0
-1.9375,-50,-64
-2,0,0
-2.0625,60,75
-2.125,0,0
-2.1875,-60,-75
-2.25,0,0
-2.3125,60,75
-2.375,0,0
-2.4375,-60,-75
-2.5,0,0
-2.5625,70,85
-2.625,0,0
-2.6875,-70,-85
-2.75,0,0
-2.8125,70,85
-2.875,0,0
-2.9375,-70,-85
-3,0,0

Manuscript/wileyNJD-AMA.bib

@@ -765,4 +765,31 @@ steel from coupon test results available. First, the theory of metal plasticity
   howpublished = {Standard, American Society of Civil Engineers},
 }
 
+@Article{Virtanen2025,
+  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
+  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific Computing in Python}},
+  journal = {Nature Methods},
+  year    = {2020},
+  volume  = {17},
+  pages   = {261--272},
+  doi     = {10.1038/s41592-019-0686-2},
+}
+
+@Article{Storn1997,
+  author   = {Storn, Rainer and Price, Kenneth},
+  journal  = {Journal of Global Optimization},
+  title    = {Differential {Evolution} – {A} {Simple} and {Efficient} {Heuristic} for global {Optimization} over {Continuous} {Spaces}},
+  year     = {1997},
+  issn     = {1573-2916},
+  month    = dec,
+  number   = {4},
+  pages    = {341--359},
+  volume   = {11},
+  abstract = {A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means of an extensivetestbed it is demonstrated that the new methodconverges faster and with more certainty than manyother acclaimed global optimization methods. The newmethod requires few control variables, is robust, easyto use, and lends itself very well to parallelcomputation.},
+  doi      = {10.1023/A:1008202821328},
+  keywords = {evolution strategy, genetic algorithm, global optimization, nonlinear optimization, Stochastic optimization},
+  language = {en},
+  urldate  = {2026-05-13},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}