PSI - Issue 75

Laurent Gornet et al. / Procedia Structural Integrity 75 (2025) 129–139 Author name / Structural Integrity Procedia (2025)

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References

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Appendix A. The code of the Gornet- fatigue’s model (Python, TensorFlow, Keras)

import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit # === Experimental data === logN_data = np.array([

0.0000000001, 1., 3.82979, 4.76596, 5.00304, 5.30699, 5.38602, 5.53799, 5.59878, 5.90881, 6.26748, 6.44985], dtype=np.float64) N = 10 ** logN_data S = np.array([699, 699, 460.26, 460.26, 464.85, 441.89, 418.94, 439.6, 451.0, 430.42, 401.72, 418.94], dtype=np.float64) # === Imposed parameters === S_sat_fixed = 699 S0_sat_fixed = 370.73 # === Gornet’s model === def stromeyer_modified(N, c, b, d): return (S_sat_fixed - S0_sat_fixed) / ((1 + c * N**b)**d) + S0_sat_fixed # === initiales Estimations === c_init = 1e-6

b_init = 2.0 d_init = 1.0 # === Ajustement des paramètres === popt, _ = curve_fit( stromeyer_modified, N, S, p0=[c_init, b_init, d_init], bounds=([1e-10, 0.5, 0.1], [1e3, 10, 5])) c_fit, b_fit, d_fit = popt # === Prediction === N_test = np.logspace(0, 9, 300) S_pred = stromeyer_modified(N_test, *popt) # === Response === plt.figure(figsize=(8, 6)) plt.semilogx(N, S, 'ro', label='Experimental data')