Pedestrian (Forman 2015)#

Model validation information

  • Performed by: Nico Erlinger, Elena Weißenbacher

  • Reviewed by: Corina Klug

Added to VIVA+ Validation Catalog on 2022-11-30 by Corina Klug

Version

Date

Performed by

LS-Dyna

2022-11-30

Nico Erlinger, Elena Weißenbacher

9.3.1

0.3.2

2023-11-23

Corina Klug

9.3.1

1.1.1

2024-06-04

Matej Kranjec

12.2.1

© 2019-2024, OpenVT Organization (OVTO)

Available openly under under Creative Commons Attribution 4.0 International License

Reference#

Experiments by Forman et al. (2015)#

Forman, J., Joodaki, H., Forghani, A., O Riley, P., Bollapragada, V., Lessley, D., Overby, B., Heltzel, S., Crandall, J., 2015a. Biofidelity Corridors for Whole‐Body Pedestrian Impact with a Generic Buck, in: 2015 IRCOBI Conference Proceedings. IRCOBI Conference, Lyon, France. 9.-11.9.2015. IRCOBI, pp. 356–372. http://www.ircobi.org/wordpress/downloads/irc15/pdf_files/49.pdf

Forman, J.L., Joodaki, H., Forghani, A., Riley, P.O., Bollapragada, V., Lessley, D.J., Overby, B., Heltzel, S., Kerrigan, J.R., Crandall, J.R., Yarboro, S., Weiss, D.B., 2015. Whole-body Response for Pedestrian Impact with a Generic Sedan Buck. Stapp Car Crash J 59, 401–444.

VIVA+ validation#

Manuscript currently under preparation

Information on the subjects/specimens#

PMHS

Sex

Height [cm]

Weight [kg]

Age [yr]

Scale factor z (height)

Scale factor x and y (weight)

V2370

m

180

73

73

1.02

0.96

V2371

m

187

82

54

1.07

1.00

V2374

m

178

78

67

1.02

1.00

V2370

V2371

V2374

Experiment [°]

pos. VIVA+ [°]

Experiment [°]

pos. VIVA+ [°]

Experiment [°]

pos. VIVA+ [°]

side view

Right lower leg

76

75

65

68

60

63

Right upper leg

95

92

94

89

97

94

Left lower leg

84

88

96

100

97

101

Left upper leg

114

108

100

99

97

97

Right lower arm

-

45

-

38

-

29

Right upper arm

63

63

49

56

17

48

Left lower arm

-

51

-

50

-

40

Left upper arm

59

64

54

62

10

53

front view

Right lower leg

84

81

85

80

81

78

Right upper leg

90

91

94

96

89

86

Left lower leg

94

94

95

100

92

97

Left upper leg

94

98

89

93

93

95

Right lower arm

-

58

-

54

-

40

Right upper arm

-

84

-

85

-

92

Left lower arm

-

55

-

54

-

43

Left upper arm

-

96

-

96

-

89

Pelvis

-

1

-

1

-

1

Upper body centreline

-

90

-

90

-

90

Shoulder

-

0

-

0

-

0

Loading and Boundary Conditions#

Simulation model of the generic buck was downloaded from the THUMS User Community (https://tuc-project.org/whole-body-pedestrian-impact)

Simulation

Experimental responses#

The displacement curves from the diagrams published by Forman et al. (2015) were digitised with WebPlotDigitizer v4.4 (https://automeris.io/WebPlotDigitizer).

Simulation metadata#

Results#

Trajectories#

Hide code cell source 
simulation_titles = ['V2370 (m, 180 cm, 73 kg, 73 yr)', 
                     'V2371 (m, 187 cm, 82 kg, 54 yr)',
                     'V2374 (m, 178 cm, 78 kg, 67 yr)']


create_subplots(
figure_title = 'Displacement',    
sim_x_data = ('HEAD', 'Head_COG_x-displacement', 'displacement'),
sim_y_data = ('HEAD', 'Head_COG_z-coordinate', 'coordinate'),
sim_x_data2 = ('PELVIS', 'Pelvis_sacrum_x-displacement', 'displacement'),
sim_y_data2 = ('PELVIS', 'Pelvis_sacrum_z-coordinate', 'coordinate'),
sim_name1_legend = 'Head',
sim_name2_legend = 'Pelvis',
exp_x_data = ('Head', 'x-coordinate', 'coordinate'),
exp_y_data = ('Head', 'z-coordinate', 'coordinate'),
exp_name1_legend = 'Head experiment',
exp_x_data2 = ('Pelvis', 'x-coordinate', 'coordinate'),
exp_y_data2 = ('Pelvis', 'z-coordinate', 'coordinate'),
exp_name2_legend = 'Pelvis experiment',
sim_data_for_calculation_x = ('BUCK', 'Buck_COG_x-displacement', 'displacement'),
x_label = ('X-coordinate [mm]'),
y_label = ('Z-coordinate [mm]'),
x_lim = [-2000, 0],
y_lim = [0, 2000],
filename_save = 'results/figures/VIVA+_Forman_et_al_2015_x-displacement_time.svg' )
../_images/51891738490c4ac9a4c39cde61caef78f0153b3019ebca7de5bd7518f83c99a8.png

Tibia and femur strains#

Hide code cell source 
create_subplots(
figure_title = 'Tibia strain',
sim_x_data = ('BONES', 'Tibia_Cortical_R_PS99', 'time'),
sim_y_data = ('BONES', 'Tibia_Cortical_R_PS99', 'strain'),
sim_x_data2 = ('BONES', 'Tibia_Cortical_R_MPS', 'time'),
sim_y_data2 = ('BONES', 'Tibia_Cortical_R_MPS', 'strain'),
sim_name1_legend = 'MPS',
sim_name2_legend = 'PS99',
x_label = 'Time [ms]',
y_label = 'Strain [-]',
x_lim = [0, 200],
filename_save = 'results/figures/Forman_et_al_2015_tibia_r_strain_time.svg')    

create_subplots(
figure_title = 'Femur strain',
sim_x_data = ('BONES', 'Femur_Cortical_R_PS99', 'time'),
sim_y_data = ('BONES', 'Femur_Cortical_R_PS99', 'strain'),
sim_x_data2 = ('BONES', 'Femur_Cortical_R_MPS', 'time'),
sim_y_data2 = ('BONES', 'Femur_Cortical_R_MPS', 'strain'),
sim_name1_legend = 'PS99',
sim_name2_legend = 'MPS',
x_label = 'Time [ms]',
y_label = 'Strain [-]',
x_lim = [0, 200],
filename_save = 'results/figures/Forman_et_al_2015_femur_r_strain_time.svg')
../_images/9ad1b417ecd961d3534269733db7152bfa2b857aded84a789fe98b1340d6af4c.png ../_images/4e9b3cf6ce3ac5405d977bb8bd76d08d543291da5ddef19b153f54376973540a.png

Energy time histories#

Hide code cell source 
create_subplots(
figure_title = 'Engery time histories',
sim_x_data = ('MODEL', 'Total_Energy', 'time'),
sim_y_data = ('MODEL', 'Total_Energy', 'energy'),
sim_x_data2 = ('MODEL', 'Internal_Energy', 'time'),
sim_y_data2 = ('MODEL', 'Internal_Energy', 'energy'),
sim_x_data3 = ('MODEL', 'Kinetic_Energy', 'time'),
sim_y_data3 = ('MODEL', 'Kinetic_Energy', 'energy'),
sim_x_data4 = ('MODEL', 'Hourglass_Energy', 'time'),
sim_y_data4 = ('MODEL', 'Hourglass_Energy', 'energy'),
sim_name1_legend = 'Total energy',
sim_name2_legend = 'Interal energy',
sim_name3_legend = 'Kinetic energy',
sim_name4_legend = 'Hourglass energy',
x_label = 'Time [ms]',
y_label = 'Energy [J]',
x_lim = [0, 200],
filename_save = 'results/figures/Forman_energy_time_histories.svg')
../_images/77006e344e6bf82f1b3dadbd7acc797223a70928e53a03bcef4074793b2b3f17.png

Head impact times#

Hide code cell source 
def calculate_head_impact_time(sim_x_data_leg, sim_y_data_leg, sim_x_data_head, sim_y_data_head):
  counter = 0
  time_array =[]

  for i in simulation_list:
    processed_data_path = os.path.join(processed_data_dir, i).replace('\\', '/')
    simData = pd.read_csv(os.path.join(processed_data_path, dynasaur_output_file_name), 
                                        delimiter=';', na_values='-', header = [0,1,2,3])


    leg_contact_force_time = np.array(simData[sim_x_data_leg]).flatten()
    leg_contact_force = np.array(simData[sim_y_data_leg]).flatten()                                       
    ind_pos_leg_contact_force = np.where(leg_contact_force > 0.01)
    ind_first_leg_contact = ind_pos_leg_contact_force[0][0] 
    first_leg_contact_time = leg_contact_force_time[ind_first_leg_contact]

    head_contact_force_time = np.array(simData[sim_x_data_head]).flatten()
    head_contact_force = np.array(simData[sim_y_data_head]).flatten()                                       
    ind_pos_head_contact_force = np.where(head_contact_force > 0.01)
    ind_first_head_contact = ind_pos_head_contact_force[0][0] 
    first_head_contact_time = head_contact_force_time[ind_first_head_contact]

    head_contact_time = first_head_contact_time-first_leg_contact_time
    head_contact_time = round(head_contact_time)    

    time_array.append([i, first_leg_contact_time, first_head_contact_time, head_contact_time])
  
  hit_array = []
  hit_array = np.append(hit_array, time_array)
  hit_array = hit_array.transpose()
  hit_array = np.reshape(hit_array, (3,4))
  return(hit_array)
Hide code cell source 
hit_array = calculate_head_impact_time(
sim_x_data_leg = ('HBM', 'HBM_Leg_Vehicle_Contact_Transducer_x_force', 'time'),
sim_y_data_leg = ('HBM', 'HBM_Leg_Vehicle_Contact_Transducer_x_force', 'force'),
sim_x_data_head = ('HBM', 'HBM_Head_Vehicle_Contact_Transducer_z_force', 'time'),
sim_y_data_head = ('HBM', 'HBM_Head_Vehicle_Contact_Transducer_z_force', 'force')
)

head_time_exp = [135, 138, 141]
hit_df = pd.DataFrame(hit_array, columns=['Simulation', 'First leg contact [ms]', 'First head contact [ms]', 'Simulation HIT [ms]'])
hit_df['Experimental HIT [ms]'] = np.array(head_time_exp)
display(hit_df)

create_subplots(
figure_title = 'Head and leg contact force',
sim_x_data = ('HBM', 'HBM_Leg_Vehicle_Contact_Transducer_x_force', 'time'),
sim_y_data = ('HBM', 'HBM_Leg_Vehicle_Contact_Transducer_x_force', 'force'),
sim_x_data2 = ('HBM', 'HBM_Leg_Vehicle_Contact_Transducer_z_force', 'time'),
sim_y_data2 = ('HBM', 'HBM_Leg_Vehicle_Contact_Transducer_z_force', 'force'),
sim_x_data3 = ('HBM', 'HBM_Head_Vehicle_Contact_Transducer_x_force', 'time'),
sim_y_data3 = ('HBM', 'HBM_Head_Vehicle_Contact_Transducer_x_force', 'force'),
sim_x_data4 = ('HBM', 'HBM_Head_Vehicle_Contact_Transducer_z_force', 'time'),
sim_y_data4 = ('HBM', 'HBM_Head_Vehicle_Contact_Transducer_z_force', 'force'),
sim_name1_legend = 'leg x force',
sim_name2_legend = 'leg z force',
sim_name3_legend = 'head x force',
sim_name4_legend = 'head z force',
x_label = 'Time [ms]',
y_label = 'Contact force [kN]',
vertical_line1 = list(map(float, (hit_df['First leg contact [ms]'].tolist()))),
vertical_line2 = list(map(float, (hit_df['First head contact [ms]'].tolist()))),
x_lim = [0, 200],
y_lim = [0, 15],   
)
Simulation First leg contact [ms] First head contact [ms] Simulation HIT [ms] Experimental HIT [ms]
0 Test_V2370 4.502251125562781 134.26713356678337 130 135
1 Test_V2371 0.80040020010005 137.7688844422211 137 138
2 Test_V2374 2.0010005002501248 135.06753376688343 133 141
../_images/f0431d8a597b6a0bd633c0f3ef30d79fc21f5c1292b109d83d0477f76256c222.png

Plotting peak strains of tibia and femur on IRCs#

Hide code cell source 
def find_peak_strains(title, x_data, y_data):
    list = []
    for i in simulation_list:
        processed_data_path = os.path.join(processed_data_dir, i).replace('\\', '/')
        simData = pd.read_csv(os.path.join(processed_data_path, dynasaur_output_file_name), 
                                    delimiter=';', na_values='-', header = [0,1,2,3])

        sim_x_data = np.array(simData[x_data]).flatten()
        sim_y_data = np.array(simData[y_data]).flatten()
        
        peak_strain_ind = np.nanargmax(sim_y_data)
        peak_time = sim_x_data[peak_strain_ind]
        peak_strain = sim_y_data[peak_strain_ind]
        list.append([i, peak_time, peak_strain])

    list = np.reshape(list, (len(simulation_list), 3))
    return list
    
def plot_peak_strains_on_irc(x_label, filename_strains_irc, peak_strain_array):
    plotTest_V2370 = { "marker" :'D', "color" : 'black',}
    plotTest_V2371 = { "marker" :'D', "color" : 'red', }
    plotTest_V2374 = { "marker" :'D', "color" : 'darkorange',}

    sim_strain_data = peak_strain_array[:,2].astype(float)
    sim_name_data = peak_strain_array[:,0]
    df_strains_irc = pd.read_csv(filename_strains_irc, sep = ";")

    plt.figure(figsize=(7,5))
    weibull_fit = Fit_Weibull_2P(failures=list(df_strains_irc.PS99),show_probability_plot=False,print_results=False, CI=0.95, CI_type="time")
    
    weibull_fit.distribution.CDF(label='Fitted Distribution',color='steelblue')
    plt.xlabel(x_label, fontweight='semibold',fontsize=10)
    plt.ylabel("Probability",fontweight='semibold',fontsize=10)

    x_values = sim_strain_data.flatten()
    axs = plt.gca()
    line = axs.lines[0]
    IRC_distrib_x = line.get_xdata()
    IRC_distrib_y = line.get_ydata()

    probability_list = []
    for x in range(len(x_values)):
        smaller_values = np.where(IRC_distrib_x < x_values[x])
        bigger_values = np.where(IRC_distrib_x > x_values[x])
        
        ind_small = smaller_values[0][-1]
        ind_big = ind_small+1
        
        if ind_small == 199:
            y_value = 0.9999    
        else:
            x_interp_array = [IRC_distrib_x[ind_small],IRC_distrib_x[ind_big]]
            y_interp_array = [IRC_distrib_y[ind_small],IRC_distrib_y[ind_big]]
            y_value = np.interp(x_values[x],x_interp_array,y_interp_array)

        simPlot = locals()["plot" + sim_name_data[x]]
        plt.scatter(x_values[x], y_value,**simPlot,label=sim_name_data[x] + ' = ' + str(round(y_value*100,1)) + '%')
        plt.legend(loc=0)
        probability_list.append(y_value*100)
    return(probability_list)
Hide code cell source 
tibia_exp_fracture = ['no', 'no', 'no']
femur_exp_fracture = ['no', 'no', 'no']

tibia_PS99_peak_strain_array = find_peak_strains(
title = 'Tibia PS99 strain',
x_data = ('BONES', 'Tibia_Cortical_R_PS99', 'time'),
y_data = ('BONES', 'Tibia_Cortical_R_PS99', 'strain')    
)

femur_PS99_peak_strain_array = find_peak_strains(
title = 'Femur PS99 strain',
x_data = ('BONES', 'Femur_Cortical_R_PS99', 'time'),
y_data = ('BONES', 'Femur_Cortical_R_PS99', 'strain')    
)

probability_list_tibia = plot_peak_strains_on_irc(
peak_strain_array = tibia_PS99_peak_strain_array,
x_label = 'Right tibia peak PS99 [-]',
filename_strains_irc = 'data/metadata/Tibia_injury-risk-curve/strains_tibia_for_IRC.csv'
)

probability_list_femur = plot_peak_strains_on_irc(
peak_strain_array = femur_PS99_peak_strain_array,
x_label = 'Right femur peak PS99 [-]',
filename_strains_irc = 'data/metadata/Femur_injury-risk-curve/Schubert-2020_Femur_All-Strain-Curves-for-FRC.csv'
)

fracture_risks_df = pd.DataFrame({'Simulation': simulation_list,
                                'Tibia fracture probability [%]': probability_list_tibia,
                                'Experiment tibia fracture': np.array(tibia_exp_fracture), 
                                'Femur fracture probability [%]': probability_list_femur,
                                'Experiment femur fracture': np.array(femur_exp_fracture),})

display(fracture_risks_df)
Simulation Tibia fracture probability [%] Experiment tibia fracture Femur fracture probability [%] Experiment femur fracture
0 Test_V2370 10.1977 no 8.48424 no
1 Test_V2371 6.79441 no 15.0103 no
2 Test_V2374 1.61808 no 7.83302 no
../_images/b03979798a83346b4609f259e3575e20896d4904f0bbe01f74e56b51cf4344f7.png ../_images/6abdd98cec19503c3f3830badf62356f987438ec85775a31211d6947fafd0d63.png