Pedestrian (Snedeker 2003)

Pedestrian (Snedeker 2003)#

Validation model information#

Performed by: Nico Erlinger
Reviewed by: Corina Klug

Added to VIVA+ Validation Catalog on: 2022-11-30

Last modified: 2023-11-23

VIVA+ Model version (this notebook run for): 0.3.2

The Jupyter notebooks are licensed under Creative Commons Attribution 4.0 International License CCBY

References#

Experiments by Snedeker et al. (2003)#

Snedeker, J.G., Muser, M.H., Walz, F.H., 2003. Assessment of pelvis and upper leg injury risk in car-pedestrian collisions: comparison of accident statistics, impactor tests and a human body finite element model. Stapp Car Crash J 47, 437–457. References

Snedeker, J.G., Muser, M.H., Walz, F.H., 2003. Assessment of pelvis and upper leg injury risk in car-pedestrian collisions: comparison of accident statistics, impactor tests and a human body finite element model. Stapp Car Crash J 47, 437–457. Snedeker, J.G., Walz, F.H., Muser, M.H., Schroeder, G., Mueller, T.L., Müller, R., 2006. Microstructural insight into pedestrian pelvic fracture as assessed by high-resolution computed tomography. Journal of Biomechanics 39, 2709–2713. https://doi.org/10.1016/j.jbiomech.2005.09.008.

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 xand y (weight)
T1	f	160	50	52	0.99	0.89
T2	f	166	74	76	1.03	1.08
T3	m	177	75	32	1.01	0.98
T4	m	180	64	78	1.03	0.90
T5	m	172	60	76	0.98	0.89

Loading and Boundary Conditions#

The vehicle shapes are differing in the different loadcases, which is shown exemplary for loadcase T1 & T5 in the figure below.

The initial velocity of the vehicle and its deceleration is prescribed. Open Issue: Implement damping to adress vehicle front oscillations (visible in energy plots).

Experimental responses#

The experimental responses from the original papers were digitalised.

Simulation metadata#

Results#

Trajectories#

../_images/8fdc0e9b1d3240b4afeb8216e3be143ed95537f30d59978f55b37a6f9ebcd7ba.png

../_images/f35eb48766353af3597024d0a022404d44522a147ba119e1d9eaff18ca3afdc6.png

Femur and Tibia strains#

../_images/9c63d745d613f862fb6a246bf98074ff9a8c802fd70326ca76108b51752cf81f.png

../_images/1e24b7e292726223553eab42014424f797b1579435a67eb5c2ad998b35ef5593.png

Energy time histories#

../_images/73770f131f820a782f140c9ddd86525b93186e1eb3fb6ed8abcf236b82ebcae4.png

Plotting peak strains of tibia and femur on IRCs#

Show code cell source 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.argmax(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):
    plotT1 = { "marker" :'D', "color" : 'black',}
    plotT2 = { "marker" :'D', "color" : 'red', }
    plotT3 = { "marker" :'D', "color" : 'darkorange',}
    plotT4 = { "marker" :'D', "color" : 'lime',}
    plotT5 = { "marker" :'D', "color" : 'blue',}

    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)

	Simulation	Tibia fracture probability [%]	Experiment tibia fracture	Femur fracture probability [%]	Experiment femur fracture
0	T1	99.99	no	8.88195	no
1	T2	99.99	no	2.07505	no
2	T3	99.99	yes	2.84773	no
3	T4	99.729	yes	6.47073	no
4	T5	98.5425	yes	3.63275	no

../_images/6b0127ec712734c522a2f72cd0eb27205f95f01781ddcb3f503dcf198b216f06.png

../_images/6c542478e9604c82d0732521d6b1bfa2a38e92a1a45fb9e525cc7f657deefbe2.png

Head impact times#

	Simulation	First leg contact [ms]	First head contact [ms]	HIT [ms]
0	T1	4.0	122.0	118
1	T2	1.0	130.0	129
2	T3	2.0	146.0	144
3	T4	3.0	102.0	99
4	T5	3.0	99.0	96

../_images/13915a2cc7727fae94abb2cfb67d6205cd7790cec44529d32c7a2e47c9ead948.png