Pedestrian (Paas 2015)

Pedestrian (Paas 2015)#

Postprocessing of femur shaft validations based on Paas et al. 2015

Performed by: Nico Erlinger
Reviewed by: Corina Klug

Added to VIVA+ Validation Catalog on: 2022-10-21

Version	Date	Performed by	LS-Dyna
0.3.2	2022-10-19	Nico Erlinger	9.3.1
1.1.0	2024-06-04	Corina Klug	12.0.0.

Available openly under under Creative Commons Attribution 4.0 International License

References#

Experimental data#

Paas, R., Masson, C., and Davidsson, J. (2015). Head boundary conditions in pedestrian crashes with passenger cars: Six-degrees-of-freedom post-mortem human subject responses. International Journal of Crashworthiness 20, 547–559. doi: 10.1080/13588265.2015.1060731

Paas, R., Östh, J., and Davidsson, J. (2015). “Which Pragmatic Finite Element Human Body Model Scaling Technique Can Most Accurately Predict Head Impact Conditions in Pedestrian-Car Crashes?” in 2015 IRCOBI Conference Proceedings, ed. International Research Council on the Biomechanics of Injury (IRCOBI), 546–576. http://www.ircobi.org/wordpress/downloads/irc15/pdf_files/64.pdf

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)
PM01	m	172	69	72	0.98	0.96
PF02	f	154	47	85	0.95	0.88
PM03	m	175	88	61	1.00	1.08
PM04	m	179	81	83	1.02	1.02
PM05	m	174	68	89	0.99	0.94

Postures

		PM01		PF02		PM03		PM04		PM05
		Experiment [°]	pos. VIVA+ [°]	Experiment [°]	pos. VIVA+ [°]	Experiment [°]	pos. VIVA+ [°]	Experiment [°]	pos. VIVA+ [°]	Experiment [°]	pos. VIVA+ [°]
side view	Right lower leg	-10	-5	-1	-2	-12	-11	-8	-6	-3	0
	Right upper leg	6	2	7	6	1	2	12	9	13	8
	Left lower leg	-15	-15	-12	-11	-15	-16	-37	-33	-29	-29
	Left upper leg	6	1	11	7	1	1	2	4	10	5
	Right lower arm	4	8	10	7	9	11	14	12	12	18
	Right upper arm	-8	-6	-2	-3	-1	-6	-5	-6	-1	-6
	Left lower arm	44	40	17	17	22	23	32	30	11	13
	Left upper arm	-1	-1	3	3	8	6	1	6	4	6

front view	Right lower leg	1	2	1	6	10	15	-6	-9	2	3
	Right upper leg	-9	-7	0	2	8	7	1	4	4	3
	Left lower leg	-8	-11	12	13	6	6	10	10	2	3
	Left upper leg	-7	-9	-2	-6	4	9	1	6	-4	-5
	Right lower arm	-7	-9	2	2	-10	-7	7	4	-10	-8
	Right upper arm	9	9	8	7	7	8	5	8	15	12
	Left lower arm	22	25	-3	1	12	11	13	17	8	7
	Left upper arm	-25	-23	-8	-6	-4	-8	-5	-8	16	15
	Pelvis	89	90	91	90	89	89	91	90	89	89
	Upper body centreline	-1	0	1	0	1	0	0	0	0	0
	Shoulder	90	89	93	90	91	90	91	90	95	90

Loading and Boundary Conditions#

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

The setup of PM01 is shown exemplary below.

Experimental responses#

Currently only trajectories are compared

Simulation metadata#

Function for plotting#

Results#

Head impact times#

	Simulation	First leg contact [ms]	First head contact [ms]	Simulation HIT [ms]	Experimental HIT [ms]
0	PM01	3.0	110.0	107	120
1	PF02	2.0	92.0	90	102
2	PM03	3.0	135.0	132	162
3	PM04	2.0	118.0	116	120
4	PM05	2.0	115.0	113	111

../_images/48429dabead41c4989794ea994863a488ed714706627133513c5eb1315918f17.png

Head COG displacements#

../_images/f9e4684af00f40415dbab6111c089c7ca4fcb0feb378d83581bba97b1f6e60b4.png

../_images/d3097b8cafad74024cd6e8ecf0831cc4dfa64393f1f96f475c138b6cf74377ed.png

../_images/3806b2b519c8b7564e02e06da61cd09b85fcf6ddd312aadf76e2602fba2a54a1.png

Tibia and femur strains#

../_images/5fed355c430a85868aa70b607d5713f7f17fe25f325ba2fb000cfada8311c104.png

../_images/11f182144b0fe1d0cbaeb303433ee37a5a902b7ba30971f0bad80229f60b2ecc.png

Energy time histories#

../_images/34db3d5ef55fe29376bc868008a304256531ff5ae384ceabf9ba3bd73431c285.png

Tibia and femur fracture risk#

	Simulation	Tibia fracture probability [%]	Experiment tibia fracture	Femur fracture probability [%]	Experiment femur fracture
0	PM01	99.7614	no	42.0278	no
1	PF02	75.5104	yes	98.5391	no
2	PM03	5.7665	yes	89.7069	no
3	PM04	60.8827	yes	38.9985	no
4	PM05	99.2236	yes	39.0745	no

../_images/a97622f32d6e116d9f1cdde48426754b80a0e8a14ad341710ee53665de948822.png

../_images/0c67a39b8b93b0cdecf60f7d633704b13ceb31e17a94f361ec323da96d192bee.png

ISO18571 objective rating for displacement-time histories#

Show code cell source Hide code cell source

from objective_rating_metrics.rating import ISO18571
def calculate_iso_score(experiment_data_file, sim_x_data, sim_y_data, exp_x_data, exp_y_data, sim_data_for_calculation_y=None,):
    ratings = []
    for i in simulation_list:
        experimental_data = pd.read_csv(experiment_data_file, delimiter=';', header=[0,1,2,3], decimal='.')
        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]) 
              
        time_ref = np.array(experimental_data[(i,) + exp_x_data]).flatten()
        x_ref = np.array(experimental_data[(i,) + exp_y_data]).flatten()

        time_comp=np.array(simData[sim_x_data]).flatten()
        x_comp = simData[sim_y_data]
        
        if sim_data_for_calculation_y is not None:
            x_comp = x_comp + simData[sim_data_for_calculation_y]
        x_comp=np.array(x_comp).flatten() 
        
        time_comp = np.delete(time_comp,np.s_[150:])
        x_comp = np.delete(x_comp, np.s_[150:])

        ind_nan = []
        ind_nan = np.where(np.isnan(x_ref))
        
        if len(ind_nan[0]) > 0:
            x_ref = x_ref[0:ind_nan[0][0]-1]
            x_time_ref = time_ref[0:ind_nan[0][0]-1]
            x_comp = x_comp[0:ind_nan[0][0]-1]
            x_time_comp = time_comp[0:ind_nan[0][0]-1]
        else:
            x_time_ref = time_ref
            x_time_comp = time_comp
            
        if len(x_ref) > len(x_comp):
            ind_end = len(x_comp)
            x_ref = x_ref[0:ind_end]
            x_time_ref = x_time_ref[0:ind_end]
            
        elif len(x_comp) > len(x_ref):
            ind_end = len(x_ref)
            x_comp = x_comp[0:ind_end]
            x_time_comp = x_time_comp[0:ind_end]   

        ref = np.vstack((x_time_ref, x_ref)).T
        comp = np.vstack((x_time_comp, x_comp)).T

        iso_rating = ISO18571(reference_curve=ref, comparison_curve=comp)
        
        ratings.append([i,
        iso_rating.corridor_rating(), 
        iso_rating.phase_rating(),
        iso_rating.magnitude_rating(),
        iso_rating.slope_rating(),
        iso_rating.overall_rating()])

    rating_array = []
    rating_array = np.append(rating_array, ratings)
    rating_array = rating_array.transpose()
    rating_array = np.reshape(rating_array, (5,6))

    return(rating_array)


# TODO implement time offset between test data and experiment


iso_ratings_x = calculate_iso_score(
sim_x_data = ('HEAD', 'x-displacement', 'time'),
sim_y_data = ('HEAD', 'x-displacement', 'displacement'),
experiment_data_file = 'data/experiment/Paas_et_al_2015_testdata.csv',
exp_x_data = ('Head', 'time'),
exp_y_data = ('Head', 'x-displacement'),
)

iso_ratings_x_df = pd.DataFrame(iso_ratings_x, columns=['Simulation', 'Corridor', 'Phase', 'Magnitude', 'Slope', 'Overall'])
print('ISO ratings for x-displacement')
display(iso_ratings_x_df)

iso_ratings_z = calculate_iso_score(
sim_x_data = ('HEAD', 'z-displacement', 'time'),
sim_y_data = ('HEAD', 'z-displacement', 'displacement'),
experiment_data_file = 'data/experiment/Paas_et_al_2015_testdata.csv',
exp_x_data = ('Head', 'time'),
exp_y_data = ('Head', 'z-displacement'),
)

iso_ratings_z_df = pd.DataFrame(iso_ratings_z, columns=['Simulation', 'Corridor', 'Phase', 'Magnitude', 'Slope', 'Overall'])
print('ISO ratings for z-displacement')
display(iso_ratings_z_df)

ISO ratings for x-displacement

	Simulation	Corridor	Phase	Magnitude	Slope	Overall
0	PM01	0.305	0.752	0.007	0.643	0.403
1	PF02	0.526	0.766	0.333	0.736	0.577
2	PM03	0.607	0.533	0.661	0.536	0.589
3	PM04	0.939	0.795	0.935	0.83	0.888
4	PM05	0.824	0.636	0.918	0.833	0.807

ISO ratings for z-displacement

	Simulation	Corridor	Phase	Magnitude	Slope	Overall
0	PM01	0.802	0.256	0.01	0.445	0.463
1	PF02	0.885	0.86	0.933	0.823	0.877
2	PM03	0.77	0.167	0.765	0.657	0.626
3	PM04	0.973	0.139	0.228	0.606	0.584
4	PM05	0.895	0.636	0.975	0.932	0.867