## 1 Introduction
The aim of this study is to reduce the cost associated with occupant injury testing. To enhance engineers' analysis of occupant biological response through force distribution visualization and reduce cost, a finite element model of a dummy has been introduced. LSTC and the University of Washington have jointly developed the Hybrid III finite element model (FEM). Scholars optimized the material properties of the Hybrid III numerical model under high-speed impact conditions by taking into account the strain rate effects. [PERSON] validated the mechanical behavior of the lumbar spine by introducing a stiffer viscoelastic material card [14]. [PERSON] validated the material properties of the pelvis flesh, lower leg flesh, heel foam, and lower leg skin under the high rate [15]. Then, [PERSON] conducted high-speed impact tests to validate that the computational results of the optimized Hybrid III 50 th FEM are in good agreement with the test results of the physical model [16,17] (The correlation and analysis score ranged from 0.653 to 0.901, and the differences in peak response ranged from 1.5% to 12.7%). In terms of applications, [PERSON] [18], [PERSON] [19], and [PERSON] [20] used Hybrid III 50 th FEM to analyze the human damage under blast. [PERSON] studied the effect of shock waves on the human head-neck based on Hybrid III 50 th FEM [21]. However, the coupling situations between the occupant and the vehicle are complex and varied. Ensuring that the transmission of explosion impacts within vehicles matches actual conditions is extremely difficult in the finite element model. The proposal of the new method can simplify these analyses. In the paper, the finite element model was utilized under simplified conditions to conduct a comparative validation of the new method. Mapping the test model using one-dimensional convolutional neural networks (1D-CNNs) constitutes a major contribution of this work, and prior studies have explored similar approaches in data calibration and damage assessment. [PERSON] explored the feasibility of using 1D-CNN for vibration-based structural damage assessment. By analyzing the vibration signals, the 1D-CNN model can effectively identify structural damage, improving the accuracy and efficiency of the evaluation [22]. [PERSON] calibrated the low-cost carbon monoxide sensor using 1D-CNN. This method improves the accuracy and reliability of the sensor and demonstrates the potential of 1D-CNN in the calibration of sensor data [23]. [PERSON] proposed a CNN-based framework to calibrate parameters of nonlinear structural models using time-history data of loads and displacements [24]. This paper aims to investigate a unique testing method that is cost-effective, capable of yielding rapid results, and incorporates a simplified physical dummy model installed on-site. The paper established a simplified physical model of the head-neck structure. Then, we built the mapping test model based on 1D-CNN. Through the fusion of virtual and real testing, the damage to occupants caused by explosions is assessed. The method holds certain significance for the cost-effectiveness testing of dummies. The head-neck part of the dummy is the research content of this paper.