Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural Networks
This study aims to develop a deep neural network model capable of predicting the Head Injury Criterion (HIC), which is traditionally obtained through time-consuming and costly headform impact tests using dynamic stiffness measurements. The correlation between dynamic stiffness and HIC was initially...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11034971/ |
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| author | Seon-Hong Kim Seounghyun Lee Taewung Kim Je-Heon Han |
| author_facet | Seon-Hong Kim Seounghyun Lee Taewung Kim Je-Heon Han |
| author_sort | Seon-Hong Kim |
| collection | DOAJ |
| description | This study aims to develop a deep neural network model capable of predicting the Head Injury Criterion (HIC), which is traditionally obtained through time-consuming and costly headform impact tests using dynamic stiffness measurements. The correlation between dynamic stiffness and HIC was initially analyzed in a simple steel plate to explore feasibility. Subsequently, this correlation was evaluated using the hoods of two actual vehicles. The results revealed significant differences compared to the steel plate analysis, primarily due to the influence of contact with the lower components beneath the hood. To overcome these physical differences, a deep neural network model incorporating various features was designed to improve HIC prediction accuracy. Additionally, an algorithm was developed to estimate the penetration of the hood into the lower components, and this feature was included as an input to enhance the performance of the model. This study demonstrates the potential of the deep neural network model as an auxiliary tool for predicting changes in HIC resulting from modifications to the hood design. |
| format | Article |
| id | doaj-art-81a9a2f20ba44caba02afc880e2f785b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-81a9a2f20ba44caba02afc880e2f785b2025-08-20T02:07:59ZengIEEEIEEE Access2169-35362025-01-011310384810386510.1109/ACCESS.2025.357936911034971Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural NetworksSeon-Hong Kim0Seounghyun Lee1Taewung Kim2Je-Heon Han3https://orcid.org/0000-0002-9930-0083Department of Mechanical Engineering, Tech University of Korea, Siheung-si, Geonggi-do, South KoreaHyundai-Motor Company, Hwaseong-si, Gyeonggi-do, Republic of KoreaDepartment of Mechanical Design Engineering, Tech University of Korea, Siheung-si, Geonggi-do, South KoreaDepartment of Mechanical Design Engineering, Tech University of Korea, Siheung-si, Geonggi-do, South KoreaThis study aims to develop a deep neural network model capable of predicting the Head Injury Criterion (HIC), which is traditionally obtained through time-consuming and costly headform impact tests using dynamic stiffness measurements. The correlation between dynamic stiffness and HIC was initially analyzed in a simple steel plate to explore feasibility. Subsequently, this correlation was evaluated using the hoods of two actual vehicles. The results revealed significant differences compared to the steel plate analysis, primarily due to the influence of contact with the lower components beneath the hood. To overcome these physical differences, a deep neural network model incorporating various features was designed to improve HIC prediction accuracy. Additionally, an algorithm was developed to estimate the penetration of the hood into the lower components, and this feature was included as an input to enhance the performance of the model. This study demonstrates the potential of the deep neural network model as an auxiliary tool for predicting changes in HIC resulting from modifications to the hood design.https://ieeexplore.ieee.org/document/11034971/Deep neural networksdynamic stiffnessheadform impact testhead injury criteria |
| spellingShingle | Seon-Hong Kim Seounghyun Lee Taewung Kim Je-Heon Han Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural Networks IEEE Access Deep neural networks dynamic stiffness headform impact test head injury criteria |
| title | Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural Networks |
| title_full | Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural Networks |
| title_fullStr | Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural Networks |
| title_full_unstemmed | Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural Networks |
| title_short | Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural Networks |
| title_sort | prediction of head injury criteria in pedestrian crashes using frequency response function based deep neural networks |
| topic | Deep neural networks dynamic stiffness headform impact test head injury criteria |
| url | https://ieeexplore.ieee.org/document/11034971/ |
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