A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions
A finite element (FE)–guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body’s response to impacts. FE modeling is ofte...
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| Veröffentlicht in: | Medical & biological engineering & computing 2021-05, Vol.59 (5), p.1065-1079 |
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| creator | Berthelson, P. R. Ghassemi, P. Wood, J. W. Stubblefield, G. G. Al-Graitti, A. J. Jones, M. D. Horstemeyer, M. F. Chowdhury, S. Prabhu, R. K. |
| description | A finite element (FE)–guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body’s response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC
15
) trends were predicted from Kriging models across varied impact velocities (10–45 mph; 16.1–72.4 km/h), locations (near side, far side, front, and rear), and angles (−45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC
15
increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC
15
under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously.
Graphical abstract
This study presents a finite element-guided mathematical surrogate modeling methodology to evaluate occupant injury response trends for a wide range of impact velocities (10–45 mph), locations, and angles (−45 to 45°). Head injury response trends were predicted and compared to previously published data to assess the efficacy of the methodology for assessing occupant response to variations in impact conditions. Velocity and location were the most influential factors on the head injury response, with the risk increasing alongside greater impact velocity and locational proximity to the driver. Additionally, the angle of impact variable was dependent on the location and, thus, had minimal independent influence on the head injury risk. |
| doi_str_mv | 10.1007/s11517-021-02349-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_pubmed_primary_33881704</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2516222041</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-886fe141729b62ba872df84d2c496b65e61409b062b035a78c980a8e1ef31a783</originalsourceid><addsrcrecordid>eNp9kc1u1DAQxy0EotvCC3BAlrhwCfVXEudYVQUqVeJSzpbjTHa9Suxgx5V64x14hL4ZT8J0t4DEAVm2NTO_mfH4T8gbzj5wxtrzzHnN24oJjluqrpLPyIa3Ck2l1HOyYVyxinGuT8hpznuGZC3US3Iipda8ZWpDHi7o6INfgcIEM4T15_cf2-IHGOhs1x3g4Z2daC4pxa1Fbo4DTD5sqV2WFK3b0TEmanOGnB_d0bmy2LBSH_Yl3dM1QRgytS7FnOmdTR5LxpAxTrOfl8mPHrvdwc67MtlE0WfdSl0Mgz-Qr8iL0U4ZXj_dZ-Trx6vby8_VzZdP15cXN5WTbb1WWjcjcMVb0fWN6K1uxTBqNQinuqZvamjwO7qeYYzJ2rbadZpZDRxGydGUZ-T9sS7O9a1AXs3ss4NpsgFiyUbUvBFCMMURffcPuo8lBXwdUriUYlIgJY7UYfYEo1mSn226N5yZRwnNUUKDwpiDhEZi0tun0qWfYfiT8lszBOQRyBgKW0h_e_-n7C-DYqsl</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2525244032</pqid></control><display><type>article</type><title>A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions</title><source>Springer Nature - Complete Springer Journals</source><source>Business Source Complete</source><creator>Berthelson, P. R. ; Ghassemi, P. ; Wood, J. W. ; Stubblefield, G. G. ; Al-Graitti, A. J. ; Jones, M. D. ; Horstemeyer, M. F. ; Chowdhury, S. ; Prabhu, R. K.</creator><creatorcontrib>Berthelson, P. R. ; Ghassemi, P. ; Wood, J. W. ; Stubblefield, G. G. ; Al-Graitti, A. J. ; Jones, M. D. ; Horstemeyer, M. F. ; Chowdhury, S. ; Prabhu, R. K.</creatorcontrib><description>A finite element (FE)–guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body’s response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC
15
) trends were predicted from Kriging models across varied impact velocities (10–45 mph; 16.1–72.4 km/h), locations (near side, far side, front, and rear), and angles (−45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC
15
increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC
15
under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously.
Graphical abstract
This study presents a finite element-guided mathematical surrogate modeling methodology to evaluate occupant injury response trends for a wide range of impact velocities (10–45 mph), locations, and angles (−45 to 45°). Head injury response trends were predicted and compared to previously published data to assess the efficacy of the methodology for assessing occupant response to variations in impact conditions. Velocity and location were the most influential factors on the head injury response, with the risk increasing alongside greater impact velocity and locational proximity to the driver. Additionally, the angle of impact variable was dependent on the location and, thus, had minimal independent influence on the head injury risk.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-021-02349-3</identifier><identifier>PMID: 33881704</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Computer Applications ; Cost analysis ; Evaluation ; Finite element method ; Head injuries ; Human Physiology ; Human response ; Imaging ; Impact analysis ; Impact velocity ; Mathematical analysis ; Mathematical models ; Methodology ; Occupant injuries ; Original Article ; Radiology ; Traffic accidents ; Trends ; Velocity</subject><ispartof>Medical & biological engineering & computing, 2021-05, Vol.59 (5), p.1065-1079</ispartof><rights>International Federation for Medical and Biological Engineering 2021</rights><rights>International Federation for Medical and Biological Engineering 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-886fe141729b62ba872df84d2c496b65e61409b062b035a78c980a8e1ef31a783</citedby><cites>FETCH-LOGICAL-c375t-886fe141729b62ba872df84d2c496b65e61409b062b035a78c980a8e1ef31a783</cites><orcidid>0000-0002-6073-4802</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-021-02349-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-021-02349-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33881704$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Berthelson, P. R.</creatorcontrib><creatorcontrib>Ghassemi, P.</creatorcontrib><creatorcontrib>Wood, J. W.</creatorcontrib><creatorcontrib>Stubblefield, G. G.</creatorcontrib><creatorcontrib>Al-Graitti, A. J.</creatorcontrib><creatorcontrib>Jones, M. D.</creatorcontrib><creatorcontrib>Horstemeyer, M. F.</creatorcontrib><creatorcontrib>Chowdhury, S.</creatorcontrib><creatorcontrib>Prabhu, R. K.</creatorcontrib><title>A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>A finite element (FE)–guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body’s response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC
15
) trends were predicted from Kriging models across varied impact velocities (10–45 mph; 16.1–72.4 km/h), locations (near side, far side, front, and rear), and angles (−45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC
15
increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC
15
under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously.
Graphical abstract
This study presents a finite element-guided mathematical surrogate modeling methodology to evaluate occupant injury response trends for a wide range of impact velocities (10–45 mph), locations, and angles (−45 to 45°). Head injury response trends were predicted and compared to previously published data to assess the efficacy of the methodology for assessing occupant response to variations in impact conditions. Velocity and location were the most influential factors on the head injury response, with the risk increasing alongside greater impact velocity and locational proximity to the driver. Additionally, the angle of impact variable was dependent on the location and, thus, had minimal independent influence on the head injury risk.</description><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Computer Applications</subject><subject>Cost analysis</subject><subject>Evaluation</subject><subject>Finite element method</subject><subject>Head injuries</subject><subject>Human Physiology</subject><subject>Human response</subject><subject>Imaging</subject><subject>Impact analysis</subject><subject>Impact velocity</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Methodology</subject><subject>Occupant injuries</subject><subject>Original Article</subject><subject>Radiology</subject><subject>Traffic accidents</subject><subject>Trends</subject><subject>Velocity</subject><issn>0140-0118</issn><issn>1741-0444</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kc1u1DAQxy0EotvCC3BAlrhwCfVXEudYVQUqVeJSzpbjTHa9Suxgx5V64x14hL4ZT8J0t4DEAVm2NTO_mfH4T8gbzj5wxtrzzHnN24oJjluqrpLPyIa3Ck2l1HOyYVyxinGuT8hpznuGZC3US3Iipda8ZWpDHi7o6INfgcIEM4T15_cf2-IHGOhs1x3g4Z2daC4pxa1Fbo4DTD5sqV2WFK3b0TEmanOGnB_d0bmy2LBSH_Yl3dM1QRgytS7FnOmdTR5LxpAxTrOfl8mPHrvdwc67MtlE0WfdSl0Mgz-Qr8iL0U4ZXj_dZ-Trx6vby8_VzZdP15cXN5WTbb1WWjcjcMVb0fWN6K1uxTBqNQinuqZvamjwO7qeYYzJ2rbadZpZDRxGydGUZ-T9sS7O9a1AXs3ss4NpsgFiyUbUvBFCMMURffcPuo8lBXwdUriUYlIgJY7UYfYEo1mSn226N5yZRwnNUUKDwpiDhEZi0tun0qWfYfiT8lszBOQRyBgKW0h_e_-n7C-DYqsl</recordid><startdate>20210501</startdate><enddate>20210501</enddate><creator>Berthelson, P. 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R. ; Ghassemi, P. ; Wood, J. W. ; Stubblefield, G. G. ; Al-Graitti, A. J. ; Jones, M. D. ; Horstemeyer, M. F. ; Chowdhury, S. ; Prabhu, R. 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R.</au><au>Ghassemi, P.</au><au>Wood, J. W.</au><au>Stubblefield, G. G.</au><au>Al-Graitti, A. J.</au><au>Jones, M. D.</au><au>Horstemeyer, M. F.</au><au>Chowdhury, S.</au><au>Prabhu, R. K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions</atitle><jtitle>Medical & biological engineering & computing</jtitle><stitle>Med Biol Eng Comput</stitle><addtitle>Med Biol Eng Comput</addtitle><date>2021-05-01</date><risdate>2021</risdate><volume>59</volume><issue>5</issue><spage>1065</spage><epage>1079</epage><pages>1065-1079</pages><issn>0140-0118</issn><eissn>1741-0444</eissn><abstract>A finite element (FE)–guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body’s response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC
15
) trends were predicted from Kriging models across varied impact velocities (10–45 mph; 16.1–72.4 km/h), locations (near side, far side, front, and rear), and angles (−45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC
15
increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC
15
under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously.
Graphical abstract
This study presents a finite element-guided mathematical surrogate modeling methodology to evaluate occupant injury response trends for a wide range of impact velocities (10–45 mph), locations, and angles (−45 to 45°). Head injury response trends were predicted and compared to previously published data to assess the efficacy of the methodology for assessing occupant response to variations in impact conditions. Velocity and location were the most influential factors on the head injury response, with the risk increasing alongside greater impact velocity and locational proximity to the driver. Additionally, the angle of impact variable was dependent on the location and, thus, had minimal independent influence on the head injury risk.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>33881704</pmid><doi>10.1007/s11517-021-02349-3</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-6073-4802</orcidid></addata></record> |
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| ispartof | Medical & biological engineering & computing, 2021-05, Vol.59 (5), p.1065-1079 |
| issn | 0140-0118 1741-0444 |
| language | eng |
| recordid | cdi_pubmed_primary_33881704 |
| source | Springer Nature - Complete Springer Journals; Business Source Complete |
| subjects | Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Computer Applications Cost analysis Evaluation Finite element method Head injuries Human Physiology Human response Imaging Impact analysis Impact velocity Mathematical analysis Mathematical models Methodology Occupant injuries Original Article Radiology Traffic accidents Trends Velocity |
| title | A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions |
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