A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions
The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting i...
Ausführliche Beschreibung
Autor*in: |
Xu, Guoji [verfasserIn] Cao, Zhiyang [verfasserIn] Wang, Jinsheng [verfasserIn] Xue, Shihao [verfasserIn] Tang, Maolin [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Ocean engineering - Amsterdam [u.a.] : Elsevier Science, 1970, 285 |
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Übergeordnetes Werk: |
volume:285 |
DOI / URN: |
10.1016/j.oceaneng.2023.115347 |
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Katalog-ID: |
ELV062557807 |
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520 | |a The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. | ||
650 | 4 | |a Barge-pier collision | |
650 | 4 | |a Impact force | |
650 | 4 | |a Finite element method | |
650 | 4 | |a Machine learning | |
650 | 4 | |a Time-frequency analysis | |
700 | 1 | |a Cao, Zhiyang |e verfasserin |0 (orcid)0000-0002-4881-0278 |4 aut | |
700 | 1 | |a Wang, Jinsheng |e verfasserin |4 aut | |
700 | 1 | |a Xue, Shihao |e verfasserin |4 aut | |
700 | 1 | |a Tang, Maolin |e verfasserin |4 aut | |
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allfields |
10.1016/j.oceaneng.2023.115347 doi (DE-627)ELV062557807 (ELSEVIER)S0029-8018(23)01731-6 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Xu, Guoji verfasserin (orcid)0000-0001-9761-2326 aut A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. Barge-pier collision Impact force Finite element method Machine learning Time-frequency analysis Cao, Zhiyang verfasserin (orcid)0000-0002-4881-0278 aut Wang, Jinsheng verfasserin aut Xue, Shihao verfasserin aut Tang, Maolin verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 285 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:285 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 285 |
spelling |
10.1016/j.oceaneng.2023.115347 doi (DE-627)ELV062557807 (ELSEVIER)S0029-8018(23)01731-6 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Xu, Guoji verfasserin (orcid)0000-0001-9761-2326 aut A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. Barge-pier collision Impact force Finite element method Machine learning Time-frequency analysis Cao, Zhiyang verfasserin (orcid)0000-0002-4881-0278 aut Wang, Jinsheng verfasserin aut Xue, Shihao verfasserin aut Tang, Maolin verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 285 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:285 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 285 |
allfields_unstemmed |
10.1016/j.oceaneng.2023.115347 doi (DE-627)ELV062557807 (ELSEVIER)S0029-8018(23)01731-6 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Xu, Guoji verfasserin (orcid)0000-0001-9761-2326 aut A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. Barge-pier collision Impact force Finite element method Machine learning Time-frequency analysis Cao, Zhiyang verfasserin (orcid)0000-0002-4881-0278 aut Wang, Jinsheng verfasserin aut Xue, Shihao verfasserin aut Tang, Maolin verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 285 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:285 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 285 |
allfieldsGer |
10.1016/j.oceaneng.2023.115347 doi (DE-627)ELV062557807 (ELSEVIER)S0029-8018(23)01731-6 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Xu, Guoji verfasserin (orcid)0000-0001-9761-2326 aut A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. Barge-pier collision Impact force Finite element method Machine learning Time-frequency analysis Cao, Zhiyang verfasserin (orcid)0000-0002-4881-0278 aut Wang, Jinsheng verfasserin aut Xue, Shihao verfasserin aut Tang, Maolin verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 285 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:285 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 285 |
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10.1016/j.oceaneng.2023.115347 doi (DE-627)ELV062557807 (ELSEVIER)S0029-8018(23)01731-6 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Xu, Guoji verfasserin (orcid)0000-0001-9761-2326 aut A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. Barge-pier collision Impact force Finite element method Machine learning Time-frequency analysis Cao, Zhiyang verfasserin (orcid)0000-0002-4881-0278 aut Wang, Jinsheng verfasserin aut Xue, Shihao verfasserin aut Tang, Maolin verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 285 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:285 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 285 |
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690 VZ 50.92 bkl A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions Barge-pier collision Impact force Finite element method Machine learning Time-frequency analysis |
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A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions |
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a novel machine learning-based framework for predicting impact force in ship-bridge pier collisions |
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A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions |
abstract |
The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. |
abstractGer |
The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. |
abstract_unstemmed |
The escalating occurrence of ship-pier collisions poses a substantial threat to coastal bridge infrastructures. To avert such accidents, various specifications have introduced several impact force models, yet engineers strive for accurate predictions. This study presents a framework for predicting impact forces by combining a precise finite element (FE) model, machine learning algorithms, and fast Fourier transform (FFT). Firstly, a reliable FE model is constructed to simulate barge collisions with a double-column pier, encompassing analyses of energy transformation, structural damage, time-frequency impact forces, and structural response. Subsequently, a machine learning approach combined with FFT is employed to predict the impact force, with a discussion on the impact force's sensitivity to barge weight and velocity. The study also presents two potential applications of the proposed framework. Numerical results demonstrate that the framework accurately predicts the duration and frequency series of impact forces. The sensitivity analysis reveals the importance of closely monitoring barge weight in comparison to velocity during the design and management stages. Additionally, the study reveals that increasing barge velocity and weight prolongs the impact duration and amplifies the response peak, with time-series responses primarily concentrated in a limited low-frequency band. In summary, this study not only proposes a novel and accurate framework for predicting the time-history of impact forces through time-frequency analysis but also offers valuable insights into preventing catastrophic ship-pier collisions and mitigating their impact on coastal bridge infrastructure. |
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