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

Gespeichert in:

Autor*in:	Xu, Guoji [verfasserIn] Cao, Zhiyang [verfasserIn] Wang, Jinsheng [verfasserIn] Xue, Shihao [verfasserIn] Tang, Maolin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Barge-pier collision Impact force Finite element method Machine learning Time-frequency analysis

Übergeordnetes Werk:	Enthalten in: Ocean engineering - Amsterdam [u.a.] : Elsevier Science, 1970, 285
Übergeordnetes Werk:	volume:285

DOI / URN:	10.1016/j.oceaneng.2023.115347

Katalog-ID:	ELV062557807

Internformat


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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.
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650		4	\|a Impact force
650		4	\|a Finite element method
650		4	\|a Machine learning
650		4	\|a Time-frequency analysis
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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
allfieldsSound	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
language	English
source	Enthalten in Ocean engineering 285 volume:285
sourceStr	Enthalten in Ocean engineering 285 volume:285
format_phy_str_mv	Article
bklname	Meerestechnik
institution	findex.gbv.de
topic_facet	Barge-pier collision Impact force Finite element method Machine learning Time-frequency analysis
dewey-raw	690
isfreeaccess_bool	false
container_title	Ocean engineering
authorswithroles_txt_mv	Xu, Guoji @@aut@@ Cao, Zhiyang @@aut@@ Wang, Jinsheng @@aut@@ Xue, Shihao @@aut@@ Tang, Maolin @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	30658977X
dewey-sort	3690
id	ELV062557807
language_de	englisch
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author	Xu, Guoji
spellingShingle	Xu, Guoji ddc 690 bkl 50.92 misc Barge-pier collision misc Impact force misc Finite element method misc Machine learning misc Time-frequency analysis A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions
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topic_title	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
topic	ddc 690 bkl 50.92 misc Barge-pier collision misc Impact force misc Finite element method misc Machine learning misc Time-frequency analysis
topic_unstemmed	ddc 690 bkl 50.92 misc Barge-pier collision misc Impact force misc Finite element method misc Machine learning misc Time-frequency analysis
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title	A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions
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title_full	A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions
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title_sort	a novel machine learning-based framework for predicting impact force in ship-bridge pier collisions
title_auth	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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title_short	A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions
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author2	Cao, Zhiyang Wang, Jinsheng Xue, Shihao Tang, Maolin
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doi_str	10.1016/j.oceaneng.2023.115347
up_date	2024-07-06T18:55:32.530Z
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A novel machine learning-based framework for predicting impact force in ship-bridge pier collisions

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