ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering

Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, w...
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Autor*in:	Guo, Shaolong [verfasserIn] Zheng, Dongjian Zhao, Lihong Liu, Xiaoke

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Soft clay strength-weakening coefficient wave loads influence factor ANN-AdaBoost algorithm

Anmerkung:	© Indian Academy of Sciences 2023

Übergeordnetes Werk:	Enthalten in: Sādhāna - Bangalore : Acad., 1978, 48(2023), 4 vom: 01. Nov.
Übergeordnetes Werk:	volume:48 ; year:2023 ; number:4 ; day:01 ; month:11

Links:	Volltext

DOI / URN:	10.1007/s12046-023-02276-z

Katalog-ID:	SPR053601815

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520			\|a Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC.
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allfields	10.1007/s12046-023-02276-z doi (DE-627)SPR053601815 (SPR)s12046-023-02276-z-e DE-627 ger DE-627 rakwb eng Guo, Shaolong verfasserin aut ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Academy of Sciences 2023 Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC. Soft clay (dpeaa)DE-He213 strength-weakening coefficient (dpeaa)DE-He213 wave loads (dpeaa)DE-He213 influence factor (dpeaa)DE-He213 ANN-AdaBoost algorithm (dpeaa)DE-He213 Zheng, Dongjian (orcid)0000-0003-3753-4455 aut Zhao, Lihong aut Liu, Xiaoke aut Enthalten in Sādhāna Bangalore : Acad., 1978 48(2023), 4 vom: 01. Nov. (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:48 year:2023 number:4 day:01 month:11 https://dx.doi.org/10.1007/s12046-023-02276-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 4 01 11
spelling	10.1007/s12046-023-02276-z doi (DE-627)SPR053601815 (SPR)s12046-023-02276-z-e DE-627 ger DE-627 rakwb eng Guo, Shaolong verfasserin aut ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Academy of Sciences 2023 Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC. Soft clay (dpeaa)DE-He213 strength-weakening coefficient (dpeaa)DE-He213 wave loads (dpeaa)DE-He213 influence factor (dpeaa)DE-He213 ANN-AdaBoost algorithm (dpeaa)DE-He213 Zheng, Dongjian (orcid)0000-0003-3753-4455 aut Zhao, Lihong aut Liu, Xiaoke aut Enthalten in Sādhāna Bangalore : Acad., 1978 48(2023), 4 vom: 01. Nov. (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:48 year:2023 number:4 day:01 month:11 https://dx.doi.org/10.1007/s12046-023-02276-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 4 01 11
allfields_unstemmed	10.1007/s12046-023-02276-z doi (DE-627)SPR053601815 (SPR)s12046-023-02276-z-e DE-627 ger DE-627 rakwb eng Guo, Shaolong verfasserin aut ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Academy of Sciences 2023 Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC. Soft clay (dpeaa)DE-He213 strength-weakening coefficient (dpeaa)DE-He213 wave loads (dpeaa)DE-He213 influence factor (dpeaa)DE-He213 ANN-AdaBoost algorithm (dpeaa)DE-He213 Zheng, Dongjian (orcid)0000-0003-3753-4455 aut Zhao, Lihong aut Liu, Xiaoke aut Enthalten in Sādhāna Bangalore : Acad., 1978 48(2023), 4 vom: 01. Nov. (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:48 year:2023 number:4 day:01 month:11 https://dx.doi.org/10.1007/s12046-023-02276-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 4 01 11
allfieldsGer	10.1007/s12046-023-02276-z doi (DE-627)SPR053601815 (SPR)s12046-023-02276-z-e DE-627 ger DE-627 rakwb eng Guo, Shaolong verfasserin aut ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Academy of Sciences 2023 Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC. Soft clay (dpeaa)DE-He213 strength-weakening coefficient (dpeaa)DE-He213 wave loads (dpeaa)DE-He213 influence factor (dpeaa)DE-He213 ANN-AdaBoost algorithm (dpeaa)DE-He213 Zheng, Dongjian (orcid)0000-0003-3753-4455 aut Zhao, Lihong aut Liu, Xiaoke aut Enthalten in Sādhāna Bangalore : Acad., 1978 48(2023), 4 vom: 01. Nov. (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:48 year:2023 number:4 day:01 month:11 https://dx.doi.org/10.1007/s12046-023-02276-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 4 01 11
allfieldsSound	10.1007/s12046-023-02276-z doi (DE-627)SPR053601815 (SPR)s12046-023-02276-z-e DE-627 ger DE-627 rakwb eng Guo, Shaolong verfasserin aut ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Academy of Sciences 2023 Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC. Soft clay (dpeaa)DE-He213 strength-weakening coefficient (dpeaa)DE-He213 wave loads (dpeaa)DE-He213 influence factor (dpeaa)DE-He213 ANN-AdaBoost algorithm (dpeaa)DE-He213 Zheng, Dongjian (orcid)0000-0003-3753-4455 aut Zhao, Lihong aut Liu, Xiaoke aut Enthalten in Sādhāna Bangalore : Acad., 1978 48(2023), 4 vom: 01. Nov. (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:48 year:2023 number:4 day:01 month:11 https://dx.doi.org/10.1007/s12046-023-02276-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 4 01 11
language	English
source	Enthalten in Sādhāna 48(2023), 4 vom: 01. Nov. volume:48 year:2023 number:4 day:01 month:11
sourceStr	Enthalten in Sādhāna 48(2023), 4 vom: 01. Nov. volume:48 year:2023 number:4 day:01 month:11
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institution	findex.gbv.de
topic_facet	Soft clay strength-weakening coefficient wave loads influence factor ANN-AdaBoost algorithm
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container_title	Sādhāna
authorswithroles_txt_mv	Guo, Shaolong @@aut@@ Zheng, Dongjian @@aut@@ Zhao, Lihong @@aut@@ Liu, Xiaoke @@aut@@
publishDateDaySort_date	2023-11-01T00:00:00Z
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author	Guo, Shaolong
spellingShingle	Guo, Shaolong misc Soft clay misc strength-weakening coefficient misc wave loads misc influence factor misc ANN-AdaBoost algorithm ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering
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topic_title	ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering Soft clay (dpeaa)DE-He213 strength-weakening coefficient (dpeaa)DE-He213 wave loads (dpeaa)DE-He213 influence factor (dpeaa)DE-He213 ANN-AdaBoost algorithm (dpeaa)DE-He213
topic	misc Soft clay misc strength-weakening coefficient misc wave loads misc influence factor misc ANN-AdaBoost algorithm
topic_unstemmed	misc Soft clay misc strength-weakening coefficient misc wave loads misc influence factor misc ANN-AdaBoost algorithm
topic_browse	misc Soft clay misc strength-weakening coefficient misc wave loads misc influence factor misc ANN-AdaBoost algorithm
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title	ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering
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title_full	ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering
author_sort	Guo, Shaolong
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author_browse	Guo, Shaolong Zheng, Dongjian Zhao, Lihong Liu, Xiaoke
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title_sort	ann-adaboost model for the strength-weakening coefficient of soft clay in port engineering
title_auth	ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering
abstract	Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC. © Indian Academy of Sciences 2023
abstractGer	Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC. © Indian Academy of Sciences 2023
abstract_unstemmed	Abstract The strength of soft clay tends to weaken under wave loads, which affects the safe operation of buildings with soft clay foundations in port engineering, and its calculation theory is not well developed. Empirical models need a large amount of onsite test data to fit the model parameters, which is time and labor consuming, and its universality is often poor. In our study, an ANN (artificial neural network) -AdaBoost model was developed to determine the strength-weakening coefficient of soft clay (SWCoSC), based on multifield test data from different port engineering projects. The main influencing factors of soft clay strength-weakening under wave loads were analysed first. Then the field test data from different engineering were selected as training samples for learning. Combined with the AdaBoost algorithm, an ANN model for SWCoSC prediction was established using a small amount of test data of similar properties or onsite soft clay. The SWCoSC had also been predicted using other artificial intelligence algorithms (XGBoost, Bagging, Random Forest, and DenseNet). The cyclic dynamic stress level has a significant impact on the SWCoSC. The case analysis showed that the ANN-AdaBoost model was feasible and effective in predicting the SWCoSC. © Indian Academy of Sciences 2023
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container_issue	4
title_short	ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering
url	https://dx.doi.org/10.1007/s12046-023-02276-z
remote_bool	true
author2	Zheng, Dongjian Zhao, Lihong Liu, Xiaoke
author2Str	Zheng, Dongjian Zhao, Lihong Liu, Xiaoke
ppnlink	359574963
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hochschulschrift_bool	false
doi_str	10.1007/s12046-023-02276-z
up_date	2024-07-03T20:42:45.455Z
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ANN-AdaBoost model for the strength-weakening coefficient of soft clay in port engineering

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