Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures
The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desire...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
de Souza, Calebe Paiva Gomes [verfasserIn] Kurka, Paulo Roberto Gardel [verfasserIn] Lins, Romulo Gonçalves [verfasserIn] de Araújo, José Medeiros [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 104 |
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| Übergeordnetes Werk: |
volume:104 |
| DOI / URN: |
10.1016/j.asoc.2021.107254 |
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ELV005856825 |
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| 245 | 1 | 0 | |a Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures |
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| 520 | |a The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. | ||
| 650 | 4 | |a Civil structures | |
| 650 | 4 | |a Damage diagnosis | |
| 650 | 4 | |a Real-time monitoring | |
| 650 | 4 | |a Artificial neural network | |
| 650 | 4 | |a Non-adaptive and adaptive algorithms | |
| 700 | 1 | |a Kurka, Paulo Roberto Gardel |e verfasserin |4 aut | |
| 700 | 1 | |a Lins, Romulo Gonçalves |e verfasserin |4 aut | |
| 700 | 1 | |a de Araújo, José Medeiros |e verfasserin |4 aut | |
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10.1016/j.asoc.2021.107254 doi (DE-627)ELV005856825 (ELSEVIER)S1568-4946(21)00177-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl de Souza, Calebe Paiva Gomes verfasserin aut Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. Civil structures Damage diagnosis Real-time monitoring Artificial neural network Non-adaptive and adaptive algorithms Kurka, Paulo Roberto Gardel verfasserin aut Lins, Romulo Gonçalves verfasserin aut de Araújo, José Medeiros verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 104 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 104 |
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10.1016/j.asoc.2021.107254 doi (DE-627)ELV005856825 (ELSEVIER)S1568-4946(21)00177-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl de Souza, Calebe Paiva Gomes verfasserin aut Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. Civil structures Damage diagnosis Real-time monitoring Artificial neural network Non-adaptive and adaptive algorithms Kurka, Paulo Roberto Gardel verfasserin aut Lins, Romulo Gonçalves verfasserin aut de Araújo, José Medeiros verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 104 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 104 |
| allfields_unstemmed |
10.1016/j.asoc.2021.107254 doi (DE-627)ELV005856825 (ELSEVIER)S1568-4946(21)00177-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl de Souza, Calebe Paiva Gomes verfasserin aut Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. Civil structures Damage diagnosis Real-time monitoring Artificial neural network Non-adaptive and adaptive algorithms Kurka, Paulo Roberto Gardel verfasserin aut Lins, Romulo Gonçalves verfasserin aut de Araújo, José Medeiros verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 104 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 104 |
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10.1016/j.asoc.2021.107254 doi (DE-627)ELV005856825 (ELSEVIER)S1568-4946(21)00177-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl de Souza, Calebe Paiva Gomes verfasserin aut Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. Civil structures Damage diagnosis Real-time monitoring Artificial neural network Non-adaptive and adaptive algorithms Kurka, Paulo Roberto Gardel verfasserin aut Lins, Romulo Gonçalves verfasserin aut de Araújo, José Medeiros verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 104 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 104 |
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10.1016/j.asoc.2021.107254 doi (DE-627)ELV005856825 (ELSEVIER)S1568-4946(21)00177-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl de Souza, Calebe Paiva Gomes verfasserin aut Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. Civil structures Damage diagnosis Real-time monitoring Artificial neural network Non-adaptive and adaptive algorithms Kurka, Paulo Roberto Gardel verfasserin aut Lins, Romulo Gonçalves verfasserin aut de Araújo, José Medeiros verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 104 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:104 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 104 |
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de Souza, Calebe Paiva Gomes @@aut@@ Kurka, Paulo Roberto Gardel @@aut@@ Lins, Romulo Gonçalves @@aut@@ de Araújo, José Medeiros @@aut@@ |
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004 DE-600 54.00 bkl Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures Civil structures Damage diagnosis Real-time monitoring Artificial neural network Non-adaptive and adaptive algorithms |
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ddc 004 bkl 54.00 misc Civil structures misc Damage diagnosis misc Real-time monitoring misc Artificial neural network misc Non-adaptive and adaptive algorithms |
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performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures |
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Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures |
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The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. |
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The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. |
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The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time. |
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| score |
7.3985004 |