Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks
The deterioration of bridges as a result of ageing is a serious problem in many countries. To prevent the failure of these deficient bridges, early damage detection which helps us to evaluate the safety of bridges is important. Therefore, the present research proposed a method to quantify damage sev...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/789384 |
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| _version_ | 1850175559045218304 |
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| author | Pang-jo Chun Hiroaki Yamashita Seiji Furukawa |
| author_facet | Pang-jo Chun Hiroaki Yamashita Seiji Furukawa |
| author_sort | Pang-jo Chun |
| collection | DOAJ |
| description | The deterioration of bridges as a result of ageing is a serious problem in many countries. To prevent the failure of these deficient bridges, early damage detection which helps us to evaluate the safety of bridges is important. Therefore, the present research proposed a method to quantify damage severity by use of multipoint acceleration measurement and artificial neural networks. In addition to developing the method, we developed a cheap and easy-to-make measurement device which can be made by bridge owners at low cost and without the need for advance technical skills since the method is mainly intended to apply to small to midsized bridges. In addition, the paper gives an example application of the method to a weathering steel bridge in Japan. It can be shown from the analysis results that the method is accurate in its damage identification and mechanical behavior prediction ability. |
| format | Article |
| id | doaj-art-c047d2385bc144cb8cc31bd691792cb3 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-c047d2385bc144cb8cc31bd691792cb32025-08-20T02:19:26ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/789384789384Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural NetworksPang-jo Chun0Hiroaki Yamashita1Seiji Furukawa2Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Ehime 790-8577, JapanWest Nippon Expressway Engineering Shikoku Company Ltd., Takamatsu, Kagawa 760-0072, JapanWest Nippon Expressway Engineering Shikoku Company Ltd., Takamatsu, Kagawa 760-0072, JapanThe deterioration of bridges as a result of ageing is a serious problem in many countries. To prevent the failure of these deficient bridges, early damage detection which helps us to evaluate the safety of bridges is important. Therefore, the present research proposed a method to quantify damage severity by use of multipoint acceleration measurement and artificial neural networks. In addition to developing the method, we developed a cheap and easy-to-make measurement device which can be made by bridge owners at low cost and without the need for advance technical skills since the method is mainly intended to apply to small to midsized bridges. In addition, the paper gives an example application of the method to a weathering steel bridge in Japan. It can be shown from the analysis results that the method is accurate in its damage identification and mechanical behavior prediction ability.http://dx.doi.org/10.1155/2015/789384 |
| spellingShingle | Pang-jo Chun Hiroaki Yamashita Seiji Furukawa Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks Shock and Vibration |
| title | Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks |
| title_full | Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks |
| title_fullStr | Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks |
| title_full_unstemmed | Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks |
| title_short | Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks |
| title_sort | bridge damage severity quantification using multipoint acceleration measurement and artificial neural networks |
| url | http://dx.doi.org/10.1155/2015/789384 |
| work_keys_str_mv | AT pangjochun bridgedamageseverityquantificationusingmultipointaccelerationmeasurementandartificialneuralnetworks AT hiroakiyamashita bridgedamageseverityquantificationusingmultipointaccelerationmeasurementandartificialneuralnetworks AT seijifurukawa bridgedamageseverityquantificationusingmultipointaccelerationmeasurementandartificialneuralnetworks |