After an earthquake, quickly identifying damage on bridges is crucial to early recoveries. However, current method of structural health monitoring on bridges rely on cameras, cable networks, and manpower. These methods require high costs and also lead to slower recovery effort. In order to address these issues, a health monitoring model of a bridge bearing and a pier using neural networks and accelerometers were proposed. Such a model ensures real time evaluation of bearing and pier damage while using only the acceleration responses of a bridge. In order to evaluate the bearing damage, a neural network was proposed with acceleration responses of girder and pier top being the input while the output being bearing displacement. In order to evaluate the pier damage, first a neural network was proposed with acceleration responses of footing, girder and pier top being the input while the output being footing displacement. Then a neural network was proposed with displacement responses of footing, pier top and acceleration responses of girder being the input while the output being pier curvature. Several different combinations of seismic motions were considered as the learning data set. The results demonstrated that the neural network estimated the bearing displacement and the pier curvature with high precision.
"},{"@language":"ja","@value":"地震時に道路橋の損傷を瞬時に判定し，迅速な道路網の復旧を実現することを目的に，本研究ではニューラルネットワークを用い，橋梁で観測された加速度応答から支承および橋脚における損傷度の判定を試みた．まず，観測された加速度応答から免震支承の変位応答をニューラルネットワークで再現できることを確認した．また，ニューラルネットワークと完全積分を併用して加速度応答から橋脚基部の曲率応答を再現できることも確認した．最後に模擬地震動を用い，免震支承の変位応答および橋脚基部の曲率応答を基に損傷程度を判定する本手法の精度を検証した．
"}],"abstractLicenseFlag":"disallow"}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1410856518026927490","@type":"Researcher","foaf:name":[{"@language":"en","@value":"MAZDA Taiji"},{"@language":"ja","@value":"松田 泰治"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"九州大学大学院 工学研究院 社会基盤部門"}]},{"@id":"https://cir.nii.ac.jp/crid/1410856518026927489","@type":"Researcher","foaf:name":[{"@language":"en","@value":"MIYATAKE Shuya"},{"@language":"ja","@value":"宮武 修也"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"九州大学大学院 工学府 建設システム工学専攻"}]},{"@id":"https://cir.nii.ac.jp/crid/1410856518026927488","@type":"Researcher","foaf:name":[{"@language":"en","@value":"KAJITA Yukihide"},{"@language":"ja","@value":"梶田 幸秀"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"九州大学大学院 工学研究院 社会基盤部門"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"21854653"}],"prism:publicationName":[{"@language":"en","@value":"Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering & Earthquake Engineering (SE/EE))"},{"@language":"ja","@value":"土木学会論文集A1（構造・地震工学）"},{"@language":"en","@value":"JJSCE A1"},{"@language":"en","@value":"J. JSCE"},{"@language":"en","@value":"J. JSCE Ser. A1"},{"@language":"en","@value":"J. JSCE, Ser. A1"},{"@language":"en","@value":"A1"},{"@language":"en","@value":"Ser. A1"},{"@language":"ja","@value":"土木学会論文集Ａ１"},{"@language":"ja","@value":"土論Ａ１"},{"@language":"ja","@value":"地震工学論文集"},{"@language":"ja","@value":"土木学会論文集Ａ１（構造・地震工学）"}],"dc:publisher":[{"@language":"en","@value":"Japan Society of Civil Engineers"},{"@language":"ja","@value":"公益社団法人 土木学会"}],"prism:publicationDate":"2022","prism:volume":"78","prism:number":"4","prism:startingPage":"I_344","prism:endingPage":"I_353"},"reviewed":"false","url":[{"@id":"https://www.jstage.jst.go.jp/article/jscejseee/78/4/78_I_344/_pdf"}],"availableAt":"2022","foaf:topic":[{"@id":"https://cir.nii.ac.jp/all?q=neural%20network","dc:title":"neural network"},{"@id":"https://cir.nii.ac.jp/all?q=structural%20health%20monitoring","dc:title":"structural health monitoring"},{"@id":"https://cir.nii.ac.jp/all?q=acceleration%20response","dc:title":"acceleration response"},{"@id":"https://cir.nii.ac.jp/all?q=bearing","dc:title":"bearing"},{"@id":"https://cir.nii.ac.jp/all?q=pier","dc:title":"pier"}],"relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360016867707068160","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"A neural network approach for structural identification and diagnosis of a building from seismic response data"}]},{"@id":"https://cir.nii.ac.jp/crid/1390001205169767936","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"ESTIMATION OF EARTHQUAKE RESPONSE OF A BUILDING USING AN ACCELEROMETER"},{"@language":"ja","@value":"１台の加速度センサのみを用いた建築構造物の振動応答推定手法"},{"@language":"ja-Kana","@value":"1ダイ ノ カソクド センサ ノミ オ モチイタ ケンチク コウゾウブツ ノ シンドウ オウトウ スイテイ シュホウ"}]},{"@id":"https://cir.nii.ac.jp/crid/1390001205358564608","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"ja","@value":"ニューラルネットワークを用いた橋脚の地震時リアルタイム損傷判定法に関する検討"},{"@language":"en","@value":"Real-time evaluation of bridge pier damages due to earthquake by using Neural Networks technique"}]},{"@id":"https://cir.nii.ac.jp/crid/1390001205669053056","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"ja","@value":"限られた階の加速度記録のみに基づく3次スプライン補間による建物全層の応答推定"},{"@language":"en","@value":"CUBIC SPLINE INTERPOLATION BASED ESTIMATION OF ALL STORY SEISMIC RESPONSES WITH ACCELERATION MEASUREMENT AT A LIMITED NUMBER OF FLOORS"},{"@language":"ja-Kana","@value":"カギラレタ カイ ノ カソクド キロク ノミ ニ モトズク 3ジ スプライン ホカン ニ ヨル タテモノ ゼン ソウ ノ オウトウ スイテイ"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282680326063488","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"A PROPOSAL OF MEASUREMENT AND ANALYSIS METHOD FOR DETECTION OF PLASTIC RESPONSE OF STRUCTURES USING SENSOR NETWORK"},{"@value":"無線センサネットワークによる構造物塑性化検知のための計測・解析手法の提案"}]}],"dataSourceIdentifier":[{"@type":"JALC","@value":"oai:japanlinkcenter.org:2009893958"},{"@type":"CROSSREF","@value":"10.2208/jscejseee.78.4_i_344"}]}