機械学習によるコンクリート構造物の打音検査手法の定量化に関する研究

• 資料種別: 博士論文
• タイトル: 機械学習によるコンクリート構造物の打音検査手法の定量化に関する研究
• 著者・編者: 新保, 弘
• 著者標目: 新保, 弘
• 出版年月日等: 2023-03-24
• 出版年（W3CDTF）: 2023-03-24
• 並列タイトル等: Study on Quantification of Hammering Test Method for Concrete Structures by Machine Learning
• 授与機関名: 法政大学 (Hosei University)
• 授与年月日: 2023-03-24
• 授与年月日（W3CDTF）: 2023-03-24
• 報告番号: 甲第577号
• 学位: 博士(工学)
• 本文の言語コード: jpn
• 件名標目: コンクリート; 打音検査; 機械学習
• 対象利用者: 一般
• 一般注記: type:Thesis; Concrete structures constructed during Japan's high-growth period are now more than 50 years old, and the damages of aging deterioration are beginning to become apparent. The rationalization and automation of their maintenance are strongly needed, given the declining working population due to the falling birthrate and aging population, and the large socioeconomic burden that is expected to be placed on maintenance and management. On the other hand, recent years have seen the development of technologies related to machine learning and artificial intelligence, along with the improvement of computer capabilities and the rapid progress of information networks. Considering these circumstances, this study empirically examines the possibility of introducing machine learning technology into the quantification of hammering test method to rationalize and improve the accuracy of structural diagnosis technology.In this paper, I first conducted hammering tests on small models subjected to artificial defects and on a real-scale specimen subjected to internal cracks caused by electrical corrosion on the steel bars. It was confirmed that the classification accuracy by machine learning of CNN (Convolutional Neural Network) with the imaged hammering sound waveforms was equivalent to that of a skilled technician. Furthermore, we confirmed the applicability of this method to the field by appropriately classifying the deterioration state in an actual structure. I proposed a quantitative evaluation method of defects at an arbitrary site based on imaged sound waveform using a trained CNN as a feature extractor. The generalization performance of the proposed method was examined through cross-validation using hammering sound data from three different sites. As a result, it was shown that the proposed method has the potential to achieve generalization performance that can quantitatively evaluate the integrity and defects of concrete structures at arbitrary sites.
• DOI: 10.15002/00026668; https://doi.org/10.15002/00026668
• 国立国会図書館永続的識別子: info:ndljp/pid/12911513; https://dl.ndl.go.jp/pid/12911513
• コレクション（共通）: 障害者向け資料
• コレクション（障害者向け資料：レベル1）: テキストデータ
• コレクション（個別）: 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• 収集根拠: 博士論文（自動収集）
• 受理日（W3CDTF）: 2023-07-08T03:42:31+09:00
• 記録形式（IMT）: application/pdf
• オンライン閲覧公開範囲: 国立国会図書館内限定公開
• デジタル化資料送信: 図書館・個人送信対象外
• 遠隔複写可否（NDL）: 可
• 掲載誌（URI）: https://doi.org/10.15002/00026668; http://hdl.handle.net/10114/00026668
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

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2024-02-02 再収集

2024-02-02 再収集