人工ニューラルネットワークによるコンクリート中の空隙調査に関する検討

カテゴリ: 論文誌(論文単位)

グループ名: 【A】基礎・材料・共通部門

発行日: 2024/07/01

タイトル(英語): Estimating Location and Thickness of a Void in a Concrete Slab using Artificial Neural Network

著者名: 上水流　直紀（福岡大学），圓谷　友紀（福岡大学），孟　志奇（福岡大学）

著者名(英語): Naoki Kamizuru (Fukuoka University), Tomonori Tsuburaya (Fukuoka University), Zhiqi Meng (Fukuoka University)

キーワード: コンクリート，空隙，周波数領域解析法，人工ニューラルネットワーク，電磁波　　concrete，void，frequency-domain analysis method，artificial neural network，electromagnetic wave

要約(英語): The investigation of voids in concrete slabs is a crucial aspect of concrete diagnostics. We have reported that it is possible to efficiently detect the presence of voids by identifying the scattered waveform through an artificial neural network (ANN). This study serves as a sequel, aiming not only to determine the presence of a void but also to detect its location and thickness. The ANN-based estimation demonstrated good accuracy even when the observed data included some noise or when dealing with models featuring weak conductivity. Additionally, a substantial amount of scattered waveform data is required to train and test the ANN. In this research, we leverage frequency domain analysis and FFT techniques to create the scattered waveform data of a concrete slab considered as a flat layered medium. This approach allows for significant time savings in computation compared to the finite difference time domain (FDTD) method used in our previous studies.

本誌: 電気学会論文誌A（基礎・材料・共通部門誌） Vol.144 No.7 （2024）

本誌掲載ページ: 258-263 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejfms/144/7/144_258/_article/-char/ja/