文献
J-GLOBAL ID:202202273919414968
整理番号:22A1175820
人工知能変形予測に基づくトンネル崩壊災害のためのマルチソース情報融合評価手法【JST・京大機械翻訳】
A Multi-Source Information Fusion Evaluation Method for the Tunneling Collapse Disaster Based on the Artificial Intelligence Deformation Prediction
著者 (8件):
Wu Bo
(College of Civil Engineering and Architecture, Guangxi University, Nanning, Guangxi, China)
,
Wu Bo
(School of Civil and Architectural Engineering, East China University of Technology, Nanchang, Jiangxi, China)
,
Wu Bo
(School of Architectural Engineering, Guangzhou City Construction College, Guangzhou, Guangdong, China)
,
Qiu Weixing
(College of Civil Engineering and Architecture, Guangxi University, Nanning, Guangxi, China)
,
Huang Wei
(College of Civil Engineering and Architecture, Guangxi University, Nanning, Guangxi, China)
,
Meng Guowang
(College of Civil Engineering and Architecture, Guangxi University, Nanning, Guangxi, China)
,
Nong Yu
(College of Civil Engineering and Architecture, Guangxi University, Nanning, Guangxi, China)
,
Huang Jingsong
(College of Civil Engineering and Architecture, Guangxi University, Nanning, Guangxi, China)
資料名:
Arabian Journal for Science and Engineering
(Arabian Journal for Science and Engineering)
巻:
47
号:
4
ページ:
5053-5071
発行年:
2022年
JST資料番号:
W4051A
ISSN:
2193-567X
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
ドイツ (DEU)
言語:
英語 (EN)