山岳トンネルの地山評価におけるニューラルネットワークの適用性

• 長谷川 信介

  応用地質（株）

• 長谷川 真吾

  東海旅客鉄道（株）

• 北岡 貴文

  京都大学大学院　工学研究科都市社会工学専攻

• 大津 宏康

  京都大学大学院　工学研究科都市社会工学専攻

書誌事項

タイトル別名

• Applicability of Neural Network in Rock Classification of Mountain Tunnel
• サンガク トンネル ノ ジヤマ ヒョウカ ニ オケル ニューラルネットワーク ノ テキヨウセイ

抄録

In construction projects of mountain tunnels, with a purpose of improving accuracies of rock classifications in preliminary survey, we have studied applicability of Artificial Neural Network (ANN). One characteristics of ANN is that it does not require defining clear formula correlating data input and output, by using its learning function. Leveraging the characteristics, accuracy of rock classification improved by using geophysical datasets (seismic velocity and resistivity) at a tunnel face and surrounding. Also, ANN has a problem of reduced applicability caused by over learning to training data. It is possible to avoid the over learning problem by increasing training dataset, but it is not easy to accumulate complete dataset of geophysical properties and actual rock classification obtained in construction stage. We found that it is important to collect various tunnel data without much deviation, for accumulating training datasets effectively in the future.

収録刊行物

• 材料

  材料 67 (3), 354-359, 2018

  公益社団法人 日本材料学会

キーワード

• Mountain tunnel
• Artificial Neural Network
• Over learning
• Database

詳細情報

• CRID: 1390282680422894592
• NII論文ID: 130006515967
• NII書誌ID: AN00096175
• DOI: 10.2472/jsms.67.354
• ISSN: 18807488
• ISSN: 05145163
• NDL書誌ID: 028915746
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I028915746
• Web Site: https://www.jstage.jst.go.jp/article/jsms/67/3/67_354/_pdf
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可