PREDICTION OF LATERAL EFFECTIVE STRESSES IN SAND USING ARTIFICIAL NEURAL NETWORK

• 資料種別: 記事
• タイトル: PREDICTION OF LATERAL EFFECTIVE STRESSES IN SAND USING ARTIFICIAL NEURAL NETWORK
• 著者標目: UNCUOGLU ERDAL; LAMAN MUSTAFA; SAGLAMER AHMET; KARA H. BEKIR
• 出版年月日等: 2008
• 出版年（W3CDTF）: 2008
• タイトル（掲載誌）: ＳＯＩＬＳ ＡＮＤ ＦＯＵＮＤＡＴＩＯＮＳ
• 巻号年月日等（掲載誌）: 48 2
• 掲載巻: 48
• 掲載号: 2
• 掲載ページ: 141-153
• 掲載年月日（W3CDTF）: 2008
• ISSN（掲載誌）: 00380806
• 出版事項（掲載誌）: The Japanese Geotechnical Society
• 本文の言語コード: en
• 件名標目: artificial neural network; at rest pressure; backpropagation; computer application; laboratory test; relative density; sand; (<b>IGC</b>: D3/E5/E13)
• 対象利用者: 一般
• DOI: 10.3208/sandf.48.141
• 関連情報（URI）: https://api.elsevier.com/content/article/PII:S0038080620304030?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0038080620304030?httpAccept=text/plain
• 参照: Lateral Earth Pressure at Rest in Soft Clay; https://cir.nii.ac.jp/crid/1360011143611166208; Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications; https://cir.nii.ac.jp/crid/1360011146365427200; Predicting Geotechnical Parameters of Sands from CPT Measurements Using Neural Networks; https://cir.nii.ac.jp/crid/1360292620521463168; At-Rest Lateral Earth Pressure of Consolidating Clay; https://cir.nii.ac.jp/crid/1360292620940463232; Training feedforward networks with the Marquardt algorithm; https://cir.nii.ac.jp/crid/1360574095690715008; Efficiency of pile groups installed in cohesionless soil using artificial neural networks; https://cir.nii.ac.jp/crid/1360855568670202752; Back-propagation neural networks for modeling complex systems; https://cir.nii.ac.jp/crid/1360855569154178304; Seismic Liquefaction Potential Assessed by Neural Networks; https://cir.nii.ac.jp/crid/1361137044545243392; At-Rest Lateral Pressure of Peat Soils; https://cir.nii.ac.jp/crid/1361137044758758144; Data Division for Developing Neural Networks Applied to Geotechnical Engineering; https://cir.nii.ac.jp/crid/1361137046271246464; Modeling soil collapse by artificial neural networks; https://cir.nii.ac.jp/crid/1361981469333576960; Backpropagation through time: what it does and how to do it; https://cir.nii.ac.jp/crid/1362262946021584896; Development of KO in soft soils; https://cir.nii.ac.jp/crid/1362544418542653184; Three-dimensional site characterisation: neural network approach; https://cir.nii.ac.jp/crid/1363388845328032256; Multilayer feedforward networks are universal approximators; https://cir.nii.ac.jp/crid/1363670319074321792; Predicting Settlement of Shallow Foundations using Neural Networks; https://cir.nii.ac.jp/crid/1363670320693887488; River Stage Forecasting Using Artificial Neural Networks; https://cir.nii.ac.jp/crid/1363951796224544384; An experimental study of soil memory and preconsolidation adjacent to an active tectonic structure: the Meers fault, Oklahoma, USA; https://cir.nii.ac.jp/crid/1364233268153226624; Neural Networks in Civil Engineering: 1989–2000; https://cir.nii.ac.jp/crid/1364233269926735616; Assessment of the coefficient of the earth pressure at rest from shear wave velocity measurements; https://cir.nii.ac.jp/crid/1364233270087720448; The coefficient of earth pressure at rest; https://cir.nii.ac.jp/crid/1372257704546560513; Lateral stresses in uniaxial strain; https://cir.nii.ac.jp/crid/1372257704546560645; Liquefaction assessment by artificial neural networks; https://cir.nii.ac.jp/crid/1372257704546560649; Indirect determination of K0 from multi-stage triaxial compression test; https://cir.nii.ac.jp/crid/1372257704546560650; Eléments de Mécanique quasistatique des milieux granulaires mouillés ou secs; https://cir.nii.ac.jp/crid/1372257704546560653; Soil parameters affecting coefficient of earth pressure at rest of cohesionless soils; https://cir.nii.ac.jp/crid/1372257704546560772; Comparison of three back-propagation training algorithms for two case studies; https://cir.nii.ac.jp/crid/1372257704546560773; Theory of the backpropagation neural network; https://cir.nii.ac.jp/crid/1372257704546560776; Critical appraisal of calibration chamber testing in sands; https://cir.nii.ac.jp/crid/1372257704546560777; Artificial intelligence techniques for the design and analysis of deep foundations; https://cir.nii.ac.jp/crid/1372257704546560778; Artificial neural network applications in geotechnical engineering; https://cir.nii.ac.jp/crid/1372257704546560780; Artificial intelligence applications in geotechnical engineering; https://cir.nii.ac.jp/crid/1372257704546560781
• 連携機関・データベース: 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• 提供元機関・データベース: Japan Link Center; https://japanlinkcenter.org/top; Crossref; https://www.crossref.org; CiNii Articles
• NII論文ID: 130004552320

• 要約等: Predicting the lateral effective stress and the coefficient of lateral earth pressure at rest values is an important task in geotechnical engineering ...