Three-dimensional site characterisation: neural network approach

被引：55

作者：

Juang, CH ^{[1
]}

Jiang, T

Christopher, RA

机构：

[1] Clemson Univ, Dept Civil Engn, Clemson, SC 29634 USA

[2] Clemson Univ, Dept Geol Sci, Clemson, SC USA

来源：

GEOTECHNIQUE | 2001年 / 51卷 / 09期

关键词：

in-situ testing; neural networks; site investigation; statistical analysis;

D O I：

10.1680/geot.51.9.799.41033

中图分类号：

P5 [地质学];

学科分类号：

0709 ; 081803 ;

摘要：

Site characterisation is an important task in geotechnical engineering practice. The ultimate goal in site characterisation is to be able to estimate in situ soil properties at any half-space point at a site based on limited tests. This estimate may be a point estimate or expressed in terms of some statistical parameters. Geostatistical and random field methods have been applied with limited success. This paper presents a new approach, based on artificial neural networks, for site characterisation. Emphasis is placed on the application of generalised regression neural networks for site characterisation. The results show that the neural network approach has the potential to be a practical tool for site characterisation.

引用

页码：799 / 809

页数：11

共 50 条

[1] The three-dimensional rotation neural network
Shuai, JW
Zheng, JC
Chen, ZX
Liu, RT
Wu, BX
[J]. PHYSICA A, 1997, 238 (1-4): : 23 - 38
[2] Application of the neural network approach to the reconstruction of a three-dimensional geoelectric structure
Spichak, VV
Popova, IV
[J]. FIZIKA ZEMLI, 1998, (01): : 39 - 45
[3] A Neural Network Approach to Medical Image Segmentation and Three-Dimensional Reconstruction
Bevilacqua, Vitoantonio
Mastronardi, Giuseppe
Marinelli, Mario
[J]. INTELLIGENT COMPUTING, PART I: INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, ICIC 2006, PART I, 2006, 4113 : 22 - 31
[4] Three-Dimensional Integrated Optical Neural Network
Sharma, Kanhaya
Grabulosa, Adria
Jung, Erik
Brunner, Daniel
[J]. MACHINE LEARNING IN PHOTONICS, 2024, 13017
[5] Three-dimensional cognitive mapping with a neural network
Voicu, H
Schmajuk, N
[J]. ROBOTICS AND AUTONOMOUS SYSTEMS, 2001, 35 (01) : 23 - 36
[6] Neural network approach to stereoscopic correspondence of three-dimensional particle tracking velocimetry
Department of Informations Systems Engineering, Osaka Sangyo University, 3-1-1, Nakagaito, Daitoshi, Osaka, 574-8530, Japan
[J]. IEEJ Trans. Electr. Electron. Eng., 6 (612-619):
[7] Neural Network Approach to Stereoscopic Correspondence of Three-Dimensional Particle Tracking Velocimetry
Sapkota, Achyut
Ohmi, Kazuo
[J]. IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2008, 3 (06) : 612 - 619
[8] GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
Hillier, Michael
Wellmann, Florian
de Kemp, Eric A.
Brodaric, Boyan
Schetselaar, Ernst
Bedard, Karine
[J]. GEOSCIENTIFIC MODEL DEVELOPMENT, 2023, 16 (23) : 6987 - 7012
[9] Three-dimensional surface registration: A neural network strategy
Liu, Heng
Yan, Jingqi
Zhang, David
[J]. NEUROCOMPUTING, 2006, 70 (1-3) : 597 - 602
[10] Chaos in a three-dimensional general model of neural network
Das, A
Das, P
Roy, AB
[J]. INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 2002, 12 (10): : 2271 - 2281

← 1 2 3 4 5 →