Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Guo-shao SU"'
Publikováno v:
Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Vol 9, Iss 1, Pp 1-21 (2023)
Abstract A series of tests for time-delayed rockburst of granite under true triaxial condition was designed and carried out. By using the true triaxial rockburst test system, an acoustic emission (AE) system, a high-speed camera system, and a digital
Externí odkaz:
https://doaj.org/article/8a4c1d8cf88c4257ab1efb990d77f8e9
Publikováno v:
工程科学学报, Vol 44, Iss 7, Pp 1160-1170 (2022)
Based on the UDEC (Universal distinct element code) modeling and grain-based model (UDEC-GBM), the effects of mineral’s (e.g., feldspar) cleavage angle, the confining effect of the cleavage angle and cleavage spacing on mechanical properties, micro
Externí odkaz:
https://doaj.org/article/41909329f20f4f8bafa0fa04ac660fce
Publikováno v:
Advances in Civil Engineering, Vol 2019 (2019)
A new response surface method (RSM) for slope reliability analysis was proposed based on Gaussian process (GP) machine learning technology. The method involves the approximation of limit state function by the trained GP model and estimation of failur
Externí odkaz:
https://doaj.org/article/f9bac3cd8bc94772b39322fb279c7250
Publikováno v:
Journal of Central South University. 27:2883-2898
To investigate the acoustic emission (AE) precursors of coarse-grained hard rock instability, an experimental study on the rockburst and slabbing process of granite was carried out using a true triaxial test system. The evolution of the AE signals wa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::38073d623079ab1ea39ced708985e588
https://doi.org/10.1007/s11771-020-4516-6
https://doi.org/10.1007/s11771-020-4516-6
Publikováno v:
IEEE Access, Vol 7, Pp 27535-27546 (2019)
The Gaussian process regression (GPR) model, which is a powerful machine learning tool for probabilistic prediction, is introduced into slope displacement prediction. Using this model, the displacements of the slope of the permanent ship lock of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5001fc3b744ec4274f02f6f7aec3eef
https://ieeexplore.ieee.org/document/8630981/
https://ieeexplore.ieee.org/document/8630981/
Publikováno v:
Advances in Civil Engineering, Vol 2019 (2019)
A new response surface method (RSM) for slope reliability analysis was proposed based on Gaussian process (GP) machine learning technology. The method involves the approximation of limit state function by the trained GP model and estimation of failur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::613f7af0506d20ea19ad01f040b251e0
Publikováno v:
Journal of Korean Tunnelling and Underground Space Association. 18:183-193
Estimation of the stability of an operating tunnel through a back analysis is a difficult concept to analyze. Specially, when a relatively thick lining is constructed as in case of a subsea tunnel, there will be a limit to the use of displacement-bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a13fa879f397c486dc41695a8a187e9
https://doi.org/10.9711/ktaj.2016.18.2.183
https://doi.org/10.9711/ktaj.2016.18.2.183
Publikováno v:
Applied Mechanics and Materials. :136-139
A KNN Classification Based MCS (Monte Carlo Simulation Method) is proposed for the reliability analysis which hindered by the implicit nature of the performance function. In the method, Markov chain is adopted to simulate a small amount of training s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c490f2d5a459c74418d74ca815a59660
https://doi.org/10.4028/www.scientific.net/amm.638-640.136
https://doi.org/10.4028/www.scientific.net/amm.638-640.136
Publikováno v:
Advanced Materials Research. :1167-1171
Gaussian process (GP) is a newly developed machine learning technology based on statistical theoretical fundamentals, which has successful application in the field of solving for highly nonlinear problems. Conventional methods for forecasting of non-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5579438d7bee4ffe60d1275a17fc0593
https://doi.org/10.4028/www.scientific.net/amr.955-959.1167
https://doi.org/10.4028/www.scientific.net/amr.955-959.1167
Publikováno v:
Applied Mechanics and Materials. :1067-1070
The performance function of large-scale complicated engineering structure is always highly nonlinear and implicit, and its reliability needs to be evaluated through a time-consuming Finite Element method (FEM). A new method, Gaussian process classifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f96bf30e317034b4b369f0190b6412e
https://doi.org/10.4028/www.scientific.net/amm.501-504.1067
https://doi.org/10.4028/www.scientific.net/amm.501-504.1067