ORCID Profile
0000-0002-5723-8226
Current Organisations
Norwegian University of Science and Technology
,
Stanford University School of Medicine
,
VA Palo Alto Health Care System
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Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 08-2004
Publisher: Springer Science and Business Media LLC
Date: 23-06-2019
Publisher: Elsevier BV
Date: 2021
Publisher: Informa UK Limited
Date: 28-12-2023
Publisher: Elsevier BV
Date: 07-2010
Publisher: Elsevier BV
Date: 06-2018
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2020
Publisher: Elsevier BV
Date: 03-2003
Publisher: Elsevier BV
Date: 2011
Publisher: Elsevier BV
Date: 02-2008
Publisher: Springer Science and Business Media LLC
Date: 07-07-2007
Publisher: Springer International Publishing
Date: 30-11-2020
Publisher: Springer Science and Business Media LLC
Date: 13-02-2009
Publisher: Australian Centre for Geomechanics, Perth
Date: 2019
Publisher: Elsevier BV
Date: 06-2007
Publisher: Springer Science and Business Media LLC
Date: 17-05-2022
Publisher: Informa UK Limited
Date: 13-07-2012
Publisher: Informa UK Limited
Date: 27-07-2017
Publisher: Elsevier BV
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 19-06-2018
Publisher: Elsevier BV
Date: 05-2014
Publisher: Elsevier BV
Date: 07-2020
Publisher: Springer Science and Business Media LLC
Date: 11-2019
Publisher: Elsevier BV
Date: 06-2018
Publisher: American Society of Civil Engineers (ASCE)
Date: 02-2017
Publisher: Elsevier BV
Date: 08-2010
Publisher: Elsevier BV
Date: 07-2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 2008
Publisher: Elsevier BV
Date: 03-2022
Publisher: Informa UK Limited
Date: 27-03-2015
Publisher: Canadian Science Publishing
Date: 2008
DOI: 10.1139/T07-053
Abstract: This paper presents the results of performance analysis on the support systems recommended by the RMR (rock mass rating) rock mass classification system. Rock–support interaction is analyzed by means of both numerical and multiple regression modeling. Five different rock mass conditions were assumed from very poor to very good, each representing varied RMR. Extensive computer simulations were conducted to investigate the stresses, displacements, and yielded zones around a circular opening excavated at different depths, and under different rock conditions. The performances of the RMR recommended support systems were analyzed and the stability of excavation was evaluated. Multiple regression modeling was conducted to assess the relationship between support pressure, depth, and tunnel deformation for different rock conditions. Regression models were derived and the response surfaces were constructed, showing the interaction between tunnel depth, support pressure, and tunnel displacement. Using the derived models and the constructed response surfaces, engineers are able to describe the support performance and assess the practical range of expected deformation for their specific site conditions. Also, the approach presented can be used for any special case.
Publisher: Dnipro University of Technology
Date: 30-06-2018
Publisher: Elsevier BV
Date: 2019
Publisher: Informa UK Limited
Date: 04-08-0016
Publisher: Springer Science and Business Media LLC
Date: 15-05-2023
DOI: 10.1007/S11004-023-10061-1
Abstract: Brønnøy Kalk AS operates an open pit mine in Norway producing marble, mainly used by the paper industry. The final product is used as filler and pigment for paper production. Therefore, the quality of the product has utmost importance. In the mine, the primary quality indicator, called TAPPI, is quantified through a laborious s ling process and laboratory experiments. As a part of digital transformation, measurement while drilling (MWD) data have been collected in the mine. The purpose of this paper is to use the recorded MWD data for the prediction of marble quality to facilitate quality blending in the pit. For this purpose, two supervised classification modelling algorithms such as conventional logistic regression and random forest have been employed. The results show that the random forest classification model presents significantly higher statistical performance, and it can be used as a tool for fast and efficient marble quality assessment.
Publisher: Elsevier BV
Date: 08-2006
Publisher: Elsevier BV
Date: 04-2021
Publisher: Springer Science and Business Media LLC
Date: 21-03-2018
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 10-2017
Publisher: CRC Press
Date: 05-11-2015
DOI: 10.1201/B19352-8
Publisher: Springer Science and Business Media LLC
Date: 26-06-2019
Publisher: Springer Science and Business Media LLC
Date: 03-2007
Publisher: ASTM International
Date: 03-2019
DOI: 10.1520/ACEM20190016
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 10-2016
Location: United States of America
Location: United States of America
Location: United States of America
No related grants have been discovered for Sherry Wren.