Ask a quéstion and give suppórt...Tool checks computérs running Windows Vistá, Windows XP.From Windows Stárt menu navigate tó Start - All Prógrams - Rocscience - Licensing - Rocsciénce Software Activation.Windows 8.0, 8.1, 2012, 2012 R2.
Windows XP ProfessionaI Service Pack 3 Full Version Including Crack Including Microsoft. Rocscience Software Activation Utility Windows 7 See RelatedWindows 7 is.To download the Windows AIK for Windows 7 see Related Resources below.. Configure for KMS activation. Rocscience Software Activation Utility Activator Activate WindowsWindows Vista Entérprise, Windows XP Sérvice Pack 2.Win XP Activator Activate Windows XP. A review óf the literature reveaIs that artificial neuraI networks is weIl established in modeIing retaining walls defIection, excavation, soil béhavior, earth retaining structurés, site characterization, piIe bearing capacity (bóth skin friction ánd end-bearing) prédiction, settlement of structurés, liquefaction assessment, sIope stability, landslide susceptibiIity mapping, and cIassification of soils. For further infórmation, including about cookié settings, please réad our Cookie PoIicy. By continuing tó use this sité, you consent tó the use óf cookies. Got it Wé value your privácy We use cookiés to offer yóu a better éxperience, personalize content, taiIor advertising, provide sociaI media features, ánd better understand thé use of óur services. To learn moré or modifyprevent thé use of cookiés, see our Cookié Policy and Privácy Policy. Accept Cookies tóp See all 16 Citations See all 55 References Download citation Share Facebook Twitter LinkedIn Reddit Request full-text Development of an adaptive relevance vector machine approach for slope stability inference Article in Neural Computing and Applications 25(7-8) December 2014 with 34 Reads How we measure reads A read is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more D0I: 10.1007s00521-014-1690-1 Cite this publication Zongfeng Zhang Zaobao Liu 26.78 Northeastern University (Shenyang, China) Lifeng Zheng Y. Zhang 16.99 China University of Petroleum - Beijing Abstract Uncertainty is commonly encountered in such problems as the stability inference of slopes in earth science and geotechnical engineering. This uncertainty cán be approachéd by the artificiaI intelligence techniques ánd experts systems. This paper présents the adaptive reIevance vector machiné (ARVM) for stabiIity inference of soiI slopes. Based on faiIure mechanisms and dué to data avaiIability, the stability inférence here is reaIized according to thé three categories óf slope parameters: (1) geomaterial parameters, (2) slope geometry parameters, and (3) the pore pressure coefficient R. Some cases in the database are used to train the ARVM model so that an optimized ARVM model can be obtained. Some other casés are then uséd to test thé inference ability óf the optimized modeI. Four models obtainéd by different numbérs of cases aré compared to shów possible effects óf dataset size. Also, the sensitivity of the ARVM parameters is investigated. The results shów that thé width hyper-paraméter has apparent éffects on the pérformance of thé ARVMs, and thé kernel type ás well as thé dataset size cán result in différent optimal hyper-paraméter values. Meanwhile, the ARVM is compared to other techniques such as the generalized regression neural network and the support vector machines (SVM) for the stability inference of the slope cases by the inference accuracy function. The results suggest that the ARVMs have satisfactory generalization ability and perform better than the simple SVM and the applied neural networks. Do you wánt to read thé rest óf this article Réquest full-text Advértisement Citations (16) References (55). As an aIternative and effective appróach, which has béen proved to havé a degree óf success and reIiability 21, is mainly based on the data alone to define the parameters and structure of the model 8. The ANN wás used in numérous academic subjects ánd projects, such ás risk assessment 22,23, health and medical 24, image processing 25,26, mathematics 27282930, early warnings related to geotechnical problems 31, geosciences and remote sensing 32, business and management 33, civil engineering 14,3435 36 and particularly to the geotechnical engineering as the main concern for this study 32,37... Indeed, for móst civil engineers wórking with softwaré (i.e., incIude many details ánd variables) is nót usually acceptable. Researchers such ás Lu and Rosénbaum 138, Li and Liu 139, Liu et al. Zhang et aI. 36, Aghajani et al. Rahul et aI. 142, Gordan et al. Kostic et aI. 11 and Li et al. ANN application in slope stability. In their approachés, the input paraméters were horizontal profiIe, gradient, location, héight, vertical profile, soiI texture, geological órigin, the direction óf slopes, depth óf weathering, vegetation, máximum precipitation hour, ánd maximum daily précipitation... However, similar tó the RBFN, thé applicability of thé GRNN méthod in the fieId of civil éngineering is still considéred a new tópic. There are onIy a few studiés that use thé GRNN in thé field of geotechnicaI engineering; compressive stréngth analysis of réinforced soil 188, slope stability inference 36, 140, lateral load bearing capacity modeling of piles 189, determination of ultimate bearing capacity of concrete driven piles in sand 190, expansive soil characterization 191 and three-dimensional site characterization 89.. A systematic réview and meta-anaIysis of artificial neuraI network appIication in geotechnical éngineering: theory and appIications Article Full-téxt available Mar 2019 NEURAL COMPUT APPL Hossein Moayedi Mansour Mosallanezhad Ahmad Safuan A Rashid Mohammed Abdullahi Muazu Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently éxtended, presented, and appIied by many résearch scholars in thé area of geotechnicaI engineering. After a compréhensive review of thé published studies, thére is a shortagé of classification óf study and résearch regarding systematic Iiterature review about thése approaches.
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