Methodology for real-time adaptation of tunnels support using the observational method

The observational method in tunnel engineering allows the evaluation in real time of the actual conditions of the ground and to take measures if its behavior deviates considerably from predictions. However, it lacks a consistent and structured methodology to use the monitoring data to adapt the support system in real time. The definition of limit criteria above which adaptation is required are not defined and complex inverse analysis procedures (Rechea et al. 2008, Levasseur et al. 2010, Zentar et al. 2001, Lecampion et al. 2002, Finno and Calvello 2005, Goh 1999, Cui and Pan 2012, Deng et al. 2010, Mathew and Lehane 2013, Sharifzadeh et al. 2012, 2013) may be needed to consistently analyze the problem. In this paper a methodology for the real time adaptation of the support systems during tunneling is presented. In a first step limit criteria for displacements and stresses are proposed. The methodology uses graphics that are constructed during the project stage based on parametric calculations to assist in the process and when these graphics are not available, since it is not possible to predict every possible scenario, inverse analysis calculations are carried out. The methodology is applied to the “Bois de Peu” tunnel which is composed by two tubes with over 500 m long. High uncertainty levels existed concerning the heterogeneity of the soil and consequently in the geomechanical design parameters. The methodology was applied in four sections and the results focus on two of them. It is shown that the methodology has potential to be applied in real cases contributing for a consistent approach of a real time adaptation of the support system and highlight the importance of the existence of good quality and specific monitoring data to improve the inverse analysis procedure.

As características geotécnicas de um maciço como factores preponderantes na planificação e organização de obras de escavação subterrâneas - uma perspectiva sobre a produção

Com o presente documento pretende-se abordar e identificar os diferentes factores que influenciam directamente a produção e execução de uma escavação subterrânea, com especial relevo sobre a influência exercida pela geotecnia do maciço intersectado. Inicialmente são focados os principais aspectos a ter em conta na caracterização geotécnica de um maciço, seguindo-se uma introdução a diferentes métodos de escavação actuais e metodologias de suporte de uma obra subterrânea, com particular realce para os utilizados em maciços brandos. Depois de tratados estes conceitos, é apresentada uma obra subterrânea em execução que foi acompanhada durante 4 meses para efeitos de desenvolvimento deste estudo. Assim, são abordados neste documento diferentes aspectos construtivos, no que diz respeito à mão-de-obra utilizada, metodologias e técnicas aplicadas, redes técnicas auxiliares instaladas, produções e rendimentos verificados. De seguida e de modo a atestar a importância da caracterização geotécnica ao longo da obra, foi feito um estudo do maciço intersectado, relativamente às descontinuidades que o intersectam, litologia, alteração, e resistência à compressão. Para este último parâmetro foram utilizadas técnicas distintas mas complementares, nomeadamente o ensaio de carga pontual (em laboratório), e o esclerómetro portátil (in situ). Por último, tendo em conta os parâmetros e características presentes e as implicações que uma obra do género acarreta, são propostas de modo sucinto, técnicas alternativas de escavação do maciço cuja viabilidade de implementação seja possível no contexto em questão.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Decision Support Model for Assessing Archaeological Survey Needs for Bridge Replacement Projects in Iowa, 2006

The Bridges Decision Support Model is a geographic information system (GIS) that assembles existing data on archaeological sites, surveys, and their geologic contexts to assess the risk of bridge replacement projects encountering 13,000- to 150-year-old Native American sites. This project identifies critical variables for assessing prehistoric sites potential, examines the quality of available data about the variables, and applies the data to creating a decision support framework for use by the Iowa Department of Transportation (Iowa DOT) and others. An analysis of previous archaeological surveys indicates that subsurface testing to discover buried sites became increasingly common after 1980, but did not become routine until after the adoption of guidelines recommending such testing, in 1993. Even then, the average depth of testing has been relatively shallow. Alluvial deposits of sufficient age, deposited in depositional environments conducive to human habitation, are considerably thicker than archaeologists have routinely tested.

Perforated exit regions for the reduction of micro-pressure waves from tunnels

The authors are grateful to the following bodies that provided financial support for the project: (i) China Scholarship Council (20117 00029), (ii) National Natural Science Foundation of China (Grant no. U1334201) and (iii) UK Engineering and Physical Sciences Research Council (Grant no. EP/G069441/1).

Perforated exit regions for the reduction of micro-pressure waves from tunnels

The authors are grateful to the following bodies that provided financial support for the project: (i) China Scholarship Council (20117 00029), (ii) National Natural Science Foundation of China (Grant no. U1334201) and (iii) UK Engineering and Physical Sciences Research Council (Grant no. EP/G069441/1).

D3M: multicast listener mobility support mechanisms over distributed mobility anchoring architectures

The explosion in mobile data traffic is a driver for future network operator technologies, given its large potential to affect both network performance and generated revenue. The concept of distributed mobility management (DMM) has emerged in order to overcome efficiency-wise limitations in centralized mobility approaches, proposing not only the distribution of anchoring functions but also dynamic mobility activation sensitive to the applications needs. Nevertheless, there is not an acceptable solution for IP multicast in DMM environments, as the first proposals based on MLD Proxy are prone to tunnel replication problem or service disruption. We propose the application of PIM-SM in mobility entities as an alternative solution for multicast support in DMM, and introduce an architecture enabling mobile multicast listeners support over distributed anchoring frameworks in a network-efficient way. The architecture aims at providing operators with flexible options to provide multicast mobility, supporting three modes: the first one introduces basic IP multicast support in DMM; the second improves subscription time through extensions to the mobility protocol, obliterating the dependence on MLD protocol; and the third enables fast listener mobility by avoiding potentially slow multicast tree convergence latency in larger infrastructures, by benefiting from mobility tunnels. The different modes were evaluated by mathematical analysis regarding disruption time and packet loss during handoff against several parameters, total and tunneling packet delivery cost, and regarding packet and signaling overhead.

Can We Predict The Clinical Outcomes Of Genitourinary Trauma Patients And Offer Them An Evidence-based Support? The Case For Telemedicine Network.

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Matrix Support And Institutional Support: Analyzing Their Construction.

This is an analysis of the theoretical and practical construction of the methodology of Matrix Support by means of studies on Paideia Support (Institutional and Matrix Support), which is an inter-professional work of joint care in recent literature and official documents of the Unified Health System (SUS). An attempt was made to describe methodological concepts and strategies. A comparative analysis of Institutional Support and Matrix Support was also conducted using the epistemological framework of Field and Core Knowledge and Practices.

Molecular phylogeny of the Myzorhynchella Section of Anopheles (Nyssorhynchus) (Diptera: Culicidae): genetic support for recently described and resurrected species

Phylogenetic relationships among species of the Myzorhynchella Section of Anopheles (Nyssorhynchus) were investigated using the nuclear ribosomal DNA second internal transcribed spacer (ITS2), the nuclear whitegene and mitochondrial cytochrome oxidase subunit I (COI) regions. The recently described Anopheles pristinus and resurrected Anopheles guarani were also included in the study. Bayesian phylogenetic analyses found Anopheles parvus to be the most distantly related species within the Section, a finding that is consistent with morphology. An. pristinus and An. guarani were clearly resolved from Anopheles antunesi and Anopheles lutzii, respectively. An. lutzii collected in the same mountain range as the type locality were found within a strongly supported clade, whereas individuals from the southern state of Rio Grande do Sul, tentatively identified as An. lutzii based on adult female external morphology, were distinct from An. lutzii, An. antunesi and from each other, and may therefore represent two new sympatric species. A more detailed examination of An. lutzii sensu latoalong its known geographic range is recommended to resolve these anomalous relationships.

Cost risk benefit analysis to support chemoprophylaxis policy for travellers to malaria endemic countries

Background: In a number of malaria endemic regions, tourists and travellers face a declining risk of travel associated malaria, in part due to successful malaria control. Many millions of visitors to these regions are recommended, via national and international policy, to use chemoprophylaxis which has a well recognized morbidity profile. To evaluate whether current malaria chemo-prophylactic policy for travellers is cost effective when adjusted for endemic transmission risk and duration of exposure. a framework, based on partial cost-benefit analysis was used Methods: Using a three component model combining a probability component, a cost component and a malaria risk component, the study estimated health costs avoided through use of chemoprophylaxis and costs of disease prevention (including adverse events and pre-travel advice for visits to five popular high and low malaria endemic regions) and malaria transmission risk using imported malaria cases and numbers of travellers to malarious countries. By calculating the minimal threshold malaria risk below which the economic costs of chemoprophylaxis are greater than the avoided health costs we were able to identify the point at which chemoprophylaxis would be economically rational. Results: The threshold incidence at which malaria chemoprophylaxis policy becomes cost effective for UK travellers is an accumulated risk of 1.13% assuming a given set of cost parameters. The period a travellers need to remain exposed to achieve this accumulated risk varied from 30 to more than 365 days, depending on the regions intensity of malaria transmission. Conclusions: The cost-benefit analysis identified that chemoprophylaxis use was not a cost-effective policy for travellers to Thailand or the Amazon region of Brazil, but was cost-effective for travel to West Africa and for those staying longer than 45 days in India and Indonesia.

Information from managing information systems as a fundamental element to support decision-making in hospitals

This article deals with the activity of defining information of hospital systems as fundamental for choosing the type of information systems to be used and also the organizational level to be supported. The use of hospital managing information systems improves the user`s decision -making process by allowing control report generation and following up the procedures made in the hospital as well.

Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved.

A novel electrocatalyst support with proton conductive properties for polymer electrolyte membrane fuel cell applications

The objective of this study is to graft the Surface of carbon black, by chemically introducing polymeric chains (Nafion (R) like) with proton-conducting properties. This procedure aims for a better interaction of the proton-conducting phase with the metallic catalyst particles, as well as hinders posterior support particle agglomeration. Also loss of active surface call be prevented. The proton conduction between the active electrocatalyst site and the Nafion (R) ionomer membrane should be enhanced, thus diminishing the ohmic drop ill the polymer electrolyte membrane fuel cell (PEMFC). PtRu nanoparticles were supported on different carbon materials by the impregnation method and direct reduction with ethylene glycol and characterized using amongst others FTIR, XRD and TEM. The screen printing technique was used to produce membrane electrode assemblies (MEA) for single cell tests in H(2)/air(PEMFC) and methanol operation (DMFC). In the PEMFC experiments, PtRu supported on grafted carbon shows 550 mW cm(-2) gmetal(-1) power density, which represents at least 78% improvement in performance, compared to the power density of commercial PtRu/C ETEK. The DMFC results of the grafted electrocatalyst achieve around 100% improvement. The polarization Curves results clearly show that the main Cause of the observed effect is the reduction in ohmic drop, caused by the grafted polymer. (C) 2009 Elsevier B.V. All rights reserved.