A spectrum-driven damage Identification technique : application and validation through the numerical simulation of the Z24 Bridge

The present paper focuses on a damage identification method based on the use of the second order spectral properties of the nodal response processes. The explicit dependence on the frequency content of the outputs power spectral densities makes them suitable for damage detection and localization. The well-known case study of the Z24 Bridge in Switzerland is chosen to apply and further investigate this technique with the aim of validating its reliability. Numerical simulations of the dynamic response of the structure subjected to different types of excitation are carried out to assess the variability of the spectrum-driven method with respect to both type and position of the excitation sources. The simulated data obtained from random vibrations, impulse, ramp and shaking forces, allowed to build the power spectrum matrix from which the main eigenparameters of reference and damage scenarios are extracted. Afterwards, complex eigenvectors and real eigenvalues are properly weighed and combined and a damage index based on the difference between spectral modes is computed to pinpoint the damage. Finally, a group of vibration-based damage identification methods are selected from the literature to compare the results obtained and to evaluate the performance of the spectral index.

Modelling Z→ττ processes in ATLAS with τ-embedded Z→μμ data

This paper describes the concept, technical realisation and validation of a largely data-driven method to model events with Z→ττ decays. In Z→μμ events selected from proton-proton collision data recorded at s√=8 TeV with the ATLAS experiment at the LHC in 2012, the Z decay muons are replaced by τ leptons from simulated Z→ττ decays at the level of reconstructed tracks and calorimeter cells. The τ lepton kinematics are derived from the kinematics of the original muons. Thus, only the well-understood decays of the Z boson and τ leptons as well as the detector response to the τ decay products are obtained from simulation. All other aspects of the event, such as the Z boson and jet kinematics as well as effects from multiple interactions, are given by the actual data. This so-called τ-embedding method is particularly relevant for Higgs boson searches and analyses in ττ final states, where Z→ττ decays constitute a large irreducible background that cannot be obtained directly from data control samples.

Numerical simulation of galvanized rebars pullout

The usage of rebars in construction is the most common method for reinforcing plain concrete and thus bridging the tensile stresses along the concrete crack surfaces. Usually design codes for modelling the bond behaviour of rebars and concrete suggest a local bond stress – slip relationship that comprises distinct reinforcement mechanisms, such as adhesion, friction and mechanical anchorage. In this work, numerical simulations of pullout tests were performed using the finite element method framework. The interaction between rebar and concrete was modelled using cohesive elements. Distinct local bond laws were used and compared with ones proposed by the Model Code 2010. Finally an attempt was made to model the geometry of the rebar ribs in conjunction with a material damaged plasticity model for concrete.

A data-driven analytics approach in the study of pneumonia’s fatalities

As huge amounts of data become available in organizations and society, specific data analytics skills and techniques are needed to explore this data and extract from it useful patterns, tendencies, models or other useful knowledge, which could be used to support the decision-making process, to define new strategies or to understand what is happening in a specific field. Only with a deep understanding of a phenomenon it is possible to fight it. In this paper, a data-driven analytics approach is used for the analysis of the increasing incidence of fatalities by pneumonia in the Portuguese population, characterizing the disease and its incidence in terms of fatalities, knowledge that can be used to define appropriate strategies that can aim to reduce this phenomenon, which has increased more than 65% in a decade.

Simulation of humidity fields in concrete: experimental validation and parameter estimation

The moisture content in concrete structures has an important influence in their behavior and performance. Several vali-dated numerical approaches adopt the governing equation for relative humidity fields proposed in Model Code 1990/2010. Nevertheless there is no integrative study which addresses the choice of parameters for the simulation of the humidity diffusion phenomenon, particularly in concern to the range of parameters forwarded by Model Code 1990/2010. A software based on a Finite Difference Method Algorithm (1D and axisymmetric cases) is used to perform sensitivity analyses on the main parameters in a normal strength concrete. Then, based on the conclusions of the sensi-tivity analyses, experimental results from nine different concrete compositions are analyzed. The software is used to identify the main material parameters that better fit the experimental data. In general, the model was able to satisfactory fit the experimental results and new correlations were proposed, particularly focusing on the boundary transfer coeffi-cient.

Numerical simulation of the punching shear behaviour of self-compacting fibre reinforced flat slabs

This paper presents the numerical simulations of the punching behaviour of centrally loaded steel fibre reinforced self-compacting concrete (SFRSCC) flat slabs. Eight half scaled slabs reinforced with different content of hooked-end steel fibres (0, 60, 75 and 90 kg/m3) and concrete strengths of 50 and 70 MPa were tested and numerically modelled. Moreover, a total of 54 three-point bending tests were carried out to assess the post-cracking flexural tensile strength. All the slabs had a relatively high conventional flexural reinforcement in order to promote the occurrence of punching failure mode. Neither of the slabs had any type of specific shear reinforcement rather than the contribution of the steel fibres. The numerical simulations were performed according to the Reissner-Mindlin theory under the finite element method framework. Regarding the classic formulation of the Reissner-Mindlin theory, in order to simulate the progressive damage induced by cracking, the shell element is discretized into layers, being assumed a plane stress state in each layer. The numerical results are, then, compared with the experimental ones and it is possible to notice that they accurately predict the experimental force-deflection relationship. The type of failure observed experimentally was also predicted in the numerical simulations.

Evaluating ubiquitous computing environments using 3D simulation

Human activity is very dynamic and subtle, and most physical environments are also highly dynamic and support a vast range of social practices that do not map directly into any immediate ubiquitous computing functionally. Identifying what is valuable to people is very hard and obviously leads to great uncertainty regarding the type of support needed and the type of resources needed to create such support. We have addressed the issues of system development through the adoption of a Crowdsourced software development model [13]. We have designed and developed Anywhere places, an open and flexible system support infrastructure for Ubiquitous Computing that is based on a balanced combination between global services and applications and situated devices. Evaluation, however, is still an open problem. The characteristics of ubiquitous computing environments make their evaluation very complex: there are no globally accepted metrics and it is very difficult to evaluate large-scale and long-term environments in real contexts. In this paper, we describe a first proposal of an hybrid 3D simulated prototype of Anywhere places that combines simulated and real components to generate a mixed reality which can be used to assess the envisaged ubiquitous computing environments [17].

Process simulation support in BPM tools: The case of BPMN

Due to the increasing acceptance of BPM, nowadays BPM tools are extensively used in organizations. Core to BPM are the process modeling languages, of which BPMN is the one that has been receiving most attention these days. Once a business process is described using BPMN, one can use a process simulation approach in order to find its optimized form. In this context, the simulation of business processes, such as those defined in BPMN, appears as an obvious way of improving processes. This paper analyzes the business process modeling and simulation areas, identifying the elements that must be present in the BPMN language in order to allow processes described in BPMN to be simulated. During this analysis a set of existing BPM tools, which support BPMN, are compared regarding their limitations in terms of simulation support.

Blast loading of masonry infills: testing and simulation

This work intends to present a newly developed test setup for dynamic out-of-plane loading using underWater Blast Wave Generators (WBWG) as loading source. Underwater blasting operations have been, during the last decades, subject of research and development of maritime blasting operations (including torpedo studies), aquarium tests for the measurement of blasting energy of industrial explosives and confined underwater blast wave generators. WBWG allow a wide range for the produced blast impulse and surface area distribution. It also avoids the generation of high velocity fragments and reduces atmospheric sound wave. A first objective of this work is to study the behavior of masonry infill walls subjected to blast loading. Three different masonry walls are to be studied, namely unreinforced masonry infill walls and two different reinforcement solutions. These solutions have been studied previously for seismic action mitigation. Subsequently, the walls will be simulated using an explicit finite element code for validation and parametric studies. Finally, a tool to help designers to make informed decisions on the use of infills under blast loading will be presented.

A sixth-order finite volume method for the 1D biharmonic operator: application to intramedullary nail simulation

A new very high-order finite volume method to solve problems with harmonic and biharmonic operators for one- dimensional geometries is proposed. The main ingredient is polynomial reconstruction based on local interpolations of mean values providing accurate approximations of the solution up to the sixth-order accuracy. First developed with the harmonic operator, an extension for the biharmonic operator is obtained, which allows designing a very high-order finite volume scheme where the solution is obtained by solving a matrix-free problem. An application in elasticity coupling the two operators is presented. We consider a beam subject to a combination of tensile and bending loads, where the main goal is the stress critical point determination for an intramedullary nail.

Search for massive supersymmetric particles decaying to many jets using the ATLAS detector in pp collisions at s√=8TeV

Results of a search for decays of massive particles to fully hadronic final states are presented. This search uses 20.3 fb−1 of data collected by the ATLAS detector in s√=8TeV proton--proton collisions at the LHC. Signatures based on high jet multiplicities without requirements on the missing transverse momentum are used to search for R-parity-violating supersymmetric gluino pair production with subsequent decays to quarks. The analysis is performed using a requirement on the number of jets, in combination with separate requirements on the number of b-tagged jets, as well as a topological observable formed from the scalar sum of the mass values of large-radius jets in the event. Results are interpreted in the context of all possible branching ratios of direct gluino decays to various quark flavors. No significant deviation is observed from the expected Standard Model backgrounds estimated using jet-counting as well as data-driven templates of the total-jet-mass spectra. Gluino pair decays to ten or more quarks via intermediate neutralinos are excluded for a gluino with mass mg~<1TeV for a neutralino mass mχ~01=500GeV. Direct gluino decays to six quarks are excluded for mg~<917GeV for light-flavor final states, and results for various flavor hypotheses are presented.

Induced magnetoelectric effect driven by magnetization in BaFe12O19 -/P(VDF-TrFE) composites

Films of BaFe12O19/P(VDF-TrFE) composites with 5, 10 and 20 %wt Barium ferrite content have been fabricated. BaFe12O19 microparticles have the shape of thin hexagonal platelets, the easy direction of magnetization remaining along the c axis, which is perpendicular to the plates. This fact allows for ferrite particles orientation in-plane and out-of-plane within the composite films, as confirmed by measured hysteresis loops. While the in-plane induced magnetoelectric effect (ME) is practically zero, these composite films show a good out-of-plane magnetoelectric effect. with maximum ME coupling coefficient changes of 3, 17 and 2 mV/cm.Oe for the 5, 10 and 20%wt Barium ferrite content films, respectively. We infer that this ME behavior appears as driven by the magnetization process arising when we applied the external magnetic field. We have also measured linear and reversible magnetoelectric effect for low applied bias field, when magnetization process is still reversible.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.