1000 resultados para Damage insurance


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This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

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Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

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The Australian income tax regime is generally regarded as a mechanism by which the Federal Government raises revenue, with much of the revenue raised used to support public spending programs. A prime example of this type of spending program is health care. However, a government may also decide that the private sector should provide a greater share of the nation's health care. To achieve such a policy it can bring about change through positive regulation, or it can use the taxation regime, via tax expenditures, not to raise revenue but to steer or influence individuals in its desired direction. When used for this purpose, tax expenditures steer taxpayers towards or away from certain behaviour by either imposing costs on, or providing benefits to them. Within the context of the health sector, the Australian Federal Government deploys social steering via the tax system, with the Medicare Levy Surcharge and the 30 percent Private Health Insurance Rebate intended to steer taxpayer behaviour towards the Government’s policy goal of increasing the amount of health provision through the private sector. These steering mechanisms are complemented by the ‘Lifetime Health Cover Initiative’. This article, through the lens of behavioural economics, considers the ways in which these assorted mechanisms might have been expected to operate and whether they encourage individuals to purchase private health insurance.

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The purpose of this study was to investigate the effects of whole-body cryotherapy (WBC) on proprioceptive function, muscle force recovery following eccentric muscle contractions and tympanic temperature (TTY). Thirty-six subjects were randomly assigned to a group receiving two 3-min treatments of −110 ± 3 °C or 15 ± 3 °C. Knee joint position sense (JPS), maximal voluntary isometric contraction (MVIC) of the knee extensors, force proprioception and TTY were recorded before, immediately after the exposure and again 15 min later. A convenience sample of 18 subjects also underwent an eccentric exercise protocol on their contralateral left leg 24 h before exposure. MVIC (left knee), peak power output (PPO) during a repeated sprint on a cycle ergometer and muscles soreness were measured pre-, 24, 48 and 72 h post-treatment. WBC reduced TTY, by 0.3 °C, when compared with the control group (P<0.001). However, JPS, MVIC or force proprioception was not affected. Similarly, WBC did not effect MVIC, PPO or muscle soreness following eccentric exercise. WBC, administered 24 h after eccentric exercise, is ineffective in alleviating muscle soreness or enhancing muscle force recovery. The results of this study also indicate no increased risk of proprioceptive-related injury following WBC.

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