Estimating the error in simulation prediction over the design space

This study addresses the assessrnent of accuracy of simulation predictions. A procedure is developed to validate a simple non-linear model defined to capture the hardening behavior of a foam material subjected to a short-duration transient impact. Validation means that the predictive accuracy of the model must be established, not just in the vicinity of a single testing condition, but for all settings or configurations of the system. The notion of validation domain is introduced to designate the design region where the model's predictive accuracy is appropriate for the application of interest. Techniques brought to bear to assess the model's predictive accuracy include test-analysis coi-relation, calibration, bootstrapping and sampling for uncertainty propagation and metamodeling. The model's predictive accuracy is established by training a metalnodel of prediction error. The prediction error is not assumed to be systcmatic. Instead, it depends on which configuration of the system is analyzed. Finally, the prediction error's confidence bounds are estimated by propagating the uncertainty associated with specific modeling assumptions

A national cross-sectional survey of dental anxiety in the French adult population

<p>Abstract</p> <p>Background</p> <p>Dental anxiety is a public health problem but no epidemiological study has been undertaken in France to evaluate its prevalence. The aim of this study was to estimate the prevalence, severity and associations of dental anxiety in a sample of the French adult population.</p> <p>Methods</p> <p>A convenience sample of 2725 adults (mean age = 47 years, SD16, minimum = 16, maximum = 101 years), representative of the French population with regard to age and urban distribution, completed a French version of the Corah Dental Anxiety scale (DAS) and a questionnaire relating to their dental appointments.</p> <p>Results</p> <p>Moderate dental anxiety (14≥DAS≥13) was revealed for 172 persons (6.2%), while 195 (7.3%) had severe dental anxiety (DAS≥15), giving an overall prevalence of dental anxiety of 13.5%. Prevalence was lower proportionally with age (P < 0.001) and was higher in French overseas territories and in the countryside (P < 0.01). Farmers and low skilled workers were significantly more anxious than executives and shopkeepers (P < 0.001). Anxiety was associated with avoidance of care (p < 0.001) and lack of regular dental appointments (p < 0.001).</p> <p>Conclusion</p> <p>Dental anxiety in France appears to concern a similar proportion of the population as in other industrialised European, Australasian or North American countries. Recommendations for prevention and management of dental anxiety are made with reference to dental education and health care services in France.</p

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

A Critical Review on the Structural Health Monitoring Methods of the Composite Wind Turbine Blades

With increasing turbine size, monitoring of blades becomes increasingly im-portant, in order to prevent catastrophic damages and unnecessary mainte-nance, minimize the downtime and labor cost and improving the safety is-sues and reliability. The present work provides a review and classification of various structural health monitoring (SHM) methods as strain measurement utilizing optical fiber sensors and Fiber Bragg Gratings (FBG’s), active/ pas-sive acoustic emission method, vibration‒based method, thermal imaging method and ultrasonic methods, based on the recent investigations and prom-ising novel techniques. Since accuracy, comprehensiveness and cost-effectiveness are the fundamental parameters in selecting the SHM method, a systematically summarized investigation encompassing methods capabilities/ limitations and sensors types, is needed. Furthermore, the damages which are included in the present work are fiber breakage, matrix cracking, delamina-tion, fiber debonding, crack opening at leading/ trailing edge and ice accre-tion. Taking into account the types of the sensors relevant to different SHM methods, the advantages/ capabilities and disadvantages/ limitations of repre-sented methods are nominated and analyzed