1,721,017 research outputs found
Bayesian strategies in reliability assessment of heritage structures
No full textReliability assessment of heritage structures is becoming an increasingly important and frequent engineering task. Probabilistic methods and Bayesian updating techniques are often combined to obtain realistic estimates, also aiming to target at the best the possible interventions. Moreover, several factors often contribute to complicate the probabilistic approach: among the others, uncertainties about the knowledge of mechanical properties of materials, due to lack of quantitative data from the structure. In this paper, a strategy to perform a Bayesian updating in assessing heritage structures, based on qualitative a posteriori judgment is proposed, where probability distribution function for material properties are updated on the base of information available on similar structures. A case study, concerning a relevant historical building is finally presented
PARAMETER IDENTIFICATION VIA GPCE-BASED STOCHASTIC INVERSE METHODS FOR RELIABILITY ASSESSMENT OF EXISTING STRUCTURES
No full textThis paper focuses on updating the probability distribution function of the parameters that govern the reliability of existing buildings using measurements of observable responses of the structure. Herein efficient stochastic approaches are presented using functional approximation for the probabilistic description of the observable structural response. It is shown through a toy example, that such update may be used for the reliability assessment of structures, for which no destructive tests are allowed. In this article different approaches are discussed, and the implementation of a method, namely a general polynomial chaos based ensemble Kalman filter is considered. The main goal of this paper is to draw attention to the perspective, that the given methods may be applied for a smart, on-line monitoring of critical structures
A Bayesian Approach to Determine the Minimum Detectable Damage
No full textThis paper proposes an approach to the evaluation of the minimum detectable damage, which takes advantage of the Bayes Theorem and of Bayesian Hypothesis Testing. Assuming that some model outputs depending on random parameters are observed, a special application of the Kalman Filter to stationary inverse problems is applied, also called Linear Bayesian Filter, which allows to obtain an analytic formulation of the posterior distribution. A method called HDI+ROPE is used, which is based on a decision rule considering a range of plausible values indicated by
the highest density interval of the posterior distribution, and its relation to a region of practical equivalence around the null value. The analytic formula for the minimum detectable damage derives from the limit condition for which it is possible to establish with certainty the presence of damage. In order to validate the formula, an application is developed to a simple linear abstract problem and to a single degree of freedom system, in which the results obtained analytically are compared with those obtained by simulation. This approach could represent a significant step
forward in the design of non-destructive tests for existing infrastructures since it allows to put in relationship structural reliability with the reliability of the measurement system, allowing also, in the particular case of Structural
Health Monitoring, to consider static and dynamic measurements
A novel probabilistic methodology for the local assessment of future trends of climatic actions
No full textClimate change could significantly affect climatic actions, so influencing not only existing structures, designed in accordance to the provisions of past Codes, but also updating of structural Codes. In the paper a new probabilistic technique for bias correction and downscaling of climate projections is presented to assess uncertainties in the future trends of climate extremes at the local scale. Referring to the relevant historical period, climate model outputs at relevant weather stations are compared with recorded daily series of maximum and minimum temperatures and water precipitations to derive appropriate monthly error probability density functions. Subsequently, new climate data series are generated by adding to the climate models output a random error term sampled from the monthly error PDFs. An extreme value analysis is finally carried out for each generated series according different time windows to assess how characteristic values (50 years return period) vary over time. The results for snow loads at the investigated weather stations confirm that the proposed methodology is suitable to reproduce recorded past climate extremes, so suggesting that it could be a useful tool to improve the climate change forecasts derived from climate models
Determinants of cognitive impairment in elderly myasthenia gravis patients
No full textThe relationship between myasthenia gravis (MG) and cognitive dysfunction has been a matter of debate because of the possible association between peripheral and central nervous system (CNS) cholinergic dysfunction. The aim of this study was to evaluate cognitive function in a series of elderly MG patients in comparison to matched controls. In all, 100 consecutive MG patients aged over 60 years and 31 matched control subjects underwent an extensive neuropsychological test battery to explore multiple cognitive domains. There were no differences in cognitive performances between patients and controls. Severe MG was associated with impaired attention, constructional praxis, and frontal control. Logistic regression analysis showed that advanced age, diabetes, and thyroid dysfunction were independently associated with cognitive impairment. This study does not support the hypothesis of CNS cholinergic involvement in MG. The impairments of attention, memory, and control tasks in MG are related to general visual motor slowness and to the concomitant presence of other diseases
Climate Change: impact on snow load on structures and consequences on built environment
No full textThe effect of climate change could significantly affect, in the mid-term future, climatic actions and then the design of new structures as well as the reliability of existing ones, designed according the provisions of current or past codes. In this wok, a suitable procedure to derive snow loads on ground under non-stationary climate conditions is proposed, combining data provided by observational dataset and outputs of climate models. The analyses are performed for the Italian Mediterranean region and the results in terms of updated snow load maps are presented for movable 30 year time windows till 2100 according different greenhouse gas emission scenarios
A comparison of stochastic inverse methods with sampling and functionalbased linear and non-linear update procedures
No full textIn this paper we focus on inverse methods enabling the calibration of input parameters when measurement of the re-sponse of an engineering system is available. Considering only stochastic approaches, different methods can be used to perform the update. In the paper, a comparison of some of these numerical procedures is presented in order to evaluate the capability of the different methods. In particular, simple analysis have been carried out focusing the attention on those aspects that are more crucial in engineering application, such as the linearity/non-linearity of the model and the influence of the prior quality.
The results obtained with some toy-examples show that these aspects highly influence the performance of the methods. The Markov Chain Monte Carlo (MCMC) method is computationally expensive, due to slow convergence rate, but it is competitive for capturing multi-modal Bayesian posterior distribution. Efficient methods, such as the Kalman Filter, are suitable for linear models but have limitations when updating the parameters of non-linear models. Non-linear filters, such as the Non Linear Minimum Mean Squared Error (NL-MMSE), lead to better results for highly nonlin-ear models
Applying Queueing Theory for Managing Waterways Systems subject to Service Interruptions
No full textThe navigability of German waterways depends on the functioning of its infrastructures, such as locks and weirs. Since most of them have almost reached the design working life, they should undergo maintenance and eventually repair. However for economic and operation reasons interventions should be prioritized. This paper proposes to model waterways networks as a system of queues and to apply queueing theory in order to define new prioritization criteria based on traffic characteristics. We especially consider the rate of abandonment in case of unexpected interruptions, accord-ing to which it is possible to express the loss in terms of ships in case of unplanned stall of one lock. A case study is finally developed in order to show how this research supports the planning and the prioritization of maintenance intervention
Seismic Reliability Assessment of a Concrete Water Tank Based on the Bayesian Updating of the Finite Element Model
No full textFailure or malfunction of complex engineered networks involves relevant social and economic aspects, so that their maintenance is of primary importance. In assessing the reliability of such networks, it should be duly considered that they are a whole made of different parts, and that some of these individual parts or structures are often crucial to assure the proper operation of the entire network. Moreover, each of these structures can be considered a complex system by itself: structural reliability theory should be thus combined with advanced numerical analysis tools in order to obtain realistic estimates of the probability of failure. Accurate estimations are especially required in seismic zones, aiming to efficiently plan future interventions. This paper presents a method for the reliability assessment of a critical element of engineered networks. The method is discussed with special reference to a relevant case study: a concrete water tank, which is a key component
of a water supply system. Special attention is devoted to the reliability assessment of the tank under seismic loads, based on a structural identification approach. The calibration of the finite element model (FEM) of the structure is carried out on probabilistic basis, applying the Bayes theorem and response surface methods. The proposed approach allows to significantly speed up the structural identification process, leading to sounder estimate of the input parameters. Finally, the seismic fragility curves of the structure are developed according to the relevant limit states, demonstrating that information regarding the global structural behavior and local checks can be effectively combined in structural reliability assessments
GPCE-based stochastic inverse methods: A benchmark study from a civil engineer’s perspective
Full text linkIn civil and mechanical engineering, Bayesian inverse methods may serve to calibrate the uncertain input parameters of a structural model given the measurements of the outputs. Through such a Bayesian framework, a probabilistic description of parameters to be calibrated can be obtained; this approach is more informative than a deterministic local minimum point derived from a classical optimization problem. In addition, building a response surface surrogate model could allow one to overcome computational difficulties. Here, the general polynomial chaos expansion (gPCE) theory is adopted with this objective in mind. Owing to the fact that the ability of these methods to identify uncertain inputs depends on several factors linked to the model under investigation, as well as the experiment carried out, the understanding of results is not univocal, often leading to doubtful conclusions. In this paper, the performances and the limitations of three gPCE-based stochastic inverse methods are compared: the Markov Chain Monte Carlo (MCMC), the polynomial chaos expansion-based Kalman Filter (PCE-KF) and a method based on the minimum mean square error (MMSE). Each method is tested on a benchmark comprised of seven models: four analytical abstract models, a one-dimensional static model, a one-dimensional dynamic model and a finite element (FE) model. The benchmark allows the exploration of relevant aspects of problems usually encountered in civil, bridge and infrastructure engineering, highlighting how the degree of non-linearity of the model, the magnitude of the prior uncertainties, the number of random variables characterizing the model, the information content of measurements and the measurement error affect the performance of Bayesian updating. The intention of this paper is to highlight the capabilities and limitations of each method, as well as to promote their critical application to complex case studies in the wider field of smarter and more informed infrastructure systems
- …
