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Reinforced concrete structures are often subjected to corrosion, inducing a decreasing reliability over time. This corrosion often exhibits spatial variation, which can be modelled by random fields. To improve the accuracy of the estimated reliability, measurements can be performed. In this work, focus will be on static strain data. Taking into account these measurements leads to updated distributions of the corrosion parameters and an updated reliability. However, modelling errors will arise due to an inadequate choice of the spatial correlation model. These are present since the perfect model is not known. By assuming an inaccurate correlation model, a bias on the distribution of the reliability index might arise. In this work, the influence of this modelling error is investigated by assuming a different correlation model in the calibration of the model and the generation of the data. Different correlation models are considered and the most robust spatial correlation model is searched for. This problem is considered first for a simply supported beam subjected to corrosion, with the aim of identifying the most robust model. Next, it is investigated whether this model also performs well for a bridge structure. [ABSTRACT FROM AUTHOR]

The aging of large concrete structures such as Nuclear Containment Buildings (NCB) or bridges involves a continuous strain evolution in time, which may affect their durability, safety and the safety of their environment. Then, the evaluation of structural integrity requires an accurate assessment of the long-term strain level. When considering probabilistic analysis of the delayed mechanical behavior of large concrete structures, the prediction results may present large uncertainties, which do not provide clear indicators aiming at supporting decisions related to structures' maintenance. In this context, Bayesian updating enables to reduce uncertainties, by combining a prior state of knowledge with noisy monitoring data of the structure response. It requires the definition of a prior probability distribution, which summarizes all available information before collecting monitoring data. In former work concerning Bayesian approaches applied to the analysis of delayed strains, the prior distribution is usually defined through expert judgement, which constitutes a quite subjective process which may have a significant influence on Bayesian updating results. Moreover, the previously cited work involved strong hypotheses related to observation noise, which is usually assumed to be perfectly known. The present contribution aims at evaluating the influence of prior distributions defined by expert judgement on Bayesian updating results, through an illustrative case study of a well instrumented 1:3 scale NCB. The present work proposes also a Bayesian framework suitable for cases where the observation noise of data is unknown. The influence of the amount of monitoring data on the uncertainty reduction provided by Bayesian updating is also studied. Results underline that the modeling choices of the analyst are of paramount importance in the framework of long-term strains predictions, regardless the quantity of available data. Furthermore, results also suggest that Bayesian updating is well suitable for providing significant uncertainty reduction, even in the case of structures which dispose of a limited amount of monitoring data. [ABSTRACT FROM AUTHOR]

The traffic environment of a bridge generally varies over its lifetime and can be affected by unexpected changes in the surroundings such as the construction of new roads. Therefore, for accurate estimation of traffic loads, changes in the traffic environment need to be continuously monitored and incorporated into traffic load predictions. To this end, this study first further develops the comprehensive probabilistic model of bridge traffic loads by introducing micro-simulation models to describe accurately congestion state. Next, a Bayesian methodology is proposed to update the parameters of the distributions in the probabilistic model of bridge traffic loads based on in-service data representing the traffic environment. Three Bayesian inference methods are used: conjugate prior distributions, Bayesian linear regression, and Gibbs sampling. Hyper-parameters of the prior model are set up appropriately based on the measurement accuracy and the degrees of belief in the prior model. The proposed Bayesian updating methodology is demonstrated by numerical examples with various scenarios of traffic environment changes and in-service weigh-in-motion (WIM) data measured on a real bridge. The results confirm that the proposed methodology can successfully incorporate changes of the traffic environment into the estimation of traffic load effects on bridges in operation. [ABSTRACT FROM AUTHOR]

In low-to-moderate seismicity regions, seismic loss functions (SLFs) are barely established due to limited observational data, making it difficult to derive decision-making on disaster prevention and management. Herein, a Bayesian framework is developed to update the SLFs with limited observational data. The proposed point-based Bayesian method updates local probability density function parameters for damage ratios at each seismic intensity, which helps to avoid an unrealistic underestimation of damage ratios in the low-to-moderate range of seismic intensities. The feasibility of the developed framework in a low-to-moderate seismicity region is verified by the comparison between the updated SLF and post-event data. [ABSTRACT FROM AUTHOR]

Bayesian estimation of spatially varying soil parameters is a challenging task in geotechnical engineering because a large number of random variables need to be learned. To address this challenge, three Bayesian methods are revisited, including Differential Evolution Adaptive Metropolis with sampling from past states [DREAM(zs)] method, Bayesian Updating with Structural reliability methods using Subset Simulation (BUS + SS), and modified BUS with Subset Simulation (mBUS + SS). The differences between the performances (i.e. convergences, computational accuracies, and efficiencies) of these three methods are not well understood. This study systematically investigates the differences of these three methods in the generation of random samples, convergence criterion, model evidence, and estimation of posterior probability of failure in slope reliability updating. Two slope examples are used for the comparative study. It is found that the BUS + SS method performs well not only in the low-dimensional Bayesian inverse problems but also in the high-dimensional Bayesian inverse problems of spatially varying soil parameters. The DREAM(zs) method is preferentially recommended to deal with the low-dimensional Bayesian inverse problems whereas the mBUS + SS method can well tackle the high-dimensional problems. [ABSTRACT FROM AUTHOR]

Ageing infrastructure is becoming an increasing challenge as a result of deterioration and greater loading demands. Modern cities were built on top of complex underground infrastructure networks many of which are still in-service beyond their design life. The safety assessment of underground structures is of utmost importance to avoid catastrophic failures and develop cost-effective renewal and rehabilitation strategies. However, the lack of design documentation and absence of data on the level of structural deterioration make determination of current structural capacity a challenge. This paper presents a probabilistic based assessment framework for underground utility service pits using Bayesian updating technique, which is used to refine the probabilistic distribution of material properties from the prior distribution constructed using published data. A case study of an underground pit located in Central Melbourne is provided. Extensive experimental testing was conducted to characterise the material properties and a full-scale masonry wall was tested to understand the failure mode due to earth pressure and traffic load. The test data was used in strength prediction models to achieve a more accurate estimate for wall capacity. Further, the strength degradation models were integrated to develop the time-dependent material models, which were eventually used to compute reliability index. [ABSTRACT FROM AUTHOR]

Recently, some have challenged the idea that there are genuine norms of diachronic rationality. Part of this challenge has involved offering replacements for diachronic principles. Skeptics about diachronic rationality believe that we can provide an error theory for it by appealing to synchronic updating rules that, over time, mimic the behavior of diachronic norms. In this paper, I argue that the most promising attempts to develop this position within the Bayesian framework are unsuccessful. I sketch a new synchronic surrogate that draws upon some of the features of each of these earlier attempts. At the heart of this discussion is the question of what exactly it means to say that one norm is a surrogate for another. I argue that surrogacy, in the given context, can be taken as a proxy for the degree to which formal and traditional epistemology can be made compatible. [ABSTRACT FROM AUTHOR]

The author of this article describes a game-theory-based economics class on how people should, and do, form beliefs, communicate, and make decisions under uncertainty. Topics include Bayesian and non-Bayesian belief updating, the value of information, communication games, advertising, political media, and social learning. The only prerequisite is introductory microeconomics. The course also seeks to convey both the value of thinking in probabilities and awareness of mechanisms of strategic influence in students' everyday lives. [ABSTRACT FROM AUTHOR]

The runout distance of landslides is a critical factor that influences landslide risk quantification and mitigation designs. Nevertheless, empirical correlation models developed in a region may be biased when it is applied to landslides in another region, partly due to different geological formation process undergone in different regions. Besides, the development of a reliable correlation model for estimating runout distance generally requires a relatively large number of data points, collection of which is difficult and time-consuming. This study proposes a Bayesian method for efficiently developing region-specific correlation models to estimate runout distance. The method integrates systematically sparse data collected in a specific region and prior knowledge embedded in existing correlation models in other regions. The method is illustrated using both real and numerical examples. The results show that the proposed method is efficient and robust in developing region-specific correlation models for estimating runout distance. [ABSTRACT FROM AUTHOR]

Physical models for carbonation-induced reinforcement corrosion and concrete cracking have been proposed mainly based on accelerated carbonation tests. However, it has been criticised for not being representable of the natural corrosion process, and may not be applicable to real structures, so it is meaningful to calibrate and modify these models with in-situ inspection data of reinforced concrete (RC) structures in actual carbonation environment. The available in-situ data with the current inspection technologies (non-destructive) include concrete cover thickness, concrete strength, carbonation depth, reinforcement corrosion proportion, cracking proportion, etc. Based on Bayesian updating theorem, the paper proposes an integrated procedure for the updating of carbonation-induced deterioration models with inspection data, and then the durability assessment of existing RC structures in remaining service lives. In the procedure, the carbonation depth model and the probability of reinforcement corrosion are firstly updated with the inspected data, and then the un-carbonated depth model, the cracking probability and the critical corrosion depth model for cracking are updated subsequently. The methodology is illustrated using a case study of a RC industrial building with an age of 33 years. The influences of prior distribution and inspection data amount on the updating effect are discussed. Especially, the uncertainty associated with the number of corroded samples due to the error in corrosion inspection is discussed, and its effect on updated corrosion probability is investigated. [ABSTRACT FROM AUTHOR]

In many countries concrete bridges are reaching the end of their service-life, showing signs of deterioration, e.g. due to corrosion. Hence, the question arises whether their safety level is still acceptable. To improve the estimate of the safety level, information extracted from different tests, e.g. proof-loading, operational modal analysis, etc., where deflections, strains or accelerations are measured, can be used. Unfortunately, in practice inspection results are often not used directly to improve our knowledge of the degree of deterioration and it is difficult to combine information from different types of tests when making inferences. In this contribution, a methodology is developed and demonstrated in order to update estimations of parameters of service life models for concrete girders subjected to chloride-induced corrosion based on heterogeneous measurement data, with focus on strains measured under proof-loading and modal parameters extracted from ambient vibration tests. A Bayesian framework is adopted, where posterior distributions of the parameters describing the corrosion process are generated based on Markov Chain Monte Carlo sampling. These updated distributions reflect the information on the actual deterioration state of the bridge, which can be extracted from limited data. The updating procedure based on strain and modal data is illustrated by application on a simply supported beam and a case study. [ABSTRACT FROM AUTHOR]

Hindcasting of past levee failures enhances insights in the performance and vulnerability of levees. The scarcity of field evidence makes identifying the cause(s) of failure difficult. Under these circumstances, multiple scenarios and model choices are possible to characterise and to model the failure. This paper shows how probabilistic Bayesian techniques advance the procedure of hindcasting of levee failures. In the developed approach, a-priori levee information, and failure observations are systematically taken into account to determine the most likely scenario and the most representative model choices to characterise the failure most accurately. Observations, such as the slip surface, are taken into account in the probability estimates. The levee failure near Breitenhagen, Germany (2013) is used as a case study. The levee failed during river floods due the instability of the landside slope. The levee failure was most likely triggered by locally weak soil conditions and unexpected high water pressures due a connection between a pond on the riverside of the levee and the aquifer. These conditions were likely caused by the occurrence of a previous breach at this location. The approach developed in this paper is expected to support a more systematic and objective method of analysis of other levee failures. [ABSTRACT FROM AUTHOR]

Focused factories are one of the new manufacturing trends for automotive suppliers. A key requirement for these suppliers is the ability to accurately estimate both product and non-product related investment in these facilities to quote profitable business. We propose a systematic Bayesian framework to estimate non-product related investment in focused factories. Our approach incorporates uncertainty in the activity-based costing method and applies Monte Carlo simulation process to generate distributions of investment for the cost centres, and for the different project phases in setting up a facility. A Bayesian-updating procedure is introduced to improve parameter estimations as new information becomes available with experience in setting up these facilities. Our approach is deployed at a leading global automotive tier-one supplier, Visteon Corporation. The efficacy of the Visteon-focused factory cost model is validated using subject matter experts as well as by comparing the model results with estimates from the typical current process. [ABSTRACT FROM PUBLISHER]

We investigate if house prices are affected by the overconfidence of households who predict house prices using imperfect public information about economic outlook. For this purpose, we develop a new measure of household overconfidence in the Bayesian framework. For the three variables we test – changes in consumption, stock returns, and changes in human capital, we find that UK households were overconfident about the signals of consumption regardless of regions. However, households in London were overconfident about the signals of stock markets whereas those remote from London were overconfident about the signals of human capital. The results of household overconfidence appear positive in the UK housing market for our sample period from 1980 to 2018, in particular, 0.5% per quarter in London. [ABSTRACT FROM AUTHOR]

This paper investigates a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at the jump times of a homogeneous Poisson process. Drift estimates are based on Kalman filter techniques and described by the conditional mean and covariance matrix of the drift given the observations. We study the filter asymptotics for increasing arrival intensity of expert opinions and prove that the conditional mean is a consistent drift estimator; it converges in the mean-square sense to the hidden drift. Thus, in the limit as the arrival intensity goes to infinity investors have full information about the drift. [ABSTRACT FROM AUTHOR]

A trial embankment constructed on soft clay has been back analysed using Bayesian updating of parameters in a one-dimensional geotechnical model. The purpose of the back analysis was to demonstrate that Bayesian updating can be used to fit the data and to predict the final settlement. There was a limited amount of laboratory test data and site investigation data available, as is normal commercial practice. The initial material parameters were deemed to be biased due to sample disturbance and its effect on laboratory test data. The monitoring data comprised surface settlement measurements at the centreline of the embankment. The embankment was constructed in two stages over a period of 7 years. Bayesian updating of parameters using settlement was able to fit the entire time-settlement history. [ABSTRACT FROM AUTHOR]

The paper presents a new methodology for reassessing hull-girder ultimate strength, assuming that new data become available on a specific time instance during the service life of the vessel. The calculation of ultimate strength is based on Smith's incremental-iterative method and is examined in probabilistic terms. Uncertainties are associated with material yield strength, Young's modulus and time-variant corrosion thickness wastage of structural members. The reassessment of ultimate strength is achieved by updating the time-variant corrosion model, taking into advantage on-site observations from thickness measurements reports, via the framework of Bayesian Updating. The results are discussed, along with recommendations for the validation of the suggested approach in practice. The proposed methodology is a tool to reassess the strength of vessel and can also be used for structural reliability analysis on vessels with similar operational and structural profile. [ABSTRACT FROM AUTHOR]

This article presents new ground-motion prediction equations for three distinct seismic regions of Iran via updating the previous global model using observed data for each region by means of Bayesian updating. The Bayesian theory has the advantage that it results in more accurate results even in situations when little data is available. This leads the way for updating global models to obtain new local models for seismotectonic regions with little available data like Iran. The proposed updated model was compared against currently available models for Iran and the results reveal the overall stability and quality performance of the proposed model. [ABSTRACT FROM PUBLISHER]

Residual life (RL) estimation plays an important role in prognostics and health management. In operating conditions, components usually experience stresses continuously varying over time, which have an impact on the degradation processes. This paper investigates a Wiener process model to track and predict the RL under time-varying conditions. The item-to-item variation is captured by the drift parameter and the degradation characteristic of the whole population is described by the diffusion parameter. The bootstrap method and Bayesian theorem are employed to estimate and update the distribution parameters of ‘a’ and ‘b’, which are the coefficients of the linear drifting process in the degradation model. Once new degradation information becomes available, the RL distributions considering the future operating condition are derived. The proposed method is tested on Lithium-ion battery devices under three levels of charging/discharging rates. The results are further validated by a simulation method. [ABSTRACT FROM PUBLISHER]

Manufacturing parts with target properties and quality in Laser-Based Additive Manufacturing (LBAM) is crucial toward enhancing the "trustworthiness" of this emerging technology and pushing it into the mainstream. Most of the existing LBAM studies do not use a systematic approach to optimize process parameters (e.g., laser power, laser velocity, layer thickness, etc.) for desired part properties. We propose a novel process optimizationmethod that directly utilizes experimental data from previous studies as the initial experimental data to guide the sequential optimization experiments of the current study. This serves to reduce the total number of time- and cost-intensive experiments needed. We verify our method and test its performance via comprehensive simulation studies that test various types of prior data. The results show that our method significantly reduces the number of optimization experiments, compared with conventional optimization methods. We also conduct a real-world case study that optimizes the relative density of parts manufactured using a Selective LaserMelting system. A combination of optimal process parameters is achieved within five experiments. [ABSTRACT FROM AUTHOR]

This study introduces a transition probability-based Bayesian updating (BU) approach for spatial classification through expert system. Transition probabilities are interpreted as expert opinions for updating the prior marginal probabilities of categorical response variables. The main objective of this paper is to provide a spatial categorical variable prediction method which has a solid theoretical foundation and yields relatively higher classification accuracy compared with conventional ones. The basic idea is to first build a linear Bayesian updating (LBU) model that corresponds to an application of Bayes’ theorem. Since the linear opinion pool is intrinsically suboptimal and underconfident, the beta-transformed Bayesian updating (BBU) model is proposed to overcome this limitation. Another type of BU approach, conditional independent Bayesian updating (CIBU), is derived based on conditional independent experts. It is shown that traditional Markovian-type categorical prediction (MCP) is equivalent to a particular CIBU model with specific parameters. As three variants of the BU method, these techniques are illustrated in synthetic and real-world case studies, comparison results with both the LBU and MCP favor the BBU model. [ABSTRACT FROM PUBLISHER]

Estimating a deterministic single value for model parameters when reconstructing the system response has a limited meaning if one considers that the model used to predict its behaviour is just an idealization of reality, and furthermore, the existence of measurements errors. To provide a reliable answer, probabilistic instead of deterministic values should be provided, which carry information about the degree of uncertainty orplausibilityof those model parameters providing one or more observations of the system response. This is widely-known as the Bayesian inverse problem, which has been covered in the literature from different perspectives, depending on the interpretation or the meaning assigned to theprobability. In this paper, we revise two main approaches: the one that uses probability as logic, and an alternative one that interprets it as information content. The contribution of this paper is to provide an unifying formulation from which both approaches stem as interpretations, and which is more general in the sense of requiring fewer axioms, at the time the formulation and computation is simplified by dropping some constants. An extension to the problem of model class selection is derived, which is particularly simple under the proposed framework. A numerical example is finally given to illustrate the utility and effectiveness of the method. [ABSTRACT FROM PUBLISHER]

This article presents a case study involving the assessment of an existing bridge, starting with simple methods and ending with a probabilistic analysis, the latter emphasising Bayesian methods. When assessing an existing bridge, it is common practice to collect information from the bridge in the form of samples. These samples are in general of small size, raising the question of how the corresponding statistical uncertainty can be taken into account on reliability estimates. The case study illustrates how Bayesian methods are especially suitable to deal with that source of uncertainty. Another strong point of the Bayesian methods is their ability to combine the information contained in the samples collected from the bridge with prior information, if any. This aspect will also be illustrated through the case study. [ABSTRACT FROM PUBLISHER]

The load-life exponents used in the modified life rating equation for rolling element bearings were determined by statistical analysis of the experimental data generated in the 1940s, following Lundberg and Palmgren's seminal work. Based on fracture mechanics arguments, the fatigue life is known to be inversely proportional to the square root of the size of the nonmetallic inclusions. However, modern high-performance vacuum induction melt–vacuum arc remelt (VIMVAR) bearing steels are clean and nonmetallic inclusions are no longer the weak link. Fatigue life predictions (L10life) for modern bearings using the modified load-life relations greatly underpredict observed life. Hence, there is a need to update parameters of these equations using more recent life data. Based on the endurance data reported in Harris and McCool (1), validation analysis of the modified life rating equation was performed to reevaluate the values of load-life exponent for both ball and cylindrical roller bearings. The results from this study indicate that the load-life exponent for ball bearings should be 4.1, instead of 3, and for roller bearings it should be 5.5, instead of 3.33. BearingL10life calculated using the corrected load-life exponents values shows better agreement with observed life. Details of the sampling technique used for reducing epistemic uncertainty in experimental data and the process of statistical reevaluation using Bayesian updating are discussed in detail. The accuracy of reevaluated results is presented using logarithmic plots of the ratio of predicted to actual fatigue lives for all data samples. [ABSTRACT FROM PUBLISHER]

Risk management for landslides involves, in addition to active and passive countermeasures, the collection of information through exploration. With such information, it is possible to reduce uncertainties, make more reliable decisions and therefore reduce risk. This paper addresses two types of exploration, namely (1) exploration (information collection) after the decision to obtain additional information is made, which is the standard type of exploration, and where one uses the new information to update risk, and (2) exploration (information collection) before the decision to obtain additional information is made, in which one conducts ‘virtual exploration’ to establish if exploration is worthwhile. The paper shows that both types of exploration can be assessed using either decision trees or Bayesian networks. Both approaches were applied to the Walton's Wood Landslide in England using infinite slope analysis and produced consistent results. The interested user can, based on what is documented in the paper, select and apply either one or both of the approaches. [ABSTRACT FROM AUTHOR]

This article presents a model of the capacity investment problem faced by pharmaceutical firms and other firms with long and risky product development cycles. These firms must balance two conflicting objectives: on one hand, the delay in scaling up production once the product is approved must be minimized and, on the other hand, the risk of investing in ultimately unused capacity must be minimized. A stylized model of this type of capacity investment problem is developed and analyzed. In this model the firm re-evaluates its capacity investment strategy as information about the potential success of the product is continually updated (for example, via clinical trial results in the case of the pharmaceutical industry). Motivated by observations of current practices in the biopharmaceutical industry, a computational study is used to explore how practices such as more frequent re-evaluations of investment decisions, stopping and restarting of projects, and the use of alternative types of capacity can, under certain conditions, help the firm reduce both the delay of the commercial launch of the new product and the risk of lost investment. [ABSTRACT FROM PUBLISHER]

Product reliability in the field is important for a wide variety of critical applications such as manufacturing, transportation, power generation, and health care. In particular, the propensity of achieving zero-downtime emphasizes the need for Remaining Useful Life (RUL) prediction for a single unit. The task is quite challenging when the unit is subject to time-varying operating conditions. This article provides a framework for predicting the RUL of a single unit under time-varying operating conditions by incorporating the results of both accelerated degradation testing andin situcondition monitoring. For illustration purposes, the underlying degradation process is modeled as a Brownian motion evolving in response to the operating conditions. The model is combined within situdegradation measurements of the unit and the operating conditions to predict the unit's RUL through a Bayesian technique. When the operating conditions are piecewise constant, statistical approaches using a conjugate prior distribution and Markov chain Monte Carlo approach are developed for cases involving linear and non-linear degradation–stress relationships, respectively. The proposed framework is also extended to handle a more complex case where the projected future operating conditions are stochastic. Simulation experiments and a case study for ball bearings are used to verify the prediction capability and practicality of the framework. In the case study, a quantile regression technique is proposed to handle load-dependent failure threshold values in RUL prediction. [ABSTRACT FROM PUBLISHER]

According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool andAISI 1018steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forgedAISI 4137chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well. [ABSTRACT FROM PUBLISHER]

Flood defence levees are crucial for flood protection, especially in riverine and deltaic areas. One of the most critical failure modes of levees is backwards internal erosion or piping. Piping reliability is largely influenced by uncertainties in ground conditions, especially by geohydrological properties such as the hydraulic conductivity of an aquifer. These properties are typically highly uncertain, which can lead to the related probabilities of failure being considerably high. Ways to reduce such uncertainties are by performing additional site investigation, incorporating information from field performance observations or monitoring. After providing some background on uplift and piping, the paper shows how Bayesian posterior analysis can be done using head monitoring observations in aquifers during extreme floods. The approach will be demonstrated by means of an illustrative example using a simplified uplift model showing also how the posterior distributions of the basic random variables change with respect to their priors. Subsequently, it will be illustrated how uncertain future measurements can be used in pre-posterior analysis to support decisions on investments in monitoring. The results suggest that the uncertainties in geohydrological conditions can be reduced significantly and that, if the monitoring cost is low compared to potential retrofitting measures, monitoring can be cost-effective. [ABSTRACT FROM AUTHOR]

In this paper, an approach for integrating the information obtained from structural health monitoring in a life-cycle bridge management framework is proposed. The framework is developed on the basis of life-cycle system performance concepts that are also presented in this paper. The performance of the bridge is quantified by incorporating prior knowledge and information obtained from structural health monitoring using Bayesian updating concepts. This performance is predicted in the future using extreme value statistics. Advanced modelling tools and techniques are used for the lifetime reliability computations, including incremental nonlinear finite element analyses, quadratic response surface modelling using design of experiments concepts, and Latin hypercube sampling, among other techniques. The methodology is illustrated on an existing bridge in the state of Wisconsin. [ABSTRACT FROM AUTHOR]

The article presents a study which describes the safety assessments of the current state and development of existing European bridge structures based on the Eurocodes. The researchers have reviewed the key elements in bridge assessments including the redundancy and robustness criteria, the proof load testing, and the field measurements and diagnostic test data. The study recommends to improve the current evaluation methods in the development of the new assessment Eurocode for bridges.

Advancements in condition monitoring techniques have facilitated the utilization of sensor technology for predicting failures of engineering systems. Within this context, failure is defined as the point where a sensor-based degradation signal reaches a pre-specified failure threshold. Parametric degradation models rely on complete signals to estimate the parametric functional form and do not perform well with sparse historical data. On the other hand, non-parametric models that address the challenges of data sparsity usually assume that signal observations can be made beyond the failure threshold. Unfortunately, in most applications, degradation signals can only be observed up to the failure threshold resulting in what this article refers to as truncated degradation signals. This article combines a non-parametric degradation modeling framework with a signal transformation procedure, allowing different types of truncated degradation signals to be characterized. This article considers (i) complete signals that result from constant monitoring of a system up to its failure; (ii) sparse signals resulting from sparse observations; and (iii) fragmented signals that result from dense observations over disjoint time intervals. The goal is to estimate and update the residual life distributions of partially degraded systems using in situ signal observations. It is showed that the proposed model outperforms existing models for all three signal types. [ABSTRACT FROM PUBLISHER]

This work outlines a probabilistic model for predicting temporal evolution of the internal corrosion damage depth in active sites of pipelines. The evolution of damage is specified by a stochastic process with stationary independent increments and is characterised by the state of damage at a given time instant from the propagation function that describes damage velocity. The propagation function is related with a Bayesian model that can incorporate all the available information such as inspections and velocities obtained from coupons or laboratory measurements. The quality of inspection is expressed in terms of detection capacity of the inspection tool and the errors in measurements. The model robustness lies in the fact that corrosion damage is specified in terms of a physical measure and is usable under practical conditions. Corrosion damage velocities are based on measurements, which implicitly consider multiple factors such as aggressive chemical environment, microstructure, operating conditions intervening in the corrosion process, as well as their variability. The application of the model is exemplified using inspection reports of two industrially operated pipeline systems that transport oil products. [ABSTRACT FROM PUBLISHER]

This paper provides an innovative approach to help clinicians decide when to start radiation therapy in prostate cancer patients. The decision is based on predictions of the time when the patient's prostate specific antigen (PSA) level reaches its lowest point (nadir). These predictions are based on a log quadratic model for how the PSA level changes over time. The distribution of the time of the PSA nadir (which might be linked to maximal tumor regression) is derived from an approximation to the ratio of two correlated normal random variables. Using a dynamic Kalman filter model, the parameter estimates are updated as new patient information becomes available. Clustering is incorporated to improve prior estimates of the curve parameters. The model balances the risk of beginning radiation therapy too soon so that hormone therapy has not achieved its maximum effect vs. waiting too long to start therapy so that there is an increased risk of tumor cells becoming resistant to the treatment. A comparison of clinically implementable policies (cumulative probability policy and threshold probability policy) based on this new approach is applied to a cohort of prostate cancer patients. It shows that our approach outperforms the current protocol. [ABSTRACT FROM PUBLISHER]

One of the most challenging aspects of the seismic assessment of existing buildings is the characterization of structural modeling uncertainties. Recent codes, such as Eurocode 8, seem to synthesize the effect of structural modeling uncertainties in the so-called confidence factors that are applied to mean material property estimates. The confidence factors are classified and tabulated as a function of discrete knowledge levels acquired based on the results of specific in-situ tests and inspections. In this approach, the effect of the application of the confidence factors on structural assessment is not explicitly stated. This work presents probabilistic performance-based proposals for seismic assessments of RC buildings based on the knowledge levels. These proposals take advantage of the Bayesian framework for updating the probability distributions for structural modeling parameters based on the results of tests and inspections. As structural modeling parameters, both the mechanical material properties and also the structural detailing parameters are considered. These proposals can be categorized based both on the amount of structural analysis effort required and on the type of structural analysis performed. An efficient Bayesian method is presented which relies on simplified assumptions and employs a small sample of structural model realizations and ground motion records in order to provide an estimate of structural reliability. As an alternative proposal suitable for code implementation, the simplified approach implemented in the SAC-FEMA guidelines is adapted to existing structures by employing the efficient Bayesian method. This method takes into account the effect of both ground motion uncertainty and the structural modeling uncertainties on the global performance of the structure, in a closed-form analytical safety-checking format. These alternative proposals are demonstrated for the case study structure which is an existing RC frame. In particular, it is shown how the parameters for the safety-checking format can be estimated and tabulated as a function of knowledge level, outcome of tests, and the type of structural analysis adopted. [ABSTRACT FROM AUTHOR]

We develop a Bayesian framework for estimating the means of two random variables when only the sum of those random variables can be observed. Mixture models are proposed for establishing conjugacy between the joint prior distribution and the distribution for observations. Among other desirable features, conjugate distributions allow Bayesian methods to be applied in sequential decision problems. [ABSTRACT FROM AUTHOR]

Preventative maintenance (PM) includes proactive maintenance actions that aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds because it can extend the life of bridges and avoid the need for unplanned essential maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed an optimisation methodology based on genetic algorithm (GA) principles, which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. To further improve the reliability of estimating the degree of deterioration of an element, which is a key element in predicting optimum PM strategies using the GA methodology, Bayesian updating is utilised. The use of Bayesian updating enables the updating of the probability of failure based on data from site inspection or laboratory experiments and the adjustment, if necessary, of the timing of subsequent PM interventions. For the case study presented in this paper, the probability of failure is expressed as the probability of corrosion initiation of a reinforced concrete element due to de-icing salt. [ABSTRACT FROM AUTHOR]

Many concrete structures can be considered as large parts of engineering systems. For these, realistic modelling of deterioration and the planning of inspection and maintenance activities requires explicit consideration of the interdependencies among the individual elements of the structure. Ageing of the infrastructure has become increasingly recognized by engineers and decision makers as being important, yet relatively few research projects have addressed this topic to date. The present paper provides an overview of the decision problems related to the planning of inspection, monitoring and maintenance activities in large engineering systems. This is followed by the presentation of a model framework for the representation of spatial and temporal variability of deterioration, illustrated by consideration of chloride-induced corrosion of the reinforcement in concrete structures. The framework is computationally highly efficient and facilitates the consistent, quantitative modelling of the risks from deterioration for the entire system at all points in time. Finally, it is demonstrated how the framework can be applied to optimize the planning of inspection and maintenance activities for large concrete structures. [ABSTRACT FROM AUTHOR]

This article reviews recent conceptual debates on cross-national and cross-sectoral policy-learning in the political science literature. It proceeds from the argument that the existing literature is characterised by the absence of a comparative assessment of the risks and potentials of different strategies of policy learning. This sin of omission does not only have significant implications for the study of policy learning but also for its practice. The authors use the normative concept of improvement-oriented learning to assess the risks and potentials of three learning strategies: imitation, Bayesian updating and deliberation. They observe that the distribution of risks and potentials is most advantageous in deliberative learning strategies, but that imitation is the most risky learning strategy, and Bayesian updating ranges somewhere in-between. [ABSTRACT FROM AUTHOR]

Condition monitoring uses sensory signals to assess the health of engineering systems. A degradation model is a mathematical characterization of the evolution of a condition signal. Our recent research focuses on using degradation models to compute residual-life distributions for degrading components. Residual-life distributions are important for providing probabilistic estimates of failure time for use in maintenance planning and spare parts inventory management. To obtain residual-life distributions, our earlier work assumed the degradation model's stochastic parameters to be normally distributed. This paper investigates the performance of these residual-life distributions when the underlying normality assumptions are not satisfied. The paper also develops methods for estimating residual-life when the stochastic parameters of the degradation model follow more general distributions. [ABSTRACT FROM AUTHOR]

The author describes a protocol for classroom experiments for courses that introduce undergraduates to signaling games. Signaling games are conceptually difficult because, when analyzing the game, students are not naturally inclined to think in probabilistic, Bayesian terms. The experimental design explicitly presents the posterior frequencies of the unobserved events. The protocol's emphasis on the posterior enhances convergence to the equilibrium prediction, relative to a treatment in which posterior frequencies are not explicitly computed. This convergence reinforces the development of the theory in subsequent lecture periods. [ABSTRACT FROM AUTHOR]

The present article considers a special class of structural systems for which condition control has proven to constitute a real problem, namely attachments such as e.g., fasteners of façade systems, anchors of retaining walls, rivets in riveted connections, riser attachments of offshore platforms, and many other similar types of structures. Such structural systems are comprised of a large number of quasi-identical components under relatively homogeneous conditions for what concerns both resistances and loads. The problem of condition control is addressed in the context of Bayesian reliability updating; utilizing proof-load tests of a few individual components for the purpose of gaining knowledge about all components. The optimization of proof-load test strategies in regard to the number of components to be tested and the load intensity to be applied is discussed. Two examples are given to illustrate the use and the benefits of the proposed methodology. [ABSTRACT FROM AUTHOR]

Deterioration models for the condition and reliability prediction of civil infrastructure facilities involve numerous assumptions and simplifications. Furthermore, input parameters of these models are fraught with uncertainties. A Bayesian methodology has been developed by the authors, which uses information obtained through health monitoring to improve the quality of prediction. The sensitivity of prior and posterior predicted performance to different input parameters of the deterioration models, and the effect of instrument and measurement uncertainty, is investigated in this paper. The results quantify the influence of these uncertainties and highlight the efficacy of the updating methodology based on integrating monitoring data. It has been found that the probabilistic posterior performance predictions are significantly less sensitive to most of the input uncertainties. Furthermore, updating the performance distribution based on ‘event’ outcomes is likely to be more beneficial than monitoring and updating of the input parameters on an individual basis. [ABSTRACT FROM AUTHOR]

Our goal is to study general properties of one Bayesian's subjective beliefs about the behavior of another Bayesian's subjective beliefs. We consider two Bayesians, A and B, who have different subjective distributions for a parameter θ and study Bayesian A's expectation of Bayesian B's posterior distribution for θ given some data Y. We show that when θ can take only two values, Bayesian A always expects Bayesian B's posterior distribution to he between the prior distributions of A and B. Conditions are given under which a similar result holds for an arbitrary real-valued parameter θ. For a vector parameter θ we present useful expressions for the mean vector and covariance matrix of A's expectation of B's posterior distribution. Examples are given illustrating the relevance of the conditions under which the results are derived. [ABSTRACT FROM AUTHOR]

Consider a problem in which you and a group of other experts must report your individual predictive distributions for an observable random variable X to some decision maker. Suppose that the report of each expert is assigned a prior weight by the decision maker and that these weights are then updated based on the observed value of X. In this situation you will try to maximize your updated, or posterior, weight by appropriately choosing the distribution that you report, rather than necessarily simply reporting your honest predictive distribution. We study optimal reporting strategies under various conditions regarding your knowledge and beliefs about X and the reports of the other experts, and under various utility functions for your posterior weight. We present the only utility functions for which it is always optimal to report your honest predictive distribution. Attention is restricted to problems in which X can take only a finite number of values. [ABSTRACT FROM AUTHOR]

This article considers a version of the so-called best-choice problem. The buyer receives a random sample of price quotations for a good and desires to buy the good at as low a price as possible. After each price quotation is received, the buyer must decide either to accept the price or not, with the objective of maximizing the probability of buying the good at the lowest price. When the distribution of the price quotation is completely known, the optimal buying policy is myopic. The main purpose of this article is to show that this myopic property holds for some interesting price distributions that are updated in a Bayesian manner as successive prices are received. [ABSTRACT FROM AUTHOR]

Bayesian estimation incorporating prior information has been a popular approach to gaining estimation efficiency. Although prior information can take a variety of forms, generalizations derived from meta-analyses have been suggested as being useful. This article shows that these priors can be problematic in light of the many empty cells observed in meta-analysis designs. Design reduction, which gives rise to an unbiased prior, is found to be the preferred solution. [ABSTRACT FROM AUTHOR]