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Prioritizing multiple potentially unstable slopes based on varying levels of criticality is an essential approach when resources are limited. This study presents a framework for planning the prioritization of unstable rock slope remediation. The prioritization of mitigation measures for these slopes is determined using deformation values derived from indirect information, such as rock classification systems. To demonstrate the application of this framework, a case study involving unstable rock slopes in southwestern China is presented. Initially, the most appropriate estimation model was chosen from among six candidates for estimating the deformation modulus of the unstable rock mass in the slope, utilizing indirect information. Subsequently, the deformation modulus, updated through Bayesian analysis, was employed to generate the corresponding deformation distributions for the rock slopes. Compared to traditional quantitative methods, the proposed framework is adaptable to various working conditions and yields more definitive results. The indirect data used in this framework are cost-effective, enabling the assessment of all unstable slopes. Moreover, the effect of the amount of indirect data and prior information on slope remediation prioritization is discussed. The proposed framework offers a valuable tool for decision-makers in planning and prioritizing mitigation measures.

Recently, the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly. However, updating the ground settlement profile, which is crucial for determining potential damage to nearby infrastructures, has received limited attention. To address this, this paper proposes a physics-guided simplified model combined with a Bayesian updating framework to accurately predict the ground settlement profile. The advantage of this model is that it eliminates the need for complex finite element modeling and makes the updating framework user-friendly. Furthermore, the model is physically interpretable, which can provide valuable references for construction adjustments. The effectiveness of the proposed method is demonstrated through two field case studies, showing that it can yield satisfactory predictions for the settlement profile.

Background: The main task of applied sport science is to inform decision-making in sports practice, that is, enabling practitioners to compare the expectable outcomes of different options (e.g. training programs). Main Body: The "evidence" provided may range from group averages to multivariable prediction models. By contrast, many decisions are still largely based on the subjective, experience-based judgement of athletes and coaches. While for the research scientist this may seem "unscientific" and even "irrational", it is important to realize the different perspectives: science values novelty, universal validity, methodological rigor, and contributions towards long-term advancement. Practitioners are judged by the performance outcomes of contemporary, specific athletes. This makes out-of-sample predictive accuracy and robustness decisive requirements for useful decision support. At this point, researchers must concede that under the framework conditions of sport (small samples, multifactorial outcomes etc.) near certainty is unattainable, even with cutting-edge methods that might theoretically enable near-perfect accuracy. Rather, the sport ecosystem favors simpler rules, learning by experience, human judgement, and integration across different sources of knowledge. In other words, the focus of practitioners on experience and human judgement, complemented—but not superseded—by scientific evidence is probably street-smart after all. A major downside of this human-driven approach is the lack of science-grade evaluation and transparency. However, methods are available to merge the assets of data- and human-driven strategies and mitigate biases. Short Conclusion: This work presents the challenges of learning, forecasting and decision-making in sport as well as specific opportunities for turning the prevailing "evidence vs. eminence" contrast into a synergy. Key Points: Generally, decision making in sports practice is based on "evidence " from scientific research or on "eminence" based on experience and subjective human judgment. Under the framework conditions of sport—specifically complexity and sparse data—both come with limitations that lead to considerable uncertainty when making decisions in practice. A broader range of strategies, smart strategy combinations and critical evaluation of performance under real-world conditions contribute to better decisions and better outcomes in the field of sport and exercise. [ABSTRACT FROM AUTHOR]

Accurate prediction of ground surface settlements induced by shield construction is of great significance for ensuring the safety of shield construction. This paper proposes a ground surface settlement prediction method for shield tunneling based on Bayesian updating. The sequential observation data during the advance of excavation is utilized to update the key soil parameters, leading to a more accurate settlement prediction for the subsequent excavation stages. Response surfaces are constructed to replace the finite element model as the forward models for higher computational efficiency. A tunnel excavation project in Hangzhou, China, is selected to illustrate the effectiveness of the proposed method. The shield excavation face passes through four soil layers, and two soil parameters (i.e., Young’s modulus and friction angle) of these soil layers are selected as random variables to be updated. The results show that the soil parameters can be effectively updated based on the observation data at multiple points and various excavation stages. The predictions of ground surface settlements are improved by using the updated soil parameters. The prediction accuracy of the proposed method increases as more stages of observation data are sequentially obtained and incorporated.

The nation's transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic health of communities, transportation resilience has gained increasing attention in recent years. Previous studies on transportation resilience have heavily emphasized network functionality during and/or following a scenario hazard event by implicitly assuming that sufficient knowledge of structural capacity and environmental/service conditions is available at the time of an extreme event. However, such assumptions often fail to consider uncertainties that arise when an extreme hazard event occurs in the future. Thus, it is essential to quantify and reduce uncertainties to better prepare for extreme events and accurately assess transportation resilience. To this end, this paper proposes a dynamic Bayesian network-based resilience assessment model for a large-scale roadway network that can explicitly quantify uncertainties in all phases of the assessment and investigate the role of inspection and monitoring programs in uncertainty reduction. Specifically, the significance of data reliability is investigated through a sensitivity analysis, where various sets of data having different reliabilities are used in updating system resilience. To evaluate the effectiveness of the model, a benchmark problem involving a highway network in South Carolina, USA is utilized, showcasing the systematic quantification and reduction of uncertainties in the proposed model. The benchmark problem result shows that incorporating monitoring and inspection data on important variables could improve the accuracy of predicting the seismic resilience of the network. It also suggests the need to consider equipment reliability when designing monitoring and inspection programs. With the recent development of a wide range of monitoring and inspection techniques, including nondestructive testing, health monitoring equipment, satellite imagery, LiDAR, etc., these findings can be useful in assisting transportation managers in identifying necessary equipment reliability levels and prioritizing inspection and monitoring efforts. [ABSTRACT FROM AUTHOR]

The nation’s transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic health of communities, transportation resilience has gained increasing attention in recent years. Previous studies on transportation resilience have heavily emphasized network functionality during and/or following a scenario hazard event by implicitly assuming that sufficient knowledge of structural capacity and environmental/service conditions is available at the time of an extreme event. However, such assumptions often fail to consider uncertainties that arise when an extreme hazard event occurs in the future. Thus, it is essential to quantify and reduce uncertainties to better prepare for extreme events and accurately assess transportation resilience. To this end, this paper proposes a dynamic Bayesian network-based resilience assessment model for a large-scale roadway network that can explicitly quantify uncertainties in all phases of the assessment and investigate the role of inspection and monitoring programs in uncertainty reduction. Specifically, the significance of data reliability is investigated through a sensitivity analysis, where various sets of data having different reliabilities are used in updating system resilience. To evaluate the effectiveness of the model, a benchmark problem involving a highway network in South Carolina, USA is utilized, showcasing the systematic quantification and reduction of uncertainties in the proposed model. The benchmark problem result shows that incorporating monitoring and inspection data on important variables could improve the accuracy of predicting the seismic resilience of the network. It also suggests the need to consider equipment reliability when designing monitoring and inspection programs. With the recent development of a wide range of monitoring and inspection techniques, including nondestructive testing, health monitoring equipment, satellite imagery, LiDAR, etc., these findings can be useful in assisting transportation managers in identifying necessary equipment reliability levels and prioritizing inspection and monitoring efforts.

Bayesian updating (BU) tools are increasingly used in nuclear engineering for inverse uncertainty quantification (IUQ) and calibration combined with uncertainty reduction. Their goals are quantifying or reducing the uncertainty of some of the uncertain model input parameters. Since it is often the case that available experimental data only allows for updating the most influential input parameters, researchers often ignore the less important ones during BU. This paper explores the consequences of neglecting the uncertainties of uncalibrated model input parameters (UMIP). It also proposes how to include them properly and which BU algorithms are the best choices for various types of inverse problems. The analysis is based on exploring two toy problems and one in nuclear engineering concerning multiplication factor calculations. The results clearly show that the improper treatment of UMIP during BU often leads to underestimating posterior uncertainties — either of the calibrated input parameters or the simulated integral parameters, depending on how the BU was conducted. The proposed methods of correct UMIP treatment will improve the rigorousness of the BU processes and boost confidence in the resulting posterior distributions. • Researchers often limit Bayesian updating to most influential model parameters. • Ignoring uncertainties of unupdated parameters leads to incorrect posterior results. • Rigorous methodology for unupdated parameters' treatment is proposed. • Most popular Bayesian updating algorithms and their applicability are discussed. [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]

This work proposes a new approach, based on Bayesian updating and extreme value statistics to determine the maximum magnitudes for truncated magnitude-frequency distributions such as the Gutenberg Richter model in the framework of Probabilistic Seismic Hazard Analyses. Only the maximum observed magnitude and the associated completeness period are required so that the approach is easy to implement and there is no need to determine and use the completeness periods for smaller events. The choice of maximum magnitudes can have a major impact on hazard curves when long return periods as required for safety analysis of nuclear power plants are considered. Here, not only a singular value but a probability distribution accounting for prior information, data and uncertainty is provided. Moreover, uncertainties related to magnitude frequency distributions, including the uncertainty related to the maximum observed magnitude are discussed and accounted for. The accuracy of the approach is validated based on simulated catalogues with various parameter values. Then the approach is applied to French data for a specific region characterized by high-seismic activity in order to determine the maximum magnitude distribution and to compare the results to other approaches. [ABSTRACT FROM AUTHOR]

Highlights • Bhattacharyya distance is proposed as a novel UQ metric in stochastic model updating. • The proposed likelihood is a capable connection between UQ metrics and updating tool. • Both Bhattacharyya and Euclidian distances are utilized in the updating framework. • Bhattacharyya distance is more comprehensive than Euclidian distance as a UQ metric. • Calculation is significantly reduced thanks to the approximate Bayesian computation. Abstract The Bhattacharyya distance is a stochastic measurement between two samples and taking into account their probability distributions. The objective of this work is to further generalize the application of the Bhattacharyya distance as a novel uncertainty quantification metric by developing an approximate Bayesian computation model updating framework, in which the Bhattacharyya distance is fully embedded. The Bhattacharyya distance between sample sets is evaluated via a binning algorithm. And then the approximate likelihood function built upon the concept of the distance is developed in a two-step Bayesian updating framework, where the Euclidian and Bhattacharyya distances are utilized in the first and second steps, respectively. The performance of the proposed procedure is demonstrated with two exemplary applications, a simulated mass-spring example and a quite challenging benchmark problem for uncertainty treatment. These examples demonstrate a gain in quality of the stochastic updating by utilizing the superior features of the Bhattacharyya distance, representing a convenient, efficient, and capable metric for stochastic model updating and uncertainty characterization. [ABSTRACT FROM AUTHOR]

This study proposes a method for inversely estimating the spatial distribution characteristic of a material’s elastic modulus using the measured value of the observation data and the distance between the measurement points. The structural factors in the structural system possess temporal and spatial randomness. One of the representative structural factors, the material’s elastic modulus, possesses temporal and spatial randomness in the stiffness of the plate structure. The structural factors with randomness are typically modeled as having a certain probability distribution (probability density function) and a probability characteristic (mean and standard deviation). However, this method does not consider spatial randomness. Even if considered, the existing method presents limitations because it does not know the randomness of the actual material. To overcome the limitations, we propose a method to numerically define the spatial randomness of the material’s elastic modulus and confirm factors such as response variability and response variance.

We investigate the cost of the opportunity delayed by working on one project with uncertain success rather than searching for a new project. We answer this question: How long should a firm work on a research project with uncertain success before abandoning it if the only alternative is to search for a new project to work on? Rather than treating the opportunity as an exogenous alternative, this approach endogenizes the opportunity value and the attendant cost of its delay. We consider cases with both a finite and an infinite number of potential projects. We derive the optimal stopping time and show that it increases with the discount rate, with the rate of arrival of project failure, and with the prior probability that the project can be successfully completed. The optimal stopping time decreases with the rates of new project arrival and project completion. [ABSTRACT FROM AUTHOR]

This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S0 mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions. [ABSTRACT FROM AUTHOR]

Climate change causes transformations to the conditions of existing agricultural practices appointing farmers to continuously evaluate their agricultural strategies, e.g., towards optimising revenue. In this light, this paper presents a framework for applying Bayesian updating to simulate decision-making, reaction patterns and updating of beliefs among farmers in a developing country, when faced with the complexity of adapting agricultural systems to climate change. We apply the approach to a case study from Ghana, where farmers seek to decide on the most profitable of three agricultural systems (dryland crops, irrigated crops and livestock) by a continuous updating of beliefs relative to realised trajectories of climate (change), represented by projections of temperature and precipitation. The climate data is based on combinations of output from three global/regional climate model combinations and two future scenarios (RCP4.5 and RCP8.5) representing moderate and unsubstantial greenhouse gas reduction policies, respectively. The results indicate that the climate scenario (input) holds a significant influence on the development of beliefs, net revenues and thereby optimal farming practices. Further, despite uncertainties in the underlying net revenue functions, the study shows that when the beliefs of the farmer (decision-maker) opposes the development of the realised climate, the Bayesian methodology allows for simulating an adjustment of such beliefs, when improved information becomes available. The framework can, therefore, help facilitating the optimal choice between agricultural systems considering the influence of climate change. [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]

Water utilities often rely on water main failure prediction models to develop an effective maintenance, rehabilitation and replacement (M/R/R) action plan. However, the understanding of water main failure becomes difficult due to various uncertainties. In this study, a Bayesian updating based water main failure prediction framework is developed to update the performance of the Bayesian Weibull proportional hazard (BWPHM) model. Applicability of the proposed framework is illustrated with modeling failure prediction of cast iron and ductile iron pipes of the water distribution network of the City of Calgary, Alberta, Canada. The Bayesian updating models have effectively improved the water main failure prediction whenever new data or information becomes available. The proposed framework can assess the model performance in the light of uncertain and evolving information and will help the water utility authorities to attain an acceptable level of service considering financial constraints. [ABSTRACT FROM AUTHOR]

Several building codes propose methodologies to account for epistemic uncertainties in the seismic assessment of masonry buildings by selecting a knowledge level and reducing material strengths by means of the associated value of the confidence factor. Previous works showed that, in the case of masonry structures, this approach has various limitations, such as the lack of proper consideration of experimental tests performed. This article focuses on the issue of imperfect knowledge on material properties of existing masonry buildings and proposes a probabilistic methodology for the assessment, based on Bayesian updating of mechanical properties. The use of a Bayesian approach allows to update the values of the material properties assumed a priori as knowledge on the building increases, by taking into account all the experimental information gathered during the assessment process. A large number of simulated assessments is carried out and the values of the confidence factors on material properties are defined through the comparison between the obtained results and those of the reference structure, assumed to be perfectly known. These factors are useful in a more general framework for the assessment of masonry buildings accounting for different sources of uncertainty. [ABSTRACT FROM AUTHOR]

In the context of value creation in systems engineering, it is important to consider the contribution of each component of the system to the overall value. This consideration is particularly important for profit-maximizing firms, whose objective is to maximize the expected utility of profit. To determine the profit of each alternative, it is necessary to obtain an estimate of the demand for each design at a given price. Because uncertainty about the demand is always present, it is essential to incorporate this uncertainty into the profit and the design analysis. This paper provides a method for characterizing the uncertainty about demand as a function of its price. We characterize the demand distribution using a scaled Poisson process and show how to estimate demand. We illustrate the results using a numeric example applied to a regional engineering firm that designs small hybrid vehicles. Finally, we provide an approximate analytic solution for the optimal price of a product that offers the highest expected utility for the design firm. Our results indicate that the more risk-averse firm has a lower optimal price than the less risk-averse firm. We also calculate the expected value of perfect information on the demand at any price via Bayesian updating. [ABSTRACT FROM PUBLISHER]

ABSTRACT In recent years, much has been written about different ways to investigate managed natural resource systems under uncertainty. Two general approaches can be identified: an external approach, in which experimentation and assessment of the resource system proceed independently of management, and an internal approach, in which management interventions are used as treatments to explore system processes and their responses to management. The latter often is described in terms of experimental management or 'learning by doing.' Surprisingly, there has been little documentation about the integration of these 2 approaches in a common framework for learning about managed resource systems, despite the fact that both are frequently pursued simultaneously. I discuss both internal and external approaches to learning in some detail, focusing on uncertainty about the processes that influence system dynamics and the potential for its reduction. I then describe a framework that allows for the incorporation of both modes of learning, and consider a number of specific variations. Finally, I discuss some questions that arise with the combined framework, along with some potential challenges and possible extensions. Integrating internal and external learning modes can potentially accelerate the rate of learning, but it also complicates the challenge of identifying efficient research and management designs. © 2014 The Wildlife Society. [ABSTRACT FROM AUTHOR]

Climate emergency is fast becoming the overriding problem of our times and rapid reductions in carbon emissions a primary policy focus that is liable to affect all aspects of society and economy. A key component in climate science is the "climate sensitivity" measure and there has been a recent attempt using Bayesian updating to narrow this measure in the interests of "firming up the science". We explore a two stage argument in this regard. First, despite good intentions, use of Bayes sits awkwardly with uncertainty in the form of known unknowns and surprise. Second, narrowing the range may have counterproductive consequences, since the problem is anthropogenic climate change, and there are asymmetric effects from under-response in the face of irreversible and ampliative effects. As such, narrowing the range using Bayes may inadvertently violate the precautionary principle. We take from this that there is a case to be made for scenario focused decision frameworks. • Climate sensitivity measures are at the heart of climate science and measures influence the urgency with which climate policy is pursued. • There has been a recent attempt to narrow the climate sensitivity range and "firm up the science" using Bayesian updating. • This is problematic for two reasons: it cannot allow for known unknowns and surprise (and climate change is quintessentially a matter of uncertainty), and narrowing the range may reduce pressure for urgent change that cannot be retrospectively remedied. • Intuitive Logics Scenario Planning provides an alternative. [ABSTRACT FROM AUTHOR]

This paper studies belief formation for two kinds of religion. The main conclusion is that they follow different mechanisms. Specifically, for religions relying on supernatural powers, people formulate beliefs based on their prior beliefs and experiences, and they may claim their beliefs for some realistic considerations. For religions based on self-sufficiency, mainly Buddhism, beliefs are formed by an awareness of suffering and dependent arising. [ABSTRACT FROM AUTHOR]

Highlights: [•] An extended form of the Taylor tool life equation is implemented that includes the dependence on both cutting speed and feed for turning. [•] The dependence on cutting speed is quantified by an exponent, p, and feed by an exponent, q; the model also includes a constant, C. [•] Bayesian inference is applied to estimate these constants using the Metropolis–Hastings algorithm of the Markov Chain Monte Carlo approach. [•] Turning tests are performed using a carbide tool and MS309 steel work material. [•] The test results are used to update initial beliefs about the Taylor tool life constants and predict tool life via a probability density function. [Copyright &y& Elsevier]

Highlights: [•] A Taylor tool life model for milling that uses an exponent, n, and a constant, C, is developed. [•] Bayesian inference is applied to estimate the two model constants using a discrete grid method. [•] Tool wear tests are performed using an uncoated carbide tool and 1018 steel work material. [•] Test results are used to update initial beliefs predict tool life using a probability density function. [Copyright &y& Elsevier]

Rational decision making under uncertainty requires forming beliefs that integrate prior and new information through Bayes’ rule. Human decision makers typically deviate from Bayesian updating by either overweighting the prior (conservatism) or overweighting new information (e.g. the representativeness heuristic). We investigated these deviations through measurements of electrocortical activity in the human brain during incentivized probability-updating tasks and found evidence of extremely early commitment to boundedly rational heuristics. Participants who overweight new information display a lower sensibility to conflict detection, captured by an event-related potential (the N2) observed around 260 ms after the presentation of new information. Conservative decision makers (who overweight prior probabilities) make up their mind before new information is presented, as indicated by the lateralized readiness potential in the brain. That is, they do not inhibit the processing of new information but rather immediately rely on the prior for making a decision. [ABSTRACT FROM AUTHOR]

System failure often involves multiple failure modes which require considering multiple performance functions. Based on measured system response data, Bayesian updating of multiple failure probability curves needs to be performed. In this paper, a new approach based on extending ISS is proposed which can update multiple failure probability curves by simultaneously considering all the multiple performance functions in one run. A new scheme of how the generated samples are used is proposed to improve the estimation of failure probability curves. Discontinuity issues on the failure curves in ISS are resolved by the proposed method. The proposed method is applied to two illustrative examples involving uncertain model parameters and nonlinear structural dynamic systems subjected to future uncertain earthquake excitations. The computational efficiency of the proposed method is compared with direct application of Subset Simulation. Significant computational savings are observed while the coefficient of variation of the failure probability estimators is kept at the same level. [ABSTRACT FROM AUTHOR]

What is the purpose of attention? One avenue of research has led to the proposal that attention might be crucial for gathering information about the environment, while other lines of study have demonstrated how attention may play a role in guiding behavior to rewarded options. Many experiments that study attention require participants to make a decision based on information acquired discretely at one point in time. In real-world situations, however, we are usually not presented with information about which option to select in such a manner. Rather we must initially search for information, weighing up reward values of options before we commit to a decision. Here, we propose that attention plays a role in both foraging for information and foraging for value. When foraging for information, attention is guided toward the unknown. When foraging for reward, attention is guided toward high reward values, allowing decision-making to proceed by accept-or-reject decisions on the currently attended option. According to this account, attention can be regarded as a low-cost alternative to moving around and physically interacting with the environment-"teleforaging"-before a decision is made to interact physically with the world. To track the timecourse of attention, we asked participants to seek out and acquire information about two gambles by directing their gaze, before choosing one of them. Participants often made multiple refixations on items before making a decision. Their eye movements revealed that early in the trial, attention was guided toward information, i.e., toward locations that reduced uncertainty about value. In contrast, late in the trial, attention was guided by expected value of the options. At the end of the decision period, participants were generally attending to the item they eventually chose. We suggest that attentional foraging shifts from an uncertainty-driven to a reward-driven mode during the evolution of a decision, permitting decisions to be made by an engage-or-search strategy. [ABSTRACT FROM AUTHOR]

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]

Accurate health prognosis is critical for ensuring equipment reliability and reducing the overall life-cycle costs. The existing gear prognosis methods are primarily either model-based or data-driven. In this paper, an integrated prognostics method is developed for gear remaining life prediction, which utilizes both gear physical models and real-time condition monitoring data. The general prognosis framework for gears is proposed. The developed physical models include a gear finite element model for gear stress analysis, a gear dynamics model for dynamic load calculation, and a damage propagation model described using Paris' law. A gear mesh stiffness computation method is developed based on the gear system potential energy, which results in more realistic curved crack propagation paths. Material uncertainty and model uncertainty are considered to account for the differences among different specific units that affect the damage propagation path. A Bayesian method is used to fuse the collected condition monitoring data to update the distributions of the uncertainty factors for the current specific unit being monitored, and to achieve the updated remaining useful life prediction. An example is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Abstract: A general probabilistic inference procedure is proposed in this paper based on the Maximum relative Entropy (MrE) approach which generalizes both Bayesian and Maximum Entropy (MaxEnt) inference methodologies. The construction of the conditional probability (likelihood function) for general model-based inference problems is discussed in detail to systematically manage uncertainties from mechanism modeling, model parameters, and measurements. Analytical and numerical examples are used to investigate the sequence effect in the probabilistic inference using point observations and moment constraints. The developed methodology is applied to the engineering fatigue crack growth problem with experimental data for demonstration and validation. Following this, a detailed comparison between the classical Bayesian inference and the MrE inference is given. [Copyright &y& Elsevier]

We study how multiattribute product choices are affected by peer influence. We propose a two-stage conjoint-based approach to examine three behavioral mechanisms of peer influence. We find that when faced with information on peer choices, consumers update their attribute preferences in a Bayesian manner. This suggests that greater uncertainty in the attribute preferences of a focal consumer and lesser uncertainty in preferences of peers both lead to greater preference revision. Greater number of peers is associated with greater preference revision, although the extent of preference revision diminishes with increasing number of peers. Furthermore, to address the significant time and costs associated with collecting sociometric data, we estimate the accuracy of predicted consumer choices when peer influence data are unavailable. Online social network membership and frequency of peer interactions provide better proxies than more common demographic similarity measures. These findings have key implications, especially for word-of-mouth marketing. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an approach for integrating the data obtained from structural health monitoring (SHM) in the life-cycle performance assessment of ship structures under uncertainty is presented. Life-cycle performance of the ship structure is quantified in terms of the reliability with respect to first and ultimate failures and the system redundancy. The performance assessment of the structure is enhanced by incorporating prior design code-based knowledge and information obtained by SHM using Bayesian updating concepts. Advanced modeling techniques are used for the hull strength computations needed for the life-cycle performance analysis. SHM data obtained by testing a scaled model of a Joint High-speed Sealift Ship is used to update its life-cycle performance. [Copyright &y& Elsevier]

Analysis of competing hypothesis, a method for evaluating explanations of observed evidence, is used in numerous fields, including counterterrorism, psychology, and intelligence analysis. We propose a Bayesian extension of the methodology, posing the problem in terms of a multinomial-Dirichlet hierarchical model. The yet-to-be observed true hypothesis is regarded as a multinomial random variable and the evaluation of the evidence is treated as a structured elicitation of a prior distribution on the probabilities of the hypotheses. This model provides the user with measures of uncertainty for the probabilities of the hypotheses. We discuss inference, such as point and interval estimates of hypothesis probabilities, ratios of hypothesis probabilities, and Bayes factors. A simple example involving the stadium relocation of the San Diego Chargers is used to illustrate the method. We also present several extensions of the model that enable it to handle special types of evidence, including evidence that is irrelevant to one or more hypotheses, evidence against hypotheses, and evidence that is subject to deception. [ABSTRACT FROM AUTHOR]

A theoretical framework is presented to explain how agents respond to information under uncertainty in contingent valuation surveys. Agents are provided with information signals and referendum prices as part of the elicitation process. Agents use Bayesian updating to revise prior distributions. An information prompt is presented to reduce hypothetical bias. However, we show the interaction between anchoring and the information prompt creates a systematic bias in willingness to pay. We test our hypotheses in an experimental setting where agents are asked to make a hypothetical, voluntary contribution to a public good. Experimental results are consistent with the model. [ABSTRACT FROM AUTHOR]

Disputes over invocation of precaution in the presence of uncertainty are building. This essay finds: (1) analysis of past WTO panel decisions and current EU-US regulatory conflicts suggests that appeals to scientific risk assessment will not resolve emerging conflicts; (2) Bayesian updating strategies, with commitments to modify policies as information emerges, may ameliorate conflicts over precaution in environmental and security affairs. [ABSTRACT FROM AUTHOR]

Willingness to support public programs for risk management often depends on individual subjective risk perceptions in the face of uncertain science. As part of a larger study concerning climate change, we explore individual updated subjective risks as a function of individual priors, the nature of external information, and individual attributes. We examine several rival hypotheses about how subjective risks change in the face of new information (Bayesian updating, alarmist learning, and ambiguity aversion). The source and nature of external information, as well as its collective ambiguity, can have varying effects across the population, in terms of both expectations and uncertainty. [ABSTRACT FROM AUTHOR]

Jeffrey updating is a natural extension of Bayesian updating to cases where the evidence is uncertain. But, the resulting degrees of belief appear to be sensitive to the order in which the uncertain evidence is acquired, a rather un-Bayesian looking effect. This order dependence results from the way in which basic Jeffrey updating is usually extended to sequences of updates. The usual extension seems very natural, but there are other plausible ways to extend Bayesian updating that maintain order-independence. I will explore three models of sequential updating, the usual extension and two alternatives. I will show that the alternative updating schemes derive from extensions of the usual rigidity requirement, which is at the heart of Jeffrey updating. Finally, I will establish necessary and sufficient conditions for order-independent updating, and show that extended rigidity is closely related to these conditions. [ABSTRACT FROM AUTHOR]

We offer an econometric framework that models consumer's consideration set formation as an outcome of her costly information search behavior. Because frequently purchased products are characterized by frequent price promotions of varying depths of discounts, a consumer faces significant uncertainty about the prices of the brands. The consumers engage in a fixed-sample search strategy that results in their discovering the posted prices of a subset of the available brands. This subset is referred to as the consumer's 'consideration set.' The proposed model is estimated using the scanner data set for liquid detergents. Our key empirical results are: (i) consumers zincur significant search costs to discover the posted prices of the brands; (ii) whereas in-store displays and feature ads do not influence consumers' quality perceptions of the brands, they significantly reduce search costs for observing the prices of the brands; (iii) per capita income of consumer's household significantly increases her search costs; and (iv) the consumers' price sensitivity is seriously underestimated if we were to assume that consumers get to know all the posted prices at zero cost. The proposed model is also estimated for the ketchup category to enable us to do cross-category comparisons of consumers' price search behavior. [ABSTRACT FROM AUTHOR]

A Bayesian framework for probabilistic assessment of the initiation of seismic soil liquefaction is described. A database, consisting of post-earthquake field observations of soil performance, in conjunction with in situ “index” test results is used for the development of probabilistically-based seismic soil liquefaction initiation correlations. The proposed stochastic model allows full and consistent representation of all relevant uncertainties. including (a) measurement/estimation errors, (b) model imperfection, (c) statistical uncertainty, and (d) inherent variabilities. Different sets of probabilistic liquefaction boundary curves are developed for the seismic soil liquefaction initiation hazard problem, representing various sources of uncertainty that are intrinsic to the problem. The resulting correlations represent a significant improvement over prior efforts, producing predictive relationships with enhanced accuracy and greatly reduced overall model uncertainty. [Copyright &y& Elsevier]

• A bivariable function considering both mainshock and aftershock intensity measures is used to develop demand model. • A Bayesian approach is used to calibrate demand models. • The Gardoni bounds is used to quantify the variability in the fragility estimates. • The uncertainties in the seismic fragility estimates are quantified. This paper presents a formulation for developing seismic demand models and estimating fragilities for reinforced concrete (RC) structures under mainshocks (MSs) only and mainshock-aftershock (MS-AS) sequences. The demand model for the MS only is a function of the MS intensity, while the model for the MS-AS sequence is a function of two variables, one describing the MS and one describing the AS. A Bayesian method is used to calibrate the demand models. The demand models are then used to estimate the fragility of RC structures. Predictive fragility curves are developed to include the uncertainties in the model parameters estimated by Bayesian approach, and confidence bounds of the fragility curves are developed to separate the effects of the uncertainty in the model parameters from the other sources of uncertainties. The proposed formulation is illustrated using a typical five-story RC frame building. The results show that the fragility curves for MS only tend to significantly underestimate the fragility curves for MS-AS sequences. Also, the uncertainty in the fragilities for the MS-AS sequence is, as expected, larger than that considering only the MS. [ABSTRACT FROM AUTHOR]

• A new probabilistic back analysis framework is proposed. • Bayesian theory is employed in the probabilistic back analysis framework. • Multiple observations could be incorporated into this Bayesian updating. • HMC algorithm-based MCMC simulation is adopted in this Bayesian updating. • The information of uncertain variables is updated in a stage-by-stage manner. The predicted performance of a geotechnical system may deviate from the in situ observation due to the uncertainties in the input geotechnical parameters, solution model, and observation error. A precise characterization of these uncertainties is a significant challenge primarily because of limited data availability. The Bayesian theory provides a means for updating these uncertainties by incorporating prior statistical information and observations. However, conventional Bayesian inference focuses on limited sources of uncertainties. This paper presents a probabilistic back analysis method for improved reliability of subsequent predictions that considers all the uncertainties. Three distinct features of this new method include: (1) multiple observations are incorporated into the Bayesian updating, (2) the statistical information of the uncertain variables is updated in a stage-by-stage manner, and (3) the posterior distributions of uncertain variables are derived with Markov Chain Monte Carlo (MCMC) simulation that is based on the Hamiltonian Monte Carlo (HMC) algorithm. Two case histories, including a braced excavation problem and a tunnel excavation problem, are analyzed to demonstrate the effectiveness of the new method. The advantages of this new back analysis method over the conventional Bayesian updating analyses are documented. [ABSTRACT FROM AUTHOR]

Accurate estimation of landslide probability or slope failure probability when there is a rainstorm event is crucial for assessment and mitigation of rainfall-induced landslide risk. Slope reliability methods provide a rigorous way of estimating landslide probability based on slope failure mechanism and probability theory. However, it is well recognized in literature that the landslide probability estimated from existing slope reliability analysis methods are often much larger than the observed landslide frequency. To improve accuracy of the slope failure probability, this study proposes a slope reliability analysis method for an existing slope at a specific site, which considers both rainfall triggering mechanism and the slope's performance records during previous rainfall events. It was found that the fact that the slope survived from previous rainfall events could be utilized to effectively reduce uncertainties in soil parameters and might reduce the estimated landslide probability by one to two orders of magnitudes. In addition, the proposed method provides a real-time slope failure probability for a given rainfall event, and the estimated slope failure probability varies as the considered rainfall event evolves with time. • A method is proposed to estimate real-time slope failure probability under a target rainfall. • Bayesian updating of uncertainties is performed using slope performance records from past rainfall events. • Uncertainties in soil and other parameters are significantly reduced by Bayesian updating. • The challenge of overestimating slope failure probability is tackled. [ABSTRACT FROM AUTHOR]

This study proposes a method for inversely estimating the spatial distribution characteristic of a material's elastic modulus using the measured value of the observation data and the distance between the measurement points. The structural factors in the structural system possess temporal and spatial randomness. One of the representative structural factors, the material's elastic modulus, possesses temporal and spatial randomness in the stiffness of the plate structure. The structural factors with randomness are typically modeled as having a certain probability distribution (probability density function) and a probability characteristic (mean and standard deviation). However, this method does not consider spatial randomness. Even if considered, the existing method presents limitations because it does not know the randomness of the actual material. To overcome the limitations, we propose a method to numerically define the spatial randomness of the material's elastic modulus and confirm factors such as response variability and response variance. [ABSTRACT FROM AUTHOR]

• Resisting model uncertainty safety factor for seismic NLFE analyses is proposed. • Several NLFE analyses of r.c. members subjected to cyclic loads are performed. • Different reinforced concrete shear walls are considered. • Different software codes and constitutive laws for concrete are adopted. • Probabilistic analysis of the model uncertainties through a Bayesian approach. This study assesses the partial safety factor corresponding to the resistance model uncertainties in the use of non-linear finite element analyses (NLFEAs) for reinforced concrete systems subjected to cyclic loads. Specifically, various walls experimentally tested are considered for this investigation and are simulated through two-dimensional (i.e., plane stress) finite element (NLFE) models. The comparison between the global resistances from the plane stress NLFE structural models and the experimental tests is carried out considering the possible modelling hypotheses available in relation to the mechanical response of reinforced concrete structural systems subjected to cyclic loads. After that, a probabilistic processing of the abovementioned epistemic uncertainties is carried out in line with a Bayesian updating. In detail, each prior distribution of the resistance model uncertainty related to a specific combination of the modelling hypotheses is computed and successively updated with the data achieved from the other models to estimate the posterior distribution. Hence, the coefficient of variation and the mean value of the resistance model uncertainties are evaluated and the corresponding partial safety factor is assessed in line with the NLFEA safety formats of reinforced concrete systems for seismic analyses. [ABSTRACT FROM AUTHOR]