BACKCALCULATION OF PAVEMENT LAYER MODULI USING EXTENDED BAYESIAN METHOD

SummaryPrior information about features of a stimulus is a strong modulator of perception. For instance, the prospect of more intense pain leads to an increased perception of pain, whereas the expectation of analgesia reduces pain, as shown in placebo analgesia and expectancy modulations during drug administration [1]. This influence is commonly assumed to be rooted in altered sensory processing and expectancy-related modulations in the spinal cord [2], are often taken as evidence for this notion. Contemporary models of perception, however, suggest that prior information can also modulate perception by biasing perceptual decision-making — the inferential process underlying perception in which prior information is used to interpret sensory information. In this type of bias, the information is already present in the system before the stimulus is observed [3]. Computational models can distinguish between changes in sensory processing and altered decision-making as they result in different response times for incorrect choices in a perceptual decision-making task (Figure S1A,B) [4]. Using a drift-diffusion model, we investigated the influence of both processes in two independent experiments. The results of both experiments strongly suggest that these changes in pain perception are predominantly based on altered perceptual decision-making.

The Relationship of Uncertainty and Complexity with Observed Bias and Interindividual Differences When Timing Audiovisual Signals

Comparison of Bayesian survival analysis and Cox regression analysis in simulated and breast cancer data sets

Comparative study on using static and dynamic finite element models to develop FWD measurement on flexible pavement structures

Strange bayes indeed: uniform topological priors imply non-uniform clade priors

ABSTRACTPavement evaluation surveys represent the key element for efficient pavement management and for assuring pavement mission capability. In the Department of Defense (DOD), the Unified Facilities Criteria (UFC) 3-260-03 Airfield Pavement Evaluation provides the current guidance for pavement structural evaluations. During structural surveys, FWD tests are executed at different locations within the same section, with the objective of obtaining a full assessment of the section’s structural capability. The availability of multiple deflection measurements for the same section raises the challenge of identifying the deflection basin best representing the entire section and its use in the backcalculation routine to determine the section’s structural strength. This manuscript proposes a fuzzy-based approach for the selection of a representative basin over multiple deflection basins collected for a specific section. The approach accounted for the spatial variability enclosed within the basin membership function obtained by fuzzy c-mean partitioning. The proposed methodology showed promising results for flexible pavements by offering more robust structural assessment that can account for spatial variability and thus minimise some aspects of mission risk that had a large effect on funding allocation and mission readiness.

Impact of HWD dynamic loading on measured deflection basins and back-calculated stiffnesses of flexible airport pavements

This study presents the structural responses of flexible pavement which were subjected to different axle group loads. Three types of axle group loads, e.g. single axle-dual wheel, tandem axle-dual wheel, and tridem axle-dual wheel, were applied over the field for the instrumented trial section. The corresponding structural responses were measured using a series of embedded instrumentations e.g. pressure cells, asphalt strain gauges, strain gauges, thermocouples, moisture sensors etc. A 3-dimensional (3-D) finite-element analysis (FEA) model and the multi-layer linear-elastic analysis (LEA) were developed to estimate structural responses which were then compared with the field measurement data. Both FEA and LEA assumed the pavement layer materials to be homogeneous, isotropic, and linear-elastic. The elastic moduli of pavement layers were determined from the falling weight deflectometer (FWD) based on the backcalculation procedure. Results from the analysis indicated that both FEA and LEA were in good agreement with the field measurement results with some exceptions for strains under the asphalt concrete surface.

Backcalculating the pavement layer properties is a well-accepted procedure for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from nondestructive testing (NDT) results is to estimate the in-situ pavement material properties. Using backcalculation procedure, flexible pavement layer thicknesses together with in-situ material properties can be estimated from the measured field data through appropriate analysis techniques. In this study, the wavelet-neuro (WN) models were developed for backcalculating the pavement layer thickness and elastic moduli from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT were used to show the capability of the WN approaches in backcalculating the pavement layer thickness and moduli and compared NN models. When the WN and NN models are examined, it has been shown that the WN model gave higher R2 values than the NN models.

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.

Bayesian approaches have been demonstrated as effective methods for reliability analysis of complex systems with small-amount data, which integrate prior information and sample data using Bayes’ theorem. However, there is an assumption that precise prior probability distributions are available for unknown parameters, yet these prior distributions are sometimes unavailable in practical engineering. A possible way to avoiding this assumption is to generalize Bayesian reliability analysis approach by using imprecise probability theory. In this paper, we adopt a set of imprecise Dirichlet distributions as priors to quantify uncertainty of unknown parameters and extend traditional Bayesian reliability analysis approach by introducing an imprecise Dirichlet model (IDM). When the prior information is rare, the result of imprecise Bayesian analysis method is too rough to support engineering decision-making, so we proposed an optimization model to reduce the imprecision of the new method. Spindles are crucial for machine tools and reliability data related to spindles of new-developed machine tools are often rare. We can then use the imprecise Bayesian reliability analysis method to assess its reliability. In this paper, we mainly investigate the reliability assessment of a motorized spindle to illustrate the effectiveness of the proposed method.

Bayesian methods have been proposed as a way of synthesizing all available evidence to inform decision making. However, few practical applications of the use of Bayesian methods for combining patient-level data (i.e., trial) with additional evidence (e.g., literature) exist in the cost-effectiveness literature. The objective of this study was to compare a Bayesian cost-effectiveness analysis using informative priors to a standard non-Bayesian nonparametric method to assess the impact of incorporating additional information into a cost-effectiveness analysis. Patient-level data from a previously published nonrandomized study were analyzed using traditional nonparametric bootstrap techniques and bivariate normal Bayesian models with vague and informative priors. Two different types of informative priors were considered to reflect different valuations of the additional evidence relative to the patient-level data (i.e., "face value" and "skeptical"). The impact of using different distributions and valuations was assessed in a sensitivity analysis. Models were compared in terms of incremental net monetary benefit (INMB) and cost-effectiveness acceptability frontiers (CEAFs). The bootstrapping and Bayesian analyses using vague priors provided similar results. The most pronounced impact of incorporating the informative priors was the increase in estimated life years in the control arm relative to what was observed in the patient-level data alone. Consequently, the incremental difference in life years originally observed in the patient-level data was reduced, and the INMB and CEAF changed accordingly. The results of this study demonstrate the potential impact and importance of incorporating additional information into an analysis of patient-level data, suggesting this could alter decisions as to whether a treatment should be adopted and whether more information should be acquired.

The purpose of this study is to determine the best estimator for estimating the shape parameters of the Pareto distribution with the known scale parameter. Estimation of these parameters is done by using the Gamma distribution as the prior distribution of the conjugate and the Uniform distribution as the non-conjugate prior distribution. A comparison of the two prior distributions is done through simulation studies with various sample sizes. The best estimator net is a method that produces the smallest posterior variance, absolute bias, and Bayes confidence interval. This study proves that the Bayes estimator by using the prior conjugate distribution produces all indicators of the goodness of the model with a smaller value than the non-conjugate prior distribution. Thus it can be concluded that the estimator with prior conjugate will produce a better predictive value than prior non-conjugate.

Over the past 20years, there has been increasing interest in the use of Bayesian statistical methods for the analysis of clinical trials used to support regulatory decisions. Bayesian methods for the analysis of clinical trials are an attractive option when good prior information is available. Yet, in many cases, prior information is scarce and only tentative or proprietary prior information exists. In these situations, it is necessary to use noninformative type or skeptical-type priors. We undertook a systematic study of Bayesian methods and applied them to 13 randomized clinical trials in metastatic breast cancer submitted to the U.S. Food and Drug Administration for registrational purposes. Across all 13 studies, there were a total of 10,521 patients using 10 experimental agents. Our results demonstrated that Bayesian analyses with noninformative priors provided similar results to more traditional analyses. We also found that early interim looks at the study results can vary widely based upon the type of prior used. Finally, we found that pre-defined threshold stopping rules need to be relatively strong to prevent trials from stopping very early. Our results suggest that, when prior information is limited and a noninformative prior is used, there is little numerical difference between Bayesian methods and more traditional analysis methods. Bayesian methods, however, may provide additional summaries of the data that are more easily interpretable than means and confidence intervals.

Estimating phylogenies in which branch lengths are expressed in units of absolute time is crucial for testing hypotheses in evolutionary biology. However, bioinformatic tools to pre- and post-process data from Bayesian divergence time estimation analyses are often not easily interoperable, and documenting methodological choices is not a generalized practice. The R package tbea is a tool-set to integrate biological, geological, and paleontological information to optimize the specification of models, their parameters, and prior distributions in divergence time estimation analyses. tbea implements statistical models to (i) better translate time information in dating sources into the specified calibration densities, (ii) improve comparisons between prior and posterior distributions for parameters of interest, (iii) carry out inference on origination times for a set of distributions, (iv) summarize different distributions into a single one, and (v) improve the reproducibility of divergence time estimation analyses allowing users to document methodological choices. We illustrate the functionalities of tbea by carrying out two worked examples, one on the phylogenetic relationships and divergence time estimation of South American Cynodontidae, and the other on the separation time of drainages east and west of the Andes in South America. It is expected that the tools available in tbea will be key when estimating events in time from sets of point estimates, and the combination of different posterior densities from the same parameter will be useful in justifying the selection of secondary calibration points, or discussing the timing of biogeographical events when multiple sources are available.