The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Ben H. Thacker - One of the best experts on this subject based on the ideXlab platform.
-
A Probabilistic Model for Internal Corrosion of Gas Pipelines
2004 International Pipeline Conference Volumes 1 2 and 3, 2004Co-Authors: Amit Kale, Ben H. Thacker, Narasi Sridhar, Chris WaldhartAbstract:Locating internal corrosion damage in gas pipelines is made difficult by the presence of large uncertainties in flow characteristics, pre-existing conditions, corrosion resistance, elevation data, and test measurements. This paper describes a preliminary methodology to predict the most probable corrosion damage location along the pipelines, and then update this prediction using inspection data. The approach computes the Probability of critical corrosion damage as a function of location along the pipeline using physical models, for flow, corrosion rate, and inspection information as well as uncertainties in elevation data, pipeline geometry and flow characteristics. The probabilistic methodology is based on the internal corrosion direct assessment (ICDA) methodology. The Probability of corrosion damage is the Probability that the corrosion depth exceeds a critical depth times the Probability of the presence of electrolytes such as water. Water is assumed present at locations where the pipeline inclination angle is greater than the critical angle. The corrosion rate is defined to be a linear combination of three candidate corrosion rate models with separate weight factors. Monte Carlo simulation and the first-order reliability method (FORM) implemented in a simple spreadsheet model are used to perform the Probability Integration. Bayesian updating is used to incorporate inspection information (e.g., in-line, excavation, etc.) and update the corrosion rate model weight factors and thereby refine the prediction of most probable damage location. This provides a systematic method for focusing costly inspections on only those locations with a high Probability of damage while allowing future predictions to better reflect field observations.Copyright © 2004 by ASME
-
Probabilistic Structural Analysis of Deep Tunnels.
1996Co-Authors: Ben H. Thacker, David S. RihaAbstract:Abstract : Accurate and efficient methods for performing probabilistic structural analysis are developed and demonstrated using highly detailed numerical tunnel models. Typical result from the probabilistic analysis include Probability of failure, cumulative distribution functions, and probabilistic sensitivities with respect to all input statistical parameters. The results are useful for survivability/vulnerability assessments, strategic planning, tunnel design, and test planning. The report describes a program comprising development and application of several advanced probabilistic analysis methods, verification and validation of deterministic numerical models, and application of probabilistic analysis methods to several different tunnel problems. Statistical properties measured in laboratory testing are used directly in the calculations. A novel probabilistic cap constitutive model is developed that allows the model parameters to be treated as non-normal dependent random variables. The probabilistic methods used are based on a class of methods known as Fast Probability Integration (FPI). For probabilistic finite element analysis, the Advanced Mean Value (AMV) method is generally recommended. The methods are verified using Latin hypercube, adaptive importance sampling, and Monte Carlo Simulation. (MM)
-
Probabilistic structural analysis using a general purpose finite element program
Finite Elements in Analysis and Design, 1992Co-Authors: David S. Riha, Harry R. Millwater, Ben H. ThackerAbstract:Abstract This paper presents an accurate and efficient method to predict the probabilistic response for structural response quantities such as stress, displacement, natural frequencies, and buckling loads, by combining the capabilities of MSC / NASTRAN , including design sensitivity analysis and fast Probability Integration. Two probabilistic structural analysis examples have been performed and ver ified by comparison with Monte Carlo simulation of the analytical solution. The first example consists of a cantilevered plate with several point loads. The random variables are the plate thickness and modulus of elasticity. The quantity of interest is the probabilistic distribution of the tip displacement. The second example is a probabilistic buckling analysis of a simply supported composite plate under in-plane loading. The random variables are the ply material properties, orientation angles, and thickness. The coupling of MSC / NASTRAN and fast Probability Integration is shown to be orders of magnitude more efficient than Monte Carlo simulation with excellent accuracy.
Peter Juslin - One of the best experts on this subject based on the ideXlab platform.
-
Compound risk judgment in tasks with both idiosyncratic and systematic risk: The "Robust Beauty" of additive Probability Integration.
Cognition, 2017Co-Authors: Joakim Sundh, Peter JuslinAbstract:In this study, we explore how people integrate risks of assets in a simulated financial market into a judgment of the conjunctive risk that all assets decrease in value, both when assets are independent and when there is a systematic risk present affecting all assets. Simulations indicate that while mental calculation according to naïve application of Probability theory is best when the assets are independent, additive or exemplar-based algorithms perform better when systematic risk is high. Considering that people tend to intuitively approach compound Probability tasks using additive heuristics, we expected the participants to find it easiest to master tasks with high systematic risk - the most complex tasks from the standpoint of Probability theory - while they should shift to Probability theory or exemplar memory with independence between the assets. The results from 3 experiments confirm that participants shift between strategies depending on the task, starting off with the default of additive Integration. In contrast to results in similar multiple cue judgment tasks, there is little evidence for use of exemplar memory. The additive heuristics also appear to be surprisingly context-sensitive, with limited generalization across formally very similar tasks.
-
Heuristics Can Produce Surprisingly Rational Probability Estimates : Comment on Costello and Watts (2014)
Psychological review, 2016Co-Authors: Håkan Nilsson, Peter Juslin, Anders WinmanAbstract:Costello and Watts (2014) present a model assuming that people's knowledge of probabilities adheres to Probability theory, but that their Probability judgments are perturbed by a random noise in the retrieval from memory. Predictions for the relationships between Probability judgments for constituent events and their disjunctions and conjunctions, as well as for sums of such judgments were derived from Probability theory. Costello and Watts (2014) report behavioral data showing that subjective Probability judgments accord with these predictions. Based on the finding that subjective Probability judgments follow Probability theory, Costello and Watts (2014) conclude that the results imply that people's Probability judgments embody the rules of Probability theory and thereby refute theories of heuristic processing. Here, we demonstrate the invalidity of this conclusion by showing that all of the tested predictions follow straightforwardly from an account assuming heuristic Probability Integration (Nilsson, Winman, Juslin, & Hansson, 2009). We end with a discussion of a number of previous findings that harmonize very poorly with the predictions by the model suggested by Costello and Watts (2014).
-
Probability theory : Not the very guide of life
Psychological review, 2009Co-Authors: Peter Juslin, Håkan Nilsson, Anders WinmanAbstract:Probability theory has long been taken as the self-evident norm against which to evaluate inductive reasoning, and classical demonstrations of violations of this norm include the conjunction error and base-rate neglect. Many of these phenomena require multiplicative Probability Integration, whereas people seem more inclined to linear additive Integration, in part, at least, because of well-known capacity constraints on controlled thought. In this article, the authors show with computer simulations that when based on approximate knowledge of probabilities, as is routinely the case in natural environments, linear additive Integration can yield as accurate estimates, and as good average decision returns, as estimates based on Probability theory. It is proposed that in natural environments people have little opportunity or incentive to induce the normative rules of Probability theory and, given their cognitive constraints, linear additive Integration may often offer superior bounded rationality.
David S. Riha - One of the best experts on this subject based on the ideXlab platform.
-
Probabilistic Structural Analysis of Deep Tunnels.
1996Co-Authors: Ben H. Thacker, David S. RihaAbstract:Abstract : Accurate and efficient methods for performing probabilistic structural analysis are developed and demonstrated using highly detailed numerical tunnel models. Typical result from the probabilistic analysis include Probability of failure, cumulative distribution functions, and probabilistic sensitivities with respect to all input statistical parameters. The results are useful for survivability/vulnerability assessments, strategic planning, tunnel design, and test planning. The report describes a program comprising development and application of several advanced probabilistic analysis methods, verification and validation of deterministic numerical models, and application of probabilistic analysis methods to several different tunnel problems. Statistical properties measured in laboratory testing are used directly in the calculations. A novel probabilistic cap constitutive model is developed that allows the model parameters to be treated as non-normal dependent random variables. The probabilistic methods used are based on a class of methods known as Fast Probability Integration (FPI). For probabilistic finite element analysis, the Advanced Mean Value (AMV) method is generally recommended. The methods are verified using Latin hypercube, adaptive importance sampling, and Monte Carlo Simulation. (MM)
-
Probabilistic structural analysis using a general purpose finite element program
Finite Elements in Analysis and Design, 1992Co-Authors: David S. Riha, Harry R. Millwater, Ben H. ThackerAbstract:Abstract This paper presents an accurate and efficient method to predict the probabilistic response for structural response quantities such as stress, displacement, natural frequencies, and buckling loads, by combining the capabilities of MSC / NASTRAN , including design sensitivity analysis and fast Probability Integration. Two probabilistic structural analysis examples have been performed and ver ified by comparison with Monte Carlo simulation of the analytical solution. The first example consists of a cantilevered plate with several point loads. The random variables are the plate thickness and modulus of elasticity. The quantity of interest is the probabilistic distribution of the tip displacement. The second example is a probabilistic buckling analysis of a simply supported composite plate under in-plane loading. The random variables are the ply material properties, orientation angles, and thickness. The coupling of MSC / NASTRAN and fast Probability Integration is shown to be orders of magnitude more efficient than Monte Carlo simulation with excellent accuracy.
Yuanping Xia - One of the best experts on this subject based on the ideXlab platform.
-
InSAR- and PIM-Based Inclined Goaf Determination for Illegal Mining Detection
Remote Sensing, 2020Co-Authors: Yuanping Xia, Yunjia WangAbstract:The determination of the depth and boundary of the goaf is of great significance for the detection of illegal mining. However, determining the current location of unknown goafs mainly relies on low-efficiency, time-consuming, and labor-intensive physical detection methods such as geomagnetic field changes. Due to their large coverage and high degree of automation, research on remote sensing methods has been conducted to locate mining activities by monitoring surface deformation. This paper proposes a method that relies on the principle of the Probability Integration method (PIM) and on synthetic aperture radar interferometry (InSAR) to retrieve the location of an underground goaf. First, the relationship between ground subsidence and the location of the mined-out area was established according to PIM; then, the location of the mined-out area was obtained by the surface deformation acquired by InSAR. The proposed method does not rely on complex nonlinear models and has complete parameters; therefore, it has higher engineering application value. A test site in the Fengfeng mining area and 11 Radarsat-2 images were used to verify the proposed method. The experimental results showed that the average relative error of the proposed method is 6.35%, which is 27.56% higher than that of similar algorithms based on complex nonlinear models. Compared to algorithms that ignore the coal seam dip, the accuracy is improved to 98.27%.
-
Goaf Locating Based on InSAR and Probability Integration Method
Remote Sensing, 2019Co-Authors: Yunjia Wang, Meinan Zheng, Dawei Zhou, Yuanping XiaAbstract:Mining goafs can cause many hazards, such as burst water, spontaneous combustion of coal seams, surface collapse, etc. In this paper, a feature-points-based method for the efficient location of mining goafs is proposed. Different interferometric synthetic aperture radar (DInSAR) is used to monitor the subsidence basin caused by mining. Using the principles of the Probability integral method (PIM), the inflection points and the boundary points of the basin monitored by DInSAR are determined and used as feature points to locate the goaf. In this paper, the necessity of locating goafs and the traditional methods used for this task are discussed first. Then, the results of verifying the proposed method by both a simulation experiment and real data experiment are presented. Six RADARSAT-2 images from 13th October 2015 to 5th March 2016 were used to acquire the subsidence basin caused by the 15235 working faces of the Jiulong mining area. The average relative errors of the simulation experiment and real data experiment were about 6.43% and 12.59%, respectively. The average absolute errors of the simulation experiment and real data experiment were about 28 m and 38 m, respectively. In the final part of this paper, the error sources are discussed to illustrate the factors that can affect the location result.
Yahui Yao - One of the best experts on this subject based on the ideXlab platform.
-
Method Combining Probability Integration Model and a Small Baseline Subset for Time Series Monitoring of Mining Subsidence
Remote Sensing, 2018Co-Authors: Hongdong Fan, Yahui YaoAbstract:Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) has high accuracy for monitoring slow surface subsidence. However, in the case of a large-scale mining subsidence areas, the monitoring capabilities of TS-InSAR are poor, owing to temporal and spatial decorrelation. To monitor mining subsidence effectively, a method known as Probability Integration Model Small Baseline Set (PIM-SBAS) was applied. In this method, mining subsidence with a large deformation gradient was simulated by a PIM. After simulated deformation was transformed into a wrapped phase, the residual wrapped phase was obtained by subtracting the simulated wrapped phase from the actual wrapped phase. SBAS was used to calculate the residual subsidence. Finally, the mining subsidence was determined by adding the simulated deformation to the residual subsidence. The time series subsidence of the Nantun mining area was derived from 10 TerraSAR-X (TSX) images for the period 25 December 2011 to 2 April 2012. The Zouji highway above the 9308 workface was the target for study. The calculated maximum mining subsidence was 860 mm. The maximum subsidence for the Zouji highway was about 145 mm. Compared with the SBAS method, PIM-SBAS alleviates the difficulty of phase unwrapping, and may be used to monitor large-scale mining subsidence.