Probabilistic Procedure Research Articles

Performance-based assessment of the seismic pseudo-static coefficient used in slope stability analysis

Pseudo-static slope stability procedures are often employed to evaluate the seismic performance of slope systems, at least in the initial evaluation stages. To yield meaningful results, these methods should rely on parameters that are representative of the existing seismic demand and the properties of the slope system being evaluated. This study proposes a performance-based probabilistic procedure to estimate the seismic pseudo-static coefficient (SPC) in a rational and transparent manner. The procedure has its cornerstone on the evaluation of seismically-induced displacement (D) hazard curves, and it provides SPC estimates that are consistent with the allowable D level that a slope system can sustain, the properties of the sliding mass, the seismic demand at the slope site, and the hazard design level or return period. The proposed procedure can be applied to evaluate the seismic performance of a wide range of slope systems potentially affected by earthquakes from different tectonic settings (i.e. subduction and shallow crustal earthquakes), and has been implemented in a computational platform that facilitates its straightforward use in engineering practice. The implementations are fully automated for South America (i.e., Peru, Chile, Ecuador), Mexico, and the United States, but the proposed framework can be applied worldwide. Finally, an illustrative example for the application of the procedure in the seismic stability assessment of a slope system is provided.

A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets

Abstract The requirement to anonymise data sets that are to be released for secondary analysis should be balanced by the need to allow their analysis to provide efficient and consistent parameter estimates. The proposal in this article is to integrate the process of anonymisation and data analysis. The first stage uses the addition of random noise with known distributional properties to some or all variables in a released (already pseudonymised) data set, in which the values of some identifying and sensitive variables for data subjects of interest are also available to an external ‘attacker’ who wishes to identify those data subjects in order to interrogate their records in the data set. The second stage of the analysis consists of specifying the model of interest so that parameter estimation accounts for the added noise. Where the characteristics of the noise are made available to the analyst by the data provider, we propose a new method that allows a valid analysis. This is formally a measurement error model and we describe a Bayesian MCMC algorithm that recovers consistent estimates of the true model parameters. A new method for handling categorical data is presented. The article shows how an appropriate noise distribution can be determined.

Probabilistic model for time to cover cracking due to corrosion

AbstractThe time to cover cracking is widely used as a service life indicator in the assessment of deterioration of corrosion‐affected reinforced concrete structures. This paper presents a general probabilistic procedure for prediction of time to cover cracking. Within this procedure, the response surface method (RSM) is employed to calibrate a new model for calculating of radial displacement required for cover cracking based on the results obtained from the finite element (FE) analysis. By taking advantage of the central limit theorem (CLT), simple but accurate probabilistic models for prediction of time to cover cracking that only rely on the knowledge of first and second moments of basic random variables are derived. Rigorous simulation analysis has proved the accuracy of these models. It is shown in the paper that using this probabilistic procedure, factors affecting randomness of time to cover cracking can be easily identified. It is also shown that the time to cover cracking is highly variable with the concrete cover, tensile strength of concrete, corrosion current density, and the model error as the most influential factors on its randomness.

Computer Simulation of Stochastic Energy Fluctuations in Tensile Test of Elasto-Plastic Porous Metallic Material

The main aim of this work is the computational implementation and numerical simulation of a metal porous plasticity model with an uncertain initial microdefects’ volume fraction using the Stochastic Finite Element Method (SFEM) based on the semi-analytical probabilistic technique. The metal porous plasticity model applied here is based on Gurson–Tvergaard–Needleman theory and is included in the ABAQUS finite element system, while the external probabilistic procedures were programmed in the computer algebra system MAPLE 2017. Hybrid usage of these two computer systems enabled the determination of fluctuations in elastic and plastic energies due to initial variations in the ratio of the metal micro-voids, and the calculation of the first four probabilistic moments and coefficients of these energies due to Gaussian distribution of this ratio. A comparison with the Monte-Carlo simulation validated the numerical efficiency of the proposed approach for any level of input uncertainty and for the first four probabilistic characteristics traditionally seen in the experimental series.

Determining the Reduction Factor in Predicting the Contribution of Concrete to Shear Strength by Using a Probabilistic Method

The reliability of a structure can be defined as the probability of the structure to fulfil its intended purpose throughout its design lifespan. In the codes, it is aimed to provide the target failure probability by means of safety factors in the form of load factors and strength reduction factors. This paper presents an investigation of the reduction factor for the contribution of concrete to the shear strength of reinforced concrete beams without stirrups designed according to the Turkish Code. In this code, the contribution of concrete to the shear strength is calculated by multiplying the diagonal cracking strength by a reduction factor of 0.8. In this study, based upon a second moment probabilistic analysis procedure, the change in the strength reduction factor was investigated for various coefficients of variation of concrete compressive strength and failure probabilities by using experimental data available in the literature. It is assumed that the considered random variables are statistically independent, and the correlation effects are not taken into account. The results show that the reduction factor decreases with the increase in the coefficient of variation of concrete compressive strength and the reduction factor given by the Turkish Code corresponds to a variation coefficient of 0.12 and a failure probability of 10−6.

Probability analysis of an embedded water tank

The design of building structures must fulfil specific regulations, in our case, different building standards, among them Eurocodes. In addition to deterministic procedures in structural design, these standards also allow probabilistic procedures. The embedded tank loaded with soil and liquid is solved by the probability analysis using ANSYS, which contains several probabilistic methods. The reinforced concrete tank is solved by the RSM probabilistic method, which uses the well-known Monte-Carlo method in the background. Input parameters (material properties of soil and reinforced concrete, load - pressure from water, geometric data - change of both wall and tank bottom thickness) are entered into the calculation with certain aberrances allowed by standards in the construction and loading of structures. The results are also sets of probabilistic variables with a certain variance, as opposed to a deterministic calculation, where only one value results. These procedures, which use statistical methods, have been at the forefront in recent decades. At the end of the paper, some results of the analysis of embedded reinforced concrete water tank (deterministic and probabilistic procedure) in state of tank failure on the second limit state are presented.

Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring

A multi-agent data-analytics-based approach to ubiquitous healthcare monitoring is presented in this paper. The proposed architecture gathers a patient’s vital data using wireless body area networks, and the transmitted information is separated into binary component parts and divided into related dataset categories using several classification techniques. A probabilistic procedure is then used that applies a normal (Gaussian) distribution to the analysis of new medical entries in order to assess the gravity of the anomalies detected. Finally, a data examination is carried out to gain insight. The results of the model and simulation show that the proposed architecture is highly efficient in applying smart technologies to a healthcare system, as an example of a research direction involving the Internet of Things, and offers a data platform that can be used for both medical decision making and the patient’s wellbeing and satisfaction with their medical treatment.

Seismic evaluation of ordinary RC buildings retrofitted with externally bonded FRPs using a reliability-based approach

Despite the extensive literature on reinforced concrete (RC) members retrofitted with fiber-reinforced polymer (FRP) composites, few studies have employed a reliability-based approach to evaluate the seismic performance of RC buildings in terms of their collapse capacity and ductility. In this study, the performance of a poorly-confined RC building structure is investigated for different FRP retrofitting schemes using different configurations and combinations of wrapping and flange-bonded FRPs, as two well-established techniques. A nonlinear pushover analysis is then implemented with a computational reliability analysis based on Latin Hypercube Sampling (LHS) to determine the collapse capacity and ductility of the case-study structure. The variations in material properties and applied loads are examined using a rational probabilistic procedure. The results demonstrate the effectiveness of the reliability approach as it is capable of providing reliable and accurate comparisons between the retrofitting schemes implemented. In addition, the failure modes of the original and retrofitted frames are scrutinized for a more detailed study. It was found that the failure mode of the case-study building is remarkably dependent on the variations of both the input parameters and the adopted retrofitting scheme.

Probabilistic modeling and simulation of multiple surface crack propagation and coalescence

In the low cycle fatigue (LCF) regime, fatigue failure of metallic materials with high strength and less impurities generally dominates by multiple surface crack propagation and coalescence, in which its final failure shows a stochastic nature on crack initiation, propagation and coalescence under cyclic loadings. According to this, the competing failure modes of multiple surface cracks and interior cracks are studied through coupling numerical simulations with fracture mechanics methods. In particular, a probabilistic procedure for modeling multiple surface crack propagation and coalescence is established by incorporating Monte Carlo simulation with experimental evidences, including surface crack density and crack length distributions measured from LCF replica tests of 30NiCrMoV12 steel. In addition, it calculates the probability of coalescence of neighboring cracks with allowance for their interactions and local plastic deformation at the crack tips. Finally, it estimates the remaining usage lives of specimens from initial state to critical cracks by propagation and coalescence of dispersed cracks.

Fragility Functions for Tall URM Buildings around Early 20th Century in Lisbon, Part 2: Application to Different Classes of Buildings

ABSTRACT This article describes the application of the procedure for the derivation of fragility functions presented in the companion article entitled Fragility functions for tall URM buildings around early 20th century in Lisbon. Part 1: methodology and application at building level. The procedure, based on the execution of non-linear analyses, was developed to be applied to unreinforced masonry buildings considering both the in-plane and out-of-plane response. Different sources of uncertainty, both epistemic and aleatory, affecting the behaviour of these unreinforced masonry buildings are discussed and treated with a probabilistic procedure. The fragility curves determined for the different classes of buildings are compared and then combined to define the final fragility curves for these unreinforced masonry buildings. The results put in evidence the high seismic vulnerability of these buildings and the urgent need for the structural intervention and for the design of retrofitting measures in order to reduce potential losses due to future earthquakes.

Concrete gravity dams model parameters updating using static measurements

The structural control of concrete gravity dams is of primary importance. In this context, numerical models play a fundamental role both to assess the vulnerability of gravity dams and to control their behaviour during normal operativity and after extreme events. In this regard, data monitoring represents an important source of information for numerical model calibrations.This study proposes a novel probabilistic procedure, defined in the Bayesian framework, to calibrate the parameters of finite elements models of dams. To this aim, monitoring data and the results of material tests are used as reference information. The computational burden is reduced by using a new hybrid-predictive model of the dam displacements. An application on an Italian dam shows the feasibility of the proposed procedure.

3D physical modeling for checking Level III probabilistic design in breakwaters: A case study in Punta Langosteira Port, Spain

ABSTRACTThe structural design of a breakwater is usually carried out by applying the verification equations existing in the literature in order to analyze the different failure modes. It is also common to perform complementary physical model tests for testing and optimization. The design of the Western Breakwater of Punta Langosteira, A Coruña, Spain was developed in accordance with the Level III probabilistic procedure defined in the Recommendations for Maritime Works (ROM) developed by the Spanish Port Authority. This paper presents the tests which were performed for the optimization of the design, the verification of the different failure modes and the verification equations used, and the determination of the stability reserve. In addition, the results have made it possible to verify the goodness of fit of the calculations, analyze the overall stability of the breakwater, and ultimately, optimize the final design.

A physics-informed semi-empirical probabilistic model for the settlement of shallow-founded structures on liquefiable ground

This paper presents a predictive model for the settlement of shallow-founded structures on liquefiable ground during earthquakes. The model is based on the results of an extensive fully coupled, three-dimensional numerical parametric study of soil–structure systems, validated with centrifuge experiments as well as a database of case history observations. The results of the numerical study provided insight into the relative importance and influence of each input parameter and the functional form of the predictive model for a structure's permanent settlement. The case history database helped validate and refine the predictive model, accounting for complexities of the ground motion and site conditions in the field. Non-linear regression and latent variable analysis were used to develop model coefficients. The uncertainty around model estimates was modelled by a lognormal distribution. An additional logistic model was provided to estimate the probability of insignificant settlement (defined as less than 1 cm). The proposed probabilistic procedure considers variations in site conditions as well as the presence and properties of a building in three dimensions. By including the case history database in its validation and adjustment, the model captures all mechanisms of settlement below the foundation, including volumetric and deviatoric strains as well as ejecta. The total uncertainty around its predictions is rigorously characterised, which is a necessary step before the benefits of performance-based seismic design can be realised in the evaluation and mitigation of the liquefaction hazard.

A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration

We present a family of methods for 2D–3D registration spanning both deterministic and non-deterministic branch-and-bound approaches. Critically, the methods exhibit invariance to the underlying scene primitives, enabling e.g. points and lines to be treated on an equivalent basis, potentially enabling a broader range of problems to be tackled while maximising available scene information, all scene primitives being simultaneously considered. Being a branch-and-bound based approach, the method furthermore enjoys intrinsic guarantees of global optimality; while branch-and-bound approaches have been employed in a number of computer vision contexts, the proposed method represents the first time that this strategy has been applied to the 2D–3D correspondence-free registration problem from points and lines. Within the proposed procedure, deterministic and probabilistic procedures serve to speed up the nested branch-and-bound search while maintaining optimality. Experimental evaluation with synthetic and real data indicates that the proposed approach significantly increases both accuracy and robustness compared to the state of the art.

Probabilistic procedure for numerical assessment of corroded pipeline strength and operability

The numerical procedure for analysis of nonuniform stress-strain state of welded pipeline elements with detected local corrosion-erosion metal losses based on Weibull statistics has been proposed for the probabilistic assessment of static strength and operability of specific structures. The multiphysics simulation of joint evolution of state of stresses, strains and subcritical damage was used for typical pipelines elements in operation for minimal conservativeness of numerical analysis. It is based on the principles of thermomechanics of elastic-plastic continuum along with tracing of ductile fracture of material. Following the number of experimental investigations of limiting state of typical pipelines with semielliptical defects Weibull parameters as functions of material properties have been determined. Using the specific examples of steel and aluminum pipeline elements, the influence of individual corrosion-erosion defects on failure probability has been studied.

Integration of physical infrastructure and social systems in communities’ reliability and resilience analysis

This paper proposes probabilistic flow-based models for the capacity and demand of critical infrastructure considering different levels of resolution. The models are set in a probabilistic procedure that captures the impact of the damaging event on the infrastructure. The procedure predicts the reduction or loss of functionality of the infrastructure in terms of their ability to provide essential goods or services. In the aftermath of a damaging event, infrastructure change their capacities, due to the damage to the physical components, and they may not be able to satisfy pre-event demands. Furthermore, the post-event demands at the nodes of the infrastructure might change because of the human response (e.g., evacuation or relocation.) The probabilistic procedure presented in this paper integrates physical infrastructure and social systems to predict the change in demand on the infrastructure. As an example, the paper applies the proposed procedure to the modeling of the potable water network of Seaside, Oregon considering a seismic event as the damaging event. The paper shows that neglecting the interdependency between physical infrastructure and social systems may result in estimates of i) higher demands on the physical systems; ii) slower recovery; and iii) smaller impacts on society in terms of population dislocation.

Probabilistic Analysis of Tunnel Liner Performance Using Random Field Theory

Tunnel liner is conventionally designed according to the deterministic design methods. However, the loads transmitted from the surrounding ground to the liner are very uncertain. In other words, the tunnel liner is subjected to nonuniform loading conditions which may affect its performance. This is mainly due to the inherent randomness and spatial variability of ground properties. In addition, since borehole drillings are usually carried out before tunnel excavation, the measured borehole data should be considered and reflected in the tunnel liner design practice, especially when it comes to the reliability‐based design using the conditional random field theory. This paper presents an investigation of the tunnel liner performance from the perspective of probabilistic analysis and reliability‐based design, and a probabilistic procedure for evaluating the tunnel liner performance is fully described. The random behavior of ground properties is accounted for by the conditional random field theory, and the performance of tunnel liner is examined via finite‐difference modeling in FLAC 3D. The evaluation procedure is performed within the Monte Carlo simulation framework. An illustrative hypothesized tunnel is introduced to demonstrate the application of the probabilistic evaluation procedure, and the effects of site characterization parameters and conditioning on the tunnel liner performance are also inspected in a series of parametric studies. Before reaching conclusions, the reliability‐based design of a real tunnel is performed with respect to the liner strengths and thickness, given target reliability levels, which provides some insights into a more reasonable and economical design of tunnel liner.

Model-based decision analysis applied to petroleum field development and management

This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.

PROBABILISTIC EVALUATION OF STORM SURGES IN SURUGA BAY EMPLOYING STOCHASTIC TYPHOON MODEL

Design procedure of coastal embankment in Japan compares tsunami height and storm surge height and employs higher value as a crown level. Estimation of tsunami or storm surge height is usually based on the historical maximum record or an assumed maximum value. Estimation of storm surge needs not only typhoon intensity but also its characteristics e.g. tracks and travel speed. It is difficult to estimate the probability of storm surges using only observed data since typhoon rarely attacks particular area and storm surges are seldom generated. This study proposes probabilistic evaluation procedure of storm surges employing stochastic typhoon model and applies to Suruga Bay, Japan.

Nonpolynomial Zigzag Theories for Random Static Analysis of Laminated-Composite and Sandwich Plates

The present paper aims to study the random static behavior of laminated composite and sandwich plates using nonpolynomial zigzag theories. The nonpolynomial zigzag theories are proposed earlier by the authors, which satisfy the transverse shear-stress continuity conditions at the layer interfaces including the traction-free boundary conditions on the top and bottom surfaces of the plate. These theories incorporate the realistic nonlinear distribution of transverse shear stresses across the thickness of the plate. A probabilistic procedure is developed using a stochastic finite element method that is in the conjunction of finite element method with a mean-centered first-order perturbation technique, to obtain the second-order statistics of deflections of the laminate. The problem is analyzed considering the material properties of laminated composite and sandwich structures to be random in nature while the other system properties are assumed to be deterministic. The stochastic results in terms of mean as well as standard deviation of the responses have been obtained for various combinations of geometric parameters, stacking sequences, span–thickness ratios, and boundary conditions and compared with those existing in the literature and Monte Carlo simulation approach.