Failure Probability Assessment Research Articles

Failure probability assessment of landslides triggered by earthquakes and rainfall: a case study in Yadong County, Tibet, China

Yadong County located in the southern Himalayan mountains in Tibet, China, is an import frontier county. It was affected by landslides after the 2011 Sikkim earthquake (Mw = 6.8) and the 2015 Gorkha earthquake (Mw = 7.8). Casualties and property damage were caused by shallow landslides during subsequent rainfall on the earthquake-destabilized slopes. Existing researches have generally examined rainfall- and earthquake-triggered landslides independently, whereas few studies have considered the combined effects of both. Furthermore, there is no previous study reported on landslide hazards in the study area, although the area is strategically applicable for trade as it is close to Bhutan and India. This study developed a new approach that coupled the Newmark method with the hydrological model based on geomorphological, geological, geotechnical, seismological and rainfall data. A rainfall threshold distribution map was generated, indicating that the southeast part of Yadong is prone to rainfall-induced landslides, especially when daily rainfall is higher than 45 mm/day. Permanent displacement predictions were used to identify landslide hazard zones. The regression model used to calculate these permanent displacement values was 71% accurate. Finally, landslide probability distribution maps were generated separately for dry and wet conditions with rainfall of varying intensities. Results can serve as a basis for local governments to manage seismic landslide risks during rainy seasons.

Fundamental study on failure probability assessment method during earthquakes

Fundamental study on failure probability assessment method during earthquakes

О применении экспертно-статистического метода при оценке вероятности аварий и отказов работы смесительно-зарядных машин

О применении экспертно-статистического метода при оценке вероятности аварий и отказов работы смесительно-зарядных машин

Failure probability assessment and prediction of corroded pipeline under earthquake by introducing in-line inspection data

Considering the transient ground deformation (TGD) and permanent ground deformation (PGD), uncertain modeling is proposed by multiple limit states with time-dependent corrosion growth and seismic damage, as well as the rehabilitation. In this work, the developed corrosion growth model implemented the correlated 3D stochastic growth for one-defect. It then embedded in the combination model involved in corroded defect and seismic loading. Further, the Bayesian method and Markov Chain Monte Carlo (MCMC) simulation were used to obtain the updating of failure probability by introducing rehabilitation and in-line inspection (ILI) data. Failure probability was performed to study this difference using Monte Carlo simulation (MCS) to compare with the effect of the different parameters. A numerical example was investigated proposed models, and the influence of assessment rehabilitation was also discussed. The results indicate the potential impact of seismic damage for the corroded pipeline is significant, which can optimize the frame reliability-based anti-seismic for pipe infrastructure.

A Simplified Approach to the Failure Probability Assessment of Thermally Stable Diamond Composite Rock Cutting Tips

Thermally Stable Diamond Composite (TSDC) has high thermal stability and high wear resistance, and hence is potential to be used as cutting tips for hard rock cutting. Understanding the failure behavior of the TSDC tips during practical rock cutting is a key to enable the TSDC cutting tips to be applied successfully in mining and construction industries. Previous research has shown that the character of random failures due to excessive bending force on TSDC tips is of a major concern, and an approach to estimation of the failure probability of TSDC tips for cutting a rock segment has been developed. However, this approach requires the acquisition of the total cutting length of rock by a tip since it is brand new to it is failed, which can limit the application of the approach. In this paper, a simplified approach is proposed and compared with the existing approach through a numerical case study.

A probabilistic Haigh diagram based on a weakest link approach

A statistical methodology for the assessment of high cycle fatigue failure probability is presented. Based on a probabilistic Haigh diagram for a unit volume in combination with a weakest link model, the approach allows failure probability quantification for arbitrarily shaped components. We demonstrate how the model can be calibrated using a maximum likelihood approach for censored data. In order to show its potential, the proposed methodology is applied on three different publicly available data sets containing tests for smooth and notched sheet specimens. In all considered cases the model predictions are in good agreement with the test data.

Probabilistic prediction of mechanical characteristics of corroded strands

Steel strands are widely used as important structural members of bridges. Their failure can be detrimental to the structure; therefore, various studies on predicting their mechanical characteristics have been conducted. However, explaining the mechanical characteristics of steel strands is difficult because of geometric complexity, difficulty in corrosion modeling, and various uncertain factors. This paper proposes a new method for the probabilistic prediction of the mechanical characteristics of corroded steel strands. First, finite element (FE) models are built for several types of corroded wires. Second, based on the FE analysis results, a nonparametric surrogate model is constructed using Gaussian process regression. Third, the ultimate strength and strain of the corroded steel strands are predicted probabilistically by conducting a Monte Carlo simulation with a theoretical strand model. As a result, the probabilistic ranges of 50% and 95% are estimated. Based on the prediction results, appropriate probabilistic distributions for the ultimate strength and strain are studied. The proposed method is applied to several specimens of corroded seven-wire strands. The prediction results are in good agreement with the test results. Additionally, a failure probability assessment is conducted as an application example based on the goodness-of-fit test.

Assessing the failure probability of gearhead of drilling rig top drive system taking into account the mode of its operation

Elements of drilling equipment are subjected to random loads with very high amplitudes during operation. Classical methods of probabilistic calculation of gearings don’t allow us to estimate the probability of their failure taking into account the real operational data obtained by telemetry when these data cannot be described by standard distribution laws. The offered technique is based on application of methods of non-parametric statistics therefore it is free from these restrictions. All its stages are realized in the Mathcad mathematical processor. The article describes the technique of failure probability of gearing of drilling rig top drive system in accordance with criterion of bending endurance of teeth. Results of its realization for failure probability assessment of gearing at its operation in the heavy loading mode agree well with failure statistics. Also, the technique allows us to determine the probability of gearing failure for any load distribution laws during operation, including, when the loading mode is defined by sample.

Probabilistic machine learning approach to bridge fatigue failure analysis due to vehicular overloading

With the rapid development of freight transportation, truck overloading becomes very common and severe, posing a great threat to the safety of bridges, and it can even result in bridge failure. Traditional approaches investigating the overloading-induced fatigue damage on bridges, such as finite element analysis (FEA) and reliability analysis, have proven to be computationally expensive and model dependent. In this study, the prediction of fatigue failure probability of bridges due to traffic overloading was investigated by using the feedforward neural network and the Monte Carlo method. Our results show that based on a finite set of training data for the bridge under consideration, the proposed machine-learning-based approach can assist in providing an instantaneous assessment of the fatigue failure probability with high accuracy.

Time-variant reliability analysis based on high dimensional model representation

Time-variant reliability analysis aims at estimating the probability that an engineering system successfully performs intended missions over a certain period of time under various sources of uncertainty. In order to perform the time-variant reliability analysis efficiently, this paper presents a high dimensional model representation (HDMR) model combined with an active learning strategy to estimate the failure probability of dynamic problem. Firstly, the HDMR meta-model is established in the augmented input space (random variables and time) based on Gaussian process regression technique. Then, a reliability analysis approach incorporating epistemic uncertainty is proposed, and a learning function is introduced to update the experimental design sequentially. Finally, the Monte Carlo simulation method is applied for time-variant failure probability assessment based on the well-developed HDMR meta-model. Two engineering applications are used to demonstrate the effectiveness of the proposed method for time-variant reliability analysis.

Expert judgments calibration and combination for assessment of river levee failure probability

In civil engineering, the utilisation of expert judgement is important for evaluating the reliability of structures whose failure behaviour is little known and difficult to quantify. This article presents the application of the approaches of elicitation, calibration and aggregation of expert opinions to evaluate the reliability of river levees. These approaches have seldom been used in the field of French hydraulic structures whereas there is growing interest for them in other countries, concerning structures reliability and other fields such as aerospace industry, nuclear industry, hydrology, statistics, economics, psychology etc. This article proposes a quantitative approach to the use of expert judgement for evaluating river levee failure probability. An application to the case of an existing river levee is presented. The approach developed assesses the expert opinions according to their calibration (statistical accuracy) and their informativeness, then aggregates them in a single calibrated opinion. Applying the proposed weighting and aggregation procedure reduces variability in the probability of failure estimate, then it provides a better probabilistic distribution of aggregated expert opinion than of individual expert opinions. The results allow identifying a trend of aggregated expert opinions that point towards over- or lack of confidence.

Analysis and management of operating risk created by turbine operation under flexible regimes

The new conditions in which coal-fired power plants, especially 200 MW units, have to operate require a considerable increase in the dynamics of their operation. The power unit start-up frequency increases and so does the frequency of changes in loads. This intensifies some wear processes, such as low- cycle fatigue and crack propagation in particular. Therefore, further operation of power units which have already been in service for a long time has to be supplemented with results of analyses and tests taking account of the intensification of wear processes. The paper presents a proposal for an extension of standard diagnostic testing of turbines by adding small punch tests (SPT) of the rotor material micro specimens. The SPT method enables a fast quasi non-destructive assessment of changes in mechanical properties, especially rotor steel embrittlement due to the turbine previous operation. The other element of the proposed testing is the analysis of the propagation rate of potential cracks in the rotor and assessment of the rotor failure probability for different scenarios of the power unit further operation.

计及风沙磨损的架空输电线路冰风荷载失效概率评估方法

计及风沙磨损的架空输电线路冰风荷载失效概率评估方法

Prioritizing Water Mains for Inspection and Maintenance Considering System Reliability and Risk

AbstractThis paper presents a method for prioritizing water mains for inspection and maintenance with consideration of municipalities with a lack of data for failure probability assessment. The met...

Assessment of the cement failure probability using statistical characterization of the strength data

Cement sheath failure may occur under the influence of induced stresses during service life of the well. For many years, analytical or numerical models have been developed to study the probability of the cement sheath failure under different circumstances. Cement compressive strength is considered as a valuable input parameter during the assessment of the cement failure probability. Usually the compressive strength data are obtained by conducting several direct laboratory experiments on cement samples. The average value of these experiments are reported and used in different cement failure models. However, this approach may results in underestimating the cement failure probability. As cement have heterogeneous structures, therefore their strength variations have a statistical character. In this work, the compressive strength data are represented by the Weibull distribution function, which then will be used in the cement failure probability calculations. Results show that using the statistically distributed compressive strength data in calculations, leads to more accurate outputs of the failure model.

Failure probability assessment of gas transmission pipelines based on historical failure-related data and modification factors

Evaluation of failure probability is one of the core contents of quantitative risk assessment. An assessment model of gas transmission pipelines failure probability based on historical failure-related data and modification factors is established, which combines a quantitative part to integrate available historical failure-related data, with a qualitative analysis to compensate for a potential lack of precise crisp statistical data. The main idea is to use the modification factors to modify the baseline failure frequency. The baseline failure frequency is estimated from the statistical historical failure-related data. The modification factors are calculated from the segment attributes of the target pipeline using algorithms developed from the analysis of statistical data and analytical models supplemented by pipeline evaluation criteria and expert judgment. The constructed model is applied to a long-distance gas transmission pipeline so that the effectiveness of the proposed model could be demonstrated. The prospect is for more efficient risk management by acting both on historical failure-related data and modification factors of gas transmission pipeline systems.

A probabilistic approach for assessing failure risk of cutting tools in underground excavation

Accurate assessment of failure risk of cutting tools is essential to the optimization of cutter and drum design as well as the operation of mechanical excavators. In reality, factors involved in the risk assessment often have many uncertainties, e.g., the variations in rock properties and cutting tip material properties. In current practice, these uncertainties have not been considered adequately. The conventional deterministic approach based on mean values or extreme values normally generates a binary assessment (safe or unsafe) and can lead to over- or under-estimation of cutting tool failure risks. To address this issue, a probabilistic risk assessment approach is proposed in this paper, with a case study on the application of the proposed approach to the failure probability assessment of cutting tools for underground roadway development with a continuous miner. In this approach, the property variations of cutting tools and rock are modelled using probability theory, the continuous rock cutting process is discretized, and the failure risk of cutting tools is estimated based on reliability theory. The new approach enables designers and operators to estimate how likely a cutting tool would fail in a given operational condition, and then adjust their design and operation decisions accordingly to achieve an optimal balance between cutting productivity and cutting tool failure risk.

Reliability-based design optimization using a generalized subset simulation method and posterior approximation

ABSTRACTThe evaluation of the probabilistic constraints in reliability-based design optimization (RBDO) problems has always been significant and challenging work, which strongly affects the performance of RBDO methods. This article deals with RBDO problems using a recently developed generalized subset simulation (GSS) method and a posterior approximation approach. The posterior approximation approach is used to transform all the probabilistic constraints into ordinary constraints as in deterministic optimization. The assessment of multiple failure probabilities required by the posterior approximation approach is achieved by GSS in a single run at all supporting points, which are selected by a proper experimental design scheme combining Sobol’ sequences and Bucher’s design. Sequentially, the transformed deterministic design optimization problem can be solved by optimization algorithms, for example, the sequential quadratic programming method. Three optimization problems are used to demonstrate the efficiency and accuracy of the proposed method.

Assessment of effect of ductile tearing on cleavage failure probability in ductile to brittle transition region

The fracture behaviour of ferritic and ferritic martensitic steels in ductile to brittle transition (DBT) region has been extensively studied in recent years and a probabilistic approach of master curve method is generally used to describe the fracture toughness of BCC steels in DBT region as a function of temperature. The assessment of cleavage failure probability however is still untouched in the upper region of ductile to brittle transition, although various extensions of master curve approach and various local approaches has been explored. Additionally the geometry and loading in tension and bending also adds up to the difficulties when cleavage failure is assisted with prior ductile tearing. In this work the cleavage fracture is investigated in upper region of DBT and a modified master curve approach is presented which can satisfactorily describe the fracture toughness as a function of temperature as well as amount of ductile tearing preceded by cleavage.

Seismic Failure Probability and Vulnerability Assessment of Steel-Concrete Composite Structures

Building collapse in earthquakes caused huge losses, both in human and economic terms. To assess the risk posed by using the composite members, this paper investigates seismic failure probability and vulnerability assessment of steel-concrete composite structures constituted by rectangular concrete filled steel tube (RCFT) columns and steel beams. To enable numerical simulation of RCFT-structure, the details of components modeling are developed using OpenSEES finite element analysis package and the validation of proposed procedure is investigated through comparisons with available experimental results. The seismic fragility and vulnerability curves of RCFT-structures are created through nonlinear dynamic analysis using an appropriate suite of ground motions for seismic loss assessment. These curves developed for three-, six- and nine-story prototypes of RCFT-structure. Fragility curves are an appropriate tool for representing the seismic failure probabilities and vulnerability curves demonstrate a probability of exceeding loss to a measure of ground motion intensity.