Probability Of Failure Research Articles

Mechanistic and Statistical Analysis of Possible Defense Mechanisms for Probabilistic Failures in Robustness, Stability and Sustainability of Static and Dynamic Complex Networks

Complex networks are ubiquitous even in the form of defense networks in many fields such as ecological immune systems, biological immune systems, brain networks, air defense systems, national security, cyber security, peace and conflict studies, and so on. In the real world, complex networks exist in both static and dynamic forms. By understanding probabilistic failures and their possible defense mechanisms in static and dynamic complex networks, scientists and researchers can make decisions in developing and implementing methodologies of many fields as we can say, from the fields of biology to fields of technology. In this research, static and dynamic scale-free and small world networks have been generated as complex networks with network generation algorithms. Besides that, probabilistic failures and their possible defense mechanisms have also been generated and designed for previously generated complex networks. On these generated probabilistic failures and their possible defense mechanism, several mechanistic and statistical analysis methods have been applied. To generate these static and dynamic complex networks and their probabilistic failures and possible defense mechanism, two modified network generation algorithms along with several network measurements algorithms have been implemented where parameters have been chosen to produce possible types of static and dynamic scale-free and small world networks, and their corresponding probabilistic failures and possible defense mechanisms. Besides analyzing probabilistic failures and possible defense mechanisms for generated complex networks; the robustness, stability and sustainability of those networks have been studied and analyzed for probabilistic failures and their possible defense mechanisms.

Longevity of different abutments placed on narrow diameter implants: Assessment of structural damage and loosening

ObjectivesTo evaluate structural damage and loosening of abutments placed on narrow diameter implants after cyclic fatigue. MethodsSixty Morse taper narrow diameter implants (Neodent, Brazil) received two types of abutments (1PA- one-piece abutment or 2PA- two-piece abutment), which were randomly divided into 3 fatigue experiments (n = 10). The implants were placed into a customized supporting holder and a software-assisted digital torque wrench secured the manufacturer recommended torque for each abutment. Cone beam computed tomography (CBCT) scans were acquired, before and after fatigue, and post-processed (software e-Vol DX) to assess damage and abutment displacement. The boundary fatigue method was adapted to use 2 × 106 cycles, 2 Hz of frequency and constant peak load of 80 N (first experiment) that varied according to the failure rate of previous specimens (second and third experiments). Failure was evaluated using CBCT scans and removal torque values. Data were used to estimate long-term torque degradation, probability of failure and Weibull modulus (software ALTA PRO9). ResultsAll 2PA specimens became loosen independently of the applied fatigue load, and structural bending was observed in 14 abutments. Eight 1PA got loosen during the fatigue experiment. The Weibull analysis showed a lower modulus (m = 1.0; 0.7, 1.4) for 1PA than for 2PA (m = 2.6; 2, 3.4) resulting in longer predicted lifetimes and slower torque degradation for 1PA than for 2PA specimens. Significance1PA showed greater long-term survival probability than 2PA. Predicting the lifetime and mechanical behavior of implant-abutment systems are useful information to clinicians during the decision-making process of oral rehabilitations.

Investigating the Reliability of an Existing Top Angle and Seat Pad Semi-Rigid Connection System Using Advanced Modelling Techniques

The investigation introduces an advanced model procedure for evaluating the structural reliability of semi-rigid frame connections in existing aged buildings. Specifically, the focus is on the top angle and seat pad (TA–SP) semi-rigid connection, which was not initially considered in the current design standards. The approach employs a plastic hinge model to predict the ultimate strength of the connection and its beam-to-column behaviour. In order to increase computational efficiency, the investigation leverages the nonlinear behaviour of the finite element (FE) model to validate critical parameters. The statistical properties of the existing connection were obtained based on past experimental data, highlighting the weakest elements in the system. The first-order and second-order reliability methods and Monte Carlo simulations were employed to estimate the reliability index. Percentile errors were assessed to understand their impact on higher-order interactions. This new technique of identification quantifies the probability of the system failure interactions. Notably, a 45% lesser error aligns with the target reliability index, while a 114.5% larger error indicates a significant deviation from actual failure probability values. Each methodology introduced adheres to the current standard, and the system reliability analysis provides a vigorous conclusions scheme framework for assessing the existing TA–SP semi-rigid connection.

Reinforcing resilience for integrated design of green and grey infrastructure with real-time control rules by considering system failures

The mutual benefits of real-time control and green and grey infrastructure for urban drainage system (UDS) renovations have drawn great attention. However, risks emerged due to equipment and structural failures, and no studies considered such failures while optimizing the renovation scheme. To address this issue, this study proposed a multi-criteria optimization method for the integrated design of green and grey infrastructure with real-time control rules, where the economic cost and system performance under normal and exceptional conditions were optimized simultaneously. Failure probabilities of equipment and structure were quantified using homogeneous Poisson process models, and failure losses were estimated with the aid of a machine learning-based surrogate model. This approach was tested with a combined UDS in China. Results indicate that: (1) More investment does not necessarily increase system resilience to failures. Equipment and structural failures can significantly lower the effectiveness of grey infrastructure and real-time control. Therefore, solutions with more investments in grey infrastructure, which indicate higher costs, experience greater failure losses. (2) System resilience to failures can be significantly improved while maintaining other objectives when compared with the traditional design scheme. The proposed method allows for a new perspective in addition to the cost-and-resilience two-objectives design of UDS renovations, especially for systems threatened by various failures.

Shallow foundation design: a comparative study of partial safety factors and full probabilistic methods

In the past two decades, Europe has witnessed a significant transition in the design codes used for assessing foundation structures, with the widespread adoption of the Eurocodes (EC). This shift remains a pertinent topic within the engineering community, particularly concerning the transition from traditional design methodologies to those prescribed by the Eurocodes, as well as the potential for fully probabilistic design. While the Eurocodes’ methodology is described as probabilistic, it is crucial to recognize that the achievement of the target reliability level is predominantly facilitated through a system of partial safety factors. These factors are integrated into the calculation algorithm as fixed values, rendering the process essentially deterministic. To refine these calculations for more accurate reliability estimates—expressed in terms of failure probability—a genuinely probabilistic framework is required, termed as fully probabilistic computation. This paper aims to elucidate the fully probabilistic calculation approach for the broader professional community, using the geotechnical application of shallow foundations as an illustrative example. We present a comparative analysis of this advanced approach with the standard foundation design according to EC7 and ČSN 731001, the latter being a precursor in Europe for implementing the partial safety factor method. The discussion extends to a practical demonstration of full probabilistic design juxtaposed against the conventional partial safety factor method, using a shallow foundation case study. Furthermore, the paper delves into the impact of the tail behavior of uncertain or spatially varying soil parameters on the theoretical probability of failure, underscoring its significance in foundation design.

Behavior of one-way steel, BFRP, and GFRP reinforced concrete slabs under monotonic and cyclic loadings: Experiments and analyses

This paper experimentally investigated the monotonic and cyclic behaviour of one-way basalt and glass fibre-reinforced polymers (BFRP and GFRP) reinforced concrete (RC) slabs compared with that of traditional steel RC ones. A total of nine 2000×700×100 mm RC slabs, including three BFRP RC (BRC), three GFRP RC (GRC), and three steel RC (SRC) slabs, were tested. The results indicated that the behaviour of BRC and GRC slabs was governed by the elastic characteristic of FRP bars, resulting in a more uniform curvature of the slabs. The behaviour and failure modes of BRC and GRC slabs were comparable with those of SRC slabs. The similarity and difference were characterized by the rupture of FRP bars and the yielding of steel bars. Postcrack stiffness of BRC and GRC slabs was significantly lower than that of SRC slabs and was marginally affected by the effect of cyclic loading. The ultimate loads and ultimate deflections of BRC and GRC slabs were ∼2.0 and 1.6–2.0 times those of SRC slabs, respectively. Concepts of ductility, safety factor, and warning index were introduced and quantified for FRP RC slabs. BRC and GRC slabs had a significantly higher ductility (8.5–10.4) than SRC slabs (5.1–6.2), showing a highly ductile behaviour. SRC slabs had a safety factor of 1.0–1.1, while BRC and GRC slabs had a significantly higher safety factor of 3.8, showing their lower failure probability of BRC and GRC slabs when they are designed at a similar load as SRC slabs. The warning index of SRC slabs was 14.4–30.2, and that of BRC and GRC slabs was 65.1–71.6 and 63.9–86.0, respectively. The strength, ductility, safety factor, recovery, and warning index of BRC and GRC slabs were high, confirming their advanced characteristics. Under cyclic loading, BRC and GRC slabs have significantly larger stiffness degradations (4.4 %–7.2 %/cycle) than SRC slabs (1.62 %−3.2 %/cycle). The results of theoretical analyses showed that the strengths of BRC and GRC slabs can be reasonably predicted. The outcomes indicate the high potential use of FRP reinforcement for slabs in engineering practice.

Invertible Bloom Lookup Tables with Listing Guarantees

The Invertible Bloom Lookup Table (IBLT) is a probabilistic concise data structure for set representation that supports a listing operation as the recovery of the elements in the represented set. Its applications can be found in network synchronization and traffic monitoring as well as in error-correction codes. IBLT can list its elements with probability affected by the size of the allocated memory and the size of the represented set, such that it can fail with small probability even for relatively small sets. While previous works only studied the failure probability of IBLT, this work initiates the worst case analysis of IBLT that guarantees successful listing for all sets of a certain size. The worst case study is important since the failure of IBLT imposes high overhead. We describe a novel approach that guarantees successful listing when the set satisfies a tunable upper bound on its size. To allow that, we develop multiple constructions that are based on various coding techniques such as stopping sets and the stopping redundancy of error-correcting codes, and Steiner systems as well as new methodologies we develop. We analyze the sizes of IBLTs with listing guarantees obtained by the various methods as well as their mapping memory and runtime overheads. Lastly, we study lower bounds on the achievable sizes of IBLT with listing guarantees and verify the results in the paper by simulations.

Seismic fragility analysis of buried pipelines under Kahramanmaraş ground motions

This paper aims to evaluate the failure probability of buried pipelines under pulse-like ground motions based on the seismic records detected during the Kahramanmaraş earthquake in Turkey. The incremental dynamic analysis (IDA) method was used to evaluate the vulnerability of continuous and segmented steel pipes, taking into account the corrosion effect in alkaline and near-neutral soil environments.The results show that pulse-like ground motion can significantly impact the fragility of buried pipelines, particularly segment steel pipelines. Moreover, the failure probability of buried pipelines tends to increase with service age, although the rate of failure gradually decreases. Empirical models notably underestimate the failure probability of buried pipelines under pulse-like ground motion, so they should be used cautiously in engineering application. These insights contribute to advancing our understanding of the seismic fragility of buried pipelines.

Reliability assessment of ballasted railway bridges considering soil-structure interaction using ensemble of surrogate models

ABSTRACT The increasing speeds of modern trains lead to excessive vibrations on the bridges, which have the potential to destabilize the ballast particles. The occurrence of this phenomenon not only increases the track maintenance cost, but can also disrupt the load path from the rail level to the bridge deck, posing a risk to the train running safety. The design regulations indirectly control this limit-state by restricting the vertical acceleration of the bridge deck. The assessments pertaining to this purpose often neglect the soil-structure interaction (SSI) effects considering that as a conservative assumption. Such effects can positively contribute by increasing the system damping, but they can also increase the bridge flexibility making it more susceptible to vibrations due to reduction on critical speed. Therefore, this study investigates the influence of considering/disregarding SSI effects on the ballast destabilization phenomenon using a probabilistic methodology. The results are classified based on the maximum permissible train speeds and the bridge span length. Due to the high computational costs of the reliability analyses, the associated limit-state is approximated by an ensemble of classification-based surrogate models using the stack-generalization concept. Subsequently, the upper/lower bounds of the failure probability in the presence of SSI effects are compared with those obtained for simply-supported bridges. It is pointed out that neglecting SSI effects for shorter span bridges may lead to an underestimation of system safety. For longer span bridges, however, this may lead to an overestimation of safety, which means that a non-conservative system can be designed.

A cascading failure propagation model for a network with a node emergency recovery function

Cascading failures are ubiquitous in physical networks such as power grids and water networks and have become a trending research topic in the field of complex networks. In real cases, once a node in a network fails, the entire system can completely collapse, causing considerable economic losses and casualties. Implementing an appropriate node repair strategy can effectively prevent system crashes in complex networks due to cascading failure. This paper presents a network cascading failure propagation model with a node emergency recovery function. Numerical simulations with typical case scenarios are conducted to analyze network cascading failure when the nodes have an emergency recovery probability. The results of the case study show that incorporating an emergency recovery probability for nodes can reduce the maximum failure probability of nodes in a network and reduce the risk of network failure. As the recovery probability increases, the network failure risk probability gradually decreases. The proposed network cascading failure propagation model with a node emergency recovery function can accurately reflect the dynamic behavior of complex network cascades and provide a reference for the control and prevention of cascading failures in actual networks.

Risk assessment and simulation of gas pipeline leakage based on Markov chain theory

Gas pipeline leakage may lead to fire, explosion and gas poisoning, thus causing a large number of injuries and property damage. This paper focuses on the chain characteristics presented by the development process of city gas pipeline leakage, based on Markov chain theory, studies the disaster-causing process and the mechanism of disaster chain formation for the leakage, establishes the disaster chain, evaluates the pipeline operation status and quantifies the failure probability of gas pipeline leakage to determine the causes of the leakage. Subsequently, Pipeline Studio is applied to simulate the consequences of gas pipeline leakage to realize the risk analysis. The analysis is based on Markov chain theory to calculate the probability of leakage and disaster-causing causes, analyze the consequences of gas pipeline leakage, and realize the diagnosis of the causes of gas pipeline leakage accidents and the prediction of the accident development trend. Finally, using European gas pipeline accident data to prove the effectiveness of the method, the result shows that gas pipeline leakage accidents are mainly caused by third-party damage and corrosion, which is consistent with the real situation and provides a reference for risk analysis and accident prevention of gas pipeline leakage.

A data-driven approach for structural integrity assessment of prestressed concrete containment vessel considering the effect of vertical ground motions

As a final barrier against the release of radioactive material, the prestressed concrete containment vessel (PCCV) plays an important role in the nuclear power plant (NPP). When subjected to seismic loads, its structural performance requires high structural integrity. During an earthquake, the PCCV experiences ground motions (GMs) consisting of horizontal and vertical components in a global coordinate system, and its structural integrity is probably overestimated if only the horizontal component is considered in numerical analyses. In this study, a novel cumulative damage index is first employed to quantitatively assess the structural integrity of PCCVs. Accordingly, the failure probability of structural integrity of the PCCV is investigated by incremental dynamic analysis (IDA) and further seismic fragility analysis considering the effect of vertical ground motions (VGMs). The results show that the effect of VGMs on structural integrity of PCCVs is gradually non-negligible with the increase of vertical intensity. Moreover, an accurate cumulative damage prediction model for the PCCV is established based on eight mainstream machine learning (ML) algorithms, and the necessity of incorporating vertical intensity measures (IMs) in ML models is investigated. The results show that the ML model considering the vertical IMs is superior in capturing the cumulative damage of the PCCVs. The best estimates are obtained by GB with R2 of 0.912, MAE of 1.301, MSE of 6.186, and RMSE value of 2.487. Finally, the SHapley Additive exPlanation (SHAP) method is adopted for interpreting the prediction results of the optimal ML model to assess the effect of each IM and its interactions on the generalization performance of the model. The proposed ML-based cumulative damage prediction model for PCCVs considering the effect of VGMs enables an accurate quantitative assessment on structural integrity of PCCVs. This provides an important reference for evaluating the potential environmental hazard due to the leaking of radioactive materials.

A probabilistic framework for external pitting corrosion growth modelling for buried steel pipelines considering soil properties

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power-law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect-specific growth models. The framework is applicable regardless of the availability of matched or non-matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where time-dependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

Optimal Load Factors for Seismic Design of Buildings

This paper reviews the load combinations for the design of buildings established in the Federal District Building Regulations (RCDF-2004) and its Complementary Technical Standards (NTC-2004). New load factors are proposed to be specified in the next version of the RCDF. The combination of gravity load (dead load plus live load) and the combination of earthquake load (dead load, live load and earthquake load) are revised. A methodology is proposed to establish optimal load factors and combinations that guarantee the minimum expected total cost during the lifetime of the structure and that the probability of failure is at least equal to that implied in the RCDF-2004. Artificial Neural Networks are used to estimate structural reliability.

Mitigating cascading failure in power grids with deep reinforcement learning-based remedial actions

Power grids are susceptible to cascading failure, which can have detrimental consequences for modern society. Remedial actions, such as proactive islanding, generator tripping, and load shedding, offer viable solutions to mitigate cascading failure in power grids. The success of applying these solutions lies in the timeliness and the appropriate choice of actions during the rapid propagation process of cascading failure. In this paper, we introduce an intelligent method that leverages deep reinforcement learning to generate adequate remedial actions in real time. A simulation model of cascading failure is first presented, which combines power flow distribution and the probabilistic failure mechanisms of components to accurately describe the dynamic cascading failure process. Based on this model, a Markov decision process is formulated to address the problem of deciding on the remedial actions as the failure propagates. Proximal Policy Optimization algorithm is then adapted for the training of underlying policies. Experiments are conducted on representative power test cases. Results demonstrate the out-performance of trained policy over benchmarks in both power preservation and decision times, thereby verifying its advantages in mitigating cascading failure in power grids.

Efficient augmented radial basis function for reliability analysis with dynamic importance sampling

Conducting reliability analysis on complex systems, particularly those characterized by minimal failure probabilities and computationally intensive models, presents significant challenges. This article introduces a reliability analysis method combining the adaptive augmented radial basis function and the dynamic importance sampling (ARBF-DIS) algorithm. This method improves the generalization capacity of the augmented radial basis function at both local and global scales through the application of an anisotropic technique and orthogonal conditions. By using efficient cross-validation, the methodology facilitates the training of the metamodel, enabling it to provide predictive variance essential for constructing an adaptive sampling learning function. A key aspect of this approach is its capability to address the issue of low failure probabilities through dynamic importance sampling, which involves calculating an optimal importance sampling function derived from the current metamodel. The effectiveness and precision of the ARBF-DIS method are demonstrated through two analytical examples and a solid rocket motor reliability problem.

Rock fragmentation mechanism of PDC cutter from the insight of cutting chips

To meet the global energy needs and demands, obtaining oil and gas resources from deep strata plays an increasingly important role in energy supply. However, it is difficult to drill since the special properties of rocks in deep formation. To further improve the breaking efficiency of polycrystalline diamond compact (PDC) bit in deep hard rock, this paper conducted several groups of experiments on cutting granite with PDC cutters. By changing the cutting depth, rake angle and linear velocity, the relationship between the mass proportion, fractal dimension and roughness index of cuttings and the mechanical specific energy (MSE) is explored, and the following conclusions are obtained: When cutting depth HC exceeds 0.8 mm, massive chips with particle size r > 2 mm begin to form and the brittle failure begins to occur, and the probability of brittle failure will increase with the further increase of cutting depth. With the increase of rake angle, more energy will be used to form new surfaces of cutting chips, resulting in a decrease in the roughness index and an increase in MSE. More massive chips will be formed with the increasing linear velocity, but MSE only decrease slightly. The variation of the fractal MSE (FMSE) that based on the fractal dimension of cutting chips is extremely consistent with the MSE. The relevant conclusions in this paper can provide some reference for the structural design of PDC bit, so as to improve the drilling efficiency in deep hard rock.

A novel single-loop estimation method for predictive failure probability-based global sensitivity analysis

The existing predictive failure probability (PFP)-based global sensitivity analysis methods may get stuck in practical application due to the large amount of calculation or insufficient precision. Therefore, a novel single-loop estimation method is proposed in this paper to effectively estimate PFP-based global sensitivity measures. Firstly, by introducing standard normal auxiliary variables and equivalent probability transformation, the overall uncertainty of each input variable is decomposed into its inherent uncertainty and distribution parameter uncertainty. Then, Bayes theorem-based formulations are derived for three PFP-based global sensitivity measures, where only the unconditional PFP and the conditional marginal and joint distributions of standard normal auxiliary variables and distribution parameters need to be known. These items can be evaluated simultaneously using only one set of functional calls. Finally, adaptive Kriging model is embedded into the proposed method in place of the time-demanding performance function to further enhance computational efficiency. The effectiveness of the proposed methods is validated through four benchmark reliability problems asnd one engineering problem.

Model for optimizing lender's decision on dealing with collateral of defaulted mortgage

AbstractIn this article, we describe a comprehensive model for obtaining a critical gross recovery rate (GRR) for the short sale of a defaulted mortgage. Our model includes the following factors: settlement period, settlement cost, discounted sale/auction price, opportunity cost, failure probability of the short sale, and lender's willingness for the short sale. The results show that using the short sale yields a lower settlement cost, shorter settlement period, but higher loss given default (LGD). The real GRR of a short sale is about 8%–9% less than the critical GRR calculated from our model. This means the lender's willingness for the short sale is high in reality. The sensitivity analyses show that the lender's likelihood of approving a short sale is low if the settlement cost, contract rate, interest rate, and failure probability of the short sale are high. The greater the expected LGD of a foreclosure, the stronger the lender's willingness to approve the short sale. Also, a higher GRR of short sale leads to a lower expected LGD of short sale. This increases the probability of approval for the short sale. Finally, the Home Affordable Foreclosure Alternatives (HAFA) program helped struggling homeowners successfully use a short sale as an alternative to foreclosure, but the HAFA program became less effective as housing prices went up. Our model and analyses should help lenders make the optimal decision about how to efficiently deal with the collateral from a defaulted mortgage to mitigate their LGD.

A method of combined metamodel and subset simulation for reliability analysis of rare events

Reliability analysis of large, complex structures with high reliability has always been a challenging task. The Subset Simulation (SUS) has proven highly effective for high-dimensional and small failure probability problems. However, the computational demands of time-consuming numerical simulations in the field of mechanical engineering remain substantial. Metamodeling techniques provide an effective means to reduce simulation computational costs. This paper introduces a novel approach, termed SUSAK, which combines the Subset Simulation with Kriging metamodels to address the challenge of small failure probability calculations. By using the original distribution of input variables representing the structural system, the SUSAK method establishes an adaptive metamodel and iteratively optimizes it with intermediate failure domain samples corresponding to the intermediate events. By replacing the performance function in subsequent calculations of the probability associated with intermediate events with metamodels, the enhanced method significantly reduces the computational burden compared to using the SUS method. This approach does not rely on solving for the design point, is not constrained by the shape of the failure domain, and retains the advantages of the SUS method in high-dimensional small failure probability calculations. As a result, it is well-suited for calculating small failure probabilities in nonlinear, discontinuous failure domains, multiple failure domains, and high-dimensional problems.