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Due to multiple implicit limit state functions needed to be surrogated, adaptive Kriging model for system reliability analysis with multiple failure modes meets a big challenge in accuracy and efficiency. In order to improve the accuracy of adaptive Kriging meta-model in system reliability analysis, this paper mainly proposes an improved AK-SYS by using a refined U learning function. The improved AK-SYS updates the Kriging meta-model from the most easily identifiable failure mode among the multiple failure modes, and this strategy can avoid identifying the minimum mode or the maximum mode by the initial and the in-process Kriging meta-models and eliminate the corresponding inaccuracy propagating to the final result. By analyzing three case studies, the effectiveness and the accuracy of the proposed refined U learning function are verified.

Failures of marine propulsion components or systems can lead to serious consequences for a vessel, cargo and the people onboard a ship. These consequences can be financial losses, delay in delivery time or a threat to safety of the people onboard. This is why it is necessary to learn about marine propulsion failures in order to prevent worst-case scenarios. This paper aims to provide a review of experimental, analytical and numerical methods used in the failure analysis of ship propulsion systems. In order to achieve that, the main causes and failure mechanisms are described and summarized. Commonly used experimental, numerical and analytical tools for failure analysis are given. Most indicative case studies of ship failures describe where the origin of failure lies in the ship propulsion failures (i.e., shaft lines, crankshaft, bearings, foundations). In order to learn from such failures, a holistic engineering approach is inevitable. This paper tries to give suggestions to improve existing design procedures with a goal of producing more reliable propulsion systems and taking care of operational conditions.

The topic of rainfall thresholds for landslide occurrence was thoroughly investigated, producing abundance of case studies at different scales of analysis and several technical and scientific advances. We reviewed the most recent papers published in scientific journals, highlighting significant advances and critical issues. We collected and grouped all the information on rainfall thresholds into four categories: publication details, geographical distribution and uses, dataset features, threshold definition. In each category, we selected descriptive information to characterize each one of the 115 rainfall threshold published in the last 9 years. The main improvements that stood out from the review are the definition of standard procedures for the identification of rainfall events and for the objective definition of the thresholds. Numerous advances were achieved in the cataloguing of landslides too, which can be defined as one of the most important variables, together with rainfall data, for drawing reliable thresholds. Another focal point of the reviewed articles was the increased definition of thresholds with different exceedance probabilities to be employed for the definition of warning levels in landslide early warning systems. Nevertheless, drawbacks and criticisms can be identified in most part of the recent literature on rainfall thresholds. The main issues concern the validation process, which is seldom carried out, and the very frequent lack of explanations for the rain gauge selection procedure. The paper may be used as a guide to find adequate literature on the most used or the most advanced approaches followed in every step of the procedure for defining reliable rainfall thresholds. Therefore, it constitutes a guideline for future studies and applications, in particular in early warning systems. The paper also aims at addressing the gaps that need to be filled to further enhance the quality of the research products in this field. The contribution of this manuscript could be seen not only as a review of the state of the art, but also an effective method to disseminate the best practices among scientists and stakeholders involved in landslide hazard management.

As our society becomes more and more dependent on IT systems, failures of these systems can harm more and more people and organizations. Diligently performing risk and hazard analysis helps to minimize the potential harm of IT system failures on the society and increases the probability of their undisturbed operation. Risk and hazard analysis is an important activity for the development and operation of critical software intensive systems, but the increased complexity and size puts additional requirements on the effectiveness of risk and hazard analysis methods. This paper presents a qualitative comparison of two hazard analysis methods, failure mode and effect analysis (FMEA) and system theoretic process analysis (STPA), using case study research methodology. Both methods have been applied on the same forward collision avoidance system to compare the effectiveness of the methods and to investigate what are the main differences between them. Furthermore, this study also evaluates the analysis process of both methods by using a qualitative criteria derived from the technology acceptance model (TAM). The results of the FMEA analysis were compared to the results of the STPA analysis, which were presented in a previous study. Both analyses were conducted on the same forward collision avoidance system. The comparison shows that FMEA and STPA deliver similar analysis results.

A massive toppling failure occurred at the edge of the Furnas Reservoir at 12:30 (UTC-03) on 8 January 2022, in Brazil. The toppling belongs to single-block toppling with a volume of about 3.32 × 102 m3 and caused 10 deaths and 32 injuries. Field investigation, numerical analysis, and base friction tests were performed to explore the failure characteristics and mechanism of the toppling. A conceptual model of the toppling mechanism was constructed and the toppling process was divided into four stages: foundation erosion and weakening stage, crack propagation and dislocation stage, opening up and rotation stage, and disintegration and collapse stage. A series of real three-dimensional numerical simulations was performed to clarify the toppling evolution and related triggering mechanism using the finite difference program FLAC3D. Two different alternatives of triggering mechanism for the toppling were comparatively analyzed, the first with a reduction in the shear strength of the weak foundation layer believed to represent the foundation weakening mechanism, and the second with removal of the weak layer believed to represent the foundation erosion mechanism. We found that the foundation weakening of the weak layer resulted in a sliding mechanism of the block, while the foundation erosion resulted in a clear toppling mechanism of the block. The base friction test was conducted to investigate the toppling process and to verify the numerical results over a limited time span. The experimental evidence demonstrated a good agreement with the numerical results as well as those observations in the field. We concluded that the slope was in a critical state due to the foundation erosion of the weak layer, while the heavy rainfall triggered the toppling. It is emphasized that the undermining of the slope foundation and/or existed cavities induced by the foundation erosion played a vital role in the formation of the toppling. Moreover, a produced vertical crack or the propagation of an existed crack in the rear part of the slope may be signs of movements and thought of precursors of the single-block toppling. Dealing with the eroded cavities was suggested to be an effective way to prevent the toppling in the Furnas Reservoir, such as backfilling the eroded cavities with masonry rubble and/or grouting. The understanding of the toppling characteristics and mechanism may offer a reference for single-block toppling issues, such as its movement characteristics and failure mechanism, and may be used for stability analysis and disaster identification of potential failures.

Climate change, energy transition, population growth and other natural and anthropogenic impacts, combined with outdated (unfashionable) infrastructure, can force Dam and Reservoir Systems (DRS) operation outside of the design envelope (adverse operating conditions). Since there is no easy way to redesign or upgrade the existing DRSs to mitigate against all the potential failure situations, Digital Twins (DT) of DRSs are required to assess system’s performance under various what-if scenarios. The current state of practice in failure modelling is that failures (system’s not performing at the expected level or not at all) are randomly created and implemented in simulation models. That approach helps in identifying the riskiest parts (subsystems) of the DRS (risk-based approach), but does not consider hazards leading to failures, their occurrence probabilities or subsystem failure exposure. To overcome these drawbacks, this paper presents a more realistic failure scenario generator based on a causal approach. Here, the novel failure simulation approach utilizes fuzzy logic reasoning to create DRS failures based on hazard severity and subsystems’ reliability. Combined with the system dynamics (SD) model this general failure simulation tool is designed to be used with any DRS. The potential of the proposed method is demonstrated using the Pirot DRS case study in Serbia over a 10-year simulation period. Results show that even occasional hazards (as for more than 97% of the simulation there were no hazards), combined with outdated infrastructure can reduce DRS performance by 50%, which can help in identifying possible “hidden” failure risks and support system maintenance prioritization.

Colluvial landslides develop in loose Quaternary deposits, with deformation generally being progressive and crack development dominant in the sliding mass surface layer. With the Tanjiawan landslide in the Three Gorges Reservoir (China) as a case study, field investigations, deformation monitoring, and groundwater level monitoring data were integrated to analyze the landslide deformation characteristics and elucidate the influence of cracks on its deformation. We used numerical simulations, including the finite element and discrete element methods, for investigating the progressive deformation mechanism of rainfall-triggered landslides in the accumulation layer and predicting the failure process. The results indicated that crack formation instigated a preferential seepage channel in the shallow layer of the sliding mass, rainfall infiltration along cracks generated water pressure, and the landslide gradually morphed from a stable into a “step-like” progressive deformation state. Preferential flow inside the cracks effectively elevated the groundwater level within the landslide, and either the number or depth of cracks significantly affected the groundwater seepage field, thereby influencing slide stability. Geological conditions controlled the deformation and failure processes of each landslide section. The uplifted bedrock on the right side blocked the sliding process of the rear sliding mass, and the middle and front sliding masses moved faster but the sliding distance was shorter. The deformation trend is deformation, crack formation, preferential flow occurrence, crack extension, and deformation. The ultimate cause of failure was a steep rise in groundwater level following short duration heavy rainfall or long duration light rainfall.

Organizations are continuously endeavoring to provide a healthy work environment without any incident, by Health, Safety, and Environment (HSE) management. As most of the activities and processes in the organizations have risk-taking nature, identification and evaluation of risks can be useful to decrease their negative effects on the system. Although Failure Mode and Effect Analysis (FMEA) technique is used widely for risk assessment, the traditional Risk Priority Number (RPN) score has shortcomings like do not considering different weights and the inherent uncertainty of risk factors as well as do not regarding all viewpoints of the experts in decision making. The aim of this study is presenting a hybrid approach based on the Linguistic FMEA, Fuzzy Inference System (FIS) and Fuzzy Data Envelopment Analysis (DEA) model to calculate a novel score for covering some RPN shortcomings and the prioritization of HSE risks. First, after identifying potential risks and assigning values to the RPN determinant factors by linguistic FMEA team members due to the differentiation of these values, FIS is used to reach a consensus opinion about these factors. Then, the outputs of FIS are used by the fuzzy DEA and its supper efficiency model to risk prioritization which can contribute to full prioritization. In addition to considering uncertainty and decreasing dependence on the team’s opinions, in this phase weights of triple factors are calculated based on mathematical programming. To show the ability of the proposed approach in terms of HSE risks prioritization, it has been implemented in an active company in the chemical industry. After identifying risks having high priority based on the proposed score, preventive/corrective actions are presented in accordance with the case study, and for more analysis of results, the self-organizing map has been applied in this study.

Risk assessment of congestive heart failure (CHF) is essential for detection, especially helping patients make informed decisions about medications, devices, transplantation, and end-of-life care. The majority of studies have focused on disease detection between CHF patients and normal subjects using short-/long-term heart rate variability (HRV) measures but not much on quantification. We downloaded 116 nominal 24-hour RR interval records from the MIT/BIH database, including 72 normal people and 44 CHF patients. These records were analyzed under a 4-level risk assessment model: no risk (normal people, N), mild risk (patients with New York Heart Association (NYHA) class I-II, P1), moderate risk (patients with NYHA III, P2), and severe risk (patients with NYHA III-IV, P3). A novel multistage classification approach is proposed for risk assessment and rating CHF using the non-equilibrium decision-tree-based support vector machine classifier. We propose dynamic indices of HRV to capture the dynamics of 5-minute short term HRV measurements for quantifying autonomic activity changes of CHF. We extracted 54 classical measures and 126 dynamic indices and selected from these using backward elimination to detect and quantify CHF patients. Experimental results show that the multistage risk assessment model can realize CHF detection and quantification analysis with total accuracy of 96.61%. The multistage model provides a powerful predictor between predicted and actual ratings, and it could serve as a clinically meaningful outcome providing an early assessment and a prognostic marker for CHF patients.