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This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios. [ABSTRACT FROM AUTHOR]

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.

The reliability of face recognition system has the characteristics of fuzziness, randomness, and continuity. In order to measure it in unconstrained scenes, we find out and quantify key broad-sense and narrow-sense influencing factors of reliability on the basis of analyzing operation states for six dynamic face recognition systems in the practical use of six public security bureaus. In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. We verify the model by comparing the evaluation results with actual achievements of these systems. Finally, we find several vulnerabilities in the system with the least reliability and put forward a few optimization strategies. The proposed method combines advantages of the improved fuzzy C-means model with those of the dynamic Bayesian network to evaluate the reliability of the dynamic face recognition systems, making the evaluation results more reasonable and realistic. It starts a new research of face recognition systems in unconstrained scenes and contributes to the research on face recognition performance evaluation and system reliability analysis. Besides, the proposed method is of practical significance in improving the reliability of the systems in use. [ABSTRACT FROM AUTHOR]

The submarine pipeline stopper is an emergency device that can quickly seal damaged pipelines. Investigating the stopper's dependability and safety is vital to ensure that subsequent maintenance activities go smoothly. However, in production environments, it is challenging to get failure data for complex systems due to high experimental costs. This work proposes a fuzzy comprehensive dynamic Bayesian network (FCDBN) based on fault tree, fuzzy evaluation, and dynamic Bayesian network. Using this method, the failure rate of the stopper can be obtained, thus solving the problem of difficult data acquisition. The time slice-based qualities are taken into account while evaluating the reliability and safety. By controlling variables, each failure rate's effect is quantified. Finally, the process of failure prediction is completed. The sealing device is least reliable and most possible to fail, according to the results. The most significant influence on reliability comes from rubber barrel shoulder upwarping. The failure rate of the stopper is highest if the sealing device fails. Based on the aforementioned findings, appropriate control measures are suggested, which can greatly lower the stopper failure risk. • Considering the possibility of degradation, a fuzzy comprehensive dynamic Bayesian network (FCDBN) is proposed and it is applied to the submarine pipeline stopper. • The main failure modes of the stopper were analyzed and the failure rates were obtained, thus filling the gap in the lack of data for the stopper. • The reliability, safety, and impact of failure modes on thestopper were analyzed. FCDBN also can be used for reverse analysis to predict the failure probability of each mode. • The results indicate that the safety of the stopper reduces over time, and the sealing device has the lowest reliability and is most prone to failure. Thus, when designing and producing, more attention should be paid to the sealing device. [ABSTRACT FROM AUTHOR]

• A novel fuzzy dynamic Bayesian network (FDBN) model is proposed for dynamic risk assessment under uncertainty. • The novel FDBN uses fuzzy numbers throughout the entire DBN modeling process. • The novel FDBN enables to quantify uncertainty propagation over time. • Quantitative probability ranges and risk ranges as well as the most likely values can be obtained. • Results demonstrate credibility and reasonability of the novel FDBN for dynamic risk assessment. Risk assessment (RA) plays a vital role in safety engineering. The conventional RA approaches have limited capabilities in handling time dependence and data uncertainty. Although dynamic Bayesian network (DBN) is robust in inference under uncertainty due to its flexible structure and capability of modeling the interdependencies of variables, it still has some defects in quantifying the uncertainty (probability range) propagation over time, and dealing with inaccurate or insufficient data (data uncertainty). This study is aimed to propose a novel fuzzy dynamic Bayesian network (FDBN) methodology to improve the ability of dynamic risk assessment (DRA) methods to quantify and propagate uncertainty arise from inaccurate or insufficient data. The methodology incorporates the fuzzy set theory (FST) with DBN to conduct DRA under data uncertainty while quantifying the uncertainty propagation over time. The proposed methodology represents the causality of variables in the time dimension and adopts expert elicitation and FST to determine the probability of causality. Triangular fuzzy numbers are used throughout the entire dynamic modeling process of DBN to completely retain the uncertainty information. A comparison between the proposed novel FDBN and crisp value based DBN verifies the credibility, rationality and robustness of the proposed methodology. A multi-variable risk assessment of the cotton warehouse is presented here to illustrate the potential of the proposed methodology in dealing with dynamic risk with uncertainty. [ABSTRACT FROM AUTHOR]