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With the continuous improvement of people ‘s living standards, people have put forward higher requirements for the safety and comfort of housing. Therefore, Inherent Defect Insurance, a financial method to guarantee the quality of construction projects, has also emerged. At present, China ‘s Inherent Defect Insurance has been gradually promoted, but its claim mechanism has not been analyzed and studied. From the perspective of construction engineering, this paper first makes a bibliometric analysis of the influencing factors of insurance claims that may be caused by construction engineering quality through VOSViewers, and the evaluation index system of inherent defects is constructed. Then, according to the influencing factors, the PSO-LSSVR model is adopted to fit the performance function of the inherent defects. Finally, based on the reliability design principle of engineering structure, the reliability index and failure probability of Inherent Defect Insurance are derived from the performance function of inherent defects. This paper also analyzes its application in insurance practice and determines the relationship between the number of insurance underwriting policies and the initial reserve of insurance at a certain risk level. This paper studies the probability of Inherent Defect Insurance by constructing the reliability model of inherent defect risks in construction quality, and analyzes the anti-risk ability of insurance companies from the perspective of claim, which provides scientific analysis methods and theoretical basis for the scientific decision-making of insurance companies.

Consideration of safety is one of the current requirements to design a new system that is often implemented by defining the system failure probability. Renewable energy systems (RESs) do have the same requirements when it comes to safety and reliability. When designing a wind turbine, as a RES, its reliability is of the highest importance. So, efficient reliability models are required to ensure the turbine is working safely to generate electricity. A new model is introduced in this paper by taking into account the wind speed and the wind angle as two effective factors. These two random variables are reported to follow the Gaussian and Weibull probability distributions, and so are employed to define a limit-state function for the turbines. This limit-state function, which is also called system performance function, will then be used to find out the system failure probability. [ABSTRACT FROM AUTHOR]

Purpose: The paper proposed a fast procedure for solving the reliability-based robust design optimization (RBRDO) by modifying the RBRDO formulation and transforming it into a series of RBRDO subproblems. Then for each subproblem, the objective function, constraint function and reliability index are approximated using Taylor series expansion, and their approximate forms depend on the deterministic design vector rather than the random vector and the uncertain estimation in the inner loop of RBRDO can be avoided. In this way, it can greatly reduce the evaluation number of performance function. Lastly, the trust region method is used to manage the above sequential RBRDO subproblems for convergence. Design/methodology/approach: As is known, RBRDO is nested optimization, where the outer loop updates the design vector and the inner loop estimate the uncertainties. When solving the RBRDO, a large evaluation number of performance functions are needed. Aiming at this issue, the paper proposed a fast integrated procedure for solving the RBRDO by reducing the evaluation number for the performance functions. First, it transforms the original RBRDO problem into a series of RBRDO subproblems. In each subproblem, the objective function, constraint function and reliability index caused are approximated using simple explicit functions that solely depend on the deterministic design vector rather than the random vector. In this way, the need for extensive sampling simulation in the inner loop is greatly reduced. As a result, the evaluation number for performance functions is significantly reduced, leading to a substantial reduction in computation cost. The trust region method is then employed to handle the sequential RBRDO subproblems, ensuring convergence to the optimal solutions. Finally, the engineering test and the application are presented to illustrate the effectiveness and efficiency of the proposed methods. Findings: The paper proposes a fast procedure of solving the RBRDO can greatly reduce the evaluation number of performance function within the RBRDO and the computation cost can be saved greatly, which makes it suitable for engineering applications. Originality/value: The standard deviation of the original objective function of the RBRDO is replaced by the mean and the reliability index of the original objective function, which are further approximated by using Taylor series expansion and their approximate forms depend on the deterministic design vector rather than the random vector. Moreover, the constraint functions are also approximated by using Taylor series expansion. In this way, the uncertainty estimation of the performance functions (i.e. the mean of the objective function, the constraint functions) and the reliability index of the objective function are avoided within the inner loop of the RBRDO. [ABSTRACT FROM AUTHOR]

Many factors influence structural reliability in practice engineering. Some factors can be measured to obtain lots of data, but others are difficult to acquire statistical data. In view of this situation, a new structural reliability analysis method is proposed using chance theory, which is composed of probability theory and uncertainty theory and can reflect random uncertainty and cognitive uncertainty simultaneously. The performance function of a structural mechanical element is defined, and when it is a random uncertain variable, the chance distribution is established. Then the calculated method of failure measures and reliability measures for the structural mechanical element is put forward. Furthermore, considering the series system and parallel system, the performance function of the structural system is proposed, and the calculated method of failure measure is determined by theoretical proof. The results can provide a new approach to analyzing structural reliability under the uncertain circumstance of lack of statistical data. [ABSTRACT FROM AUTHOR]

Introduction: The present study examines the fatigue reliability of subway tunnel invert structures subjected to train-induced loads, a critical factor in ensuring the safety and longevity of these vital infrastructures.Methods: By integrating higher-order moment theory, fatigue equations, and cumulative damage theory, this research analyzes the impact of train loads on invert structures, taking into account factors such as tension stress, compression stress, train axle weight, speed, and lining thickness.Results: The findings indicate that the reinforcement of the invert structure is required under tension stress, while it is not necessary for compression stress. Notably, the train’s axle weight and speed exert a significant influence on the fatigue reliability index, with speed displaying a marginally greater effect than axle weight. Conversely, the lining thickness demonstrates a negligible impact on the reliability index. As time progresses, the fatigue reliability index diminishes, resulting in an elevated probability of failure.Discussion: In light of these findings, it is imperative to conduct regular inspections and maintain the tensile state of the invert structure, which warrants the most attention. To safeguard the safety and longevity of subway tunnel invert structures, it is essential to concentrate on the aspects related to tensile stress and closely monitor train loads, specifically axle weight and speed.

This paper presents a composite fuzzy learning finite-time prescribed performance control (PPC) approach for uncertain nonlinear systems with dead-zone inputs. First, a finite-time performance function is constructed by a quartic polynomial. Subsequently, with the help of an error transformation function, the restriction problem of the tracking error performance is transformed into a stability problem of an equivalent transformation system. In order to ensure that all signals of the closed-loop system are bounded, a finite-time PPC method combined with fuzzy logic systems (FLSs) is proposed. Although the tracking error can be guaranteed to be limited within a predefined range, the proposed finite-time PPC method only uses instantaneous data, which cannot guarantee the accurate estimation of unknown functions under the influence of dead-zone inputs. Therefore, based on the persistent excitation (PE) condition, a predictive error is defined by using online recorded data and instantaneous data, and a corresponding composite learning finite-time PPC method with parameter updating the law, which can not only achieve the control aim of the former method but also improve the control effect, is designed. The simulation results verified that the composite learning finite-time PPC method is more effective than the finite-time PPC method without learning.

• Propose an efficient augmented integral method for probability distribution evaluation of performance functions. • Justify the necessity and the selection of an optimal auxiliary random variable. • Eliminate the effect of the auxiliary random variable using the deconvolution of PDFs. • Verify the accuracy and efficiency of the proposed method. The paper proposes an efficient augmented integral method for probability distribution evaluation of performance functions. In the proposed method, the performance function is augmented by adding an auxiliary random variable, whose probability density function (PDF) and cumulative distribution function (CDF) are formulated as the integrations of the original performance function with respect to basic random variables. The optimal auxiliary random variable is determined to provide an accurate estimation of the integrations by investigating the geometric properties of integrands and a distribution parameter optimization approach based on moment analysis. According to the convolution formula, the relationship between the PDFs of the augmented performance function and the original performance function is clarified. Then, the PDF of the original performance function is calculated by solving an unconstrained optimization problem that is established using the convolution formula. Finally, four numerical examples are investigated to demonstrate the efficiency and accuracy of the proposed method for structural reliability analysis. The results indicate that the proposed method can evaluate the probability distribution of performance functions accurately and efficiently, even when the performance functions are strongly nonlinear and implicit. [ABSTRACT FROM AUTHOR]

Reliability-based Design Optimization (RBDO) is a nested optimization approach. This paper emphasizes the importance of improving computational efficiency by reducing the number of evaluations of the performance function, which is the bottleneck for practical engineering applications. Aim at this issue, the paper proposes to reduce the function evaluation number caused by gradient calculations as the important direction for improving RBDO's efficiency, as it takes up the majority of the total evaluation number of RBDO. Within the above important direction, the paper introduces a new hybrid inverse reliability method for searching the minimum performance target point (MPTP). It integrates the traditional Advanced Mean Value (AMV) with an innovative MPTP-searching approach, where the new format of gradient computation is dependent on one variable other than muliti-ones. This can effectively eliminate a drawback of conventional gradient computation, whose evaluation number is influenced by the dimensionality of random variables. Furthermore, its operational simplicity is highlighted by the absence of chaos control factors, making it more convenient for real engineering applications compared to methods like Chaos Control (CC) or Modified Chaos Control (MCC). Lastly, the numerical examples are investigated to validate the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Many factors influence structural reliability in practice engineering. Some factors can be measured to obtain lots of data, but others are difficult to acquire statistical data. In view of this situation, a new structural reliability analysis method is proposed using chance theory, which is composed of probability theory and uncertainty theory and can reflect random uncertainty and cognitive uncertainty simultaneously. The performance function of a structural mechanical element is defined, and when it is a random uncertain variable, the chance distribution is established. Then the calculated method of failure measures and reliability measures for the structural mechanical element is put forward. Furthermore, considering the series system and parallel system, the performance function of the structural system is proposed, and the calculated method of failure measure is determined by theoretical proof. The results can provide a new approach to analyzing structural reliability under the uncertain circumstance of lack of statistical data.

Simulating the higher-order statistical moments of response functions is a great challenge to research community in the filed of probabilistic analysis, and generally the polynomial surrogate model can be applied to evaluate these statistical moments. In this study, an analytical solution is developed for calculating the first four raw moments of response functions. Then, a novel criterion is proposed to choose the best analytical formulas that gives the most accurate values of response functions. The intrusive and non-intrusive PC expansion models are considered. Several applications are provided to demonstrate the accuracy and efficiency of the developed model, in which Monte Carlo methods are adopted as an exact criterion for comparison. [ABSTRACT FROM AUTHOR]

This article investigates the finite-time tracking control problem of nonlinear pure-feedback systems with nonaffine input signals. By using a low-pass filter, the nonaffine system is transformed into a pure-feedback system with an affine input. To characterize the dynamic and steady-state performance, a new performance function is defined, which is intergraded into adaptive fuzzy rules to design a novel fractional-power-based output tracking controller. It is shown that the tracking error converges to a predefined range at a settling time according to the traditional finite-time stability theory. Simulation results obtained for two examples reveal the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper attempts to apply the resilience concept to the mining sector, especially to mining machinery and production systems. The quantitative analysis method using the linear recovery function has been applied. As the core part of the proposed method, it is assumed that in the mining machinery fleet, the performance function falls to a “zero” value immediately after the occurrence of a failure. Therefore, the resilience calculation process runs through the concept of time to repair and machine maintainability. As a case study for the proposed concept, the operation and failure data of the drum shearer machine in Parvadeh longwall mine in Iran is applied. The data pertains to a coal cutting operation in a whole longwall panel over the period of two years. In total, the calculations encompass over 2600 hours of actual operation and 171.8 hours of repair time, which reveals that the studied shearer has a resilience of 96.7 percent. Along with the case study results, it is confirmed by this paper that resilience as a developing concept could be adequately applied to coal mining systems as a support measure for production assurance and reliability.

For structural performance functions with characteristics of multivariable, non-normality, small failure probability and nonlinearity, it is usually difficult to pursue satisfactory accuracy by means of traditional probabilistic methods such as first-order reliability method and second-order reliability method. According to the idea of adaptive subdivision, a backward recursive method based on the universal generating function is suggested for structural reliability calculation. In the sensitive region, the variable space is subdivided partially, the discrete interval length is reduced, and the random variables are discretized nonuniformly and adaptively by the backward recursion operation. Compared with the current method of forward recursion operator and the corresponding similar items merging operation, the backward recursion operator can not only avoid low accuracy owing to subdividing the state combinations in the sensitive region, but avert the high cost of calculation owing to merging state combinations in the nonsensitive region of the variable space as well. Three examples are presented to demonstrate the rationality of the backward recursive algorithm. The results indicate that the accuracy of the proposed method is significantly higher than that of traditional methods, and the computational efficiency of the proposed method meets the engineering requirement. The research work provides a new path for reliability calculation with consideration of complex performance functions. [ABSTRACT FROM AUTHOR]

Based on the machine learning algorithms, prediction models for landslide displacement with step-like behavior in the reservoir area were established for landslides prevention and reduction; these models could predict a given test set very well. However, due to the length and the sequence of the training set in prediction models, the predictive ability of these prediction models could not be evaluated accurately if just validated with a given test set. To solve the problem, a hybrid reliability model was proposed. Complimentary ensemble empirical mode decomposition (CEEMD) algorithm was used to decompose the accumulated displacement into the trend displacement and the periodic displacement firstly. The Gauss function was used to predict the trend displacement, and the random forest (RF) and back propagation neural network (BPNN) algorithms were employed to predict the periodic displacement. Furthermore, a novel performance function for the reliability analysis of the displacement prediction model was derived to address failure probabilities in different cases. The Baijiabao landslide in the Three Gorges Reservoir Area was taken as an example for reliability analysis of the prediction model for landslide displacement with step-like behavior, and the predictive ability of the CEEMD-RF model and the CEEMD-BPNN model were compared. The results indicated that the CEEMD-RF model and CEEMD-BPNN model both can accurately predict the accumulated displacement of the given test set; the predictive value obtained with the CEEMD-RF model or the CEEMD-BPNN model showed uncertainties of the prediction model for landslide displacement and the predictive ability of the CEEMD-RF model was more reliable than the CEEMD-BPNN model under different cases. The failure probability proposed in the paper could evaluate the predictive ability of the model more accurately and comprehensively compared with the existing assessment indices. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the reliability analysis of small sample data or implicit structural function, a novel structural reliability analysis model based on support vector machine (SVM) and neural network direct integration method (DNN) is proposed. Firstly, SVM with good small sample learning ability is used to train small sample data, fit structural performance functions and establish regular integration regions. Secondly, DNN is approximated the integral function to achieve multiple integration in the integration region. Finally, structural reliability was obtained bjr DNN. Numerical examples are investigated to demonstrate the effectiveness of the present method, which prorides a feasible way for the structural reliability analysis. [ABSTRACT FROM AUTHOR]

Sustainable management of Urban Drainage Systems (UDSs) is challenging since the hydraulic characteristics and pollutants discharged into catchment areas are constantly changing. Quantifying the resilience of UDSs is a crucial step towards long-term and sustainable management of these chatments in urban areas. This study was performed to evaluate the resilience of UDSs against flooding through combining hydraulic performance of stormwater drainage network with the system performance function. To conduct this performance evaluation extreme rainfall intensity values for different return year periods ranging from 2 to 100 years were used for catchment drained into the West Flood-Diversion (WFD) system in Tehran. Both hydraulic and hydrodynamic simulations were performed for these stormwater drainage system under two scenarios: with and without BMPs. To further evaluate the systems performance and resilience, additional simulation analysis was performed using one specific flooded node under extreme rainfalls with different return periods. The results showed that adding a vegetative swale to the drainage system would decrease the nodal flooding duration and the flood volume up to 15% and 47%, respectively. The results also shown that the resilience against flooding will increase from about 0.4% up to 6% under the above-mentioned conditions. From the results of this study, we can conclude that employing some types of BMP along with the existence low-impact development measures within the traditional urban drainage areas may be sufficient to increase the system resilience and sustainable control of flooding.

A process model for forming technical-and-economic indices in estimating mechanical equipment for feed grain processing for livestock is offered. Predictive estimate of the perspectiveness of the operating devices representation and development is obtained.

The performances of first-order reliability method (FORM) are highly important owing to its accuracy and efficiency in the structural reliability analysis. In the gradient methods-based sensitivity analysis, the iterative formula of FORM is established using the gradient vector which it may not compute for some structural problems with discrete or non-continuous performance functions. In this study, the probabilistic Gaussian global-best harmony search (GGHS) optimization is implemented to search for the most probable point in the structural reliability analysis. The proposed GGHS approach for reliability analyses is performed based on two main adjusted processes using the random Gaussian generation. The accuracy and efficiency of the GGHS are compared with original harmony search (HS) algorithm and three modified versions of HS as improved HS, global-best HS, and improved global-best HS based on a mathematical and three structural problems. The obtained results illustrated that the PGGHS is more efficient than other modified versions of HS and provides the accurate results for discrete performance functions compared to original FORM-based gradient method. [ABSTRACT FROM AUTHOR]

Copyright of Bagh-e Nazar is the property of Nazar Research Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty. When an input variable is described by a specific interval rather than a certain probability distribution, the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods. Generally, the non-probabilistic importance analysis methods involve the Monte Carlo simulation (MCS) and the optimization-based methods, which both have high computational cost. In order to overcome this problem, this study proposes an interval important analytical method avoids the time-consuming optimization process. First, the original performance function is decomposed into a combination of a series of one-dimensional subsystems. Next, the interval of each variable is divided into several subintervals, and the response value of each one-dimensional subsystem at a specific input point is calculated. Then, the obtained responses are taken as specific values of the new input variable, and the interval importance is calculated by the approximated performance function. Compared with the traditional non-probabilistic importance analysis method, the proposed method significantly reduces the computational cost caused by the MCS and optimization process. In the proposed method, the number of function evaluations is equal to one plus the sum of the subintervals of all of the variables. The efficiency and accuracy of the proposed method are verified by five examples. The results show that the proposed method is not only efficient but also accurate. [ABSTRACT FROM AUTHOR]

The reliability of stay cables is critical to the safety of cable-stayed bridges. This paper investigates and compares reliability assessments of stay cables by using identified cable tension forces and monitored bridge loads. One-year monitoring data from a cable-stayed bridge was used to characterize the probability distributions of cable forces and pertinent bridges loads including temperature of the cable, wind load, and vehicle load. The results show that, for the bridge under study, the cable temperature, the wind load, and the vehicle weight obey the Beta distribution, whereas the axle weight obeys the lognormal distribution, indicating deviations from the design codes. Subsequently, two performance functions are proposed to compute the cable reliability index, where one directly uses the monitored cable forces and the other is based on the monitored loads and the finite element method simulation of the bridge. The computed index based on the monitored cable forces and the performance function I is larger than that based on the monitored loads and the performance function II. The reasonings attributed to the differences and the implication of the present findings in structural design and optimization are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the continuous improvement of people 's living standards, people have put forward higher requirements for the safety and comfort of housing. Therefore, Inherent Defect Insurance, a financial method to guarantee the quality of construction projects, has also emerged. At present, China 's Inherent Defect Insurance has been gradually promoted, but its claim mechanism has not been analyzed and studied. From the perspective of construction engineering, this paper first makes a bibliometric analysis of the influencing factors of insurance claims that may be caused by construction engineering quality through VOSViewers, and the evaluation index system of inherent defects is constructed. Then, according to the influencing factors, the PSO-LSSVR model is adopted to fit the performance function of the inherent defects. Finally, based on the reliability design principle of engineering structure, the reliability index and failure probability of Inherent Defect Insurance are derived from the performance function of inherent defects. This paper also analyzes its application in insurance practice and determines the relationship between the number of insurance underwriting policies and the initial reserve of insurance at a certain risk level. This paper studies the probability of Inherent Defect Insurance by constructing the reliability model of inherent defect risks in construction quality, and analyzes the anti-risk ability of insurance companies from the perspective of claim, which provides scientific analysis methods and theoretical basis for the scientific decision-making of insurance companies., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Zeyu Chen reports financial support was provided by Hebei Provincial Department of Housing and Urban-Rural Development. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The uncertainty of the parameters involved in the bridge vortex-induced vibration (VIV) response is hard to be described accurately and the probabilistic description remains scarce. The present work aims at giving a contribution to the probabilistic evaluation of the bridge VIV response considering uncertainty influence of parameters. A self-anchored suspension bridge was selected as an example to analyze the uncertainty influence on the probabilistic evaluation of VIV response. The probability density function of the wind speed at the deck level of the bridge was established. The modal analysis was carried out and the natural vibration characteristics were checked. Then three performance functions involving different considerations of the maximum response models of bridge VIV were introduced. The numerical characteristics (coefficient of variation (COV), skewness and kurtosis) in the parameter space was propagated to access its influence on the exceeding probability of the bridge VIV response utilizing the second-order fourth-moment (SOFM) method. The influence from the different distribution type of the parameters was investigated. Results showed that the influence degree of COV, skewness and kurtosis of parameters on the exceeding probability of VIV response for the bridge is different and the distribution type of parameter has little effect based on the SOFM method. [ABSTRACT FROM AUTHOR]

In order to accurately evaluate fatigue reliability performances of A-type frame, a fuzzy fatigue reliability calculation method based on response surface method is proposed, considering the uncertainties of A-type frame in the actual manufacture and use stage. The fatigue reliability performance function of A-type frame was constructed based on response surface method to evaluate the fatigue reliability performance of A-type frame under the condition of downhill and turning braking with full load and its fatigue reliability was found to be less than 90%. Then, the fuzzy fatigue reliability performance was optimized by genetic algorithm. According to the optimized results, a new flexible front lateral stabilizer for this steering system was presented, which could guarantee its fatigue reliability performance. The final fatigue reliability of the optimized A-type frame reached up to 98%. [ABSTRACT FROM AUTHOR]