Search

Due to the complexity of spill fire, predicting heat release rate (HRR) is a challenging aspect, therefore, identifying key contributors to uncertainty is essential to develop reliable models for fire risk assessment. We propose a framework that couples Artificial-Neural-Network (ANN) and Monte Carlo Uncertainty Quantification (UQ) to quantify the uncertainties of spill fire parameters on peak HRR variance. The ANN spill fire model, trained on experimental spill fire data, shows good agreement with measured HRR values. By coupling with Dakota for Monte Carlo uncertainty propagation, the framework identifies major contributors to peak HRR uncertainty for fixed and continuous unconfined spill fires. This is achieved by investigating two uncertainty sources: data scarcity and input parameters. UQ results indicate that the confidence intervals widen at points where data are scarcer. Sensitivity analysis shows that in the case of fixed quantity spills, the fuel amount and properties parameters contribute to 54.4% and 22.6% of peak HRR variance, respectively. For continuous spills, the discharge rate and fuel properties parameters account for 41.8% and 39.3% of peak HRR variance, respectively. This work can be utilized in developing more advanced predictive ANN models for spill fire scenarios, ultimately enhancing fire probabilistic safety analysis in nuclear power plants. [ABSTRACT FROM AUTHOR]

AbstractThis study delves into the impact of material and fabrication uncertainties on the natural frequencies of rim-driven rotating structures. Utilizing a higher-order shear deformation theory (HSDT), an isogeometric analysis (IGA) model predicts these frequencies. Sensitivity analysis via a radial basis function network (RBFN) model, validated with Monte Carlo simulation, highlights parameter influence. Parametric investigations unveil sensitivity patterns and influential parameters. Employing a stochastic approach, the study compares the effects of random material properties on the free vibration response of rotating and non-rotating plates. Additionally, the RBFN-based surrogate model assesses reliability parameters concerning resonance failure in rim-driven rotating composite structures. [ABSTRACT FROM AUTHOR]

Uranium is one of the most critical components in the nuclear field due to its chemical and radiological properties. Since few studies were conducted on uranium concentration in drinking water in Iranian cities, this study aimed to determine the concentration of uranium and flouridein the samples of drinking water collected from tap water in Shiraz, Fars province. Fifty samples were examined for uranium and fluoride presence using the ICP-MS and spectrophotometry methods. Hazard Quotient (HQ) and sensitivity analysis (SA) using the Monte Carlo Simulation technique with 10,000 iterations were applied to determine the human carcinogenic and non-carcinogenic effects of Uranium and Fluoride in three different groups (children, teenagers, and adults). The results showed that uranium and fluoride concentrations varied between 0.18–1.12 μg/L and 0.06–0.56 mg/L, respectively, which is less than the EPA recommendation for both elements. pH, EC, and TDS values were in accordance with the standard, and a positive correlation was observed between fluoride and total dissolved solids (TDS). The highest risk of carcinogenicity was found in the adult group with a value of 3.24E-9, lower than the allowable limit (1E-6). The highest hazard quotient (HQ) for uranium and fluoride was obtained in children with 0.13 and 0.66. Monte Carlo simulation results showed that the hazard quotient (HQ) for both elements in all groups was less than one and consistent with the values of the deterministic method. Also, water swallowing rate and contaminant concentration were the most influential parameters in the sensitivity analysis. [ABSTRACT FROM AUTHOR]

It is essential to effectively identify the sources and health risks of heavy metals in groundwater in rural areas. Therefore, the sources and health risks of heavy metals in groundwater in rural areas of Huaibei Plain were investigated. The results showed that the mean concentrations of Cr, Fe, Mn, Cu, Zn, Ni, As, Cd, and Pb were 0.38, 86.48, 90.94, 0.20, 0.97, 1.18, 0.009 and 0.16 μg/L. Multivariate statistical analysis combined with positive matrix factorization (PMF) was used to quantitatively identify four sources (agricultural activities (24.01%), industrial activities (22.27%), natural sources (26.45%), and traffic sources (27.28%)). The probabilistic health risk assessment results based on Monte Carlo simulation showed that the non-carcinogenic risk for adults and children was negligible. However, 17.52% of children and 1.99% of adults exceeded the maximum acceptable safety limit (1.0E-6) for cancer risk. Sensitivity analysis showed that daily intake, Cr, and Fe were the main factors influencing the health risk assessment results. These results have important implications for groundwater management in rural areas of the Huaibei Plain. [ABSTRACT FROM AUTHOR]

Probabilistic analysis has been widely used to assess the inherent uncertainty of variables in laterally loaded pile systems, but the calculation is still difficult and time-consuming. The present study presents an efficient probabilistic analysis framework for a laterally loaded pile system. The performance of the system is defined as the lateral deflection at the pile head and maximum bending moment of the pile shaft, corresponding to two failure modes. Within this framework, the spatial variability of the soil and the correlation between failure modes are considered by the random field theory and the First-Order Reliability Method, respectively. Moreover, the Sequential Compounding Method is used as an efficient tool to determine the system reliability indexes. The framework is confirmed by comparing the reliability indexes of failure modes and systems with those of the Monte Carlo Simulation Method. Furthermore, a parametric analysis and system sensitivity analysis are performed. The results show that the auto-correlation distance, allowable lateral displacement at the pile head, and allowable bending moment of the pile shaft have a great influence on reliability indexes of failure modes and system, and the major parameter of soil in affecting pile is the elastic modulus compared with the undrained shear strength. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the spatial distribution and zoning of nitrate and fluoride concentrations in the drinking water distribution network of Behbahan city and the health risk caused by their concentration using sensitivity analysis and the Monte Carlo simulation during 2018 to 2019.In this cross-sectional study, 90 samples were collected from 45 points of the city's drinking water distribution network in the two high-rainy and low-rainy seasons.Nitrate and fluoride concentration was measured byDR 5000 spectrophotometer and its non-carcinogenic impacts were calculated using EDI and HQ.The results showed that the average nitrate concentrations in high and low rainy seasons in the study area were 15.05 and 13.35 mg l−1, respectively.Also, the average concentrations of fluoride in high and low-rainy seasons were 0.67 and 0.76 mg L−1, respectively.The fluoride concentration in the high-rainy season showed a decreasing trend from north to south.The nitrate concentration decreased from north to south and southwest.The amount of nitrate HQ in the age groups corresponding to children, adolescents, adults and infants were 0.5, 0.4, 0.34, and 0.07, respectively.The fluoride HQ levels in the age groups corresponding to children, adolescents, adults and infants were 0.7, 0.5, 0.05, and 0.1, respectively.These results showed that children were the significant at-risk group due to these ions.According to the Monte Carlo simulator, 95th percentile caused by exposure to nitrate and fluoride ions in all age groups was more than one.Based on the sensitivity analysis, the most efficient factor in the health risk caused by nitrate and fluoride ions was the concentration of these ions in the drinking water distribution network. [ABSTRACT FROM AUTHOR]

This study is the first to conduct a global sensitivity analysis to identify the crucial variables that have an impact on the energy consumption of substations. The peak cooling and heating energy consumption, as well as the annual cooling and heating energy consumption of a substation in Shandong, are all simulated basing the Monte Carlo method. The simulation outputs are discussed by uncertainty analysis to obtain more accurate energy consumption thresholds. Subsequently, the treed Gaussian process and the standardized rank regression coefficient are used to perform a global sensitivity analysis of the simulation outputs. The results of the two global sensitivity analyses are practically the same, demonstrating that robustness can be ensured by simultaneously using two methods based on different theories. In addition, this study provides an efficient method for the energy-saving retrofitting of the existing substation and the energy-saving design of green substations in the future. [ABSTRACT FROM AUTHOR]

The time-dependent mechanism reliability and sensitivity analysis with imprecise probability distributions is investigated in this article. First, the interval model is employed to describe the uncertainty of the parameters of input variables due to the lack of data and knowledge. Next, the form of time-dependent failure probability and global reliability sensitivity (GRS) main effect index in the case of interval distribution parameters is analyzed. Then, the calculation methods of failure probability and GRS main effect index under the above circumstance are discussed. Finally, combined with the optimization algorithm, the Monte Carlo simulation (MCS) method and the method based on envelope function (MEVF) are proposed. By comparing with the MCS results, several numerical and engineering examples are analyzed to demonstrate the significance as well as the effectiveness of MEVF. [ABSTRACT FROM AUTHOR]

The human health risks of cadmium (Cd), lead (Pb), and nickel (Ni) in fifty regular and slim cigarette brands available in the Serbian market were evaluated. Distributions on per-cigarette basis concentrations of these metals, as determined by graphite furnace atomic absorption spectrometry, were used as inputs in the probabilistic risk assessment. The contents of these metals in cigarettes varied significantly. Larger quantities of the studied metals were observed in regular cigarettes than in slim cigarettes, but the concentration levels per cigarette tobacco mass were higher in slim cigarettes. The metal concentrations in counterfeit cigarettes were marginally higher than those in authentic brands. Based on the mean concentrations, Pb was the most abundant toxic metal, followed by Ni and Cd. The calculated values of the cumulative hazard index (HI) and incremental lifetime cancer risk (ILCR) indicated a low noncarcinogenic risk and low but not negligible carcinogenic risk from Cd, Pb, and Ni. The mean HI and ILCR values obtained using probabilistic and deterministic approaches were similar. A Monte Carlo simulation was employed to minimize the uncertainty of health risk estimation. The sensitivity analysis revealed that the most influential factor was cigarette mass followed by Cd content. [ABSTRACT FROM AUTHOR]

Investigating the effect of the uncertainty in the parameters on time-varying chatter stability is critical to refrain from the time-varying regenerative chatter in turning. In this study, the influence of tool wear on the cutting force coefficient (CFC) in turning processing is taken into consideration, and CFC variation during cutting time is described by the Gamma process. The model of the time-varying chatter stability in turning is established based on the motional and mechanical properties of the cutting process in turning. Time-varying reliability (TVR) is estimated based on the uncertain and time-varying characteristics of the turning process parameters. The method of moment-independent time-varying global sensitivity analysis (TV-GSA) based on the cumulative failure probability (CFP) is proposed to measure the effect of parameters on the CFP of the chatter stability in turning. Furthermore, for reducing the computational cost of moment-independent TV-GSA based on CFP, the active learning Kriging model is established to replace the nonlinear and implicit limit state function of the chatter stability in turning. The dynamic model of the turning chatter is validated by an illustrative example. And the results of the proposed method are compared with the results of the Monte Carlo simulation to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Water, as the primary human needs, play a vital role in all aspects of our life, such as social environment, production, the economy, food security, and politics. In the present investigation, Heavy Metals (HM) concentrations including Cr, Pb, and Cd, have been ascertained in drinking water resources. Besides, point estimation using Hazard Quotient (HQ) and Hazard Index (HI) formula and sensitivity analysis (SA) using the Monte Carlo Simulation technique, by 10,000 repetitions in Oracle Crystal Ball®, were applied to demonstrate the non-carcinogenic effects of HMs on different exposed populations (Infant, children, teenagers, and adults groups). According to the results, the concentrations of Cr, Pb, and Cd in water resources ranged from 2.2 to 173.7 µg/L, 0.5 to 10 µg/L, and 0.2 to 0.9 µg/L, respectively. The results also showed that HI values were in the order of Pb>Cd >Cr. The results of SA, to determine the most effective parameter in increasing non-carcinogenic risk, showed that the concentration parameter of studied HMs had the most effect on increasing sensitivity in the four studied exposed groups. [ABSTRACT FROM AUTHOR]

The work proposes a new family of survival models called the Odd log-logistic generalized Neyman type A long-term. We consider different activation schemes in which the number of factors M has the Neyman type A distribution and the time of occurrence of an event follows the odd log-logistic generalized family. The parameters are estimated by the classical and Bayesian methods. We investigate the mean estimates, biases, and root mean square errors in different activation schemes using Monte Carlo simulations. The residual analysis via the frequentist approach is used to verify the model assumptions. We illustrate the applicability of the proposed model for patients with gastric adenocarcinoma. The choice of the adenocarcinoma data is because the disease is responsible for most cases of stomach tumors. The estimated cured proportion of patients under chemoradiotherapy is higher compared to patients undergoing only surgery. The estimated hazard function for the chemoradiotherapy level tends to decrease when the time increases. More information about the data is addressed in the application section. [ABSTRACT FROM AUTHOR]

During farming practices in rural areas, various fertilizers are used, which can lead to the contamination of groundwater resources with nitrate and, consequently, can affect the health of consumers. This study aimed to investigate the drinking suitability of the groundwater resources in rural areas of Divandarreh County, Kurdistan province, Iran using drinking water quality index (DWQI) and also estimation of the non-carcinogenic health risk induced by nitrate due to the drinking route. Sixty groundwater samples collected (2018) from active dug-wells and twelve parameters (TDS, pH, TH, EC, HCO3−, K+, Na+, Mg2+, Ca2+, Cl−, SO42-, and NO3−) were measured to calculate DWQI. Also, non-carcinogenic risk assessment was carried out for four exposed groupsusing two different approaches: deterministic andprobabilistic by Monte Carlo simulation. The results of WQI showed that 61.66, 31.66, and 6.66% of samples fall within the class of excellent, good, and poor quality, respectively. The nitrate concentration in drinking water ranged from 36.06 ± 14.32 mg/L. The HQ mean for infants, children, teenagers, and adults were 0.90158, 1.17205, 0.90158, and 0.70436, respectively.Probability estimation showed the HQ values for the 5th, and 95th percentile in infants, children, teenagers, and adult groups were (0.52–2.53), (0.27–1.54), (0.25–1.40), and (0.15–0.71), respectively. Sensitivity analysis results showed that the most effective parameter in the non-carcinogenic risk in all exposed groups was NO3− concentration. Generally, nitrate concentration was relatively high and required more attention especially in agriculture and management of the use of fertilisers. [ABSTRACT FROM AUTHOR]

The uncertainties of parameters are inevitable in practical engineering problems, which may cause unexpected variations of objectives and constraints. Tolerance design methods can specify the allowable variation of uncertain parameters, thereby maintaining product quality while achieving lower manufacturing cost. In this paper, a novel tolerance design method is proposed, in which the 'reverse model' is used to directly analyse the robustness of the preexisting alternatives and the Kriging model is introduced to reduce computational cost. Firstly, the sensitivity region is generated by the 'reverse model' that maps the given acceptable objective and feasibility variations into the parameter space to analyse the robustness. Secondly, interval uncertainty is introduced to describe tolerance within the sensitive region. Thirdly, a nested double-loop structure is adopted to implement the search process. Two numerical examples and one engineering case are used to illustrate the effectiveness of the proposed method. Verification of robustness for the obtained interval is performed by Monte Carlo Method, and the worst-case analysis is applied to verify whether the tolerance is the maximum interval. The obtained maximum tolerance that meets the performance requirements reflects the trade-off between quality and manufacturing cost. [ABSTRACT FROM AUTHOR]

This study aimed to assess the groundwater quality and human health risks of nitrate, nitrite, and fluoride contamination in rural areas of Khaf County, Iran. For this aim, 28 groundwater samples were collected from open dug wells during August 2018. Twelve parameters (pH, EC, TDS TH, Cl-, SO42-, HCO3−, NO3−, F−, Ca2+, Mg2+, and Na+) have been used to evaluate the water quality index. Also, point estimation using HQ formula and sensitivity analysis using the Monte Carlo Simulation (MCS) method, by 10,000 repetitions in Oracle Crystal Ball®, were applied to determine the additional risk of non-carcinogenicity in Nitrate, Nitrite, and Fluoride in four groups: infant, children, teenager, and adult. WQI showed that 7%, 60%, 10% and 21% of groundwater samples fall within the class of excellent, good, poor, and very poor quality, respectively. The HQ values of nitrate for infant, children, teenagers, and adults in 32%, 14%, 10% and 7% of the samples from rural areas were above the safe limit of 1. The HQ values of nitrite were mostly acceptable for infants, children, teenagers, and adults in all the rural areas because the mean HQ values of fluoride were lower than 1. Also, the calculated HQ values of fluoride for infants, children, and teenagers in 60.7%, 21.4% and 10% of the rural areas were above the safe limit. The results of HQ point estimation showed that nitrate and fluoride had non-carcinogenic risk in most exposed population groups, but exposure to nitrite did not have non-carcinogenic risk. The results of sensitivity analysis, to determine the most effective parameter in increasing non-carcinogenic risk, showed that the concentration parameter of Nitrate, Nitrite, and Fluoride had the most effect on increasing sensitivity in the four studied exposed groups. [ABSTRACT FROM AUTHOR]

This paper addresses the influence of material and fabrication uncertainties on natural frequencies of curvilinear fiber laminate. Material properties and fiber orientation are considered uncertain. The sensitivity of input parameters to natural frequencies is obtained using high-dimensional model representation tool, and the results are compared with Monte Carlo simulation. This sensitivity pattern is established through various parametric studies. Highly sensitive parameters are identified and analyzed. Further stochastic analysis of natural frequencies of curvilinear fiber laminate is performed using polynomial neural network. The accuracy and efficiency of polynomial neural network are compared and conclusions are drawn. [ABSTRACT FROM AUTHOR]

In this work, a combination of the spectral stochastic finite element method (SSFEM) and the time-domain spectral element method (TSEM), referred to as the stochastic time domain spectral element method (STSEM), is presented to compute the stochasticity of strain and stress of a higher-order sandwich composite beam with spatial variability in the material properties. The method proposed in this work employs the efficiencies of both SSFEM and TSEM for the uncertainty analysis of a sandwich beam. The material properties of face sheets and core are considered as Gaussian random fields, which are discretized using the Karhunen-Loéve expansion, and polynomial chaos expansion is used to represent the response quantity. A numerical example is considered for which, first, a sensitivity analysis is performed to identify the most sensitive material properties. Then, the proposed STSEM is used to demonstrate the computational efficiency and numerical accuracy in comparison with Monte-Carlo simulation. Moreover, the effect of core depth on strain and stress variability is also examined. [ABSTRACT FROM AUTHOR]

Sensitivity analysis investigates how the change in the output of a computational model can be attributed to changes of its input parameters. Identifying the input parameters that propagate more uncertainty on the ruin probability associated with insurance risk models is a challenging problem. In this paper, we consider the classical risk model, where an epistemic-uncertainty veils the true values of the claim size distribution rate and the Poisson arrival rate. Based on the available data for calibrating the probability distributions that model gaps of knowledge on these rates, and using the Taylor-series expansion methodology, we obtain the ruin probability under polynomial form in uncertain rates as a computational model. Specifically, we get a new sensitivity estimate of the ruin probability with respect to uncertain parameters. We provide a coherent framework within which we can accurately characterize statistically the uncertain ruin probability. In addition, we use the Markov's inequality to estimate the risk incurred by working with uncertain ruin probability rather than that evaluated at fixed parameters. A series of numerical experiments are presented to illustrate the potential of the proposed approach. [ABSTRACT FROM AUTHOR]

In the existing literature, little is known about the dynamic behaviour of the optimal portfolio in terms of market inputs and arbitrary stochastic factor dynamics in an incomplete market with a stochastic volatility. In this paper, to study optimal portfolio behaviour, we compute and analyze the mean and the variance of the optimal portfolio and of their adjustment speed in terms of market inputs in an incomplete market. The incompleteness arises from the additional source of uncertainty of the volatility in Heston's stochastic volatility model. Conducting sensitivity analysis for the mean and the variance of the optimal portfolio process as well as its adjustment speed to the market parameters, we find several interesting behavioural patterns of investors towards asset price and its volatility shocks. Our results are robust and convergent by the agreement from two simulation methods for different time step increments and the number of Monte Carlo simulation paths. [ABSTRACT FROM AUTHOR]

In this paper, a multiple period replenishment problem based on (s,S) policy is investigated for a supply chain (SC) comprising one retailer and one manufacturer with uncertain demand. Novel mixed-integer linear programming (MILP) models are developed for centralised and decentralised decision-making modes using two-stage stochastic programming. To compare these decision-making modes, a Monte Carlo simulation is applied to the optimization models’ policies. To deal with demand uncertainty, scenarios are generated using Latin Hypercube Sampling method and their number is reduced by a scenario reduction technique. In large test problems, where CPLEX solver is not able to reach an optimal solution in the centralised model, evolutionary strategies (ES) and imperialist competitive algorithm (ICA) are applied to find near optimal solutions. Sensitivity analysis is conducted to show the performance of the proposed mathematical models. Moreover, it is demonstrated that both ES and ICA provide acceptable solutions compared to the exact solutions of the MILP model. Finally, the main parameters affecting difference between profits of centralised and decentralised SCs are investigated using the simulation method. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method to assess the reliability of closed-ring grids by combining the total loss of continuity approach and the classical Monte Carlo simulation. The proposed method is called the Modified Monte Carlo Simulation (MMCS). The MMCS method is tested by Roy Billinton Test System (RBTS) Bus-2 and Bus-4 in three different scenarios: radial, open-ring, and closed-ring. Besides, the method proposed is applied to a real 6-bus closed-ring distribution system. The sensitivity analysis based on fuzzy logic is applied to determine the uncertainties associated with the reliability input data. It is shown that the MMCS method is appropriate to assess the reliability of both open-ring and closed-ring systems, and the sensitivity analysis is crucial to draw the real picture of reliability assessment, and an essential tool for power utilities to determine the most risk parameters that significantly deteriorate system reliability. [ABSTRACT FROM AUTHOR]

It is of great significance to analyze the influence mechanism of MIG welding parameters during thermo-mechanical coupling to improve the MIG welding process. In this paper, the Python script and the birth and death element method are used to realize the transient growth of a weld, the moving load of the welding heat source was realized by a Fortran subroutine, and a thermo-mechanical coupling model for the SUS301L-HT stainless steel MIG welding process is established. The temperature field and the thermal stress field are calculated. On this basis, a response surface model is established by the BOX-Behnen Design (BBD) method, and the sensitivity of welding process parameters to the influence of welding process is calculated by combining the Monte Carlo method with the response surface model. The calculation shows that the welding current and the welding speed have the most significant effect during the MIG welding of SUS301L-HT stainless steel, while the arc voltage has a relatively weak effect on the welding. The V-shaped angle has a great influence on the deformation angle of the welded workpiece. [ABSTRACT FROM AUTHOR]

This study designed, techno-economically analyzed and compared the heat-assisted extraction (HAE)- and ultrasound-assisted extraction (UAE)-technology-based processes (HAE-tBP and UAE-tBP) for the production of crude anthocyanins powder (CAnysP) from Hibiscus sabdariffa calyx (HSC) with the assistance of SuperPro Designer software. Both the base cases and industrial setups of 600 L extractor capacities for HAE- and UAE-tBPs were analyzed and compared. When building the process flowsheets, peculiarities of the technicalities of both HAE and UAE extractors were taken into consideration. Materials and energy demands, production costs and unit production cost (UPC) of CAnysP were the parameters used for process analysis. Uncertainty and sensitivity analyses were conducted for the process with least CAnysP UPC using Monte Carlo simulations in Oracle Crystal Ball software. The HAE-tBP recorded the highest number of batches of 5278 per annum. HSC of 15.551 kg was required to produce 7.433 and 10.022 kg/batch CAnysP by 600 L extractor setup of HAE- and UAE-tBPs, respectively. The UAE-tBP achieved lower CAnysP UPC (164.28 US$/kg) compared with HAE-tBP (176.31 US$/kg) with 80.08% certainty. Out of the variables considered for sensitivity analysis of UAE-tBP UPC, ultrasound treatment time contributed the highest (+62%), while anthocyanins recovery contributed the least (+0.2%) to the variance in UPC. [ABSTRACT FROM AUTHOR]

The concentrations of 18 organophosphorus, carbamate, pyrethroid, and nicotinoid pesticides were measured, by use of gas chromatography coupled to mass spectrophotometry, in six cultivars of walnut from five geographical regions of Iran, including, Azarshahr, Damavand, Farouj, Shahmirzad, and Tuyserkan. Assessments of risks posed to humans were conducted by calculating the hazard indices (HIs), by use of the Monte Carlo Simulations. The 95th centile of HIs for humans based on exposure via ingestion of walnuts was estimated to be 1.68, which represented di minimis to moderate concern for human consumers. The most influential parameters, determined by sensitivity analysis conducted during the MCS, was concentration, which ranged from 0.71to 0.97. The results indicate that while the walnuts are, in general safe to eat, uses of organophosphorus, pesticides on walnut cultivation in Iran is not completely without risks so that guidelines should be established and a monitoring program should be established. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic reliability and global sensitivity analysis method of double random vibration system is proposed, considering structure uncertainty and excitation uncertainty of complex mechanical and structural systems under random load. Based on the Miner criterion of fatigue failure of structures, the dynamic responses of structures under the condition of double random vibration are analyzed, and the dynamic reliability analysis method is established. Furthermore, the moment-independent global sensitivity index of random structures is proposed, which can quantitatively reflect the effects of random structure parameters on dynamic reliability under random excitation and provide reference for structure reliability optimization design. Based on the proposed dynamic reliability moment-independent global sensitivity analysis (GSA) index, this paper combines the computationally efficient active learning Kriging (ALK) method to establish the solving method. Finally, the rationality and correctness of the dynamic reliability GSA index and the ALK method proposed in this paper are illustrated by comparing the results with those gained by Monte Carlo simulation (MCS) method. The results prove that the GSA has significantly guided meaning and referenced value in the dynamic reliability design and optimization for the double random vibration system, especially for the engineering practice. [ABSTRACT FROM AUTHOR]

The cure fraction models are useful to model lifetime data with long-term survivors. In this paper, we introduce a flexible cure rate survival model where the model parameters are related to covariates in different regression structures. The regression model allows to model jointly the location, scale and shape effects. The maximum likelihood method is employed to estimate the model parameters. We provide Monte Carlo simulation experiments to verify the performance of the maximum likelihood estimates for different sample sizes and cure rate percentages. Furthermore, some diagnostic measures to assess departures from model assumptions as well as to detect outlying observations are also considered. Finally, applications to real data are presented to show the usefulness of the new cure rate model. [ABSTRACT FROM AUTHOR]

To assess the health risks caused by soil heavy metal in China's mining areas, concentration data for eight heavy metals in 77 mines were collected from previous literature. Monte Carlo simulation was used to analyze the corresponding carcinogenic risks and noncarcinogenic risks, and sensitivity analysis was carried out for each parameter. The results showed that among the different types of mining areas, multi-metal mines have the highest risk of carcinogenesis, followed by tungsten and antimony mines. Their carcinogenic risk values are all greater than 10−4, which is unacceptable. Pb is a heavy metal with highest noncarcinogenic risk. The log-transformed value is 3.2, which is much larger than the threshold of 0; Pb is followed by As and Hg. Therefore, Pb, As, and Hg are the heavy metals that should be controlled preferentially in polluted mining areas. Sensitivity analysis showed that the soil ingestion rate, exposure frequency, and pollutant concentration level are the factors that have the greatest impacts on health risks. More attention should be paid to these factors when addressing heavy metal pollution in mining areas. In addition, for the surveyed mines, children had a greater health risk than adults, so children should be given extra attention. [ABSTRACT FROM AUTHOR]

The focus of this work is general methods for prioritization or screening of project sites based on the favorability of multiple spatial criteria. We present a threshold-based transformation of each underlying spatial favorability factor into a continuous scale with a common favorability interpretation across all criteria. We compare several methods of computing site favorability and propagating uncertainty from the data to the favorability metrics. Including uncertainty allows decision makers to determine if seeming differences among sites are significant. We address uncertainty using Taylor series approximations and analytical distributions, which are compared to computationally intensive Monte Carlo simulations. Our methods are applied to siting direct-use geothermal energy projects in the Appalachian Basin, where our knowledge about any particular site is limited, yet sufficient data exist to estimate favorability. We consider four factors that contribute to site favorability: the thermal resource described by the depth to 80°C rock, natural reservoir productivity described by rock permeability and thickness, potential for induced seismicity, and the estimated cost of surface infrastructure for heat distribution. Those factors are combined in three ways. We develop favorability uncertainty propagation and sensitivity analysis methods. All methods are general and can be applied to other multi-criteria spatial screening problems. [ABSTRACT FROM AUTHOR]

Today, it is essential to use a hydrological model to assess and predict the water availability of river basins due to climate change. Event-based rainfall–run-off modeling is carried out using HEC-HMS. In the present study, linear imaging self-scanning sensor (LISS-III) satellite images are used to delineate the river basin using ArcGIS. Penman–Montieth model is used for the estimation of evapotranspiration loss. SCS-CN method and Snyder's method are compared for the predictive capability. From the study, it is found that SCS-CN method is much suitable for the study area. Uncertainty analysis was carried out on SCS-CN parameters. In the present study, five events are considered for the period 2011–2014. Three events are used for the model development. Model is validated for the independent two events. The objective of the present study is to develop the rainfall–run-off model for the lower Tapi basin and carry out the uncertainty analysis. For the uncertainty analysis, Monte Carlo method adopted. Nash–Sutcliffe coefficient is used as an objective function, which is to be maximized. Results shows that curve number 85–90, initial abstraction 5–10 mm, and percentage impervious of about 5–10% are optimized parameter range values for study area under consideration. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for conducting probabilistic assessments for structural integrity applications based on the Monte Carlo method. This starts with the definition of the underlying procedure for assessing the failure mechanisms of interest, followed by the statistical modelling of the key input parameters, leading to the estimation of desired probabilities. A case-study assessing a plant component (the tubeplate) for creep-fatigue crack initiation using the R5 Volume 2/3 procedure was conducted to provide context and demonstrate the utilities of implementing a probabilistic framework. Expanding on previous work, this paper highlights the main areas of focus for the proposed probabilistic methodology: probabilistic representation of input parameters, correlations, treatment of loading uncertainties, conducting post-assessment sensitivity analyses, the extrapolation of assessment location probabilities to component-level and, thereafter, population-level estimates. Through presenting a case-study implementing the full probabilistic methodology, the aim is to promote wider application and acceptance within the international structural integrity community, and further development of the methodology and constituent methods. [ABSTRACT FROM AUTHOR]

We develop a semiparametric Bayesian approach to missing outcome data in longitudinal studies in the presence of auxiliary covariates. We consider a joint model for the full data response, missingness, and auxiliary covariates. We include auxiliary covariates to "move" the missingness "closer" to missing at random. In particular, we specify a semiparametric Bayesian model for the observed data via Gaussian process priors and Bayesian additive regression trees. These model specifications allow us to capture nonlinear and nonadditive effects, in contrast to existing parametric methods. We then separately specify the conditional distribution of the missing data response given the observed data response, missingness, and auxiliary covariates (i.e., the extrapolation distribution) using identifying restrictions. We introduce meaningful sensitivity parameters that allow for a simple sensitivity analysis. Informative priors on those sensitivity parameters can be elicited from subject-matter experts. We use Monte Carlo integration to compute the full data estimands. Performance of our approach is assessed using simulated datasets. Our methodology is motivated by, and applied to, data from a clinical trial on treatments for schizophrenia. for this article are available online. [ABSTRACT FROM AUTHOR]

Exploratory bifactor analysis (EBFA) represents a methodological advancement for implementing a bifactor model in exploratory factor analysis (EFA). However, little is known about how to properly employ the procedure. The current rotation criteria available for EBFA make it more likely to "get stuck" in local minima, contributing to possible group factor collapse, than more traditional EFA rotations. Thus, getting a proper solution is a more complex and involved process than typical EFA and may require a sensitivity analysis. This article examines EBFA through a sensitivity analysis and subsequent simulation of parameters thought to contribute to group factor collapse. Results support the use of sensitivity analysis, as the problematic variable was shown to greatly increase the likelihood of factor collapse. The hypothesis that estimation start values contribute to factor collapse was not supported. Accompanying R syntax for all analyses are provided to facilitate reproducibility. [ABSTRACT FROM AUTHOR]

Parameter uncertainty has fuelled criticisms on the robustness of results from computable general equilibrium models. This has led to the development of alternative sensitivity analysis approaches. Researchers have used Monte Carlo analysis for systematic sensitivity analysis because of its flexibility. But Monte Carlo analysis may yield biased simulation results. Gaussian quadratures have also been widely applied, although they can be difficult to apply in practice. This paper applies an alternative approach to systematic sensitivity analysis, Monte Carlo filtering and examines how its results compare to both Monte Carlo and Gaussian quadrature approaches. It does so via an application to rural development policies in Aberdeenshire, Scotland. We find that Monte Carlo filtering outperforms the conventional Monte Carlo approach and is a viable alternative when a Gaussian quadrature approach cannot be applied or is too complex to implement. [ABSTRACT FROM AUTHOR]

The presented ALPHA cost tool is a novel highly flexible bottom-up parametric hybrid cost estimation framework. It combines the benefits of both methods with the aim of providing cost information during all product development phases. The software offers full transparency to the user and advanced two-level uncertainty management to not only understand any project's cost structure but also aid to identify its cost driving parameters. The implementation of sensitivity analysis makes the intrinsic uncertainty inevitable embedded in cost estimation become graspable. Gaussian error propagation offers direct feedback without extra calculation time while classic Monte Carlo Simulation gives detailed insight through post estimation analysis. From the vast number of commercially available or self-developed cost tools many probably already incorporate uncertainty measures similar to those proposed here. But this article shows both the potential of the additionally obtainable information from uncertainty propagation and demonstrates a way of integrating these risk considerations into a self-developed cost tool. [ABSTRACT FROM AUTHOR]

Longitudinal count responses are often analyzed with a Poisson mixed model. However, under overdispersion, these responses are better described by a negative binomial mixed model. Estimators of the corresponding parameters are usually obtained by the maximum likelihood method. To investigate the stability of these maximum likelihood estimators, we propose a methodology of sensitivity analysis using local influence. As count responses are discrete, we are unable to perturb them with the standard scheme used in local influence. Then, we consider an appropriate perturbation for the means of these responses. The proposed methodology is useful in different applications, but particularly when medical data are analyzed, because the removal of influential cases can change the statistical results and then the medical decision. We study the performance of the methodology by using Monte Carlo simulation and applied it to real medical data related to epilepsy and headache. All of these numerical studies show the good performance and potential of the proposed methodology. [ABSTRACT FROM AUTHOR]

Considering deterioration process of a beam structure subjected to cyclic load, the reliability and sensitivity analysis is presented. Gamma process is introduced to describe the deterioration of properties and the accuracy of the model is verified by experiments of structures under cycle load. A set of experiments is designed to investigate the deterioration process of the beam under cycle load. For arbitrarily distributed random material and geometric variables, the stochastic perturbation method and Edgeworth series are used to approximate the statistical characteristics of vibration and failure probability. Monte Carlo simulation is employed to show the validity of the method proposed. Additionally, the effects of material and geometric parameters are discussed to provide important information for design and use of beam structures. [ABSTRACT FROM AUTHOR]

The uncertainty and robustness of Computable General Equilibrium (CGE) models can be assessed by conducting a Systematic Sensitivity Analysis (SSA). Different methods have been used in the literature for SSA of CGE models such as Gaussian Quadrature and Monte Carlo methods. This article explores the use of Quasi-random Monte Carlo methods based on the Halton and Sobol' sequences as means to improve the efficiency over regular Monte Carlo SSA, thus reducing the computational requirements of the SSA. The findings suggest that by using low-discrepancy sequences, the number of simulations required by the regular MC SSA methods can be notably reduced, hence lowering the computational time required for SSA of CGE models. [ABSTRACT FROM AUTHOR]

Hazard rate functions are often used in modeling of lifetime data. The Exponential Power Series (EPS) family has a monotone hazard rate function. In this article, the influence of input factors such as time and parameters on the variability of hazard rate function is assessed by local and global sensitivity analysis. Two different indices based on local and global sensitivity indices are presented. The simulation results for two datasets show that the hazard rate functions of the EPS family are sensitive to input parameters. The results also showthat the hazard rate function of the EPS family is more sensitive to the exponential distribution than power series distributions. [ABSTRACT FROM AUTHOR]

Probabilistic risk assessment for bridges under natural hazards is of great interest to engineers for the development of risk mitigation strategies and implementation plans. The present study evaluates risk of an existing highway bridge in California, USA, under the integrated impact of regional seismic and flood hazards. A sensitivity study combining tornado diagram and first-order second moment reliability analyses is conducted to screen significant uncertain parameters to which bridge response is mostly sensitive. A rigorous uncertainty analysis, employing random sampling and Monte Carlo simulation techniques, is performed to obtain variations in fragility and risk curves of the bridge. Observed variations in risk curves at various risk levels are quantified through 90% confidence intervals and coefficients of variation (COV) of risk. It is observed that uncertainty in the estimated risk increases due to the presence of flood hazard at the bridge site, although mean risk does not vary with flood hazard level. Research outcome signifies that the variation in risk due to parameter uncertainty and varied flood hazard level should not be ignored to ensure bridge safety under the stated multi-hazard condition. [ABSTRACT FROM AUTHOR]

In this work, we extend upon the results of Raissi and Seshaiyer [A multi-fidelity stochastic collocation method for parabolic partial differential equations with random input data, Int. J. Uncertain. Quantif. 4(3) (2014), pp. 225-242]. In Raissi and Seshaiyer (2014), the authors propose to use deterministic model reduction techniques to enhance the performance of sampling methods like Monte-Carlo or stochastic collocation. However, in order to be able to apply the method proposed in Raissi and Seshaiyer (2014) to non-linear problems a crucial step needs to be taken. This step involves local improvements to reduced-order models. This paper is an illustration of the importance of this step. Local improvements to reduced-order models are achieved using sensitivity analysis of the proper orthogonal decomposition. [ABSTRACT FROM AUTHOR]

This paper presents a study of a new procedure, which is based on integrating a powerful nonparametric test for the two-sample problem and EWMA control scheme to online sequential monitoring. The proposed procedure, based on individual observation per sample, can be used to monitor the location and the scale parameters of a univariate continuous distribution, simultaneously. An iterative computation procedure is developed for computing the monitoring statistics. A search algorithm for the control limit based on Monte- Carlo simulation and bisection method is derived and a table is provided. The sensitivity analysis on the procedure is studied in detail. Monte-Carlo simulation results show that the proposed procedure is quite robust to nonnormally distributed data, and moreover, it is efficient in detecting various process shifts. A real data example from a chemical reaction process is shown to illustrate the application of our proposed procedure. [ABSTRACT FROM AUTHOR]

Iran, as a seismic country, is situated over the Himalayan-Alpied seismic belt and has faced many destructive earthquakes throughout history. Therefore, it is very important to evaluate the possible damage to the existing infrastructure based on statistical and spatial analysis. In this study, a new model is developed to analyse seismic damages based on seismic hazard assessment and extraction of the vulnerability function for all features of fuel infrastructure. To consider uncertainty analysis in the model, Monte Carlo simulation is used based on 10,000 iterations. The results of hazard analysis indicated that peak ground acceleration is about 0.18 g and there is slight to moderate damages to the desired fuel infrastructure in the study area. Moreover, sensitivity analysis is also performed to determine how median, standard deviation (or beta), grid size, attenuation relationships, liquefaction and landslide susceptibility impact the seismic loss. Last but not least, the effect of input parameters of earthquake scenarios including magnitude, focal depth and focal distance are also analysed in conjunction with regression analysis. The results of the study show that magnitude and focal distance are the most sensitive parameters in which the expected damage to the fuel infrastructure is reduced by about 25% if the epicentre of the earthquake is moved from 10 to 25 km. [ABSTRACT FROM AUTHOR]

Geographic information system (GIS)-based multi-criteria decision analysis (MCDA) is commonly used to solve a range of complex spatial problems. We have incorporated an analytical network process (ANP) as a central part of MCDA and used this approach for land subsidence susceptibility mapping (LSSM). We used an ANP approach in combination with the concepts of global sensitivity and uncertainty analyses, in order to minimize any associated errors. This approach was tested for a LSSM application in north-western Iran, using a methodology that consisted of three distinct phases. An ANP approach was used in the first phase to derive criteria weightings by analysing relevant criteria, which included topographic, hydrologic, climatologic, geological, and anthropological indicators. The second phase involved evaluating the uncertainty and sensitivity of areas susceptible to subsidence as a function of the derived weightings, using Monte Carlo simulations. The third phase then used a land subsidence inventory database to validate the results and to measure any improvement in accuracy following sensitivity analysis. Results indicated a 6% improvement in the accuracy of the ANP method as a result of using an integrated global sensitivity analysis. [ABSTRACT FROM AUTHOR]

Runs rules are commonly used to increase the sensitivity of the Shewhartchart, for detecting small and moderate mean shifts. This paper presents a group runs (GR) revisedm-of-k(R−m/k) runs rule chart for the mean. The proposed GRR−m/kruns rule chart has superior zero-state and steady-state performances to the basic GR, synthetic andR−m/kruns rule charts, for detecting small and moderate shifts, while maintaining the same efficiency toward large shifts. For zero-state case, the proposed chart can be designed to outperform the exponentially weighted moving average (EWMA) chart, except for small shifts. However, the EWMA chart prevails for steady-state case. [ABSTRACT FROM AUTHOR]

The novel methodology for imperfection sensitivity analysis, presented in Barbero et al. [1], is here applied for the evaluation of limit load of composite cylindrical shells. Koiter’s perturbation method is used to calculate the imperfection paths emanating from mode interaction bifurcations, and the Monte Carlo method is used to test a large number of modes and all possible interactions among them. The computational cost is low because of the efficiency of Koiter’s method. The demands of Koiter’s method for accurate evaluations of higher order derivatives of the potential energy are met by a mixed, corotational element. [ABSTRACT FROM AUTHOR]

Due to the over-parameterized models in detailed thermal simulation programs, modellers undertaking validation or calibration studies, where the model output is compared against field measurements, face difficulties in determining those parameters which are primarily responsible for observed differences. Where sensitivity studies are undertaken, the Morris method is commonly applied to identify the most influential parameters. They are often accompanied by uncertainty analysis using Monte Carlo simulations to generate confidence bounds around the predictions. This paper sets out a more rigorous approach to sensitivity analysis (SA) based on a global SA method with three stages: factor screening, factor prioritizing and fixing, and factor mapping. The method is applied to a detailed empirical validation data set obtained within IEA ECB Annex 58, with the focus of the study on the airflow network, a simulation program sub-model which is subject to large uncertainties in its inputs. [ABSTRACT FROM PUBLISHER]

Finite element reliability analysis (FERA) has been used to evaluate the reliability of structures. In FERA, approximate methods are commonly used to estimate the mean and variance of the structural response, while its probability distribution is primarily derived based on the Monte Carlo simulation (MCS) method. This paper advances FERA by combining it with the multiplicative dimensional reduction method (M-DRM). The proposed M-DRM allows fairly accurate estimation of the statistical moments, as well as the probability distribution of the structural response. The distribution of the response is obtained using fractional moments, which are calculated from the M-DRM, along with the maximum entropy principle. The variance of the response, based on global sensitivity measures, is obtained as a by-product of the analysis. The proposed approach is integrated with the OpenSees software and is illustrated through examples of nonlinear finite element analyses of reinforced concrete and steel frames. The paper shows that the proposed approach is an accurate and efficient alternative for FERA. [ABSTRACT FROM AUTHOR]

Sulphide corrosion in concrete sewers is the most common form of deterioration and should be investigated in failure assessment of sewage systems. Corrosion parameters are considered as random variables because of data scarcity and uncertainties involved in the corrosion affected concrete sewers. Sensitivity analysis is widely accepted as a necessary part of failure assessment of structures and infrastructure, in which the effect of random variables on the failure can be analysed. In the current study, the results of sensitivity analysis of a corrosion affected concrete sewer in the UK showed that among eight random variables, alkalinity of concrete and relative depth of the flow have the most effect on the probability of sewer failure. The analysis showed less significant contribution of some variables in failure functions. Therefore, it would not be necessary to consider those parameters as random variables and they can be treated as deterministic constant values for further studies. [ABSTRACT FROM AUTHOR]

Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variability of a quantity of interest (output of the model). One of the statistical tools used to quantify the influence of each input variable on the output is the Sobol sensitivity index. We consider the statistical estimation of this index from a finite sample of model outputs. We study asymptotic and non-asymptotic properties of two estimators of Sobol indices. These properties are applied to significance tests and estimation by confidence intervals. [ABSTRACT FROM AUTHOR]

Stochastic sensitivity analyses rarely measure the extent to which realized simulations cover the search space. Rather, simulation lengths are typically chosen according to expert judgement. In response, this paper recommends a novel application of Good-Turing estimators of missing distributional mass. Using the United Nations Development Programme's Human Development Index, the empirical performance of such coverage metrics are compared to alternative measures of convergence. The former are advantageous – they provide probabilistic estimates of simulation coverage and permit calculation of strict bounds on estimates of pairwise dominance (for all possible weight vectors, how often country X dominates country Y). [ABSTRACT FROM AUTHOR]