Abstract

     Considering environmental factors such as temperature in structural health monitoring progress has been a consensus. However, the uncertainty of monitoring data usually makes it difficult. In this paper, the uncertainty factor has been introduced into the anomaly diagnosis process, a Markov chain-Monta Carlo (MCMC) anomaly diagnosis method based on temperature-induced response has been proposed. First, a novel diagnosis index has been developed based on the temperature data and static strain response data collected by the SHM system, the MCMC process is used to analyze the diagnosis index, and the posterior frequency distribution histogram of the actual diagnosis index is obtained. Finally, by analyzing the histogram of an unknown state and the initial state (baseline state) of the structure, the anomaly probability of the unknown condition is obtained, which can be used for anomaly probability diagnosis of components. The availability of the method is evaluated by a laboratory truss structure test under a series of working conditions and is verified by field monitoring data of a hanger roof structure. The results show that the method can make better use of the temperature effect of the structure for anomaly diagnosis, and the uncertainty is well considered.

Introduction

     Large building structures, including truss structures, are often used in airports, stations, factories, stadiums, and other important civil infrastructures [1], such structures often encounter sudden load changes (snow loads, high wind loads), changes in restraint conditions, member damage due to material degradation, and other structural anomalies during construction or service. Monitoring and diagnosing these structural anomalies using sensors placed on the surface of the structure is an efective means to ensure the safety of the structure throughout its life, and in recent years, with the development of sensor technology and intelligent algorithms, structural anomaly diagnosis (SAD) technique is becoming an increasingly important area.

     The vibration-based method is one of the most widely used SAD methods, which refects the abnormal state of the structure by monitoring the changes of vibration features [2–4]. However, the structural vibration features (e.g., natural frequencies) are not only related to the state of the structure itself, but also to environmental factors such as temperature, which will have an impact on the accuracy of SAD [5–7]. Although diferent solutions have been proposed for vibration-based SAD under environmental changes, these methods are still greatly limited by other defects in long-term practical monitoring, such as low sensitivity of vibration features to small local damage of the structure, the complicated method of sensor arrangement, and the large data transmission and storage caused by the high sampling frequency of the monitoring process, etc. [8].

Summary and conclusions

     In this paper, a temperature-based anomaly diagnosis of truss structure system using Markov chain-Monte Carlo method is proposed, which not only introduces the uncertainty method into a SAD method based on static response monitoring, but also actively uses the temperature efect of the structure for SAD. In this method, a novel diagnosis index based on T-stress-induced strain that takes uncertainties into account is proposed, which is obtained from the stress-induced strain and temperature measured by sensors arranged in the focus area of the structure. Then, the diagnosis index measurement data set is processed by MCMC to obtain the posterior relative frequency distribution histogram of the actual diagnosis index, and the histogram of the baseline state and an unknown state are analyzed to obtain the structural anomaly probability of the members under this unknown state. As two aspects of anomaly diagnosis, the structural damage diagnosis and state change diagnosis were verifed by a truss test and feld monitoring data of the BDIA hanger roof structure respectively, and the following conclusions were obtained:

Abstract

     A tolerance framework is developed to address a group multiple criteria ranking problem with indirect preference information, which is referred to as the interaction, importance and tolerance of criteria as well as pairwise comparisons among alternatives and criteria. Choquet integral preference model is employed to capture the interaction, importance and tolerance of criteria, all of which are specific to decision makers (DMs). Some mandatory/sufficient requirements concerning criteria which are global or local, also called the tolerance attitudes of DMs, are quantified as tolerability constraints. Preference disaggregation analysis is extended to solve this type of tolerability constraints for preference elicitation. Confronted with the inconsistency issue (the feasibility of the whole preference constraints translated from indirect preference information), cause oriented strategy and consequence oriented strategy are established by regression-based mixed 0–1 integer linear programs with the objectives of the most credible minimal inconsistent preference constraints and the most credible maximal preference constraints in the context of group decision making. Considering a wide range of the minimal unsatisfied subsets of preference constraints responsible for the inconsistency and the maximum satisfied subsets of preference constraints as a consistent result, to reach a robust decision, stochastic multicriteria acceptability analysis (SMAA)-like simulation analysis is generated to examine the whole instances of compatible preference models in the modified feasible preference polyhedron and compute the result in a probabilistic form. Simulation experiment is conducted to investigate the influence of global tolerance attitudes of DMs on preference elicitation in conservative and radical scenarios. Finally, the application of the proposed approach to a credit ranking of small and medium-sized enterprises (SMEs) and the comparison analysis with objective methods are presented and discussed for the effectiveness of the proposed tolerance framework.

Introduction

     Many real-world decision problems in different fields can be formulated as multiple criteria decision making (MCDM) as the performance of alternatives can be decomposed as the evaluation of alternatives on considered criteria and ranked by the comprehensive score which is a straightforward intuition for decision makers (DMs) to comprehend. The preference model in MCDM would be paid more attention to the property that is consistent with observable properties of human decision process (i.e., intuitive reasoning, common sense, and expert knowledge), otherwise it would deteriorate a justifiable decision. The theory of fuzzy measures and integrals Liginlal and Ow (2006) has emerged to characterize the preference of DMs with the realistic hypothesis about preferential dependence among criteria and give the opportunity to represent and interpret the typical human decision process, which is such a predominant character that can address numerous practical preference modelling.

     The DMs have distinguished aggregation behaviors in context of group MCDM, also called the tolerance attitudes of DMs in some literatures. From a mathematical perspective, the tolerance attitudes of DMs are equivalent to the tolerance (or intolerance) of criteria represented by the aggregation operator (Li, Yao, Sun, & Wu, 2018). In a strategic investment decision problem, when searching for most qualified candidates among prospective alternatives, every candidate failing at most k criteria would be rejected. It implies k-intolerance attitudes of the DMs which can be perceived as a selection policy. In the field of fuzzy aggregation operator field, ordered weighted averaging (OWA) operator was studied which used the global tolerance of criteria to measure the optimism degree of DMs (Kim and Ahn, 2018, Zeng et al., 2018). Based on the k-tolerance (or k-intolerance) properties of the Bonferroni mean aggregation operator (Beliakov & James, 2013), Dutta, Figueira, and Das (2019) studied orness measure to define the or‐like degree of the Bonferroni mean and its variants. Considering non-additive value function, previous researchers constructed the optimization model using fuzzy measure to capture tolerance attitudes required from DMs without any post optimality analysis (Li et al., 2018, Yao et al., 2018). It is doubted that the decision from the inferred preference model is robust in case of the existence of the multiple feasible near-optimal solutions. As noted by Dujmović and Larsen (2007), the requirements (or expectations) on criteria combining by preference model cannot be neglected during preference aggregation. Subsequently, concise mathematical formulas for them (Hudec and Mesiar, 2020, Marichal, 2004) were defined to express a global or local tolerance degree of the DMs.

Conclusion

     In this paper, under a robust perspective, we present a tolerance framework to address a group multiple criteria ranking problem with indirect preference information such as the interaction, importance and tolerance of criteria as well as pairwise comparison among alternatives and criteria. Based on indirect preference information, Choquet integral preference model is elicited to capture the interaction, importance and tolerance of criteria for the final decision. With respect to the tolerance attitudes of DMs such as the mandatory/sufficient requirements on criteria for specific alternatives, they are quantified as tolerability constraints for preference elicitation. Once inconsistency issue (the feasibility of the whole preference constraints) in preference constructive learning process occurs, in the tolerance framework, two developed strategies including cause oriented strategy and consequence oriented strategy are applied. Then identify the minimal unsatisfied subsets of preference constraints responsible for the inconsistency or the maximum satisfied subsets of preference constraints as a specific set of consistent preference information by regression-based mixed 0-1 integer linear programs. By following the idea of treating all possible sets of preference parameters in line with indirect preference information, SMAA-like simulation algorithm is constructed to generate the rank result in the probabilistic form, which is used to generate a solution considered by exploring the whole instances of the compatible preference model. The proposed method can deal with indirect preference information involved with not only less cognitive effort but also the tolerance attitudes on criteria for the DMs, which has been paid insufficient attention in the literatures. From the illustration and application, it suggested that the proposed unified framework for MCDA has two characteristics: expressiveness of the underlying preference model which can reconstruct indirect preference information provided by the DMs and robustness of the final result which address uncertainties and imprecision observed in the actual decision support processes.

Deviations from nominal design values of mechanical characteristics of seismic isolators are inevitable due to uncertainties and/or errors in material properties, element dimensions, construction methods, quality control, and/or installation steps, etc. In addition, due to uncertainties existing in active fault mechanisms affecting the site and local soil conditions, earthquake ground motions show record-to-record variability. Moreover, environmental conditions and/or time-dependent factors such as aging, temperature, and travel, etc. may also cause additional deviations in nominal values of characteristics of seismic isolators and thus in structural responses over the service life of structures. Therefore, in order to capture the dynamic behavior of base-isolated buildings realistically and design reliable base-isolated buildings, influence of such uncertainty should be taken into account. In this study, seismic reliability of realistic fully three-dimensional benchmark multi-story buildings equipped with nonlinear isolation systems is investigated under historical near-fault earthquakes. In order to take into account the uncertainties in the isolation system characteristics, pre-yield stiffness, post-yield stiffness, and yield displacement parameters are assumed as random variables, while the record-to-record variability nature of the ground motions is considered by using large sets of historical near-fault ground motions with or without forward-directivity effects. Two different levels of nominal isolation periods are taken into account while three different levels of uncertainty are considered for the random isolator characteristics. The reliability of the buildings is investigated in terms of structural integrity, isolation system safety, and the safety of the vibration-sensitive contents of the buildings using the peak bearing displacements and the peak floor accelerations obtained from the nonlinear time history analyses conducted in the framework of the Monte Carlo simulations. The plots including the average reliabilities for the displacement and the acceleration limit values provide a comprehensive picture in terms of the reliability level of the structural systems and of the vibration sensitive contents.

Introduction

Seismically isolated structures are generally mission-critical structures housing vibration-sensitive equipment which must go on serving even during an earthquake. However, such equipment may fail if floor accelerations sustained during such motions exceed certain limit values. In addition, the isolators may fail different failure modes [1] and endanger safety of the whole structure. Isolation system displacements may exceed the design limits for the base displacements and therefore cause buckling and/or rupture of isolators may occur [2]. Buckling is a specific concern for stability of elastomeric isolators when the critical axial load capacities of the isolators are exceeded along with the increases in the horizontal isolator displacements [3,4]. In such a case, the subject isolator displacements become unconstrained and increase freely [5]. In another failure mode, cavitation damages occur at a critical tensile stress value inside a rubber layer and gain importance for horizontal displacement levels corresponding to shear strains above 100% [6,7]. Finally shear failure could be observed at very high shear (i.e., on the order of approximately 300–500%) strain levels [4,8,1]. Above all these, in case one of the abovementioned failure modes occur in a significant number of isolators almost simultaneously, this may lead to the collapse of the whole isolation system [1]. Therefore, for obtaining reliable seismically isolated structural systems, it is economically and sometimes vitally important to determine the actual mechanical properties of isolation elements, which are used in the dynamic analyses of the subject systems, and take into account the factors that may lead to deviations in those mechanical characteristics.

1- Introduction

2- Literature review

3- Methodology

4- Analysis

5- Discussion and conclusions

References

Abstract

The purpose of the study is to analyse the balanced scorecard (BSC)-based evaluation of the new service development (NSD) in Turkish banking sector. The proposed model includes fuzzy ANP (FANP), Monte Carlo Simulation, fuzzy TOPSIS (FTOPSIS), and fuzzy VIKOR (FVIKOR) respectively. FANP has been used for weighting the criteria, Monte Carlo Simulation has been applied to provide the stochastic values of BSC-based dimensions of NSD in banking sector. FTOPSIS and FVIKOR have been considered to rank the banks by their dimension performances. The novelty of the study is to provide an integrated model including FANP, FTOPSIS, FVIKOR, and Monte Carlo Simulation respectively. Additionally, BSC-based analysis of NSD has been applied for evaluating Turkish banking sector. The results demonstrate that the comparative analysis is coherent for ranking the alternatives and the stochastic values facilitate to obtain the immense expert evaluations under the fuzzy environment. It is identified that the performance of the foreign banks is lower than private and state banks. Hence, it can be said that especially foreign banks should develop new services to attract the attention of their customers. Within this framework, customer expectations should be defined by conducting a detailed analysis. As a result, it can be possible to increase comparative advantage in comparison with the other banks.

Introduction

By the globalization, the use of knowledge makes the multinational firms efficient and it eases to monitor the competitive policies in determining strategy includes versatile information for the production and service sector. Due to the effective use of information, the companies have some opportunities to determine the adequacy of the service and products in the commercialization process. Accordingly, knowledge-based system which is called the new economy has allowed the globalized firms to use the multifaced data obtained from the customers and other participants by the beginning of the 1990s. Moreover, ease of reaching the information and communication technologies with the new economy has enhanced the importance of analysing the existing internal and external environmental data in the incremental and radical innovation and development process. In the increasing competitive environment, the product development processes have begun to host the service development processes by the 1980s. Thus, the new economic process to emphasize a sustainable progress for each stage of the NSD process from the design to the commercialization in the interactive way. Similarly, multidimensional assessment of innovative strategies and decisions on the NSD in the service industry is a novel issue in the competitive market environment. The critical success factors of innovative service thinking are defined in the strategy and knowledge management, process formulation [3]. Several factors such as strategy, formalized development process, integrated development teams, and customer interaction are highly related to examine the key strategic factors in developing new services [28]. Nowadays, some research interests arise to gain the advantages of the globalized service quality improvement. Organizational competency is one of the prominent issues in the NSD. Interaction in the organizational team increases the expertise for the service improvement [78]. Thus, innovative thoughts are highly valuable by considering functional teams, and learning orientation processes. Additionally, the internal and external integration practises bring the achievement on the new service applications, despite some costs in the idea generation stage [35] and interventional culture is an influential factor in the development of new services within the learning culture [76]. Another important debate in the service development is customer-oriented innovation policies. The active role of customers at each stage contributes to creating the service innovation [15] with social networks [73], learning alignment [79], harmony between client and directly related personnel in the NSD.

The design of an optimal sand control method and production management is a complex problem due to the simultaneous influence of various factors. Typical effective variables for choosing an optimum sand control method include geological, technical, economical, and expert’s experience on similar projects. Some technical factors, which affect the optimum method, are the type of exclusion, gravel size of gravel pack and pre-packed screen, slot width and liner slot length, and productivity index reduction. The situation could be more complicated due to the uncertainty associated with various contributing factors. Therefore, it is crucial to develop a novel approach in order to select the best sand control method with a maximum level of confidence. In this study, to select an optimal sand control method, Multi Criteria Decision Matrix (MCDM) techniques including Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and, ELimination and Choice Expressing REality (ELECTRE) are used. To simulate fluid flow, an integrated model of reservoir, well, and surface facility is used based on actual oil field data collected from the south of Iran. Then, Design of Experiment (DOE) and Response Surface Methodology (RSM) are applied to optimize the controllable variables of the best selected sand control method by MCDM. Finally, Monte Carlo Simulation (MCS) is applied to perform sensitivity and uncertainty analysis in order to determine the crucial factors that control net present value (NPV). The results show that the best sand control method based on AHP, TOPSIS, and ELECTRE is the slotted liner. After that, three different methods of pre-packed, gravel pack, and wire wrapped are respectively the most efficient sand control methods based on an average score of all the MCDM techniques. The results also indicate that although the pre-packed screen has the highest NPV, it is not the best sand control method due to the influence of other efficient criteria. The result of sensitivity analysis using MCS in terms of contribution to total variance shows that slot width, slot density, and slot height controls 60.5%, 38.8%, and 0.7% of the NPV variation within the range of factors, respectively.

Introduction

Methods of sand control were first utilized in water wells and then were later applied in oil and gas wells. There are several methods of sand control, which used to control the sand production, include mechanical and chemical methods. Mechanical methods involve the use of screens to retain the formation sand (with or without gravel) or use of gravel to hold formation sand (with or without a screen to retain the gravel) including gravel pack, pre-packed screen, slotted liner, and wire wrapped. Chemical methods employ a liquid resin which is injected from a wellbore into the unconsolidated rock surrounding the well. Chemical methods involve in-situ sand consolidation techniques and resin-coated gravel pack. The design of an optimum sand control is a complicated process because choosing an optimum sand control method depends on different effective factors. These factors include the type of exclusion, gravel size of gravel pack and pre-packed screen, slot width and length of the slotted liner, PI reduction and operating costs. Optimum selection is further intricate due to the uncertainty associated with variables influencing sand control methods. Therefore, it is crucial to select the most appropriate method in terms of minimum skin (pressure drop), cost, and maximum net present value (NPV). Many authors have studied various well completion methods under different downhole conditions. Some of them have discussed sand production consequences, while, few specialists have worked on sand control method selection. Tausch and Corley (1958) found the economics and selection of the sand control method, based on bridging and consolidation of sand grains, is a function of the expected producing rate, time periods of workovers, location, and condition of wells. Tiffin et al. (1998) proposed new criteria for screen and gravel selection for sand control. These criteria are mainly based on reservoir sand size distribution. Hodge et al. (2002) developed a valuation method for a stand-alone screen design, and gravel packed completion with consideration of plugging resistance and sand retention. Denney (2002) worked on field and laboratory tests to evaluate the relative effectiveness of two types of sand control methods used in the field with respect to optimizing operating expense. Farrow et al.

The purpose of this study is to review and compare simulation methods for describing the transport of charge clouds in silicon based semiconductor detectors and investigate the effects on energy spectrum for silicon based photon counting strip detectors. Charge clouds and detailed carrier transport are sim ulated and compared using two different approaches including analytical and Monte Carlo schema. The results of the simulations are evaluated using pulse height spectra (PHS) for a silicon strip detector with edge on geometry at two energies (25 and 75 keV) at various x-ray absorption locations relative to the pixel boundary and detector depth. The findings confirm carrier diffusion plays a large role in the charge sharing effect in photon counting detectors, in particular when the photon is absorbed near the pixel boundary far away from the pixel electrode. The results are further compared in terms of the doublecounting probability for x-ray photons absorbed near the pixel boundary as a function of the threshold energy. Monte Carlo and analytical models show reasonable agreement (2% relative error in swank factor) for charge sharing effects for a silicon strip detector with edge-on geometry. For 25 keV mono-energetic photons absorbed at 5 µm from the pixel boundary, the theoretical threshold energy at 10% double-counting probability based on charge sharing is 5.5, 8.5 and 9.2 keV for absorption depths of 50, 250 and 450 µm from the electrode, respectively. The transport of charge clouds affects the spectral characteristics of photon counting detectors and the double-counting probability results show the theoretical threshold energy to avoid double-counting as a function of x23 ray energy and x-ray interaction locations for silicon and can be considered for future studies of charge sharing effects.

Introduction

Photon-counting detectors with energy discrimination capabilities have been recently developed for many medical x-ray imaging applications[1, 2] promising several advances including the ability to estimate the energy of transmitted photons at each pixel location. This technological development could enable improved material decomposition, higher spatial resolution, and implementa tion of beam hardening corrections.In most cases, photon counting detectors with energy discrimination can achieve higher signal-to-noise ratio[3] leading to improvements in existing modalities or allowing novel applications.[1, 4–8] In addition, photon-counting detectors can be used in spectral CT applications.[9– 15] One major challenge for photon-counting detectors is a phenomenon gen erally known as charge sharing. Under an externally applied bias, a cloud of charge carriers created by the energy imparted by an absorbed x-ray photon travels within the semiconductor and reaches the detector electrode. Near a pixel boundary, the cloud may be divided and detected simultaneously by multiple pixels recording energies lower than the energy carried by the x-ray quantum. The distribution of energy causes distortions in the spectral response. The significance of this effect depend on the detection material, charge carrier mobil ity, pixel size, absorption location with respect to the pixel boundary, depth of interaction within the active detector layer, temperature, applied bias (including non-uniform electric field affects due to Frisch grid structures).

Besides seismic base isolation [1,2] and other strengthening techniques [3], the use of fluid viscous dampers (FVDs) as energy dissipation devices has rapidly increased in the last few years [4] for seismic retrofitting of existing civil engineering structures. Their appealing properties include the large energy dissipation capability, the low maintenance required, and the generation of forces that are out of phase with the elastic forces, thereby not increasing the stress in the structure. Experimental evidence [5–9] reveals that the constitutive behavior of FVDs can be described by a fractional velocity power law f = cu u | | sgn( ̇ ̇) NL α d d (1) where cd is the damping coefficient, α is a velocity exponent, u̇ represents the relative velocity at the ends of the device and sgn(⋅) is the signum function. The exponent α is responsible for the nonlinear damping of FVDs and depends upon the hydraulic circuit employed. Typically, α ranges from 0.10 to 0.50 for seismic applications. As a result of the nonlinear constitutive behavior of FVDs, linear methods of analysis, e.g., the response spectrum method, are no longer applicable. In this regard, attempts have been made to determine an equivalent viscous damping ratio due to the added nonlinear FVDs [10], or an equivalent damping coefficient of energy-equivalent FVDs associated with the same energy dissipation as the nonlinear FVDs [11]. The above-mentioned relationships, extremely useful for preliminary design purposes, are amplitude-dependent due to the nonlinear nature of FVDs. Furthermore, they are strictly valid under the hypothesis of harmonic motion. Since the earthquake-induced motion is not really harmonic but is generally modelled as a random process, the estimation of the equivalent damping coefficient can be alternatively performed within the framework of the stochastic linearization technique (SLT) [12]. The SLT was applied in a number of research papers and to a general class of nonlinear behaviors, not just confined to the one shown in (1), e.g.: in the context of tuned liquid column damper optimization [13,14], characterized by quadratic-times-signum-like damping; in the field of nonlinear energy sinks optimization [15], featured by a cubic stiffness; for optimizing the performance of hysteretic dampers [16], represented by a Bouc-Wen model. The SLT has also been successfully applied to derive equivalent linear properties of bilinear systems [17,18] in the framework of response spectrum analysis [19,20]. Coming back to the nonlinearity of FVDs given by (1), which is of interest to the present note, in the literature the SLT has been applied by introducing the stochastic nature of the earthquake input via a powerspectral-density (PSD) function S ω ü ( ) g , for example the Kanai-Tajimi PSD [21–23], or a particular spectrum-compatible PSD function [24,25]. All these quoted papers used a popular force-based Gaussian SLT (F-G-SLT). In this note, it is demonstrated that this variant of SLT is not the best option for the kind of nonlinearity induced by the FVDs. After a critical assessment of six alternative formulations of SLT, we identify a so-called equal-energy non-Gaussian SLT (EE-NG-SLT) that, without increased computational effort, is better able to capture the nonlinear response of the system.

Cost is one of the most important elements throughout the project life cycle, which can determine the success or failure of a project. High accuracy of cost estimation and control is important to prevent cost overrun at the end of the project (Ali and Kamaruzzaman, 2010). The advancement of computer programs has facilitated the usage of simulation techniques to perform complex mathematical and statistical analyses (Ashworth, 2004). Simulation techniques are used to analyze and assess uncertainties causing issues to project budget. The accuracy of cost estimation can be improved through high level of work definition and risk analysis (Potts and Ankrah, 2014). By using simulation techniques, cost estimator has a better understanding of factors causing cost variance, which enable him/her to make better decision and produce accurate estimation. The detailed cost estimation is converted to a project budget for controlling and monitoring purpose. Project budget or cost baseline is estimated by aggregating the cost of project activities and the cost of contingencies (Norman et al., 2010). Contingencies are included in the budget to respond to anticipated (known) risks at the activities level (Cernauskas and Kumiega, 2008) and to unanticipated (unknown) risks at the project level (Adafin et al., 2014). Project managers and consultants rely on conventional methods to estimate contingencies such as self-experience, intuition, and judgment (Adafin et al., 2014). The usage of traditional techniques of cost estimation may result in over-budgeted or under-budgeted projects. Non-traditional methods such as Monte Carlo simulation, case-based reasoning, neural networks, and multiple regression analysis can be used to forecast cost using probability distribution and improve the accuracy of the estimation (Aram et al., 2014; Kim et al., 2004). However, there is a low uptake of simulation techniques for cost estimation and control in the construction industry (Chou, 2011). Probabilistic estimation is more scientific approach to estimate contingencies but it is seldom used in the construction industry (Uher, 1996). In addition, Akintoye and Fitzgerald (2000) find that complex statistical formula and range estimation (probabilistic technique) are among the least techniques used by contractors for cost estimation in the UK.

RECENTLY, many practical problems with the uncertain feature, referred as uncertainty quantification (UQ), have received unprecedented attentions [1]. Due to the existence of uncertainty, the radar cross section (RCS) data obtained from dynamic/static measurement or electromagnetic simulation always have some mutual difference, which is even obvious sometimes. This leads to the question about the reliability of the dynamic targets’ RCS, which has also become a bottleneck in RCS research. Therefore, the uncertainty issue is not only an inevitable problem but also a valuable study topic in this field. Extensive efforts have been devoted to analyze and evaluate RCS measurement uncertainty [2], [3]. However, in the measurement process, results are often interfered by a variety of factors simultaneously, such as attitude disturbance of dynamic targets, system noise, frequency drift, environmental clutter, and so on. It is difficult to extract the impact of one of the abovementioned uncertainties from the measurement data. Therefore, there is a great necessity to study the impact of the specific uncertainty on RCS data by means of simulation. The RCS simulation of complex target is a complicated computational process which needs to take many practical factors into account, such as sheltering, multiple scattering [4], and so on. Such simulation systems are difficult to describe with all-inclusive mathematical models. Hence, the reliability of the dynamic RCS simulation has to be considered. A more feasible way of RCS UQ is the sampling-based statistical methods. Monte Carlo (MC) method is considered to be one of the most popular UQ methods [5], [6]. Its simplicity and nonintrusive characters simplify the implementation by repeating uncertain experiments and sample statistics with sufficient amounts. Although the convergence efficiency of such method is relatively low, it is widely used in the analysis of various complex electromagnetic problems because of its strong adaptability and less constraint on conditions. These advantages of MC method make it a good choice in UQ study, since it is a classical samplingbased statistical approach, the comparison and evaluation of numerical simulation data are critical for UQ.