Variations In Input Parameters Research Articles

Application of signal to noise ratio and grey relational analysis to minimize forces and vibrations during precise ball end milling

In this paper, a method for the minimization of cutting forces and vibrations during precise ball end milling of hardened 55NiCrMoV6 steel is developed. The aim of this work concentrates on the optimal selection of surface inclination angle α and tool’s overhang l, which enables the minimization of cutting forces and vibrations in order to improve the machined surface quality. The experiment includes the measurement of cutting forces and acceleration of vibrations during the milling tests with variable input parameters. The next step focuses on the optimization of the ball end milling process with the consideration of the acquired signals. This procedure is carried out by the minimization of process responses with the application of signal to noise S/N ratio and grey relational analysis (GRA). Subsequently, the obtained optimal values of process input parameters are validated during the ball end milling tests involving the measurements of machined surface topographies. Research reveals that surface inclination angle and tool’s overhang have significant influence on generated forces and vibration values. Moreover, the selection of the optimal values of α and l enables significant improvement of machined surface quality.

Radiative detached divertor with acceptable separatrix Zeff

The feasibility study is performed for the radiative detached divertor (RDD) concept, which characterizes a variety of detached plasmas with impurity amounts providing the highest levels of divertor radiation without X-point MARFE (XPM), based on a set of restrictive criteria for the leading plasma parameters (LPPs) including, e.g., tolerable peak power loads below 1 MW/m2, low separatrix densities, allowable impurity concentrations, and acceptable Zeff values for a DIII-D like tokamak. For this, extensive simulations with the 2-D edge plasma transport code were done scanning the deuterium and impurity inventories practically for all impurity elements from beryllium to neon and the analysis of LPP variations in these scans is presented. It is shown that, for a given D inventory, the total radiation fraction with an increase in the impurity inventory reaches a flat top level, frad = 0.85 ± 0.01, whereas the higher frad corresponds to XPM. This critical fraction is the same for all elements and values of the D inventory. Successful RDD solutions with a flat top radiation meeting all ad hoc LPP criteria are found for some elements. Boron and nitrogen are shown to be the most promising elements for seeding, since they are capable of providing alone the successful RDD at the lowest concentrations. Several important effects on impurity radiation are considered including: cross-field impurity transport in regions with strong temperature gradients, multi-species thermal force, charge-exchange of impurity ions with D atoms originating from recombination, impurity entrainment by parallel flows, flows caused by inner/outer divertor asymmetries, and Mach ∼ 1 flows reached inside radiation-ionization fronts. The impurity radiation profiles of various elements are analyzed suggesting three patterns differing in the radiation front position with respect to the D ionization source. The modeled relocation of D from the pedestal into divertor regions; an enhanced pedestal enrichment for impurities with high first ionization potential; and RDD capability of confining inside the divertors the large D inventories as neutral atoms, are considered as effects of increasing impurity inventory. The responses of a flat-top RDD to the input parameters variation are studied. For instance, dependencies of LPPs on the input power increase, which express the sharp and even bifurcation transitions between two distinct branches corresponding to the detached and attached divertors, are discussed.

Uncertainty propagation by using spectral methods: A practical application to a two-dimensional turbulence fluid model

To reduce the computational cost of the uncertainty propagation analysis, which is used to study the impact of input parameter variations on the results of a simulation, a general and simple to apply methodology based on decomposing the solution to the model equations in terms of Chebyshev polynomials is discussed. This methodology, based on the work by Scheffel [Am. J. Comput. Math. 2, 173–193 (2012)], approximates the model equation solution with a semi-analytic expression that depends explicitly on time, spatial coordinates, and input parameters. By employing a weighted residual method, a set of nonlinear algebraic equations for the coefficients appearing in the Chebyshev decomposition is then obtained. The methodology is applied to a two-dimensional Braginskii model used to simulate plasma turbulence in basic plasma physics experiments and in the scrape-off layer of tokamaks, in order to study the impact on the simulation results of the input parameter that describes the parallel losses. The uncertainty that characterizes the time-averaged density gradient lengths, time-averaged densities, and fluctuation density level are evaluated. A reasonable estimate of the uncertainty of these distributions can be obtained with a single reduced-cost simulation.

Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal

Distribution entropy (DistEn) is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters—the embedding dimension m, and the number of bins M which replaces the tolerance parameter r that is used by the existing approximation entropy (ApEn) and sample entropy (SampEn) measures. The performance of DistEn can also be affected by the data length N. In our previous studies, we have analyzed stability and performance of DistEn with respect to one parameter (m or M) or combination of two parameters (N and M). However, impact of varying all the three input parameters on DistEn is not yet studied. Since DistEn is predominantly aimed at analysing short length heart rate variability (HRV) signal, it is important to comprehensively study the stability, consistency and performance of the measure using multiple case studies. In this study, we examined the impact of changing input parameters on DistEn for synthetic and physiological signals. We also compared the variations of DistEn and performance in distinguishing physiological (Elderly from Young) and pathological (Healthy from Arrhythmia) conditions with ApEn and SampEn. The results showed that DistEn values are minimally affected by the variations of input parameters compared to ApEn and SampEn. DistEn also showed the most consistent and the best performance in differentiating physiological and pathological conditions with various of input parameters among reported complexity measures. In conclusion, DistEn is found to be the best measure for analysing short length HRV time series.

Uncertainties in corn stover feedstock supply logistics cost and life-cycle greenhouse gas emissions for butanol production

The success and sustainable operation of a commercial cellulosic biorefinery are highly dependent on price, quality, and availability of feedstocks. Currently, corn stover is a primary feedstock choice for cellulosic ethanol production and the same feedstock can be used for butanol production, which is a potential alternative biofuel. However, butanol production requires different levels of feedstock and logistical resources when compared to ethanol due to about 20% lower theoretical yield. A thorough analysis of cost, life-cycle energy use and life-cycle greenhouse gas emissions associated with a feedstock supply logistics system for butanol production is yet to be done. Additionally, quality standards for corn stover feedstock are yet to be determined, which could determine cost of the feedstock for commercial applications. Thus, this study aimed to fulfill these research gaps using techno-economic analysis and life-cycle assessment methodologies. Variability in logistical resources and other input parameters gathered from recent literature were used for analyses in this study. Average corn stover supply logistics cost ($/metric ton(t), dry), life-cycle energy use (MJ/t, dry) and life-cycle greenhouse gas emissions (kg CO2e/t, dry) were 86.9, 1073.7 and 83.8, which could increase at 95% certainty to 159–504, 1672–3817 and 138–271, respectively, depending on the types of probability distributions of the input parameters. However, the mode value of logistics cost ($/t, dry), life-cycle energy use (MJ/t, dry) and life-cycle greenhouse gas emissions (kg CO2e/t, dry) of 80–120, 913–1231 and 79–120, respectively, closely represents the results of the static model. The average estimated feedstock cost for butanol production was about 1.3 times and the minimum 95% certainty value is about 2 times more than the targeted feedstock cost ($/metric ton, dry) of 66.4, excluding preprocessing. Overall, the location of the biorefinery, quality of corn stover, sustainable agricultural practices, and optimum utilization of transportation resources were key factors that enable a sustainable feedstock supply logistics system.

Sensitivity analysis of heat losses in collective heat distribution systems using an improved method of EPBD calculations

This paper studies the sensitivity of heat losses in collective heat distribution systems using a simplified calculation method. The approach is applied to a specific type of heat distribution system where the network distributes heat for both space heating and sanitary hot water. The proposed methodology takes design parameters influencing the thermal performance of the heat distribution system into account. Among other parameters, the recirculation control strategy and the length of service pipes are taken into account. The present study assesses the impact of potential variations in the input parameters on the total heat loss and distribution efficiency. To that aim, a simulation study was conducted with a multilevel factorial design, consisting of the combinations of parameters which were varied from the levels at which they were set. Results show how sensitive the solution is to the different parameter values. Furthermore, the potential of the improved calculation method, which allows combining different design conditions within a single heating distribution system, was demonstrated.

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

ABSTRACTThe increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

Is SCENA a good approach for side-stream integrated treatment from an environmental and economic point of view?

The environmental and economic benefits and burdens of including the first Short Cut Enhanced Nutrient Abatement (SCENA) into a real municipal wastewater treatment plant were evaluated using life cycle assessment (LCA) and life cycle cost (LCC). The implications of accomplishing nitrogen (N) removal and phosphorus (P) recovery via nitrite in the side stream were assessed taking into account the actual effluent quality improvement, the changes in the electricity and chemical consumption, N2O, CO2 and CH4 emissions and the effects of land application of biosolids, among others. In addition, a case-specific estimation of the P availability when sludge is applied to land, therefore replacing conventional fertilizer, was performed. Furthermore, to account for the variability in input parameters, and to address the related uncertainties, Monte Carlo simulation was applied.The analysis revealed that SCENA in the side stream is an economic and environmentally friendly solution compared to the traditional plant layout with no side-stream treatment, thanks to the reduction of energy and chemical use for the removal of N and P, respectively. The uncertainty analysis proved the validity of the LCA results for global warming potential and impact categories related to the consumption of fossil-based electricity and chemicals, while robust conclusions could not be drawn on freshwater eutrophication and toxicity-related impact categories. Furthermore, three optimization scenarios were also evaluated proving that the performance of the WWTP can be further improved by, for instance, substituting gravitational for mechanical thickening of the sludge or changing the operational strategy to the chemically enhanced primary treatment, although this second alternative will increase the operational cost by 5%. Finally, the outcomes show that shifting P removal from chemical precipitation in the main line to biologically enhanced uptake in the side stream is key to reducing chemicals use, thus the operational cost, and increasing the environmental benefit of synthetic fertilizers replacement.

Optimizing gradient conditions in online comprehensive two-dimensional reversed-phase liquid chromatography by use of the linear solvent strength model.

The linear solvent strength model was used to predict coverage in online comprehensive two-dimensional reversed-phase liquid chromatography. The prediction model uses a parallelogram to describe the separation space covered with peaks in a system with limited orthogonality. The corners of the parallelogram are assumed to behave like chromatographic peaks and the position of these pseudo-compounds was predicted. A mix of 25 polycyclic aromatic compounds were used as a test. The precision of the prediction, span 0-25, was tested by varying input parameters, and was found to be acceptable with root mean square errors of 3. The accuracy of the prediction was assessed by comparing with the experimental coverages. Less than half of experimental coverages were outside prediction ± 1 × root mean square error and none outside prediction ± 2 × root mean square error. Accuracy was lower when retention factors were low, or when gradient conditions affected parameters not included in the model, e.g. second dimension gradient time affects the second dimension equilibration time. The concept shows promise as a tool for gradient optimization in online comprehensive two-dimensional liquid chromatography, as it mitigates the tedious registration and modeling of all sample constituents, a circumstance that is particularly appealing when dealing with complex samples.

Observations by Evaluating the Uncertainty of Stress Distribution in Truss Structures Based on Probabilistic and Possibilistic Methods

Load-bearing mechanical structures like trusses face uncertainty in loading along with uncertainty in stress and strength, which are due to uncertainty in their development, production, and usage. According to the working hypothesis of the German Collaborative Research Center SFB 805, uncertainty occurs in processes that are not or only partial deterministic and can only be controlled in processes. The authors classify, compare, and evaluate four different direct methods to describe and evaluate the uncertainty of normal stress distribution in simple truss structures with one column, two columns, and three columns. The four methods are the direct Monte Carlo (DMC) simulation, the direct quasi-Monte Carlo (DQMC) simulation, the direct interval, and the direct fuzzy analysis with α-cuts, which are common methods for data uncertainty analysis. The DMC simulation and the DQMC simulation are categorized as probabilistic methods to evaluate the stochastic uncertainty. On the contrary, the direct interval and the direct fuzzy analysis with α-cuts are categorized as possibilistic methods to evaluate the nonstochastic uncertainty. Three different truss structures with increasing model complexity, a single-column, a two-column, and a three-column systems are chosen as reference systems in this study. Each truss structure is excited with a vertical external point load. The input parameters of the truss structures are the internal system properties such as geometry and material parameters, and the external properties such as magnitude and direction of load. The probabilistic and the possibilistic methods are applied to each truss structure to describe and evaluate its uncertainty in the developing phase. The DMC simulation and DQMC simulation are carried out with full or “direct” sample sets of model parameters such as geometry parameters and state parameters such as forces, and a sensitivity analysis is conducted to identify the influence of every model and state input parameter on the normal stress, which is the output variable of the truss structures. In parallel, the direct interval and the direct fuzzy analysis with α-cuts are carried out without altering and, therefore, they are direct approaches as well. The four direct methods are then compared based on the simulation results. The criteria of the comparison are the uncertainty in the deviation of the normal stress in one column of each truss structure due to varied model and state input parameters, the computational costs, as well as the implementation complexity of the applied methods.

The influence of DEM resolution on seismic landslide hazard assessment based upon the Newmark displacement method: a case study in the loess area of Tianshui, China

The estimation uncertainties and spatial variability of input parameters are gaining more attention and have frequently been taken into account in seismic slope deformation predictions, but the influence of DEM resolution has rarely been investigated. In this paper, the impact of DEM resolution on the seismic landslide hazard is investigated through the comparison between various seismic landslide hazard assessments evaluated from multi-resolution (10, 30, and 90 m) DEMs in the loess area of Tianshui, China, using the Newmark displacement method. The results show that the 90 m relatively coarse resolution DEM underestimates the estimated Newmark displacement. The 30 and 10 m resolution DEMs basically give the similar predictions while the 10 m higher-resolution gives higher resolution at the local convex topography such as scarps and ridge crests. It suggests that the ability of Newmark method to predict landslide occurrence accurately not only depends on the assigned shear strengths, but also is controlled predominantly by the distribution of slope angles. Making accurate maps of seismic landslide hazards will require high-resolution DEM, apart from the associated ground-motion parameters and assigned shear strengths.

Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs

The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237–48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pKa≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among cell types. Despite this extensive lysosomal sequestration in the individual cells types, the maximal change in the overall predicted tissue Kpu was <3-fold for lysosome-rich tissues investigated here. Accounting for the variability in cellular physiological model input parameters, in particular lysosomal pH and fraction of the cellular volume occupied by the lysosomes, only partially explained discrepancies between observed and predicted Kpu data in the lung. Improved understanding of the system properties, e.g., cell/organelle composition is required to support further development of mechanistic equations for the prediction of drug tissue distribution. Application of this revised mechanistic model is recommended for prediction of Kpu in lysosome-rich tissue to facilitate the advancement of physiologically-based prediction of volume of distribution and drug exposure in the tissues.

Experimental investigation and optimization of abrasive water jet cutting parameters for the improvement of cut quality in carbon fiber reinforced plastic laminates

The utilization of composite materials has nowadays increased in aerospace applications due to their less weight and superior mechanical properties. Nevertheless, machining of composite materials without damage is quite challenging through conventional system due to their inherent heterogeneity, anisotropy, and thermal sensitivity. To overcome this problem, abrasive water jet machining process can be employed. It is a non-conventional machining processes with high accuracy, high flexibility and with no heat generation. However, there are more challenges in cutting fiber reinforced plastics with this technique. Hence, this work deals with the assessment of the optimum process parameters in abrasive water jet cutting of carbon fiber reinforced plastic composite. Cutting experiments were conducted by varying input parameters such as the traverse rate, standoff distance on three laminates of different thickness. Analysis of variance through response surface methodology technique was used to study the effect of each input parameters on the output responses such as kerf taper and surface roughness. Optimum parameters that provide the best machining quality were found using numerical and graphical optimization techniques. The results showed that increasing the traverse rate results in increased surface roughness and taper angle of the cut kerf. Hence lower traverse rate is preferable when machining quality is of high importance.

ANFIS modeling of surface roughness in abrasive waterjet machining of carbon fiber reinforced plastics

This study discusses the development of an Adaptive neuro–fuzzy inference system (ANFIS) model for determining the surface roughness (Ra) during machining of multidirectional woven fabric Carbon fiber reinforced plastics (CFRP) using Abrasive waterjet machining (AWM). Three variable input parameters—Jet pressure (JP), Traverse speed (TS), and Standoff distance (SOD)—were selected to assess the roughness of the CFRP along the traverse direction of the cut surface. The experimental results show that a lower JP deteriorated the finish by creating surface rupture. On the other hand, a poor surface finish was observed in the case of machining at higher TS and SOD. Further, the developed ANFIS model was used to validate the results and it was found that the predicted values were in good agreement with a 95 % confidence level. It was also evident that the ANFIS technique is helpful for better prediction of the experimental data with minimum error. Finally, the cut surface morphology was analyzed using a 3D non-contact surface profilometer and the results are discussed.

Optimisation of machining parameters during ball end milling of hardened steel with various surface inclinations

This paper proposes a method for the reduction of forces and the improvement of efficiency during finish ball end milling of hardened 55NiCrMoV6 steel. The primary objective of this work concentrates on the optimal selection of milling parameters (cutting speed – vc, surface inclination angle α), which enables the simultaneous minimisation of cutting force values and increased process efficiency. The research includes the measurement of cutting forces (Fx, Fy, Fz) during milling tests with variable input parameters and calculation of process efficiency accounting for cutting parameters and surface inclination. The paper then focuses on the multi-criteria optimisation of the ball end milling process in terms of cutting forces and efficiency. This procedure is carried out with the application of the response surface method, based on the minimisation of a total utility function. The work shows that surface inclination angle has a significant influence on the cutting force values. Minimal cutting forces and relative high efficiency can be achieved with cutting speed vc=375m/min and surface inclination angle α=15°.

Tribological behaviour predictions of r-GO reinforced Mg composite using ANN coupled Taguchi approach

This paper deals with the fabrication of reduced graphene oxide (r-GO) reinforced Magnesium Metal Matrix Composite (MMC) through a novel solvent based powder metallurgy route. Investigations over basic and functional properties of developed MMC reveals that addition of r-GO improvises the microhardness upto 64 HV but however decrement in specific wear rate is also notified. Visualization of worn out surfaces through SEM images clearly explains for the occurrence of plastic deformation and the presence of wear debris because of ploughing out action. Taguchi coupled Artificial Neural Network (ANN) technique is adopted to arrive at optimal values of the input parameters such as load, reinforcement weight percentage, sliding distance and sliding velocity and thereby achieve minimal target output value viz. specific wear rate. Influence of any of the input parameter over specific wear rate studied through ANOVA reveals that load acting on pin has a major influence with 38.85% followed by r-GO wt. % of 25.82%. ANN model developed to predict specific wear rate value based on the variation of input parameter facilitates better predictability with R-value of 98.4% when compared with the outcomes of regression model.

Selection and scaling of spectrum-compatible ground motion records using hybrid coded genetic algorithms

This paper presents a new searching framework for optimal scaling of earthquake ground motion records as inputs for dynamics analysis. Two hybrid-coded Genetic Algorithms (GA), named real-permutation and binary-permutation GA, are eff ectively used to solve an applicable optimization problem in earthquake engineering field. Methodologies are outlined to choose a set of ground motions, with a good level of t to the design spectrum, and the corresponding scales simultaneously during a hybrid-coded process. The eff ects of di erent parameters used in design of algorithms are investigated through sensitivity analysis to suggest a set of proper input values. Analysis showed that the sensitivity of the binary-permutation GA results to input parameters variations is less than that of real-permutation GA. The paper also concludes that binary-permutation GA is slightly more reliable than real-permutation GA; accordingly, it is recommended as asuitable algorithm to select and scale spectrum-compatible ground motion records.

Rapid implementation of the repair-misrepair-fixation (RMF) model facilitating online adaption of radiosensitivity parameters in ion therapy

Introduction: Treatment planning for ion therapy must account for physical properties of the beam as well as differences in the relative biological effectiveness (RBE) of ions compared to photons. In this work, we present a fast RBE calculation approach, based on the decoupling of physical properties and the ratio commonly used to describe the radiosensitivity of irradiated cells or organs.Material and methods: In the framework of the mechanistic repair-misrepair-fixation (RMF) model, the biological modeling can be decoupled from the physical dose. This was implemented into a research treatment planning system for carbon ion therapy.Results: The presented implementation of the RMF model is very fast, allowing online changes of . For example, a change of including a complete biological modeling and a recalculation of RBE for voxel takes 4 ms on a 4 CPU, 3.2 GHz workstation.Discussion and conclusion: The derived decoupling within the RMF model allows fast changes in , facilitating online adaption by the user. This provides new options for radiation oncologists, facilitating online variations of the radiobiological input parameters during the treatment plan evaluation process as well as uncertainty and sensitivity analyses.

Soil Moisture Stochastic Model in Pinus tabuliformis Forestland on the Loess Plateau, China

As an important restrictive factor of ecological construction on the Loess Plateau, the study of soil moisture dynamics is essential, especially under the impact of climate change on hydrological processes. In this study, the applicability of the Laio soil moisture stochastic model on a typical plantation Pinus tabuliformis forestland on the Loess Plateau was studied. On the basis of data concerning soil properties, climate, and plants of the typical forestland during the period 2005–2015 in the Chinese National Ecosystem Research Network (Ji County Station) in Ji County, Shanxi, model results were acquired and compared with observed soil moisture from 2005 to 2015 in the study area. The genetic algorithm method was used to optimize model parameters in the calibration process. In the calibration and validation periods, the relative error between numerical characteristics of simulated and observed soil moisture values was mostly within 10%, and model evaluation index J was close to 1, indicating that the Laio model had good applicability in the study area. When calibrating the model, it was recommended to use soil moisture data with a sampling interval of no more than 10 days so as to reduce the loss of soil moisture fluctuation information. In the study area, the Laio model was strongly sensitive to variations of input parameters, including maximum evapotranspiration rate Emax, average rainfall depth α, and average rainfall frequency λ, which should be paid more attention for stable and reliable simulation results. This study offers a method to obtain soil moisture data at ungauged sites. Results from this study provide guidance for Laio model application on the Loess Plateau.

Physically Based Susceptibility Assessment of Rainfall-Induced Shallow Landslides Using a Fuzzy Point Estimate Method

The physically based model has been widely used in rainfall-induced shallow landslide susceptibility analysis because of its capacity to reproduce the physical processes governing landslide occurrence and a higher predictive capability. However, one of the difficulties in applying the physically based model is that uncertainties arising from spatial variability, measurement errors, and incomplete information apply to the input parameters and analysis procedure. Uncertainties have been recognized as an important cause of mismatch between predicted and observed distributions of landslide occurrence. Therefore, probabilistic analysis has been used to quantify the uncertainties. However, some uncertainties, because of incomplete information, cannot be managed satisfactorily using a probabilistic approach. Fuzzy set theory is applicable in this case. In this study, in order to handle uncertainty propagation through a physical model, fuzzy set theory, coupled with the vertex method and the point estimate method, was adopted for regional landslide susceptibility assessment. The proposed approach was used to evaluate susceptibility to rainfall-induced shallow landslides for a regional study area, and the analysis results were compared with landslide inventory to evaluate the performance of the proposed approach. The AUC values arising from the landslide susceptibility analyses using the proposed approach and probabilistic analysis were 0.734 and 0.736, respectively. However, when the COV values of the input parameters were reduced, the AUC values of the proposed approach and the probabilistic analysis were reduced to 0.722 and 0.688, respectively. It means that the performance of the fuzzy approach is similar to that of probabilistic analysis but is more robust against variation of input parameters. Thus, at catchment scale, the fuzzy approach can respond appropriately to the uncertainties inherent in physically based landslide susceptibility analysis, and is especially advantageous when the amount of quality data is very limited.