A new hybrid reliability‐based design optimization method under random and interval uncertainties

Zhang, Jinhao; Gao, Liang; Xiao, Mi

doi:10.1002/nme.6440

Cited by 32 publications

(9 citation statements)

References 71 publications

(93 reference statements)

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“…Unlike the black box models (e.g., ANNs), MARS models are deterministic, which means that in the final regression form the input variables are identified and the interactions between them are specified. Therefore, the MARS models are much easier to be interpreted than the other techniques [48][49][50]. Considering X as the only independent variable and Y as the dependent variable (target value), it can be seen in Figure 3 that the space of X variable is divided into three sub-regions with three different equations.…”

Section: Multivariate Regression Spline (Mars)mentioning

confidence: 99%

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Zounemat‐Kermani

Keshtegar

Kisi

et al. 2021

Water

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This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity effects are increased by using a nonlinear map and transferring input data from a polynomial to an exponential basic function. The accuracy, precision, and over/under prediction tendencies of the response surface method, kriging, improved kriging, multilayer perceptron neural network using the Levenberg–Marquardt (MLP-LM) as well as a conjugate gradient (MLP-CG), radial basis function neural network (RBFNN), multivariate adaptive regression spline (MARS), M5Tree and support vector regression (SVR) were compared. Overall, all the applied models were highly capable of predicting monthly EP in both stations with a mean absolute error (MAE) < 0.77 mm and a Willmott index (d) > 0.95. Considering periodicity as an input parameter, the MLP-LM provided better results than the other methods among the soft computing models (MAE = 0.492 mm and d = 0.981). However, the improved kriging method surpassed all the other models based on the statistical measures (MAE = 0.471 mm and d = 0.983). Finally, the outcomes of the Mann–Whitney test indicated that the applied soft computational models do not have significant superiority over the statistical ones (p-value > 0.65 at α = 0.01 and α = 0.05).

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Section: Multivariate Regression Spline (Mars)mentioning

confidence: 99%

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Zounemat‐Kermani

Keshtegar

Kisi

et al. 2021

Water

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“…The input data set is controlled by applying the radial input data in feature space. By transferring input data from original to radial map, the radial basis function (RBF) is applied in RM5Tree as follows (Chen et al 1991;Xiao et al 2020;Zhang et al 2020a):…”

Section: Radial M5 Model Treementioning

confidence: 99%

Modelling Reference Evapotranspiration using a Novel Regression-based Method: Radial basis M5 Model Tree

Kişi

Keshtegar

Zounemat‐Kermani

et al. 2021

Preprint

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In the current study, an ability of a novel regression-based method is evaluated in modelling daily reference evapotranspiration (ET0), which is an important issue in water resources management plans and helps farmers in irrigation planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The radial-based kernel function was used to control the input variables in modelling process of M5 model tree. The new model results were compared with traditional M5 model tree (M5Tree), response surface method (RSM) and two neural networks (multi-layer perceptron neural networks, MLPNN & radial basis function neural network, RBFNN) with respect to several statistical indices. Daily climatic data (relative humidity, RH, solar radiation, SR, wind speed, air temperature, T) recorded at three stations in Turkey, Mediterranean Region, were used. The effect of each weather data on ET0 was also investigated by utilizing three different input scenarios with various combinations of input variables. On the whole, the RM5Tree provided the best results (NSE > 0.997) followed by the MLPNN (NSE > 0.990), and M5Tree (NSE > 0.945) in modelling daily ET0. The SR was observed as the most effective input parameter on ET0 which was followed by the T and RH. However, the findings of the third modelling scenario revealed that taking into account of all variables would considerably increase models’ accuracies for the three stations.

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“…They obtained a more reliable optimization solution than the deterministic optimization based on the NSGA algorithm. Zhang et al [19] presented a new method for hybrid reliability-based design optimization under random and interval uncertainties, and used a ten-bar truss example and a design of piezoelectric energy harvester to verify the accuracy and effectiveness of the method. Combined with the response surface method and a multi-island genetic algorithm, Li et al [20] applied an efficient reliability-based design optimization process to optimize the reliability of lattice boom.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Reliability-Based Optimization of Control Arm Using MCS and NSGA-II Coupled with Entropy Weighted GRA

et al. 2021

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Lightweight design is one of the important ways to reduce automobile fuel consumption and exhaust emissions. At the same time, the fatigue life of automobile parts also greatly affects vehicle safety. This paper proposes a multi-objective reliability optimization method by integrating Monte Carlo simulation (MCS) with the NSGA-II algorithm coupled with entropy weighted grey relational analysis (GRA) for lightweight design of the lower control arm of automobile Macpherson suspension. The dynamic load histories of the control arm were extracted through dynamic simulations of a rigid-flexible coupling vehicle model on virtual proving ground. Then, the nominal stress method was used to predict its fatigue life. Six design variables were defined to describe the geometric dimension of the control arm, while mass and fatigue life were taken as optimization objectives. The multi-objective optimization design of the control arm was carried out based on the Kriging surrogate model and NSGA-II algorithm. Aiming at the uncertainty of design variables, the reliability constraint was added to the multi-objective optimization to improve the reliability of the fatigue life of the control arm. The optimal design of the control arm was determined from Pareto solutions by entropy weighted grey relational analysis (GRA). The optimization results show that the mass of the control arm was reduced by 4.1% and the fatigue life was increased by 215.8% while its reliability increased by 7.8%. The proposed multi-objective reliability optimization method proved to be feasible and effective for lightweight design of a suspension control arm.

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A new hybrid reliability‐based design optimization method under random and interval uncertainties

Cited by 32 publications

References 71 publications

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Modelling Reference Evapotranspiration using a Novel Regression-based Method: Radial basis M5 Model Tree

Multi-Objective Reliability-Based Optimization of Control Arm Using MCS and NSGA-II Coupled with Entropy Weighted GRA

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