The Experts below are selected from a list of 23034 Experts worldwide ranked by ideXlab platform
Behrooz Keshtegar - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy relaxed-finite step size Method to enhance the instability of the fuzzy first-order Reliability Method using conjugate discrete map
Nonlinear Dynamics, 2018Co-Authors: Behrooz Keshtegar, Mansour BagheriAbstract:Fuzzy Reliability analysis can be implemented using two discrete optimization maps in the processes of Reliability and fuzzy analysis. Actually, the efficiency and robustness of the iterative Reliability Methods are two main factors in the fuzzy-based Reliability analysis due to the huge computational burdens and unstable results. In the Structural fuzzy Reliability analysis, the first-order Reliability Method (FORM) using discrete nonlinear map can provide a C membership function. In this paper, a discrete nonlinear conjugate map is proposed using a relaxed-finite step size Method for fuzzy Structural Reliability analysis, namely Fuzzy conjugate relaxed-finite step size Method fuzzy CRS. A discrete conjugate map is stabilized using two adaptive factors to compute the relaxed factor and step size in FORM. The framework of the proposed fuzzy Structural Reliability Method is established using two linked iterative discrete maps as an outer loop, which constructs the membership function of the response using alpha level set optimization based on genetic operator, and the inner loop, implemented for Reliability analysis using proposed conjugate relaxed-finite step size Method. The fuzzy CRS and fuzzy HL-RF Methods are compared to evaluate the membership functions of five Structural problems with highly nonlinear limit state functions. Results demonstrated that the fuzzy CRS Method is computationally more efficient and is strongly more robust than the HL-RF for fuzzy-based Reliability analysis of the nonlinear Structural Reliability problems.
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An efficient-robust Structural Reliability Method by adaptive finite-step length based on Armijo line search
Reliability Engineering & System Safety, 2018Co-Authors: Behrooz Keshtegar, Subrata ChakrabortyAbstract:Abstract The robustness of iterative formula as well as its computational efficiency is the essential characteristic of interest for effective Reliability analysis of structures by first order Reliability Method (FORM). A robust and efficient iterative algorithm termed as finite-based Armijo search direction (FAL) Method is proposed in the present study for FORM-based Structural Reliability analysis. A finite-step size is proposed using the Armijo rule and sufficient descent condition to achieve the stabilization of the FORM algorithm. The FAL is adaptively adjusted based on the information obtained from the iterative algorithm at each iteration and Armijo rule. The robustness and efficiency of the proposed FAL Method is elucidated using several problems. The results obtained by the proposed Method are compared with various existing Reliability Methods based on steepest descent search direction. The results of the numerical study indicate that the FAL approach is more robust and efficient than the other existing FORM schemes and improves the robustness of FORM formula. Thus, the FAL can be successfully implemented as a robust FORM-based iterative Reliability analysis procedure.
Mansour Bagheri - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy relaxed-finite step size Method to enhance the instability of the fuzzy first-order Reliability Method using conjugate discrete map
Nonlinear Dynamics, 2018Co-Authors: Behrooz Keshtegar, Mansour BagheriAbstract:Fuzzy Reliability analysis can be implemented using two discrete optimization maps in the processes of Reliability and fuzzy analysis. Actually, the efficiency and robustness of the iterative Reliability Methods are two main factors in the fuzzy-based Reliability analysis due to the huge computational burdens and unstable results. In the Structural fuzzy Reliability analysis, the first-order Reliability Method (FORM) using discrete nonlinear map can provide a C membership function. In this paper, a discrete nonlinear conjugate map is proposed using a relaxed-finite step size Method for fuzzy Structural Reliability analysis, namely Fuzzy conjugate relaxed-finite step size Method fuzzy CRS. A discrete conjugate map is stabilized using two adaptive factors to compute the relaxed factor and step size in FORM. The framework of the proposed fuzzy Structural Reliability Method is established using two linked iterative discrete maps as an outer loop, which constructs the membership function of the response using alpha level set optimization based on genetic operator, and the inner loop, implemented for Reliability analysis using proposed conjugate relaxed-finite step size Method. The fuzzy CRS and fuzzy HL-RF Methods are compared to evaluate the membership functions of five Structural problems with highly nonlinear limit state functions. Results demonstrated that the fuzzy CRS Method is computationally more efficient and is strongly more robust than the HL-RF for fuzzy-based Reliability analysis of the nonlinear Structural Reliability problems.
Subrata Chakraborty - One of the best experts on this subject based on the ideXlab platform.
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An efficient-robust Structural Reliability Method by adaptive finite-step length based on Armijo line search
Reliability Engineering & System Safety, 2018Co-Authors: Behrooz Keshtegar, Subrata ChakrabortyAbstract:Abstract The robustness of iterative formula as well as its computational efficiency is the essential characteristic of interest for effective Reliability analysis of structures by first order Reliability Method (FORM). A robust and efficient iterative algorithm termed as finite-based Armijo search direction (FAL) Method is proposed in the present study for FORM-based Structural Reliability analysis. A finite-step size is proposed using the Armijo rule and sufficient descent condition to achieve the stabilization of the FORM algorithm. The FAL is adaptively adjusted based on the information obtained from the iterative algorithm at each iteration and Armijo rule. The robustness and efficiency of the proposed FAL Method is elucidated using several problems. The results obtained by the proposed Method are compared with various existing Reliability Methods based on steepest descent search direction. The results of the numerical study indicate that the FAL approach is more robust and efficient than the other existing FORM schemes and improves the robustness of FORM formula. Thus, the FAL can be successfully implemented as a robust FORM-based iterative Reliability analysis procedure.
Huifeng Tan - One of the best experts on this subject based on the ideXlab platform.
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A robust and efficient Structural Reliability Method combining radial-based importance sampling and Kriging
Science China Technological Sciences, 2017Co-Authors: Bo Xiong, Huifeng TanAbstract:Simulation based Structural Reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where Structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However, the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient Method in the same direction. The present Method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present Method are validated by five representative examples, where the present Method is compared mainly with two fundamental Reliability Methods based on active learning Kriging.
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New Structural Reliability Method with focus on important region and based on adaptive support vector machines
Advances in Mechanical Engineering, 2017Co-Authors: Bo Xiong, Huifeng TanAbstract:Support vector machine has been shown to be an effective classification tool for Reliability analysis. Its training set governs the computational cost of the whole Reliability analysis. To reduce t...
Bo Xiong - One of the best experts on this subject based on the ideXlab platform.
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A robust and efficient Structural Reliability Method combining radial-based importance sampling and Kriging
Science China Technological Sciences, 2017Co-Authors: Bo Xiong, Huifeng TanAbstract:Simulation based Structural Reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where Structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However, the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient Method in the same direction. The present Method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present Method are validated by five representative examples, where the present Method is compared mainly with two fundamental Reliability Methods based on active learning Kriging.
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New Structural Reliability Method with focus on important region and based on adaptive support vector machines
Advances in Mechanical Engineering, 2017Co-Authors: Bo Xiong, Huifeng TanAbstract:Support vector machine has been shown to be an effective classification tool for Reliability analysis. Its training set governs the computational cost of the whole Reliability analysis. To reduce t...
