The Experts below are selected from a list of 75354 Experts worldwide ranked by ideXlab platform
Chris P. Pantelides - One of the best experts on this subject based on the ideXlab platform.
-
comparison of fuzzy set and Convex Model theories in structural design
Mechanical Systems and Signal Processing, 2001Co-Authors: Chris P. Pantelides, Sara GanzerliAbstract:A methodology for the treatment of uncertainty in the loads applied to a structural system using Convex Models is presented and is compared to the fuzzy set finite-element method. The analytical results for a beam, a truss and a frame structure indicate that the two methods based on Convex Model or fuzzy set theory are in good agreement for equivalent levels of uncertainty applied to linear structures. Convex Model or fuzzy set theories have shown that the worst-case scenario response of all possible load combinations cannot be captured simply by load factorisation, as is the current design practice in building codes. Design problems including uncertainty with a large number of degrees of freedom, that are not computationally feasible using conventional methods described in building codes, can be solved easily using Convex Model or fuzzy set theory. These results can be used directly and efficiently in the analyses required for the optimal design of structural systems, thus enabling optimisation of complex structural systems with uncertainty.
-
Optimum structural design via Convex Model superposition
Computers & Structures, 2000Co-Authors: Sara Ganzerli, Chris P. PantelidesAbstract:Abstract A new approach is presented for implementing a multidimensional Convex Model for the optimal design of structures subjected to bounded but uncertain loads. This is a non-probabilistic method for including uncertainty in the design. In previous studies, a two-stage optimization process was used to predict the effect of uncertainties, Modeled using Convex theory, on the response constraints. Here, a superposition method is used to find the response of structures with load uncertainties. The Convex Model is implemented on the effect of the uncertain loads, i.e., the displacements and stresses. The advantage of the method is that one optimization process is eliminated, and the constraints do not need to be expressed explicitly as a function of the design variables.
-
Load and resistance Convex Models for optimum design
Structural Optimization, 1999Co-Authors: Sara Ganzerli, Chris P. PantelidesAbstract:This paper is concerned with the optimal design of structures that are affected by uncertainties present in the loads applied to the structure, and by uncertainties affecting the internal resistance of the structural members. The magnitude of the applied loads and the modulus of elasticity of the structural members are assumed to vary within deterministic bounds. These uncertainties are idealized using a nonprobabilistic method, the Convex Model. The two types of uncertainties are considered simultaneously by employing the Cartesian product of Convex sets. Two different Convex Models are examined to account for the uncertainties: the ellipsoidal Convex Model, and the uniform bound Convex Model. The optimum designs of a truss using the two Convex Models are compared to a worst case scenario optimum design in order to evaluate their performance. It is shown that it is not possible to identify a single worst case scenario that would be able to account for all possible combinations of uncertainties. However, both the ellipsoidal and the uniform bound Convex Model designs are found to be superior to the worst case scenario design in terms of constraint violations.
-
Design of Trusses under Uncertain Loads Using Convex Models
Journal of Structural Engineering, 1998Co-Authors: Chris P. Pantelides, Sara GanzerliAbstract:The optimal design of trusses subjected to loads considered to be uncertain in both magnitude and direction is investigated. A non-probabilistic ellipsoidal Convex Model is established for considering the uncertainties using three different criteria. The Convex Model is described as a set of constraints on the upper and lower limits of the load magnitudes and directions. The optimal design of the trusses is performed using two different optimization objectives. The first objective function to be minimized is the structural volume; constraints are imposed on the stresses and buckling loads of the members and on the joint displacements. Another objective function to be minimized is a selected displacement; constraints are implemented on the stresses and buckling loads of the members and on the structural volume. The presented method yields optimal designs that violate stress, displacement, and buckling constraints with less frequency than the assumed worst load condition. This method offers an alternative t...
-
Optimal design of structures under uncertain loads using Convex Models
1997Co-Authors: Chris P. Pantelides, Sara GanzerliAbstract:The optimal design of trusses subjected to loads considered to be uncertain in both magnitude and direction is investigated. A non-probabilistic ellipsoidal Convex Model is established for considering the uncertainties. The Convex Model is described as a set of constraints on the upper and lower limits of the load magnitudes and directions. The optimal design of the truss is performed using as objective the minimization of the structural volume. Constraints are imposed on the stresses and buckling loads of the members and the joint displacements. The present method yields an optimal design which is superior in terms of constraint violations when compared to the optimal design obtained from an assumed worst loading condition.
Chao Jiang - One of the best experts on this subject based on the ideXlab platform.
-
Non-probabilistic reliability-based topology optimization with multidimensional parallelepiped Convex Model
Structural and Multidisciplinary Optimization, 2017Co-Authors: Jing Zheng, Zhen Luo, Chao JiangAbstract:In this paper, a new non-probabilistic reliability-based topology optimization (NRBTO) method is proposed to account for interval uncertainties considering parametric correlations. Firstly, a reliability index is defined based on a newly developed multidimensional parallelepiped (MP) Convex Model, and the reliability-based topology optimization problem is formulated to optimize the topology of the structure, to minimize material volume under displacement constraints. Secondly, an efficient decoupling scheme is applied to transform the double-loop NRBTO into a sequential optimization process, using the sequential optimization & reliability assessment (SORA) method associated with the performance measurement approach (PMA). Thirdly, the adjoint variable method is used to obtain the sensitivity information for both uncertain and design variables, and a gradient-based algorithm is employed to solve the optimization problem. Finally, typical numerical examples are used to demonstrate the effectiveness of the proposed topology optimization method.
-
Construction of Convex Models for non-probabilistic correlation quantification and uncertainty analysis
2015Co-Authors: Chao JiangAbstract:Uncertainty widely exists in practical engineering problems, which are commonly related to material properties, loads, etc. Non-probabilistic Convex Models need to be provided only the variation interval bounds of parameters rather than their exact probability distributions; thus, such Models can be applied to uncertainty analysis of complex structures when experimental information is inadequate for probability Modelling. Convex Model utilizes a Convex set to quantify the uncertainty domain of the uncertain-but-bounded parameters. It is not a single mathematical Model; it means a series of Models. The ellipsoid Model and the parallelepiped Models are Convex Models which could take correlation between uncertain parameters into consideration. In this work, a unified method for construction of these Convex Models—one ellipsoid Model and five parallelepiped Models, is proposed. The analytic mathematical expression of each Convex Model can be formulated once the correlation matrix of the uncertain-but-bounded parameters is obtained. Two kinds of correlation coefficient between uncertain parameters are utilized to construct a Convex Model, and the comparisons are made. A concept of “unbiasedness” is proposed to evaluate the adaptability of each Convex Model to a certain group of samples. The indexes such as “fitness” and “ratio of uncertainty volume” are presented as a reference for decision making among the various kinds of Convex Models. For some certain group of uncertain-but-bounded parameters and corresponding sample points, the construction procedure of a Convex Model quantifying the uncertainty is discussed in detail by several numerical examples. With the Convex uncertainty domain, such as a multidimensional ellipsoid or parallelepiped obtained, subsequent uncertainty analysis such as reliability analysis, optimization design, etc. can be carried out.
-
a response surface based structural reliability analysis method by using non probability Convex Model
Applied Mathematical Modelling, 2014Co-Authors: Chao Jiang, R G BiAbstract:Abstract Due to its weak dependence on the amount of the uncertainty information, the non-probability Convex Model approach can be used to deal with the problems without sufficient information. In this paper, by integrating the response surface (RS) technique with the Convex Model approach, a new structural reliability analysis method is developed for many complex engineering problems with black-box limit-state functions. Using the newly developed correlation analysis technique for non-probability Convex Model, the multi-dimensional ellipsoid is efficiently constructed to characterize the uncertain parameters. A quadratic polynomial without cross terms is adopted to parameterize the black-box limit-state function, based on which the functional values as well as the first-order gradients can be explicitly calculated. At each iteration, the created RS is combined with the i HL-RF algorithm to obtain an approximate reliability index. A sequential procedure is subsequently formulated to update the RS and hence improve the precision of the reliability analysis. Four numerical examples and one engineering application are investigated to demonstrate the effectiveness of the presented method.
-
a non probabilistic structural reliability analysis method based on a multidimensional parallelepiped Convex Model
Acta Mechanica, 2014Co-Authors: Chao Jiang, Q F Zhang, Y H QianAbstract:Compared with a probability Model, a non-probabilistic Convex Model only requires a small number of experimental samples to discern the uncertainty parameter bounds instead of the exact probability distribution. Therefore, it can be used for uncertainty analysis of many complex structures lacking experimental samples. Based on the multidimensional parallelepiped Convex Model, we propose a new method for non-probabilistic structural reliability analysis in which marginal intervals are used to express scattering levels for the parameters, and relevant angles are used to express the correlations between uncertain variables. Using an affine coordinate transformation, the multidimensional parallelepiped uncertainty domain and the limit-state function are transformed to a standard parameter space, and a non-probabilistic reliability index is used to measure the structural reliability. Finally, the method proposed herein was applied to several numerical examples.
-
non probabilistic Convex Model process a new method of time variant uncertainty analysis and its application to structural dynamic reliability problems
Computer Methods in Applied Mechanics and Engineering, 2014Co-Authors: Chao Jiang, B Y NiAbstract:In this paper, we propose a method for time-variant uncertainty analysis, namely, the “non-probabilistic Convex Model process”, which provides an effective mathematical tool for the analysis of structural dynamic uncertainty when lacking relevant information. In the Convex Model process, we express the variables at any time with intervals and establish the corresponding auto-covariance function and correlation coefficient function to depict the correlation between variables at different times. We also define several important characteristic parameters for the uni- and bi-dimensional Convex Model processes, including the mid-value function, variance function, auto-covariance function, and cross-covariance function; we provide the definition for the stationary Convex Model process and its ergodicity. Then, by combining the Convex Model process with the first-passage failure mechanism, we propose a non-probabilistic analysis Model of structural dynamic reliability and formulate the solving algorithm based on Monte Carlo simulation. Finally, through the analysis of numerical examples, we verify the effectiveness of the Convex Model process and the Model of dynamic reliability analysis proposed in this paper.
Huanlin Zhou - One of the best experts on this subject based on the ideXlab platform.
-
A novel experimental data-driven exponential Convex Model for reliability assessment with uncertain-but-bounded parameters
Applied Mathematical Modelling, 2020Co-Authors: Zeng Meng, Zhuohui Zhang, Huanlin ZhouAbstract:Abstract The Convex Model is commonly applied to quantify the uncertain-but-bounded parameters. However, the typical interval and ellipsoid Models may lead to inaccurate approximation for existing experimental data, which may incur either too risk or conservative for safety assessment. To this end, this study aims to create a novel data-driven exponential Convex Model to achieve accurate approximation for experiment data, in which the dimension reduction minimum volume method plays the key role. Furthermore, a novel relaxed exponential nominal value method (RENVM) is developed to evaluate the corresponding non-probabilistic reliability index robustly and efficiently, and the sensitivities are also derived based on the straight forward perturbation method to guarantee its efficiency. Through numerical and experimental studies, the accuracy and validity of the proposed data-driven exponential Convex Model are validated compared to the interval and ellipsoid Models, and the robustness and efficiency of the proposed RENVM are also demonstrated for solving both linear and nonlinear problems.
-
new target performance approach for a super parametric Convex Model of non probabilistic reliability based design optimization
Computer Methods in Applied Mechanics and Engineering, 2018Co-Authors: Zeng Meng, Huanlin ZhouAbstract:Abstract Non-probabilistic reliability-based design optimization (NRBDO) is an essential tool to deal with an optimization Model with uncertain-but-bounded variables. A major disadvantage of NRBDO is that it conventionally suffers from excessively high computational costs. In the present study, a new target performance approach (TPA) is proposed to eliminate high computational cost incurred by complicated equality constraints in non-probabilistic reliability analysis in which the target performance constraint is applied to substitute for the non-probabilistic reliability index constraint. To further enhance the optimization efficiency, a new NRBDO algorithm is developed based on the proposed TPA in which the target performance iterative method (TPIM) is established to search for the target concern point while a Convex approximate method is used to further improve the convergence. A mathematical example, three numerical examples, and two complex engineering examples, i.e., an axially compressed stiffened shell in a launch vehicle and a tower crane in civil engineering, are utilized to demonstrate the effectiveness of the proposed method in comparison with that of other existing methods. The results indicate that the proposed method is promising in terms of complicated engineering problems in the absence of prior knowledge of uncertain parameters.
-
Super parametric Convex Model and its application for non-probabilistic reliability-based design optimization
Applied Mathematical Modelling, 2018Co-Authors: Zeng Meng, Huanlin ZhouAbstract:Abstract In this study, we attempt to propose a new super parametric Convex Model by giving the mathematical definition, in which an effective minimum volume method is constructed to give a reasonable enveloping of limited experimental samples by selecting a proper super parameter. Two novel reliability calculation algorithms, including nominal value method and advanced nominal value method, are proposed to evaluate the non-probabilistic reliability index. To investigate the influence of non-probabilistic Convex Model type on non-probabilistic reliability-based design optimization, an effective approach based on advanced nominal value method is further developed. Four examples, including two numerical examples and two engineering applications, are tested to demonstrate the superiority of the proposed non-probabilistic reliability analysis and optimization technique.
Zeng Meng - One of the best experts on this subject based on the ideXlab platform.
-
A novel experimental data-driven exponential Convex Model for reliability assessment with uncertain-but-bounded parameters
Applied Mathematical Modelling, 2020Co-Authors: Zeng Meng, Zhuohui Zhang, Huanlin ZhouAbstract:Abstract The Convex Model is commonly applied to quantify the uncertain-but-bounded parameters. However, the typical interval and ellipsoid Models may lead to inaccurate approximation for existing experimental data, which may incur either too risk or conservative for safety assessment. To this end, this study aims to create a novel data-driven exponential Convex Model to achieve accurate approximation for experiment data, in which the dimension reduction minimum volume method plays the key role. Furthermore, a novel relaxed exponential nominal value method (RENVM) is developed to evaluate the corresponding non-probabilistic reliability index robustly and efficiently, and the sensitivities are also derived based on the straight forward perturbation method to guarantee its efficiency. Through numerical and experimental studies, the accuracy and validity of the proposed data-driven exponential Convex Model are validated compared to the interval and ellipsoid Models, and the robustness and efficiency of the proposed RENVM are also demonstrated for solving both linear and nonlinear problems.
-
new target performance approach for a super parametric Convex Model of non probabilistic reliability based design optimization
Computer Methods in Applied Mechanics and Engineering, 2018Co-Authors: Zeng Meng, Huanlin ZhouAbstract:Abstract Non-probabilistic reliability-based design optimization (NRBDO) is an essential tool to deal with an optimization Model with uncertain-but-bounded variables. A major disadvantage of NRBDO is that it conventionally suffers from excessively high computational costs. In the present study, a new target performance approach (TPA) is proposed to eliminate high computational cost incurred by complicated equality constraints in non-probabilistic reliability analysis in which the target performance constraint is applied to substitute for the non-probabilistic reliability index constraint. To further enhance the optimization efficiency, a new NRBDO algorithm is developed based on the proposed TPA in which the target performance iterative method (TPIM) is established to search for the target concern point while a Convex approximate method is used to further improve the convergence. A mathematical example, three numerical examples, and two complex engineering examples, i.e., an axially compressed stiffened shell in a launch vehicle and a tower crane in civil engineering, are utilized to demonstrate the effectiveness of the proposed method in comparison with that of other existing methods. The results indicate that the proposed method is promising in terms of complicated engineering problems in the absence of prior knowledge of uncertain parameters.
-
Super parametric Convex Model and its application for non-probabilistic reliability-based design optimization
Applied Mathematical Modelling, 2018Co-Authors: Zeng Meng, Huanlin ZhouAbstract:Abstract In this study, we attempt to propose a new super parametric Convex Model by giving the mathematical definition, in which an effective minimum volume method is constructed to give a reasonable enveloping of limited experimental samples by selecting a proper super parameter. Two novel reliability calculation algorithms, including nominal value method and advanced nominal value method, are proposed to evaluate the non-probabilistic reliability index. To investigate the influence of non-probabilistic Convex Model type on non-probabilistic reliability-based design optimization, an effective approach based on advanced nominal value method is further developed. Four examples, including two numerical examples and two engineering applications, are tested to demonstrate the superiority of the proposed non-probabilistic reliability analysis and optimization technique.
-
non probabilistic reliability based design optimization of stiffened shells under buckling constraint
Thin-walled Structures, 2015Co-Authors: Zeng Meng, Gang Li, Bo Wang, Kai ZhangAbstract:Stiffened shells are affected by numerous uncertainty factors, such as the variations of manufacturing tolerance, material properties and environment aspects, etc. Due to the expensive experimental cost of stiffened shell, only a limited quantity of statistics about its uncertainty factors are available. In this case, an unjustified assumption of probabilistic Model may result in misleading outcomes of reliability-based design optimization (RBDO), and the non-probabilistic Convex method is a promising alternative. In this study, a hybrid non-probabilistic Convex method based on single-ellipsoid Convex Model is proposed to minimize the weight of stiffened shells with uncertain-but-bounded variations, where the adaptive chaos control (ACC) method is applied to ensure the robustness of search process of single-ellipsoid Convex Model, and the particle swarm optimization (PSO) algorithm together with smeared stiffener Model are utilized to guarantee the global optimum design. A 3 m-diameter benchmark example illustrates the advantage of the proposed method over RBDO and deterministic optimum methods for stiffened shell with uncertain-but-bounded variations.
Sara Ganzerli - One of the best experts on this subject based on the ideXlab platform.
-
comparison of fuzzy set and Convex Model theories in structural design
Mechanical Systems and Signal Processing, 2001Co-Authors: Chris P. Pantelides, Sara GanzerliAbstract:A methodology for the treatment of uncertainty in the loads applied to a structural system using Convex Models is presented and is compared to the fuzzy set finite-element method. The analytical results for a beam, a truss and a frame structure indicate that the two methods based on Convex Model or fuzzy set theory are in good agreement for equivalent levels of uncertainty applied to linear structures. Convex Model or fuzzy set theories have shown that the worst-case scenario response of all possible load combinations cannot be captured simply by load factorisation, as is the current design practice in building codes. Design problems including uncertainty with a large number of degrees of freedom, that are not computationally feasible using conventional methods described in building codes, can be solved easily using Convex Model or fuzzy set theory. These results can be used directly and efficiently in the analyses required for the optimal design of structural systems, thus enabling optimisation of complex structural systems with uncertainty.
-
Optimum structural design via Convex Model superposition
Computers & Structures, 2000Co-Authors: Sara Ganzerli, Chris P. PantelidesAbstract:Abstract A new approach is presented for implementing a multidimensional Convex Model for the optimal design of structures subjected to bounded but uncertain loads. This is a non-probabilistic method for including uncertainty in the design. In previous studies, a two-stage optimization process was used to predict the effect of uncertainties, Modeled using Convex theory, on the response constraints. Here, a superposition method is used to find the response of structures with load uncertainties. The Convex Model is implemented on the effect of the uncertain loads, i.e., the displacements and stresses. The advantage of the method is that one optimization process is eliminated, and the constraints do not need to be expressed explicitly as a function of the design variables.
-
Load and resistance Convex Models for optimum design
Structural Optimization, 1999Co-Authors: Sara Ganzerli, Chris P. PantelidesAbstract:This paper is concerned with the optimal design of structures that are affected by uncertainties present in the loads applied to the structure, and by uncertainties affecting the internal resistance of the structural members. The magnitude of the applied loads and the modulus of elasticity of the structural members are assumed to vary within deterministic bounds. These uncertainties are idealized using a nonprobabilistic method, the Convex Model. The two types of uncertainties are considered simultaneously by employing the Cartesian product of Convex sets. Two different Convex Models are examined to account for the uncertainties: the ellipsoidal Convex Model, and the uniform bound Convex Model. The optimum designs of a truss using the two Convex Models are compared to a worst case scenario optimum design in order to evaluate their performance. It is shown that it is not possible to identify a single worst case scenario that would be able to account for all possible combinations of uncertainties. However, both the ellipsoidal and the uniform bound Convex Model designs are found to be superior to the worst case scenario design in terms of constraint violations.
-
Design of Trusses under Uncertain Loads Using Convex Models
Journal of Structural Engineering, 1998Co-Authors: Chris P. Pantelides, Sara GanzerliAbstract:The optimal design of trusses subjected to loads considered to be uncertain in both magnitude and direction is investigated. A non-probabilistic ellipsoidal Convex Model is established for considering the uncertainties using three different criteria. The Convex Model is described as a set of constraints on the upper and lower limits of the load magnitudes and directions. The optimal design of the trusses is performed using two different optimization objectives. The first objective function to be minimized is the structural volume; constraints are imposed on the stresses and buckling loads of the members and on the joint displacements. Another objective function to be minimized is a selected displacement; constraints are implemented on the stresses and buckling loads of the members and on the structural volume. The presented method yields optimal designs that violate stress, displacement, and buckling constraints with less frequency than the assumed worst load condition. This method offers an alternative t...
-
Optimal design of structures under uncertain loads using Convex Models
1997Co-Authors: Chris P. Pantelides, Sara GanzerliAbstract:The optimal design of trusses subjected to loads considered to be uncertain in both magnitude and direction is investigated. A non-probabilistic ellipsoidal Convex Model is established for considering the uncertainties. The Convex Model is described as a set of constraints on the upper and lower limits of the load magnitudes and directions. The optimal design of the truss is performed using as objective the minimization of the structural volume. Constraints are imposed on the stresses and buckling loads of the members and the joint displacements. The present method yields an optimal design which is superior in terms of constraint violations when compared to the optimal design obtained from an assumed worst loading condition.