The Experts below are selected from a list of 11706 Experts worldwide ranked by ideXlab platform
Hong Hao - One of the best experts on this subject based on the ideXlab platform.
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Non-probabilistic method to consider uncertainties in Structural Damage Identification based on Hybrid Jaya and Tree Seeds Algorithm
Engineering Structures, 2020Co-Authors: Zhenghao Ding, Hong HaoAbstract:Abstract This paper proposes a novel non-probabilistic Structural Damage Identification approach by developing a hybrid swarm intelligence technique based on Jaya and Tree Seeds Algorithm (TSA), taking into account the high-level uncertainties in the measurements and finite element modelling. The Damage in structure is simulated as reduction of elemental stiffness, and Structural Damage Identification is formulated as an optimization problem. To overcome the challenge for Structural Damage Identification with a limited number of measurement data, an objective function based on the modal data and sparse regularization technique is defined. To make the optimization algorithm more powerful and robust, a hybridization of the K-means clustering based Jaya and TSA is proposed. Jaya algorithm is taken as the core in the hybridization. The clustering strategy is employed to replace solutions with low-quality objective values in the Jaya algorithm. Then the search strategy of the TSA is introduced into the best-so-far solution of each cycle. The proposed hybridization algorithm is termed as “ C-Jaya-TSA”. To enhance the capacity of the proposed algorithm to consider uncertainties, a non-probabilistic method is also integrated to calculate the interval bound (lower and upper bounds) of the elemental stiffness changes by using the interval analysis method. To better quantify the Structural Damage extents, Damage Measure Index (DMI) values are introduced for representing Structural Damage states. The DMI value can be viewed as a combination of deterministic stiffness reduction and the Possibility of Damage Existence (PoDE). Numerical benchmark functions, numerical studies and experimental investigations are conducted to verify the accuracy and performance of the proposed method. The Identification results show that the developed C-Jaya-TSA integrated with the non-probabilistic interval analysis method is a promising tool to accurately identify the Structural Damage, even high-level uncertainties exist.
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Structural Damage Identification with uncertain modelling error and measurement noise by clustering based tree seeds algorithm
Engineering Structures, 2019Co-Authors: Zhenghao Ding, Hong HaoAbstract:Abstract This paper proposes a novel Structural Damage Identification approach by using the clustering based Tree Seeds Algorithm, termed as C-TSA, taking into account of both the finite element modeling errors and measurement noise. In order to make the standard TSA more powerful and robust, K-means cluster technique is introduced into the standard TSA before starting the seeds search, which is beneficial to enhance the algorithm’s global optimization performance. The objective function based on the modal data is formulated for Structural Damage Identification. Numerical studies on benchmark functions and a 61-bar truss structure are conducted to investigate the accuracy and robustness of the proposed approach. The finite element modelling errors and noises in the measurement data are considered. Experimental verifications on a laboratory steel frame structure model is conducted to further validate the accuracy of the proposed approach. The results from the numerical and experimental studies are compared with those obtained from several latest evolutionary algorithms. The Identification results demonstrate that the proposed approach is more competitive and robust for Structural Damage Identification even considering the modelling errors and measurement noises.
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development and application of a deep learning based sparse autoencoder framework for Structural Damage Identification
Structural Health Monitoring-an International Journal, 2019Co-Authors: Chathurdara Sri Nadith Pathirage, Hong Hao, Wanquan Liu, Ruhua WangAbstract:This article proposes a deep sparse autoencoder framework for Structural Damage Identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems ...
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Structural Damage Identification using improved Jaya algorithm based on sparse regularization and Bayesian inference
Mechanical Systems and Signal Processing, 2019Co-Authors: Zhenghao Ding, Hong HaoAbstract:Abstract Structural Damage Identification can be considered as an optimization problem, by defining an appropriate objective function relevant to Structural parameters to be identified with optimization techniques. This paper proposes a new heuristic algorithm, named improved Jaya (I-Jaya) algorithm, for Structural Damage Identification with the modified objective function based on sparse regularization and Bayesian inference. To improve the global optimization capacity and robustness of the original Jaya algorithm, a clustering strategy is employed to replace solutions with low-quality objective values and a new updated equation is used for the best-so-far solution. The objective function that is sensitive and robust for effective and reliable Damage Identification is developed through sparse regularization and Bayesian inference and used for optimization analysis with the proposed I-Jaya algorithm. Benchmark tests are conducted to verify the improvement in the developed algorithm. Numerical studies on a truss structure and experimental validations on an experimental reinforced concrete bridge model are performed to verify the developed approach. A limited quantity of modal data, which is distinctively less than the number of unknown system parameters, are used for Structural Damage Identification. Significant measurement noise effect and modelling errors are considered. Damage Identification results demonstrate that the proposed method based on the I-Jaya algorithm and the modified objective function based on sparse regularization and Bayesian inference can provide accurate and reliable Damage Identification, indicating the proposed method is a promising approach for Structural Damage detection using data with significant uncertainties and limited measurement information.
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Development and application of a deep learning–based sparse autoencoder framework for Structural Damage Identification:
Structural Health Monitoring, 2018Co-Authors: Chathurdara Sri Nadith Pathirage, Hong Hao, Wanquan Liu, Ruhua WangAbstract:This article proposes a deep sparse autoencoder framework for Structural Damage Identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems ...
Ruhua Wang - One of the best experts on this subject based on the ideXlab platform.
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development and application of a deep learning based sparse autoencoder framework for Structural Damage Identification
Structural Health Monitoring-an International Journal, 2019Co-Authors: Chathurdara Sri Nadith Pathirage, Hong Hao, Wanquan Liu, Ruhua WangAbstract:This article proposes a deep sparse autoencoder framework for Structural Damage Identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems ...
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Development and application of a deep learning–based sparse autoencoder framework for Structural Damage Identification:
Structural Health Monitoring, 2018Co-Authors: Chathurdara Sri Nadith Pathirage, Hong Hao, Wanquan Liu, Ruhua WangAbstract:This article proposes a deep sparse autoencoder framework for Structural Damage Identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems ...
Q.w. Yang - One of the best experts on this subject based on the ideXlab platform.
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Structural Damage Identification based on best achievable flexibility change
Applied Mathematical Modelling, 2011Co-Authors: Q.w. Yang, B.x. SunAbstract:Abstract A new method based on best achievable flexibility change is presented in this paper to localize and quantify Damage in structures. Central to the Damage localization approach is the computation of the Euclidean distances between the measured flexibility change and the best achievable flexibility changes. The location of Damage can be identified by searching for a value that is considerably smaller than others in these distances. With location determined, a simple extent algorithm is then developed. Three examples are used to demonstrate the efficiency of the method. Results indicate that the proposed procedure may be useful for Structural Damage Identification.
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A Flexibility-Based Method for Structural Damage Identification Using Ambient Modal Data
International Journal of Space Structures, 2009Co-Authors: Q.w. YangAbstract:Structural Damage Identification using ambient vibration modes has become a very important research area in recent years. The main issue surrounding the use of ambient vibration modes is the mass normalization of the measured mode shapes. This paper presents a promising approach that extends the flexibility sensitivity technique to tackle the ambient vibration case. By introducing the mass normalization factors, manipulating the flexibility sensitivity equation, the unknown Damage parameters and mass normalization factors can be computed simultaneously by the least-square technique. The effectiveness of the proposed method is illustrated using simulated data with measurement noise on three examples. It has been shown that the proposed procedure is simple to implement and may be useful for Structural Damage Identification under ambient vibration case.
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Structural Damage Identification based on residual force vector
Journal of Sound and Vibration, 2007Co-Authors: Q.w. Yang, J.k. LiuAbstract:Structural Damage Identification methods based on the residual force vector are studied in this paper. Using the residual force vector, the node residual force vector is defined to locate the suspected Damaged elements preliminarily. Then three Damage quantification techniques are studied to identify Damages more precisely. The first is the algebraic solution of the residual force equation, the second is the MREU technique and the third is the natural frequency sensitivity method. A mode shape expansion technique based on the best achievable eigenvector concept is presented to solve the incomplete measurement problem. These Damage detection methods are demonstrated on a numerical example and the measurement noises are discussed.
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A new Structural Damage Identification method
Journal of Sound and Vibration, 2006Co-Authors: J.k. Liu, Q.w. YangAbstract:A new method is presented to provide an insight in to the characterizations of Structural Damages. The present algorithm makes use of an original finite element model and a subset of measured eigenvalues and eigenvectors. The proposed method detects Damages in a decoupled fashion. First, a theory is developed to determine the number of Damaged elements. With the Damage number determined, the localization and quantification algorithms are then developed. A plane truss structure is analyzed as a numerical example to verify the present method. Results show that the method is accurate and robust in Structural Damage Identification when the number of measured modes is more than the number of Damaged elements with or without noise.
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A coupled method for Structural Damage Identification
Journal of Sound and Vibration, 2006Co-Authors: Q.w. Yang, J.k. LiuAbstract:This paper presents a coupled method for Structural Damage Identification. Firstly, the Damage localization criterion (DLC) is defined to determine the Damaged degree-of-freedom (dof). Then the Damaged elements can be ascertained according to the relation between the element number and the dof number. The natural frequency sensitivity method is employed to obtain Damage extents with the Damaged elements determined. The presented method is demonstrated on a simply supported beam. Results show that the method is simple and effective for Structural Damage detection.
Chathurdara Sri Nadith Pathirage - One of the best experts on this subject based on the ideXlab platform.
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development and application of a deep learning based sparse autoencoder framework for Structural Damage Identification
Structural Health Monitoring-an International Journal, 2019Co-Authors: Chathurdara Sri Nadith Pathirage, Hong Hao, Wanquan Liu, Ruhua WangAbstract:This article proposes a deep sparse autoencoder framework for Structural Damage Identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems ...
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Development and application of a deep learning–based sparse autoencoder framework for Structural Damage Identification:
Structural Health Monitoring, 2018Co-Authors: Chathurdara Sri Nadith Pathirage, Hong Hao, Wanquan Liu, Ruhua WangAbstract:This article proposes a deep sparse autoencoder framework for Structural Damage Identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems ...
Zhenghao Ding - One of the best experts on this subject based on the ideXlab platform.
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Non-probabilistic method to consider uncertainties in Structural Damage Identification based on Hybrid Jaya and Tree Seeds Algorithm
Engineering Structures, 2020Co-Authors: Zhenghao Ding, Hong HaoAbstract:Abstract This paper proposes a novel non-probabilistic Structural Damage Identification approach by developing a hybrid swarm intelligence technique based on Jaya and Tree Seeds Algorithm (TSA), taking into account the high-level uncertainties in the measurements and finite element modelling. The Damage in structure is simulated as reduction of elemental stiffness, and Structural Damage Identification is formulated as an optimization problem. To overcome the challenge for Structural Damage Identification with a limited number of measurement data, an objective function based on the modal data and sparse regularization technique is defined. To make the optimization algorithm more powerful and robust, a hybridization of the K-means clustering based Jaya and TSA is proposed. Jaya algorithm is taken as the core in the hybridization. The clustering strategy is employed to replace solutions with low-quality objective values in the Jaya algorithm. Then the search strategy of the TSA is introduced into the best-so-far solution of each cycle. The proposed hybridization algorithm is termed as “ C-Jaya-TSA”. To enhance the capacity of the proposed algorithm to consider uncertainties, a non-probabilistic method is also integrated to calculate the interval bound (lower and upper bounds) of the elemental stiffness changes by using the interval analysis method. To better quantify the Structural Damage extents, Damage Measure Index (DMI) values are introduced for representing Structural Damage states. The DMI value can be viewed as a combination of deterministic stiffness reduction and the Possibility of Damage Existence (PoDE). Numerical benchmark functions, numerical studies and experimental investigations are conducted to verify the accuracy and performance of the proposed method. The Identification results show that the developed C-Jaya-TSA integrated with the non-probabilistic interval analysis method is a promising tool to accurately identify the Structural Damage, even high-level uncertainties exist.
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Structural Damage Identification with uncertain modelling error and measurement noise by clustering based tree seeds algorithm
Engineering Structures, 2019Co-Authors: Zhenghao Ding, Hong HaoAbstract:Abstract This paper proposes a novel Structural Damage Identification approach by using the clustering based Tree Seeds Algorithm, termed as C-TSA, taking into account of both the finite element modeling errors and measurement noise. In order to make the standard TSA more powerful and robust, K-means cluster technique is introduced into the standard TSA before starting the seeds search, which is beneficial to enhance the algorithm’s global optimization performance. The objective function based on the modal data is formulated for Structural Damage Identification. Numerical studies on benchmark functions and a 61-bar truss structure are conducted to investigate the accuracy and robustness of the proposed approach. The finite element modelling errors and noises in the measurement data are considered. Experimental verifications on a laboratory steel frame structure model is conducted to further validate the accuracy of the proposed approach. The results from the numerical and experimental studies are compared with those obtained from several latest evolutionary algorithms. The Identification results demonstrate that the proposed approach is more competitive and robust for Structural Damage Identification even considering the modelling errors and measurement noises.
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Structural Damage Identification using improved Jaya algorithm based on sparse regularization and Bayesian inference
Mechanical Systems and Signal Processing, 2019Co-Authors: Zhenghao Ding, Hong HaoAbstract:Abstract Structural Damage Identification can be considered as an optimization problem, by defining an appropriate objective function relevant to Structural parameters to be identified with optimization techniques. This paper proposes a new heuristic algorithm, named improved Jaya (I-Jaya) algorithm, for Structural Damage Identification with the modified objective function based on sparse regularization and Bayesian inference. To improve the global optimization capacity and robustness of the original Jaya algorithm, a clustering strategy is employed to replace solutions with low-quality objective values and a new updated equation is used for the best-so-far solution. The objective function that is sensitive and robust for effective and reliable Damage Identification is developed through sparse regularization and Bayesian inference and used for optimization analysis with the proposed I-Jaya algorithm. Benchmark tests are conducted to verify the improvement in the developed algorithm. Numerical studies on a truss structure and experimental validations on an experimental reinforced concrete bridge model are performed to verify the developed approach. A limited quantity of modal data, which is distinctively less than the number of unknown system parameters, are used for Structural Damage Identification. Significant measurement noise effect and modelling errors are considered. Damage Identification results demonstrate that the proposed method based on the I-Jaya algorithm and the modified objective function based on sparse regularization and Bayesian inference can provide accurate and reliable Damage Identification, indicating the proposed method is a promising approach for Structural Damage detection using data with significant uncertainties and limited measurement information.
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Structural Damage Identification based on modified artificial bee colony algorithm using modal data
Inverse Problems in Science and Engineering, 2018Co-Authors: Zhenghao Ding, Renzhi YaoAbstract:AbstractA method for Structural Damage Identification based on a modified Artificial Bee Colony algorithm is presented. A new formula is introduced to the onlooker bee phase to improve the convergence rate and the Tournament Selection Strategy is adopted instead of roulette to enhance global search ability of the algorithm. Test functions are introduced as benchmarks to verify the proposed algorithm. And then two numerical examples, including a supported beam and a plate, are conducted to investigate the efficiency and correctness of the proposed method. Final estimated results show that the present technique can produce more accurate Damage Identification results, comparing with other evolutionary algorithms, even with a few noise contaminated measurements.
