A multivariate adaptive regression splines based damage identification methodology for web core composite bridges including the effect of noise
A multivariate adaptive regression splines based damage identification methodology for web core composite bridges including the effect of noise
A novel computationally efficient damage identification methodology for web core fiber-reinforced polymer composite bridges has been developed in this article based on multivariate adaptive regression splines in conjunction with a multi-objective goal-attainment optimization algorithm. The proposed damage identification methodology has been validated for several single and multiple damage cases. The performance of the efficient multivariate adaptive regression splines-based approach for the inverse system identification process is found to be quite satisfactory. An iterative scheme in conjunction with the multi-objective optimization algorithm coupled with multivariate adaptive regression splines is proposed to increase damage identification accuracy. The effect of noise on the proposed damage identification algorithm has also been addressed subsequently using a probabilistic framework. The multivariate adaptive regression splines-based damage identification algorithm is general in nature; therefore, in future it can be implemented to other structures.
composite bridge, multi-objective optimization, multivariate adaptive regression splines, noise, Sobol sequence, Structural damage identification
885-903
Mukhopadhyay, Tanmoy
2ae18ab0-7477-40ac-ae22-76face7be475
1 October 2018
Mukhopadhyay, Tanmoy
2ae18ab0-7477-40ac-ae22-76face7be475
Mukhopadhyay, Tanmoy
(2018)
A multivariate adaptive regression splines based damage identification methodology for web core composite bridges including the effect of noise.
Journal of Sandwich Structures and Materials, 20 (7), .
(doi:10.1177/1099636216682533).
Abstract
A novel computationally efficient damage identification methodology for web core fiber-reinforced polymer composite bridges has been developed in this article based on multivariate adaptive regression splines in conjunction with a multi-objective goal-attainment optimization algorithm. The proposed damage identification methodology has been validated for several single and multiple damage cases. The performance of the efficient multivariate adaptive regression splines-based approach for the inverse system identification process is found to be quite satisfactory. An iterative scheme in conjunction with the multi-objective optimization algorithm coupled with multivariate adaptive regression splines is proposed to increase damage identification accuracy. The effect of noise on the proposed damage identification algorithm has also been addressed subsequently using a probabilistic framework. The multivariate adaptive regression splines-based damage identification algorithm is general in nature; therefore, in future it can be implemented to other structures.
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Published date: 1 October 2018
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© The Author(s) 2017.
Keywords:
composite bridge, multi-objective optimization, multivariate adaptive regression splines, noise, Sobol sequence, Structural damage identification
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Local EPrints ID: 483553
URI: http://eprints.soton.ac.uk/id/eprint/483553
ISSN: 1099-6362
PURE UUID: a02b4619-d4dd-43b8-82b0-c161831f8fb9
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Date deposited: 01 Nov 2023 18:00
Last modified: 18 Mar 2024 04:10
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Author:
Tanmoy Mukhopadhyay
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