Water Science and Engineering 2019, 12(3) 188-195 DOI:   https://doi.org/10.1016/j.wse.2019.09.002  ISSN: 1674-2370 CN: 32-1785/TV

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Fireworks algorithm (FWA)
Radial basis function (RBF) network
Surrogate model
Inverse analysis
Structural health monitoring.

Health diagnosis of concrete dams using hybrid FWA with RBF-based surrogate model

Si-qi Dou a,*, Jun-jie Li a,b, Fei Kang a

a School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China b Institute of Technology, Tibet University, Lhasa 850000, Chin


Structural health monitoring is important to ensuring the health and safety of dams. An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to diagnose the health condition of concrete dams. The damage of concrete dams is diagnosed by identifying the elastic modulus of materials using the displacement changes at different reservoir water levels. FWA is a global optimization intelligent algorithm. The proposed hybrid algorithm combines the FWA with the pattern search algorithm, which has a high capability for local optimization. Examples of benchmark functions and pseudo-experiment examples of concrete dams illustrate that the hybrid FWA improves the convergence speed and robustness of the original algorithm. To address the time consumption problem, an RBF-based surrogate model was established to replace part of the finite element method in inverse analysis. Numerical examples of concrete dams illustrate that the use of an RBF-based surrogate model significantly reduces the computation time of inverse analysis with little influence on identification accuracy. The presented hybrid FWA combined with the RBF network can quickly and accurately determine the elastic modulus of materials, and then determine the health status of the concrete dam.

Keywords Fireworks algorithm (FWA)   Radial basis function (RBF) network   Surrogate model   Inverse analysis   Structural health monitoring.  
Received 2018-11-08 Revised 2019-05-31 Online: 2019-09-30 
DOI: https://doi.org/10.1016/j.wse.2019.09.002

This work was supported by the National Key R&D Program of China (Grants No. 2016YFC0401600 and 2017YFC0404906), the National Natural Science Foundation of China (Grants No. 51769033 and 51779035), and the Fundamental Research Funds for the Central Universities (Grants No. DUT17ZD205 and DUT19LK14).

Corresponding Authors: Si-qi Dou
Email: dousiqi@yeah.net
About author:


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