|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|
|Current Issue | Archive | Search [Print] [Close]|
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|
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|
Broomhead, D.S., Lowe, D., 1988. Multivariable functional interpolation and adaptive networks. Complex Systems, 2(3), 321-355.
Cochran, W.G., 1977. Sampling Techniques, 3rd edition. John Wiley and Sons Inc., New York.
Ding, Z.H., Lu, Z.R., Liu, J.K., 2018. Parameters identification of chaotic systems based on artificial bee colony algorithm combined with cuckoo search strategy. Science China Technological Sciences, 61(3), 417-426. https://doi.org/10.1007/s11431-016-9026-4.
Dou, S.Q., Li, J.J., Kang, F., 2017. Parameter identification of concrete dams using swarm intelligence algorithm. Engineering Computations, 34(7), 2358-2378. https://doi.org/10.1108/EC-03-2017-0110.
Gu, Y.C., Gu, C.S., 2008. Optimized back analysis on improved of multiple parameters based objective function. Journal of Hydraulic Engineering, 39(8), 969-975 (in Chinese). https://doi.org/10.13243/j.cnki.slxb.2008.08.003.
Hooke, R., Jeeves, T.A., 1961. Direct search solution of numerical and statistical problems. Journal of the ACM, 8(2), 212. https://doi.org/10.1145/321062.321069.
Jin, R., Chen, W., Simpson, T.W., 2001. Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization, 23(1), 1-13. https://doi.org/10.1007/s00158-001-0160-4.
Kang, F., Li, J.J., Li, H.J., 2013. Artificial bee colony algorithm and pattern search hybridized for global optimization. Applied Soft Computing, 13(4), 1781-1791. https://doi.org/10.1016/j.asoc.2012.12.025.
Kang, F., Li, J.S., Li, J.J., 2016. System reliability analysis of slopes using least squares support vector machines with particle swarm optimization. Neurocomputing, 209, 46-56. https://doi.org/10.1016/j.neucom.2015.11.122.
Kang, F., Li, J.S., Wang, Y., Li, J.J., 2017. Extreme learning machine-based surrogate model for analyzing system reliability of soil slopes. European Journal of Environmental & Civil Engineering, 21(11), 1341-1362. https://doi.org/10.1080/19648189.2016.1169225.
Levy, S., Steinberg, D.M., 2010. Computer experiments: A review. AStA-Advances in Statistical Analysis, 94(4), 311-324. https://doi.org/10.1007/s10182-010-0147-9.
Li, J.Z., Zheng, S.Q., Tan, Y., 2014. Adaptive fireworks algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE, Beijing, pp. 3214-3221. https://doi.org/10.1109/CEC.2014.6900418.
Liu, S.H., Bauer, E., 2016. Preface for special section on long-term behavior of dams. Water Science and Engineering, 9(3), 173-174. https://doi.org/10.1016/j.wse.2016.11.004.
Maier, G., Bocciarelli, M., Bolzon, G., Fedele, R., 2006. Inverse analyses in fracture mechanics. International Journal of Fracture, 138(1-4), 47-73. https://doi.org/10.1007/s10704-006-7153-7.
Nobahari, M., Ghasemi, M.R., Shabakhty, N., 2017. Truss structure damage identification using residual force vector and genetic algorithm. Steel and Composite Structures, 25(4), 485-496. https://doi.org/10.12989/scs.2017.25.4.485.
Park, J., Sandberg, I.W., 1991. Universal approximation using radial-basis-function networks. Neural Computation, 3(2), 246-257. https://doi.org/10.1162/neco.19126.96.36.199.
Tan, Y., Zhu, Y.C., 2010. Fireworks algorithm for optimization. Lecture Notes in Computer Science. Springer. pp. 355-364.
Wu, S.Y., Cao, W., Zheng, J., 2016. Analysis of working behavior of Jinping-I arch dam during initial impoundment. Water Science and Engineering, 9(3), 240-248. https://doi.org/10.1016/j.wse.2016.11.001.
Wu, Z.R., Gu, Y.C., Gu, C.S., Guo, H.Q., Su, H.Z., 2008. Establishing time-dependent model of deformation modulus caused by bedrock excavation rebound by inverse analysis method. Science China Technological Sciences, 51(2), 1-7. https://doi.org/10.1007/s11431-008-6001-6.
Xiang, Y., Su, H.Z., Wu, Z.R., 2004. Inverse analysis of mechanical parameters based on dam safety monitoring data. Journal of Hydraulic Engineering, 35(8), 98-102 (in Chinese). https://doi.org/10.13243/j.cnki.slxb.2004.08.018.
Xiang, Y., Fu, S.Y., Zhu, K., Yuan, H., Fang, Z.Y., 2017. Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm. Water Science and Engineering, 10(1), 70-77. https://doi.org/10.1016/j.wse.2017.03.005.
Xu, B.S., Miao, Y.D., Li, B., Lei, T., Xu, H.J., 2009. Inverse analysis of mechanical parameters of dam based on PSO. Water Power, 35(3), 102-104 (in Chinese).
Zheng, S.Q., Janecek, A., Tan, Y., 2013. Enhanced fireworks algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE, Beijing, pp. 2069-2077. https://doi.org/10.1109/CEC.2013.6557813.
Zheng, S.Q., Janecek, A., Li, J.Z., Tan, Y., 2014. Dynamic search in fireworks algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE, Beijing, pp. 3222-3229. https://doi.org/10.1109/CEC.2014.6900485.
|Copyright by Water Science and Engineering|