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Abstract: Over recent decades, the artificial neural networks (ANNs) have been applied as an effective approach for detecting damage in construction materials. However, to achieve a superior result of defect identification, they have to overcome some shortcomings, for instance slow convergence or stagnancy in local minima. Therefore, optimization algorithms with a global search ability are used to enhance ANNs, i.e. to increase the rate of convergence and to reach a global minimum. This paper introduces a two-stage approach for failure identification in a steel beam. In the first step, the presence of defects and their positions are identified by modal indices. In the second step, a feedforward neural network, improved by a hybrid particle swarm optimization and gravitational search algorithm, namely FNN-PSOGSA, is used to quantify the severity of damage. Finite element (FE) models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method. For comparison, a traditional ANN is also used to estimate the severity of the damage. The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.

使用前馈神经网络结合混合粒子群优化和引力搜索算法对钢板进行损伤检测

[1]Anitescu C, Atroshchenko E, Alajlan N, et al., 2019. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 59(1):345-359.

[2]Dawari VB, Vesmawala GR, 2013. Modal curvature and modal flexibility methods for honeycomb damage identification in reinforced concrete beams. Procedia Engineering, 51:119-124.

[3]Du YC, Stephanus A, 2018. Levenberg-Marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor. Sensors, 18(7):2322.

[4]Garcia-Perez A, Amezquita-Sanchez JP, Dominguez-Gonzalez A, et al., 2013. Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 14(9):615-630.

[5]Guo HW, Zhuang XY, Rabczuk T, 2019. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 59(2):433-456.

[6]Ho VL, Tran NH, de Roeck G, et al., 2019. System identification based on vibration testing of a steel I-beam. Proceedings of the 1st International Conference on Numerical Modelling in Engineering, p.254-268.

[7]Ho VL, Hoang TN, de Roeck G, et al., 2020. Effects of measuring techniques on the accuracy of estimating cable tension in a cable-stay bridge. Proceedings of the 13th International Conference on Damage Assessment of Structures, p.433-445.

[8]Kennedy J, Eberhart R, 1995. Particle swarm optimization. Proceedings of International Conference on Neural Networks, p.1942-1948.

[9]Khatir S, Behtani A, Tiachacht S, et al., 2017. Delamination detection in laminated composite using virtual crack closure technique (VCCT) and modal flexibility based on dynamic analysis. Journal of Physics: Conference Series, 842:012084.

[10]Le-Duc T, Nguyen QH, Nguyen-Xuan H, 2020. Balancing composite motion optimization. Information Sciences, 520:250-270.

[11]Liu HB, Song G, Jiao YB, et al., 2014. Damage identification of bridge based on modal flexibility and neural network improved by particle swarm optimization. Mathematical Problems in Engineering, 2014:640925.

[12]Mirjalili S, Hashim SZM, Sardroudi HM, 2012. Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Applied Mathematics and Computation, 218(22):11125-11137.

[13]Mirjalili S, Wang GG, dos S. Coelho L, 2014. Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Computing and Applications, 25(6):1423-1435.

[14]Moser P, Moaveni B, 2011. Environmental effects on the identified natural frequencies of the dowling hall footbridge. Mechanical Systems and Signal Processing, 25(7):2336-2357.

[15]Nguyen HD, Bui TT, de Roeck G, et al., 2019. Damage detection in Ca-Non bridge using transmissibility and artificial neural networks. Structural Engineering and Mechanics, 71(2):175-183.

[16]Nguyen HD, Ho LV, Bui-Tien T, et al., 2020a. Damage evaluation of free-free beam based on vibration testing. Applied Mechanics, 1(2):142-152.

[17]Nguyen TQ, Vuong LC, Le CM, et al., 2020b. A data-driven approach based on wavelet analysis and deep learning for identification of multiple-cracked beam structures under moving load. Measurement, 162:107862.

[18]Pandey AK, Biswas M, 1994. Damage detection in structures using changes in flexibility. Journal of Sound and Vibration, 169(1):3-17.

[19]Pandey AK, Biswas M, 1995. Experimental verification of flexibility difference method for locating damage in structures. Journal of Sound and Vibration, 184(2):311-328.

[20]Pandey AK, Biswas M, Samman MM, 1991. Damage detection from changes in curvature mode shapes. Journal of Sound and Vibration, 145(2):321-332.

[21]Peeters B, de Roeck G, 2001. One-year monitoring of the Z24-bridge: environmental effects versus damage events. Earthquake Engineering & Structural Dynamics, 30(2):149-171.

[22]Rashedi E, Nezamabadi-Pour H, Saryazdi S, 2009. GSA: a gravitational search algorithm. Information Sciences, 179(13):2232-2248.

[23]Rashid T, 2016. Make Your Own Neural Network, 1st Edition. CreateSpace Independent Publishing Platform, North Charleston, SC, USA.

[24]Samaniego E, Anitescu C, Goswami S, et al., 2020. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 362:112790.

[25]Sharma B, Venugopalan K, 2014. Comparison of neural network training functions for hematoma classification in brain CT images. IOSR Journal of Computer Engineering, 16(1):31-35.

[26]Tran NH, Bui TT, 2019. Damage detection in a steel beam structure using particle swarm optimization and experimentally measured results. Science Journal of Transportation, 9:3-9.

[27]Tran-Ngoc H, Khatir S, de Roeck G, et al., 2019. An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. Engineering Structures, 199:109637.

[28]Valian E, Mohanna S, Tavakoli S, 2011. Improved cuckoo search algorithm for feed forward neural network training. International Journal of Artificial Intelligence & Applications, 2(3):36-43.

[29]Wickramasinghe WR, Thambiratnam DP, Chan THT, 2015. Use of modal flexibility method to detect damage in suspended cables and the effects of cable parameters. Special Issue: Electronic Journal of Structural Engineering, 14(1):133-144.

[30]Xia Y, Hao H, Zanardo G, et al., 2006. Long term vibration monitoring of an RC slab: temperature and humidity effect. Engineering Structures, 28(3):441-452.

[31]Zhang JR, Zhang J, Lok TM, et al., 2007. A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation, 185(2):1026-1037.

[32]Zhu JJ, Huang M, Lu ZR, 2017. Bird mating optimizer for structural damage detection using a hybrid objective function. Swarm and Evolutionary Computation, 35:41-52.