Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (5): 13-19.doi: 10.6040/j.issn.1672-3961.0.2019.509

• Civil Engineering • Previous Articles     Next Articles

Fuzzy control of structure vibration mode based on BP neural network algorithm

Zhiwei WANG(),Nan GE*(),Chunwei LI   

  1. College of Architecture Engineering, North China University of Science and Technology, Tangshan 063009, Hebei, China
  • Received:2019-09-04 Online:2020-10-20 Published:2020-10-19
  • Contact: Nan GE E-mail:1912564765@qq.com;genanas@163.com

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Abstract:

In order to control the seismic response of civil engineering structures more reasonably and conveniently, a fuzzy control algorithm based on BP neural network was proposed. The neural network was trained with the structural seismic dynamic response data to establish the structural analysis model, and the time-domain modal coordinates were taken as the controlled variables to reduce the order of the system, so that the number of fuzzy reasoning required to establish the modal fuzzy control rules was within the acceptable range, and the system energy minimum was taken as the control target to formulate the control rules. The fuzzy control numerical model of structural dynamic response was established to evaluate the damping effect of the proposed algorithm based on the calculated value of seismic dynamic response. The results showed that the trained BP neural network could accurately predict the seismic dynamic response of the structure and establish fuzzy control rules accordingly. Using mode fuzzy control only for the first mode of the structure could achieve satisfactory damping effect. When active mass driver(AMD) optimal control amplitude was used as the control domain of each floor, the damping effect of modal fuzzy control was different from it. A better damping effect could be obtained by increasing the control field.

Key words: neural network, state variables, fuzzy mode control, principle modes, inter-storey drift

CLC Number: 

  • TU352.1

Fig.1

Identification model structure of artificial BP neural network system"

Fig.2

Plan of 20-story building structure model"

Fig.3

The first three mode shapes"

Fig.4

Fuzzy control charts with different control forces"

Table 1

Maximum Inter-storey displacement and storey acceleration of seismic dynamic response"

楼层 模糊控制UA=3 模糊控制UA=7 无控制
层间位移/mm 层间位移减震效率/% 楼层加速度/(m·s-2) 楼层加速度减震效率/% 层间位移/mm 层间位移减震效率/% 楼层加速度/(m·s-2) 楼层加速度减震效率/% 层间位移/mm 楼层加速度/(m·s-2)
1 19.81 16.13 2.88 -10.34 12.15 48.59 2.17 16.85 23.62 2.61
2 19.65 15.80 5.55 -8.53 12.11 48.13 4.05 20.79 23.34 5.12
3 19.40 15.15 7.86 -6.35 12.04 47.34 5.76 22.09 22.86 7.39
4 19.14 14.10 9.49 -0.83 11.97 46.31 7.31 22.35 22.28 9.42
5 18.92 12.53 10.62 6.57 11.82 45.33 7.70 32.19 21.63 11.36
6 18.63 10.57 11.08 14.08 11.52 44.71 7.36 42.92 20.84 12.90
7 18.55 6.48 11.54 17.56 11.11 44.00 8.50 39.25 19.83 13.99
8 18.38 1.15 11.88 19.14 10.69 42.50 10.42 29.07 18.59 14.69
9 18.02 -5.21 12.25 23.70 10.28 40.00 11.66 27.40 17.13 16.06
10 17.42 -11.92 12.52 27.46 9.83 36.85 12.21 29.25 15.57 17.25
11 16.56 -17.77 12.54 29.64 9.40 33.15 12.15 31.84 14.06 17.83
12 15.43 -18.63 13.67 23.01 9.01 30.71 11.89 33.03 13.01 17.75
13 14.07 -15.46 15.12 11.71 8.60 29.44 11.53 32.65 12.18 17.12
14 12.52 -10.82 16.27 -0.61 8.07 28.61 10.78 33.36 11.30 16.17
15 10.92 -6.49 17.16 -11.72 7.37 28.13 9.75 36.51 10.26 15.36
16 9.38 -4.21 17.79 -14.14 6.51 27.68 8.45 45.75 9.00 15.58
17 7.77 -3.28 18.16 -11.65 5.46 27.39 8.43 48.15 7.53 16.26
18 6.03 -3.21 18.47 -3.18 4.26 27.14 9.64 46.14 5.84 17.90
19 4.12 -3.33 19.48 -1.44 2.92 26.91 11.58 39.70 3.99 19.20
20 2.09 -3.43 20.24 -1.38 1.48 26.73 12.73 36.24 2.02 19.96

Fig.5

Time history of structural seismic dynamic response"

Fig.6

Structural amplitudes for seismic dynamic responses"

Fig.7

Time history of storey control force"

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