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. 2024 Mar 15;14(1):6334.
doi: 10.1038/s41598-024-56960-z.

Dynamic constitutive identification of concrete based on improved dung beetle algorithm to optimize long short-term memory model

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Dynamic constitutive identification of concrete based on improved dung beetle algorithm to optimize long short-term memory model

Ping Li et al. Sci Rep. .

Abstract

In order to improve the accuracy of concrete dynamic principal identification, a concrete dynamic principal identification model based on Improved Dung Beetle Algorithm (IDBO) optimized Long Short-Term Memory (LSTM) network is proposed. Firstly, the apparent stress-strain curves of concrete containing damage evolution were measured by Split Hopkinson Pressure Bar (SHPB) test to decouple and separate the damage and rheology, and this system was modeled by using LSTM network. Secondly, for the problem of low convergence accuracy and easy to fall into local optimum of Dung Beetle Algorithm (DBO), the greedy lens imaging reverse learning initialization population strategy, the embedded curve adaptive weighting factor and the PID control optimal solution perturbation strategy are introduced, and the superiority of IDBO algorithm is proved through the comparison of optimization test with DBO, Harris Hawk Optimization Algorithm, Gray Wolf Algorithm, and Fruit Fly Algorithm and the combination of LSTM is built to construct the IDBO-LSTM dynamic homeostasis identification model. The final results show that the IDBO-LSTM model can recognize the concrete material damage without considering the damage; in the case of considering the damage, the IDBO-LSTM prediction curves basically match the SHPB test curves, which proves the feasibility and excellence of the proposed method.

Keywords: Dung beetle optimization algorithm; Dynamic constitutive model of concrete; Lens imaging reverse learning; Long short-term memory network; PID control.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Algorithm 1
Algorithm 1
α selection strategy
Algorithm 2
Algorithm 2
Breeding position renewal strategy of dung beetles
Algorithm 3
Algorithm 3
IDBO pseudo-code
Figure 1
Figure 1
Convergence curve of the test function.
Figure 2
Figure 2
LSTM cell structure.
Figure 3
Figure 3
Flow chart of IDBO optimizing LSTM parameters.
Figure 4
Figure 4
Concrete sample.
Figure 5
Figure 5
Concrete specimen after loading.
Figure 6
Figure 6
Comparison of model identification results. Annotation*: the damage-free curve (red dashed line) is obtained by using strain and strain rate as inputs and stress as output. The damage curve (blue line) is obtained by using strain, strain rate, and time as inputs, and stress as output.
Figure 7
Figure 7
Identification results of specimens with a strain rate of 143.13 s−1.
Figure 8
Figure 8
Damage evolution curves.

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References

    1. Wang, L., Hu, S., Yang, L., Dong, X. & Wang, H. Talk about dynamic strength and damage evolution. Explos. Shocks02, 169–179. 10.11883/1001-1455(2017)02-019-11 (2017) (in Chinese).10.11883/1001-1455(2017)02-019-11 - DOI
    1. Holmquist, T. J., Johnson, G. R., & Cook, W. H. A computational constitutive model for concrete subjected to large strains high strain rates and high pressure. In Proceeding of the Fourteenth International Symposium on Ballistics. American Defense preparedness Association, Vol. 2, 591–600 (1993).
    1. Taylor, L. M., Chen, E. P. & Kuszmaul, J. S. Microcrack-induced damage accumulation in brittle rock under dynamic loading. Comput. Methods Appl. Mech. Eng.55(3), 301–320 (1986).10.1016/0045-7825(86)90057-5 - DOI
    1. Sun, Z. Study on dynamic large deformation intrinsic properties and damage evolution of two PP/PA blended polymers. Ningbo Univ.10.7666/d.d013942 (2005) (in Chinese).10.7666/d.d013942 - DOI
    1. Mahmoudi, H., Bitaraf, M., Salkhordeh, M., & Soroushian, S. A rapid machine learning-based damage detection algorithm for identifying the extent of damage in concrete shear-wall buildings. In Structures, Vol. 47, 482–499. Elsevier. 10.1016/j.istruc.2022.11.041(2023).

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