Vol. 20, No. 4, pp. 385-396 (2024)
RESEARCH ON DATA-DRIVEN INTELLIGENT DESIGN METHOD FOR
ENERGY DISSIPATOR OF FLEXIBLE PROTECTION SYSTEMS
Ze-Huan He 1, Zhi-Xiang Yu 1, 2, *, Lin-Xu Liao 1, Yang-Feng Lyu 1 and Yong-Ding Tian 1
1 School of Civil Engineering, Southwest Jiaotong University, Chengdu, China
2 National Engineering Laboratory for Prevention and Control of Geological Disasters in Land Transportation, Chengdu, China
*(Corresponding author: E-mail:This email address is being protected from spambots. You need JavaScript enabled to view it.)
Received: 10 March 2024; Revised: 9 August 2024; Accepted: 5 September 2024
DOI:10.18057/IJASC.2024.20.4.6
![]() |
Export Citation: Plain Text | RIS | Endnote |
ABSTRACT
The brake ring, an essential buffer and energy dissipator within flexible protection systems for mitigating dynamic impacts from rockfall collapses, presents notable design challenges due to its significant deformation and strain characteristics. This study introduces a highly efficient and precise neural network model tailored for the design of brake rings, utilizing BP neural networks in conjunction with Particle Swarm Optimization (PSO) algorithms. The paper studies the key geometric parameters, including ring diameter, tube diameter, wall thickness, and aluminum sleeve length, with performance objectives centered on starting load, maximum load, and energy dissipation. A comprehensive dataset comprising 576 samples was generated through the integration of full-scale tests and simulations, which facilitated the training of the neural network for accurate forward predictions linking physical parameters to performance outcomes. Furthermore, a PSO-based reverse design model was developed to enable effective back-calculation from desired performance outcomes to specific geometric configurations. The BP neural network exhibited high accuracy, evidenced by a fit of 0.991, and the mechanical performance of the designed products aligned with target values in over 90% of cases, with all engineering errors remaining within acceptable limits. The proposed method significantly reduces the design time to under 5 seconds, thereby vastly improving efficiency in comparison to traditional approaches. This advancement offers a rapid and reliable reference for the design of critical components in flexible protection systems.
KEYWORDS
Flexible protective systems, Energy dissipator, Brake ring, Data-driven, Intelligent design
REFERENCES
[1] Yang J, Duan S, Li Q, et al. A review of flexible protection in rockfall protection. Natural Hazards, 2019, 99: 71-89.
[2] JIN Yuntao, YU Zhixiang, LUO Liru, et al. A study on energy dissipation mechanism of a guided flexible protection system under rockfall impact. Journal of Vibration and Shock, 2021, 40 (20): 177-185+192.(in Chinese)
[3] LIU Zhanhui, LU Zhimou, LI Yongle, et al. Study on Impact Resistance of Flexible Shed Tunnel for Bridges in Mountainous Areas. Journal of The China Railway Society,2023,45(03):129-136. (in Chinese)
[4] Muraishi H, Samizo M, Sugiyama T. Development of a flexible low-energy rockfall protection fence. Quarterly Report of RTRI, 2005, 46(3): 161-166.
[5] Castro-Fresno D, Del Coz Díaz J J, Nieto P J G, et al. Comparative analysis of mechanical tensile tests and the explicit simulation of a brake energy dissipater by FEM. International Journal of Nonlinear Sciences and Numerical Simulation, 2009, 10(8): 1059-1085.
[6] Lambert S, Nicot F. Multi-scale analysis of an innovative flexible rockfall barrier. 2013.
[7] del Coz Díaz J J, Nieto P J G, Castro-Fresno D, et al. Nonlinear explicit analysis and study of the behaviour of a new ring-type brake energy dissipator by FEM and experimental comparison. Applied Mathematics and Computation, 2010, 216(5): 1571-1582.
[8] Castanon-Jano L, Blanco-Fernandez E, Castro-Fresno D, et al. Energy dissipating devices in falling rock protection barriers. Rock Mechanics and Rock Engineering, 2017, 50: 603-619.
[9] QI Xin, XU Hu, YU Zhixiang, et al. Dynamic mechanical property study of break rings in Flexible Protective System. Engineering Mechanics, 2018, 35(9):9. (in Chinese)
[10] Lu XZ, Liao WJ, Zhang Y, Huang YL, Intelligent structural design of shear wall residence using physics-enhanced generative adversarial networks, Earthquake Engineering & Structural Dynamics, 2022, 51(7): 1657-1676.
[11] Zhao PJ, Liao WJ, Huang YL, Lu XZ, Intelligent beam layout design for frame structure based on graph neural networks, Journal of Building Engineering, 2023, 63, Part A: 105499.
[12] Bao Yuequan, Li Hui. Artificial Intelligence for Civil Engineering. China Civil Engineering Journal, 2019, 52(5): 1-11. (in Chinese)
[13] Joshi D A, Menon R, Jain R K, et al. Deep learning-based concrete compressive strength prediction model with hybrid meta-heuristic approach. Expert Systems with Applications, 2023, 233: 120925.
[14] Yi Peng, Xinyi Yu, Yixin Huang, et al. Asphalt pavement raveling recognition based on deep learning algorithms, Intelligent Transportation Infrastructure, 2024.
[15] Yongding Tian, Junhao Zhang, et al. Comprehensive review of noncontact sensing Technologies for Bridge Condition Monitoring and Assessment, Intelligent Transportation Infrastructure, 2024.
[16] Chen W, Xu J, Dong M, et al. Data-driven analysis on ultimate axial strain of FRP-confined concrete cylinders based on explicit and implicit algorithms. Composite Structures, 2021, 268: 113904.
[17] Xu J, Chen Y, Xie T, et al. Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques. Construction and Building Materials, 2019, 226: 534-554.
[18] Pham T M, Hadi M N S. Predicting stress and strain of FRP-confined square/rectangular columns using artificial neural networks[J]. Journal of Composites for Construction, 2014, 18(6): 04014019.
[19] MA Gao, LIU Kang. Prediction of Compressive Strength of CFRP-confined Concrete Columns Based on BP Neural Network. Journal of Hunan University(Natural Sciences) ,2021,48(09):88-97.
[20] ZHOU Zhong, ZHANG Junjie, et al. Prediction model of sewage treatment in tunnel green construction based on PSO-BP neural network. Journal of Railway Science and Engineering, 2022, 19(5): 1450-1458. (in Chinese)
[21] Liao L, Yu Z, Yang X, et al. An automated computation method for flexible protection systems based on neural networks. Computers and Geotechnics, 2024, 165: 105932.
[22] Cascardi A, Micelli F, Aiello MA. An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns. Eng Struct 2017;140:199–208.
[23] Jiang K, Han Q, Bai Y, Du X. Data-driven ultimate conditions prediction and stress-strain model for FRP-confined concrete. Compos Struct 2020;112094.
[24] WANG Min, SHI Saoqing, YANG Youkiu. Static tensile test and FEM dynamic simulation for a ring-brake energy dissipater. Journal of Vibration and Shock,2011,30(04):188-193. (in Chinese)
[25] TIAN Zhenhua, SHI Shaoqing, et al. Mechanical Testing on the Ring⁃brake Energy Dissipater and Influence of Thickness on Its Energy Dissipation Performance. Journal of Logistical Engineering University, 2014, 30(3): 8-12. (in Chinese)
[26] LIU Chengqing, CHEN Linya, CHENG Chi, et al. Full scale test and FEM simulation to ring-type brake energy dissipater in falling rock protection. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(6): 1245-1254. (in Chinese)
[27] The MathWorks. <http://www.mathworks.com/products/matlab/>; 2007.
[28] DAI Yimin, LI Yixin, XU Ying, et al. Prediction of hail impact force induced by Wind-Hail coupling based on GA-BP Neural Network. Engineering Mechanics, 2024. 1-10. (in Chinese)
[29] CHENG Qing, MA Rui, JIANG Zhengwu, et al. Compressive Strength Prediction and Mix Proportion Design of UHPC Based on GA-BP Neural Network. Journal of Building Materials, 2020, 23(1): 176-183+191.(in Chinese)
[30] CHEN W, XU J, DONG M, et al. Data-driven analysis on ultimate axial strain of FRP-confined concrete cylinders based on explicit and implicit algorithms. Composite Structures, 2021, 268: 113904.
[31] POLI R, KENNEDY J, BLACKWELL T. Particle swarm optimization: An overview. Swarm Intelligence, 2007, 1(1): 33-57.
[32] LI Guangbao, GAO Dong, LU Yong, et al. Internal surface treatment of gas-liquid-solid technology based on PSO-BP algorithm and FLUENT. Journal of Jilin University (Engineering and Technology Edition), 2024, 1-12. (in Chinese)
[33] YANG Junqi, FAN Xiaojun, ZHAO Yuehua, et al. Prediction of carbon emissions in Shanxi Province based on PSO-BP neural network. Journal of Environmental Engineering Technology: 1-15. (in Chinese)