橡胶-砂混凝土耗能特性智能预测模型研究<sup>*</sup>

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Abstract Research purposes: Aseismic layer design is important to ensure the stability of underground engineering structures in strong earthquake area in the western China. The development and application of aseismic materials are the key to enriching the design of aseismic layer structures and ensure their performance. A comprehensive understanding of the energy consumption characteristics of rubber-sand concrete lays the foundation for its effective application in underground engineering aseismic layers. In this paper, the energy consumption characteristics of rubber-sand concrete were tested by Hopkinson pressure bar test, and four different swarm intelligence optimization algorithms were used to optimize the back-propagation neural network algorithm based on the test results, so as to build four hybrid intelligent prediction models.
Research conclusions: (1) The importance of affecting the energy consumption performance of rubber-sand concrete ranges from high to low, with rubber content > cement content > rubber particle size. (2) The optimal population numbers for the hybrid intelligent models are 150 (PSO-BPNN), 75 (FOA-BPNN), 75 (LSO-BPNN), and 80 (SSA-BPNN). (3) The LSO-BPNN hybrid intelligent model has the highest prediction accuracy for the proportion of transmission energy of rubber-sand concrete, while the other models have prediction performance of PSO-BPNN, FOA-BPNN, and SSA-BPNN. (4) The proposed hybrid intelligent model can be used to develop suitable rubber-sand concrete for aseismic layer materials in underground engineering such as railway tunnelling, and provide guidance for aseismic design to ensure safe construction and stable operation for railway tunnelling.

Key words： underground engineering aseismic layer rubber-sand concrete energy consumption characteristics intelligent prediction model

Received: 27 July 2023

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TU93+

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	Articles by authors
	MEI Xiancheng
	MA Yalina
	LI Jianhe
	DING Changdong
	CHEN Xingqiang
	CUI Zhen
	BAI Qiangqiang

Cite this article:

MEI Xiancheng,MA Yalina,LI Jianhe, et al. Study on Intelligent Prediction Model for Energy Consumption Characteristics of Rubber-sand Concrete[J]. Journal of Railway Engineering Society, 2024, 41(7): 113-120.

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https://tdgcxb.crec.cn/EN/ OR https://tdgcxb.crec.cn/EN/Y2024/V41/I7/113

[1]	赵勇, 田四明. 中国铁路隧道数据统计[J]. 隧道建设, 2017(5): 3-4.Zhao Yong, Tian Siming.Statistics of Railway Tunnels in China[J]. Tunnel Construction, 2017(5): 3-4.
[2]	Mei X, Sheng Q, Cui Z, etc. Experimental Investigation on the Mechanical and Damping Properties of Rubber-sand-concrete Prepared with Recycled Waste Tires for Aseismic Isolation Layer[J]. Soil Dynamics and Earthquake Engineering, 2023, 165: 107718.
[3]	Reda M T, El-Died A S, El-Wahab M A, etc. Mechanical, Fracture, and Microstructural Investigations of Rubber Concrete[J]. Journal of Materials in Civil Engineering, 2008(10): 640-649.
[4]	Eldin N N, Senouci A B.Rubber-tire Particles as Concrete Aggregate[J]. Journal of Materials in Civil Engineering, 1993(4): 478-496.
[5]	郑万虎. 橡胶混凝土疲劳性能的试验研究[D]. 广州:广东工业大学, 2011.Zheng Wanhu.Experimental Study on Flexural Fatigue Performance of Rubberized Concrete[D]. Guangzhou:Guangdong University of Technology, 2011.
[6]	Potter S H, Becker J S, Johnston D M, etc. An Overview of the Impacts of the 2010-2011 Canterbury Earthquakes[J]. International Journal of Disaster Risk Reduction, 2015, 14: 6-14.
[7]	Zhou Y, Zou S, Wen J, etc. Study on the Damage Behavior and Energy Dissipation Characteristics of Basalt Fiber Concrete Using SHPB Device[J]. Construction and Building Materials, 2023, 368: 130413.
[8]	赵碧华, 刘永胜. 超短钢纤维混凝土的SHPB试验研究[J]. 混凝土, 2007(8): 55-57.Zhao Bihua, Liu Yongsheng.Impact Compressive Experiment of Spur-short Steel Fiber Reinforced Concrete by SHPB[J]. Concrete, 2007(8): 55-57.
[9]	巫绪涛, 胡俊, 谢思发. EPS混凝土的动态劈裂强度和能量耗散[J]. 爆炸与冲击, 2013(4): 369-374.Wu Xutao, Hu Jun, Xie Sifa.Dynamic Splitting-tensile Strength and Energy Dissipation Property of EPS Concrete[J]. Explosion and Shock Waves, 2013(4): 369-374.
[10]	Mei X, Li C, Sheng Q, etc. Development of a Hybrid Artificial Intelligence Model to Predict the Uniaxial Compressive Strength of a New Aseismic Layer Made of Rubber-sand Concrete[J]. Mechanics of Advanced Materials and Structures, 2022: 1-18.
[11]	Gregori A, Castoro C, Venkiteela G.Predicting the Compressive Strength of Rubberized Concrete Using Artificial Intelligence Methods[J]. Sustainability, 2021, 13(14): 7729.
[12]	Gupta T, Patel K A, Siddique S, etc. Prediction of Mechanical Properties of Rubberised Concrete Exposed to Elevated Temperature Using ANN[J]. Measurement, 2019, 147: 106870.
[13]	Han B, Wu Y, Liu L.Prediction and Uncertainty Quantification of Compressive Strength of High‐strength Concrete Using Optimized Machine Learning Algorithms[J]. Structural Concrete, 2022.
[14]	Zorlu K, Gokceoglu C, Ocakoglu F, etc. Prediction of Uniaxial Compressive Strength of Sandstones Using Petrography-based Models[J]. Engineering Geology, 2008(3-4): 141-158.
[15]	李晓锋, 李海波, 刘凯, 等. 冲击荷载作用下岩石动态力学特性及破裂特征研究[J]. 岩石力学与工程学报, 2017(10): 2393-2405.Li Xiaofeng, Li Haibo, Liu Kai, etc. Dynamic Properties and Fracture Characteristics of Rocks Subject to Impact Loading[J]. Chinese Journal of Rock Mechanics and Engineering, 2017(10): 2393-2405.
[16]	Mei X, Sheng Q, Cui Z, etc. Experimental Study on Energy Absorption Property of Viscoelasticity Damping Layer[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing: 2021.
[17]	Pan W.A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example[J]. Knowledge-Based Systems, 2012, 26: 69-74.
[18]	Qi C, Chen Q, Andy F, etc. Constitutive Modelling of Cemented Paste Backfill: A Data-mining Approach[J]. Construction and Building Materials, 2019, 197: 262-270.