Prediction of Slip and Torsion Performance of Right-Angle Fasteners Based on Machine Learning Methods

doi:10.16183/j.cnki.jsjtu.2022.399

Abstract

Abstract:

Aiming at the issue of large CPU costs and low calculation accuracy in the design of right-angle fasteners in scaffolding structures, prediction models of fastener anti-slip performance and torsion performance based on machine learning are proposed. A three-dimensional solid model of right-angle fasteners is established using the finite element method, the effectiveness of the numerical simulation method is verified through test results, and the comprehensive influence of various design parameters on the performance of fasteners is revealed by the parametric analysis method. The database is established by combining the test and numerical simulation results, and the fastener stiffness prediction models are proposed based on random forest (RF), support vector machine (SVM) and K-most proximity algorithm (K-NN), respectively. The expressions for the measured point displacement of the anti-slip model and the stiffness prediction of the torsion model are proposed in combination with genetic expression programming. The results indicate that SVM and GEP can predict the displacement and torsional stiffness of right-angle fasteners more accurately, which is important for guiding the safety design of fasteners in engineering scaffolding structures.

Key words: rectangular fasteners, anti-slip performance, torsional performance, parameter analysis, machine learning

CLC Number:

TU731

BAO Zhujie, LI Zhen, WANG Feiliang, PANG Bo, YANG Jian. Prediction of Slip and Torsion Performance of Right-Angle Fasteners Based on Machine Learning Methods[J]. Journal of Shanghai Jiao Tong University, 2024, 58(2): 242-252.

Figures/Tables 16

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Tab.2

Tab.3

Tab.4

Tab.5

Fig.11

References 32

[1]	孙世梅, 梁友, 闫伟, 等. 2010-2016年建筑施工脚手架坍塌事故统计分析[J]. 吉林建筑大学学报, 2018, 35(4): 33-36.
	SUN Shimei, LIANG You, YAN Wei, et al. Statistical analysis of scaffolding collapse accidents in building construction 2010-2016[J]. Journal of Jilin Jianzhu University, 2018, 35(4): 33-36.
[2]	HU X P, PENG G, NIU D T, et al. Experimental study on mechanical properties of steel tube-coupler connections in corroded scaffolds[J]. Journal of Constructional Steel Research, 2021, 176: 106383. doi: 10.1016/j.jcsr.2020.106383 URL
[3]	蔡雪峰, 周继忠, 庄金平. 钢管扣件节点抗滑性能研究[J]. 土木工程学报, 2009, 42(3): 60-64.
	CAI Xuefeng, ZHOU Jizhong, ZHUANG Jinping. Joint slip resistance performance of fasteners in steel pipe formwork supports[J]. China Civil Engineering Journal, 2009, 42(3): 60-64.
[4]	宋建学, 史瑞. 脚手架扣件抗滑移试验及分析[J]. 建筑技术, 2011, 42(10): 937-938.
	SONG Jianxue, SHI Rui. Anti-slip test and analysis of scaffold fasteners[J]. Construction Technology, 2011, 42(10): 937-938.
[5]	JIA L, LIU H, CHEN Z, et al. Mechanical properties of right-angle couplers in steel tube-coupler scaffolds[J]. Journal of Constructional Steel Research, 2016, 125: 43-60. doi: 10.1016/j.jcsr.2016.06.005 URL
[6]	CHANG C L, SONG J X. Parameters survey and experiment on steel scaffold components[J]. Advanced Materials Research, 2012, 368: 1513-1516.
[7]	何夕平, 王璜, 赵雪会. 扣件不同紧固力矩对工字钢悬挑钢管外脚手架变形影响分析[J]. 西安建筑科技大学学报, 2018, 50(2): 196-201.
	HE Xiping, WANG Huang, ZHAO Xuehui. Analysis of the deformation influence on the external scaffold of I-steel cantilever steel pipe in different fastening torque[J]. Journal of Xi’an University of Architecture & Technology, 2018, 50(2): 196-201.
[8]	陈志华, 陆征然, 王小盾. 钢管脚手架直角扣件刚度的数值模拟分析及试验研究[J]. 土木工程学报, 2010, 43(9): 100-108.
	CHEN Zhihua, LU Zhengran, WANG Xiaodun. Numerical analysis and experimental study of the stiffness of right-angle couplers in tubular steel scaffolds[J]. China Civil Engineering Journal, 2010, 43(9): 100-108.
[9]	陆征然, 陈志华, 王小盾, 等. 扣件式钢管满堂支撑体系稳定性的有限元分析及试验研究[J]. 土木工程学报, 2012, 45(1): 49-60.
	LU Zhengran, CHEN Zhihua, WANG Xiaodun, et al. Experimental and theoretical study of the bearing capacity of fastener steel tube full-hall formwork support system[J]. China Civil Engineering Journal, 2012, 45(1): 49-60.
[10]	贾莉, 刘红波, 陈志华, 等. 扣件式钢管满堂脚手架整体稳定试验与有限元分析[J]. 建筑结构学报, 2017, 38(6): 114-122.
	JIA Li, LIU Hongbo, CHEN Zhihua, et al. Experimental research and FEA on bearing capacity of full hall steel tube and coupler scaffold support system[J]. Journal of Building Structures, 2017, 38(6): 114-122.
[11]	中国建筑科学院. 建筑施工扣件式钢管脚手架安全技术规范: JGJ 130—2011[S]. 北京: 中国建筑工业出版社, 2011.
	China Academy of Building Research. Technical code for safety of steel tubular scaffold with couplers in construction: JGJ 130—2011[S]. Beijing: China Construction Industry Press, 2011.
[12]	SU L Y, FU L P. Analysis on the stability of double-fastener type tubular scaffold[C]// International Conference on Advances in Civil Engineering. UK: IOP Publishing, 2019, 330: 022083.
[13]	中华人民共和国国家质量监督检验检疫总局. 钢管脚手架扣件: GB 15831—2006[S]. 北京: 中国建筑金属结构, 2006.
	General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China. Steel tube scaffold couplers: GB 15831—2006[S]. Beijing: China Construction Metal Structure Association, 2006.
[14]	中国建筑一局有限公司. 建筑施工临时支撑结构技术规范: JGJ 300—2013[S]. 北京: 中国建筑工业出版社, 2013.
	China Construction First Group Corporation Limited. Technical specifications for temporary support structures for building construction: JGJ 300—2013[S]. Beijing: China Construction Industry Press, 2013.
[15]	全国铸造标准化技术委员会. 可锻铸铁件: GB/T 9440—2010[S]. 北京: 中华人民共和国国家质量监督检验检疫总局, 2010.
	National Technical Committee for Standardization of Foundry. Forged iron castings: GB/T 9440—2010[S]. Beijing: General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, 2010.
[16]	全国钢标准化技术委员会. 低压流体输送用焊接钢管: GB/T 3091—2015[S]. 北京: 中华人民共和国国家质量监督检验检疫总局, 2015.
	National Technical Committee for Standardization of Steel. Welded steel pipes for low-pressure fluid transportation: GB/T 3091—2015[S]. Beijing: General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, 2015.
[17]	张洪, 刘彬彬. 应用深度学习识别法兰螺栓连接状态[J]. 应用声学, 2021, 40(3): 350-357.
	ZHANG Hong, LIU Binbin. The recognition of flange bolt connection state based on deep learning[J]. Journal of Applied Acoustics, 2021, 40(3): 350-357.
[18]	ZHANG Z, DU F, ZHANG L, et al. Monitoring of piezoelectric impedance for bolt loosening using general regression neural network[J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(4): 639-645.
[19]	NA W S. Bolt loosening detection using impedance based non-destructive method and probabilistic neural network technique with minimal training data[J]. Engineering Structures, 2021, 226: 111228. doi: 10.1016/j.engstruct.2020.111228 URL
[20]	SUN W C, GUAN Z Q, ZENG Y, et al. Bolt loosening detection of rocket connection structure based on variational modal decomposition and support vector machines[J]. Applied Sciences-Basel, 2022, 12(12): 6266.
[21]	WANG F R, CHEN Z, SONG G B. Monitoring of multi-bolt connection looseness using entropy-based active sensing and genetic algorithm-based least square support vector machine[J]. Mechanical Systems and Signal Processing, 2020, 136: 106507. doi: 10.1016/j.ymssp.2019.106507 URL
[22]	ERALIEV O, LEE K H, LEE C H. Vibration-based loosening detection of a multi-bolt structure using machine learning algorithms[J]. Sensors, 2022, 22(3): 1210. doi: 10.3390/s22031210 URL
[23]	KHAN N, SALEEM M R, LEE D, et al. Utilizing safety rule correlation for mobile scaffolds monitoring leveraging deep convolution neural networks[J]. Computers in Industry, 2021, 129: 103448. doi: 10.1016/j.compind.2021.103448 URL
[24]	SAROTHI S Z, AHMED K S, KHAN N I, et al. Machine learning-based failure mode identification of double shear bolted connections in structural steel[J]. Engineering Failure Analysis, 2022, 139: 106471. doi: 10.1016/j.engfailanal.2022.106471 URL
[25]	SAKHAKARMI S, ARTEAGA C, CHO C, et al. Scaffold safety analysis: Focusing on deep learning[C]// Construction Research Congress 2020:Computer Applications. New York, USA: Amer Soc Civil Engineers, 2020: 218-225.
[26]	赵平, 吴昊, 贾建国. 插销式脚手架在异形结构中的安全性能研究[J]. 工业建筑, 2015, 45(2): 101-106.
	ZHAO Ping, WU Hao, JIA Jianguo. Study of safety performance of plug-in scaffold for special-shaped structure[J]. Industrial Construction, 2015, 45(2): 101-106.
[27]	CHO C, KIM K, PARK J, et al. Data-driven monitoring system for preventing the collapse of scaffolding structures[J]. Journal of Construction Engineering and Management, 2018, 144(8): 4018077-1-13.
[28]	LUO S X, QIAO A M, TANG Q G. Fault diagnosis method for attached lifting scaffold based on support vector machine[J]. Journal of Engineering, 2020, 2020(13): 495-498.
[29]	PANG B, WANG F, YANG J, et al. Evaluation on the progressive collapse resistance of infilled reinforced concrete frames based on numerical and semi-analytical methods[J]. Engineering Structures, 2022, 267: 114684. doi: 10.1016/j.engstruct.2022.114684 URL
[30]	AZIM I, YANG J, IQBAL M F, et al. Prediction of catenary action capacity of RC beam-column substructures under a missing column scenario using evolutionary algorithm[J]. KSCE Journal of Civil Engineering, 2021, 25(3): 891-905. doi: 10.1007/s12205-021-0431-0
[31]	秦大同, 谢里阳. 现代机械设计手册[M]. 第2版. 北京: 化学工业出版社, 2011.
	QIN Datong, XIE Liyang. Modern mechanical design manual[M]. 2nd ed. Beijing: Chemical Industry Press, 2011.
[32]	FERREIRA C. Gene expression programming: A new adaptive algorithm for solving problems[J]. Complex Systems, 2001, 13(2): 87-129.

螺栓拧紧力矩/(N·m)	螺栓预紧力/kN
20	11.11
30	16.67
40	22.22
50	27.78
60	33.33

算法	模型超参数	滑移模型优化值	扭转模型优化值
RF	树的数量	100	99
	最大深度	7	7
SVM	正则化常数,c	2 070	7 000
	核函数类型	径向基核函数	径向基核函数
	核函数系数,γ	1	0.3
K-NN	邻域数	2	2

模型	训练集					测试集
模型	R²	RMSE	MAE	Mean	COV	R²	RMSE	MAE	Mean	COV
RF	1.00	3.39	1.34	1.05	0	0.99	0.89	0.23	0.98	0.04
SVM	0.99	0.65	0.33	0.99	0.24	0.98	0.29	0.10	0.99	0.08
K-NN	0.88	0.86	0.35	1.08	0.17	0.74	0.24	0.06	1.00	0.12

模型	训练集					测试集
模型	R²	RMSE	MAE	Mean	COV	R²	RMSE	MAE	Mean	COV
RF	0.97	416.47	311.78	0.97	0.28	0.90	217.19	169.43	1.00	0.08
SVM	0.95	335.53	236.37	1.00	0.23	0.90	231.72	148.35	0.98	0.07
K-NN	0.97	406.41	339.70	1.04	0.10	0.94	264.78	211.11	0.97	0.16

参数类型	优化值
染色体	30
基因	3
头部长度	10
基因连接运算符	+
函数集	+,-, *, /, ^2
变异概率	0.013 8
反转率	0.005 46
IS转座概率	0.005 46
RIS转座概率	0.005 46