Visibility graph analysis on time series of shield tunneling parameters based on complex network theory

Abstract Shield tunneling operation affects project scheduling, cost estimation, and safety risk during metro construction. Given the importance of this process, tunnel engineers should understand the complexities of shield tunneling parameters. This study addresses the characteristics of shield tunneling parameters by introducing a visibility graph model implemented in a complex network of shield tunneling in metro construction. Time series data are collected for the analysis of six parameters generated in each segment ring tunneling cycle. These parameters are total thrust, torque, penetration rate, rotation rate, advance speed, and working chamber pressure. Bridging time series and the complex network with visibility graph algorithm indicates that all degree distributions of the construct networks follow the power law, establishing the scale-free time series of the parameters. Small-world features and hierarchical structures also indicate that the fluctuation and variance of the parameters are attracted, limited, or affected by a previous shield operation of segment rings. In addition, findings reveal turning points in the parameters’ time series with vital node identification based on complex network analysis. Implications relevant for shield operators and managers are proposed to improve shield tunneling performance, efficiency, and safety.

[1]  Hongtao Zhou,et al.  Smart construction site in mega construction projects: A case study on island tunneling project of Hong Kong-Zhuhai-Macao Bridge , 2018 .

[2]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[3]  Yang Li,et al.  Predicting profitability of listed construction companies based on principal component analysis and support vector machine—Evidence from China , 2015 .

[4]  Jürgen Kurths,et al.  Recurrence networks—a novel paradigm for nonlinear time series analysis , 2009, 0908.3447.

[5]  Hu Shi,et al.  Determination of the cutterhead torque for EPB shield tunneling machine , 2011 .

[6]  Jamal Rostami,et al.  Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground , 2016 .

[7]  Min-Yuan Cheng,et al.  Hybrid intelligence approach based on LS-SVM and Differential Evolution for construction cost index estimation: A Taiwan case study , 2013 .

[8]  Na Wang,et al.  Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory , 2012 .

[9]  Po-Cheng Chen,et al.  An enforced support vector machine model for construction contractor default prediction , 2011 .

[10]  Lieyun Ding,et al.  PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River , 2013 .

[11]  Hu Shi,et al.  Energy saving of cutterhead hydraulic drive system of shield tunneling machine , 2014 .

[12]  Rebecca J. Yang,et al.  Stakeholder-associated risks and their interactions in complex green building projects: a social network model , 2014 .

[13]  A. Snarskii,et al.  From the time series to the complex networks: The parametric natural visibility graph , 2012, 1208.6365.

[14]  Rotoli Francesco,et al.  Complex railway systems: capacity and utilisation of interconnected networks , 2016 .

[15]  E. Ben-Jacob,et al.  Challenges in network science: Applications to infrastructures, climate, social systems and economics , 2012 .

[16]  Lieyun Ding,et al.  A review of metro construction in China: Organization, market, cost, safety and schedule , 2017 .

[17]  H. Copur,et al.  Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model , 2014 .

[18]  Robert Galler,et al.  Tunnel boring machine performance prediction with scaled rock cutting tests , 2014 .

[19]  Xiang Li,et al.  Bridging Time Series Dynamics and Complex Network Theory with Application to Electrocardiogram Analysis , 2012, IEEE Circuits and Systems Magazine.

[20]  Jinjun Tang,et al.  Exploring dynamic property of traffic flow time series in multi-states based on complex networks: Phase space reconstruction versus visibility graph , 2016 .

[21]  Nuh Bilgin,et al.  A model to predict daily advance rates of EPB-TBMs in a complex geology in Istanbul , 2017 .

[22]  A. N. Jiang,et al.  Feedback analysis of tunnel construction using a hybrid arithmetic based on Support Vector Machine and Particle Swarm Optimisation , 2011 .

[23]  Youlun Xiong,et al.  Optimization-based model of tunneling-induced distributed loads acting on the shield periphery , 2012 .

[24]  Jing Wang,et al.  Graphic analysis and multifractal on percolation-based return interval series , 2015 .

[25]  Francesco Serinaldi,et al.  Irreversibility and complex network behavior of stream flow fluctuations , 2016 .

[26]  G. Ambika,et al.  Can recurrence networks show small-world property? , 2015, 1509.04528.

[27]  Wei-Dong Dang,et al.  Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series , 2016, Scientific Reports.

[28]  Yilan Kang,et al.  The energy method to predict disc cutter wear extent for hard rock TBMs , 2012 .

[29]  Duanbing Chen,et al.  Vital nodes identification in complex networks , 2016, ArXiv.

[30]  Guofang Gong,et al.  Electro-hydraulic control of high-speed segment erection processes , 2017 .

[31]  Hu Shi,et al.  Modeling and analysis of thrust force for EPB shield tunneling machine , 2012 .

[32]  Lieyun Ding,et al.  Application of 4D visualization technology for safety management in metro construction , 2013 .

[33]  M. Cheng,et al.  Risk Preference Based Support Vector Machine Inference Model for Slope Collapse Prediction , 2012 .

[34]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[35]  Haifeng Ma,et al.  Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO , 2011 .

[36]  Yun Long.  Visibility graph network analysis of gold price time series , 2013 .

[37]  Jin-Li Guo,et al.  Fractal analysis on human dynamics of library loans , 2012 .