Pipejacking clogging detection in soft alluvial deposits using machine learning algorithms

https://doi.org/10.1016/j.tust.2021.103908Get rights and content

Highlights

  • Potential for AI techniques to detect clayey clogging in pipejacking is explored.

  • Data decomposition into feature-based sub-series accentuates their features.

  • The use of slurry density, torque and speed can be useful in detecting clogging.

Abstract

‘Clogging’ is a common issue encountered during tunnelling in clayey soils which can impede tunnel excavation, cause unplanned downtimes and lead to significant additional project costs. Clogging can result in a drastic reduction in performance due to reduced jacking speeds and the time needed for cleaning if it cannot be fully mitigated. The data acquired by modern tunnel boring machines (TBMs) have grown significantly in recent years presenting a substantial opportunity for the application of data-driven artificial intelligence (AI) techniques. In this study, a baseline assessment of clogging in slurry-supported pipejacking is performed using a combination of TBM parameters and semi-empirical diagrams proposed in the literature. The potential for one-class support vector machines (OCSVM), isolation forest (IForest) and robust covariance (Robcov) to assess the tendency for clogging is then explored in this work. The proposed approach is applied to a pipejacking case history in Taipei, Taiwan, involving tunnelling in soft alluvial deposits. The results highlight an exciting potential for the use of AI techniques to detect clogging during slurry-supported pipejacking.

Introduction

Fine-grained soils have a strong influence on pipejacking performance for their tendency to trigger various issues (Bai et al., 2021, Cheng et al., 2020b, Cheng et al., 2020a, Cheng et al., 2021, Duan et al., 2021, Ong and Choo, 2016, Soomro et al., 2020, Tan and Wei, 2012, Wei et al., 2019, Xu et al., 2018, Xu et al., 2019, Xue et al., 2021, Zhang et al., 2019, Zhang et al., 2020a), one of which is ‘clogging’. Clogging refers to the adherence of fine-grained soils to cutters at the cutterhead, openings on the cutting wheel, screw conveyor and/or conveyor belt. Clogging can therefore cause unplanned downtimes and, consequently, a significant increase in operation costs (Heuser et al., 2012, Hollmann and Thewes, 2013, Spagnoli et al., 2011a, Thewes and Burger, 2005, Thewes and Hollmann, 2014, Zumsteg et al., 2016). For pipejacking, clogging can be described as the attraction between soil particles and cutters and the adhesion between water in the soil and the cutters (Fountaine, 1954, Kang et al., 2018, Kang et al., 2019, Sass and Burbaum, 2008, Spagnoli et al., 2019a, Spagnoli et al., 2019b). There are four potential mechanisms governing adhesion of clay to a cutter, namely adhesion of clay particles on a cutter surface, inherent cohesion, bridging of clay particles over a cutting wheel opening, and an inability for the clay to dissolve in water (Thewes, 1999, Jia, 2004, Kang et al., 2019). Previous research has shown that clogging caused by adhesion of clay to a cutter can significantly reduce shield tunnelling performance (Spagnoli et al., 2011b, Spagnoli et al., 2012a, Spagnoli et al., 2012b, Spagnoli et al., 2014, van Baalen, 1999, van Baalen et al., 2001, Zhang et al., 2018).

Various approaches have been proposed to evaluate the potential for clogging to occur such as the use of plasticity index measurements, semi-empirical diagrams, and laboratory-based drilling tests. Hu and Rostami (2020) described the importance of soil conditioning during tunnelling and the role of soil rheology in tuning the desired characteristics of the conditioned soil. Using a novel device, those authors established a relationship between soil rheological parameters, soil type and conditioning parameters for soft ground tunnelling. Using a new framework and new devices, Peila et al. (2015) noted that the effectiveness of a polymer in clay conditioning is strongly dependent on the plasticity index of the clay. For low plasticity clay, the use of polymers can cause an increase in the volume of foam needed because of the water absorption effect of the polymer itself. However, this can also lead to a more homogeneous conditioned soil with long-lasting mechanical properties. Alberto-Hernandez et al. (2017) used the relationship between cohesion (soil-soil strength) and adhesion (soil-structure strength) to evaluate clogging potential, though this method is limited to situations where soil cohesion is greater than adhesion. Hollmann and Thewes (2013) reported relevant factors for the development of clogging and presented a new classification diagram which uses changes in water content to estimate changes in the consistency of fine-grained soils. Thewes and Hollmann (2016) explored the risk of clogging in various ground conditions and for different shield types and presented a summary of methods to characterise soil ‘stickiness’ and laboratory experiments to assess clogging potential. A newly developed diagram for assessing clogging risks for all types of shields and a new testing scheme for evaluating sedimentary rocks in terms of clogging were also introduced. Feinendegen et al. (2010) recommended a cone pull-out test to detect the adhesion/clogging propensity of a rock or soil, combined with a newly developed scheme for classifying the clogging potential. Following an extended test campaign using soils with different clay contents and minerals, de Oliveira et al. (2018) developed a new device which adds an impulse via dropping of a ‘beater’ from a predetermined height. This combination could give a more reliable evaluation of the potential for clogging to occur along earth pressure balance (EPB) machine tunnel drives. Further, a laboratory routine to characterise the clogging and fluidity of soils, including mixed soils by considering different fractions of clay was proposed by de Oliveira et al., 2019a, de Oliveira et al., 2019b, de Oliveira et al., 2019c. However this routine can still be improved by doing this exercise of preliminary assessment and subsequent backanalysis.

Kang et al. (2019) evaluated the clogging potential of mixed bentonite-kaolin specimens using a combination of a semi-empirical diagram and the drilling test. Kang et al. (2019) also investigated the dependence of the clogging potential on the plasticity indices of the mixtures. The results revealed that mixtures with bentonite had a higher clogging potential than pure kaolin and the drilling tests proved an effective means of quantifying clogging potential and evaluating the performance of additives. Zumsteg et al. (2016) investigated the effect of clay mineralogy and the composition of the supporting slurry on clogging potential using novel ‘stickiness’ tests including a ‘mixing test’ and a model tunnel boring machine (TBM) cutterhead test. It was reported that increased slurry strength is likely to increase the possibility of clogging for mixed face conditions. In contrast, polymer additives to slurry can achieve both high slurry resistance and low clogging potential by protecting clay aggregate surface from penetration of water (Zumsteg et al., 2016).

Although previous studies performed over the past decade have greatly enhanced our understanding of the clogging process and the role of key influencing factors (Zumsteg et al., 2013, Ryu et al., 2019), the influence of clogging on the response of tunnelling parameters (e.g. cutterwheel torque and jacking speed) remains unclear. Further, the prevention and mitigation of clogging during shield tunnelling is still highly dependent on the operator’s accumulated site experience. The objectives of this paper are: (1) to characterise the role of tunnelling parameters in the development of clogging during slurry-supported pipejacking, (2) to evaluate the clogging potential using a combination of existing pipejacking parameters and the semi-empirical approach (Hollmann and Thewes, 2013), and (3) to explore the feasibility for artificial intelligence techniques to evaluate the clogging potential.

Section snippets

Particle-size distribution

The shear strength of coarse-grained soils (with less than 5% fines) is derived from interparticle friction and geometrical interference (e.g. dilation, particle crushing, and rearrangement) rather than cohesion and which in turn minimises clogging issues during tunnel excavation. In contrast, coarse-grained soils (e.g. sands and gravel) with fines content > 5% have been shown to increase the potential for clogging (Ni and Cheng, 2010). In this context, soil particles > 0.075 mm become part of

Methodology

In this work, three slurry-supported pipejacking drives undertaken in the soft alluvial deposits at Taipei, Taiwan were analysed. Following acquisition of the soil particle size distribution, plasticity Ip and consistency index Ic, a baseline assessment of the clogging potential was completed using existing pipejacking records, aided by the semi-empirical approach (Hollmann and Thewes, 2013). The time history of pipejacking performance parameters are typically non-stationary which can lead to

Data screening

As all case histories involved periods of tunnelling in gravel, the corresponding datapoints were first filtered out of the datasets. The authors’ previous research suggests Tc > 15 Amp and V ≥ 100 r/min characterises tunnelling in gravel, though this may vary between projects with different geology (Cheng et al., 2017). The data were logged at 2 m intervals of jacked distance to capture the clogging-induced decline in V.

Data scaling

The OCSVM, IForest and Robcov algorithms were implemented using the Python

Project overview

Fig. 4a shows the location of four drives in the soft alluvial deposits located in the Shulin district in Taipei County, Taiwan. Due to data completeness, only three (drives B, C and D) out of the four drives were selected for the present analyses and measured 126 m, 75 m and 102 m in length respectively and were located approximately 10.5 m below ground level. The tunnelling was undertaken using a slurry-supported shield machine with a 1.5 m diameter cutterhead. A 30 mm overcut was created by

Baseline assessment results

To benchmark predictions, a baseline assessment of clogging potential during pipejacking in the clayey soils was completed using measurements of the support slurry density ρ, jacking speed V, and cutterwheel torque TC, in conjunction with the semi-empirical approach. During pipejacking in clayey soils, the cutterwheel is jacked into the soil cutting face and the plastically deformable soil is pushed to both sides in the form of ‘lumps’. Water in the support slurry can transform the consistency

Conclusions

This paper has established a baseline assessment of clogging potential during slurry-supported pipejacking using a combination of existing tunnelling parameters and the semi-empirical approach and has explored the potential for the use of anomaly detection approaches to assess clogging potential. Based on the results and discussion, the main conclusions drawn are as follows:

  • (a)

    Pre-process procedures including data screening and a seasonal-trend decomposition using Loess smoothing were utilised to

CRediT authorship contribution statement

Xue-Dong Bai: Data curation, Formal analysis, Validation, Software, Writing - original draft. Wen-Chieh Cheng: Conceptualization, Methodology, Writing - review & editing, Supervision, Funding acquisition. Brian B. Sheil: Writing - review & editing. Ge Li: Data curation, Formal analysis, Validation, Writing - original draft.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors convey their thanks and sincerely acknowledge financial supports from the Special fund for Basic Scientific Research of Central Colleges, Chang’an University, under Grant No. 300102269502. The third author is funded by the Royal Academy of Engineering under the Research Fellowship Scheme.

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