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For this review of heavy duty robots Jack Hollingum went to Scotland to see a tunnel‐boring machine just completed by James Howden, and met Dr Owen Davies, Technical Manager, who considered the opportunities for automation and robotics in such machines.

The purpose of this paper is to apply the theory of sociomateriality to exhibit how the social and material are entangled and (re)configured over time and in practice in a particular organization of study.

The authors conduct an ethnographic case study of the North-South metro line project in Amsterdam and use the methods of participant-observation, in-depth interviewing and a desk study.

The authors showcase the process of sociomaterial entanglement by focussing on the history and context of the project, the agency and performativity of the material and sociomaterial (re)configuration via ritual performance. The authors found the notion of performativity not only concern the enactment of boundaries between the social and material, but also the blurring of such boundaries.

Sociomateriality theory remains difficult to grasp. The implication is the need to provide new lenses to engage this theory empirically.

The authors provide a multi-layered lens for organization researchers to engage sociomateriality theory at a contextual, organizational and practice level.

Insights from a historical and contextual perspective can help practitioners to become aware of the diverse and dynamic ways in which social and material entities are entangled and (re)configured over time and in practice.

The authors provide a unique empirical account to exhibit the entanglement and (re)configuration between the social and material in a particular organization of study. This paper studies a tangible organizational setting whereas prior research in sociomateriality mainly focussed on routines in IT and IS. Finally, the authors suggest the ethnographic method to study sociomaterial entanglement from a historical and contextual perspective.

This paper aims to present an adaptive sliding mode control (ASMC) for tunnel boring machine cutterhead telescopic system with uncertainties to achieve a high-precision trajectory in complex strata. This method could be applied to solve the problems caused by linear and nonlinear model uncertainties.

First, an integral-type sliding surface is defined to reduce the static tracking error. Second, a projection type adaptation law is designed to approximate the linear and nonlinear redefined parameters of the electrohydraulic system. Third, a nonlinear robust term with a continuous approximation function is presented for handling load force uncertainty and reducing sliding mode chattering. Moreover, Lyapunov theory is applied to guarantee the stability of the closed-loop system. Finally, the effectiveness of the proposed controller is proved by comparative experiments on a scaled test rig.

The linear and nonlinear model uncertainties lead to large variations in the dynamics of the mechanism and the tracking error. To achieve precise position tracking, an adaptation law was integrated into the sliding mode control which compensated for model uncertainties. Besides, the inherent sliding mode chattering was reduced by a continuous approximation function, while load force uncertainty was solved by a nonlinear robust feedback. Therefore, a novel ASMC for tunnel boring machine cutterhead telescopic system with uncertainties can improve its tracking precision and reduce the sliding mode chattering.

To the best of the authors’ knowledge, the ASMC is proposed for the first time to control the tunnel boring machine cutterhead telescopic system with uncertainties. The presented control is effective not only in control accuracy but also in parameter uncertainty.

The purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability.

This paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end.

This paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data.

This paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability.

Despite the many advantages this type of equipment offers, there are still some major drawbacks. Linear cutting machine (LCM) cannot accurately simulate the true rock-cutting process as 1. it does not account for the circular path along which tunnel boring machine (TBM) disk cutters cut the tunnel face, 2. it does not accurately model the position of a disk cutter on the cutterhead, 3. it cannot perfectly replicate the rotational speed of a TBM. To enhance the knowledge of these issues and in order to mimic the real rock-cutting process, a new lab testing equipment was developed by Hyundai Engineering and Construction.

A new testing machine called rotary cutting machine (RCM) is designed to simulate the excavation process of hard-rock TBMs and includes features such as TBM cutterhead, RPM simulation, constant normal force mode and constant penetration rate mode. Two sets of tests were conducted on Hwandeung granite using different disk cutter sizes to analyze the cutting forces in various excavation modes. The results are analyzed using statistical analysis and dimensional analysis. A new model is generated using dimensional analysis, and its results are compared against the results of actual cases.

The effectiveness of the new RCM test was demonstrated in its ability to apply various modes of excavation. Initial analysis of chip size revealed that the thickness of the chips is largely dependent on the cutter spacing. Tests with varying RPM showed that an increase in RPM results in an increase in the normal force and rolling force. The cutting coefficient (CC) demonstrated a linear correlation with penetration. The optimal specific energy is achieved at an S/p ratio of around 15. However, a slightly lower S/p ratio can also be used in the design if the cutter specifications permit. A dimensional analysis was utilized to develop a new RCM model based on the results from approximately 1200 tests. The model's applicability was demonstrated through a comparison of TBM penetration data from 26 tunnel projects globally. Results indicated that the predicted penetration rates by the RCM test model were in good agreement with actual rates for the majority of cases. However, further investigation is necessary for softer rock types, which will be conducted in the future using concrete blocks.

The originality of the research lies in the development of Hyundai Engineering and Construction’s advanced full-scale laboratory rotary cutting machine (RCM), which accurately replicates the excavation process of hard-rock tunnel boring machines (TBMs). The study provides valuable insights into cutting forces, chip size, specific energy, RPM and excavation modes, enhancing understanding and decision-making in hard-rock excavation processes. The research also presents a new RCM model validated against TBM penetration data, demonstrating its practical applicability and predictive accuracy.

The purpose of this paper is to introduce the numerical methods in tunnel engineering and their capabilities to indicate the fracture and failure in all kinds of tunneling methods such as New Austrian Tunneling Method, tunnel boring machine and cut‐cover. An essential definition of numerical modeling of tunnels to determine the interaction between geo‐material (soil and rock) surrounding the tunnel structure is discussed.

Tunnel geo‐material (soil and rock) interaction requires advanced constitutive models for the numerical simulation of linear, nonlinear, time‐dependent, anisotropic, isotropic, homogenous and nonhomogeneous behaviors. The numerical models discussed in this paper are developed in finite element method (FEM), finite deference method (FDM), boundary element method and discrete element method and these tools are used to illustrate the behavior of tunnel structure deformation under different loads and in complicated conditions. The disadvantage of this method is the tunnel lining assumed an independent structure under fixed load which is unable to model soil‐lining interaction. Predicting the effect of all natural factors on tunnels is the most difficult method. The above‐mentioned numerical methods are very simple and quick to use and the results are conservative and practical for users. One of the most significant advantages of the numerical method is in predicting the critical area surrounding the tunnel and the tunnel structure before making the tunnel construction due to different loads.

Numerical modeling is used as control method in reducing the risk of tunnel construction failures. Since some factors such as settlement and deformation are not completely predictable in rock and soil surrounding the tunnel, using numerical modeling is a very economical and capable method in predicting the behavior of tunnel structures in various complicated conditions of loading. Another benefit of using numerical simulation is in the colorful illustrations predicting the tunnel behavior before, during and after construction and operation.

There are not many conducted studies using numerical models to tunnel structures that estimate the critical zones. As some of the methods available have limitation in simulating and modeling the whole tunnel design factors, numerical modeling seems to be the best option, because it is fast, economical, accurate and more interesting in predicating critical zones in tunnel. However, what softwares predict are not always the same as real ground nature conditions in which there is tunnel.

Evaluation of the quality and performance of a tunnel lining during the installation of segments are the main objects of tunneling projects. Because the quality is affected by several attributes, the purpose of this paper is an appropriate multivariate data analysis that is helpful in extracting applicable knowledge of the data collected regarding the related attributes of the initial installed rings.

Principal component analysis (PCA) is used to analyze the data obtained by the quality control team. The authors use canonical correlation analysis (CCA) to extract some linear combinations of the original attributes of the two groups that produce the largest correlations with the second set of variables.

The authors reduce the dimensionality of the original data set for further analyses, and use a small number of uncorrelated variables rather than a larger set of correlated variables to take effective and efficient action to control the quality of the tunnel lining. The authors also explore the correlation structure and relationship between two main groups of characteristics used for assessing the quality of the installed rings. Then, instead of a large number of the original characteristics in the two groups, the authors can easily control these few to attain a reasonable quality for the tunnel lining.

This is a case study, and for each ring selected for inspection, 16 different characteristics are measured and the observations are recorded. The authors use PCA and CCA to analyse the data and interpret the results. Although the methods are not new, applying them to this data results in useful and informative outcomes and interpretation.

Integrating construction and site layout planning in mechanized tunnel infrastructure projects is essential due to the mutual impacts of construction planning and site layout decisions. Simulation can incorporate site layout planning and construction planning of tunneling projects in a unified environment. However, simulation adoption by industry practitioners has remained relatively limited due to the special skills required for building and using simulation models. Therefore, this paper aims to create a simple-to-use simulation tool that supports site layout and construction operation planning of tunneling projects. This tool intends to promote the simulation application in site layout planning.

The current paper proposes simulation as a decision support tool (DST) to provide an integrated environment for modeling tunnel construction operations, site layout and capturing the mutual impacts. A special purpose simulation (SPS) tool was customized and developed for typical mechanized tunneling projects, by tunnel boring machines, to facilitate building the model and allow access to users with limited simulation knowledge.

The results show that the developed SPS tool is of great assistance to construction industry practitioners to analyze a variety of site layout and construction plan scenarios and make informed decisions based on its comprehensive and intuitive outputs.

The main contribution of this research is to promote simulation application in site layout planning of tunneling projects through the development of a simple-to-use tool, which has sufficient details for site layout planning and constraints. The developed DST enables planners to make decisions simultaneously on the site layout, other construction planning variables and identify the most efficient plan.

In shield tunneling projects, human, shield machine and underground environment are tightly coupled and interacted. Accidents often occur under dysfunctional interactions among them. Therefore, this paper aims to develop a multi-agent based safety computational experiment system (SCES) and use it to identify the main influential factors of various aspects of human, shield machine and underground environment.

The methods mainly comprised computational experiments and multi-agent technologies. First, a safety model with human-machine-environment interaction consideration is developed through the multi-agent technologies. On this basis, SCES is implemented. Then computational experiments are designed and performed on SCES for analyzing safety performance and identifying the main influential factors.

The main influential factors of two common accidents are identified. For surface settlement, the main influential factors are ranked as experience, soil density, soil cohesion, screw conveyor speed and thrust force in descending order of influence levels; for mud cake on cutter, they are ranked as soil cohesion, experience, cutter speed and screw conveyor speed. These results are consistent with intuition and previous studies and demonstrate the applicability of SCES.

The proposed SCES provides comprehensive risk factor identification for shield tunneling projects and also insights to support informed decisions for safety management.

A safety model with human-machine-environment interaction consideration is developed and computational experiments are used to analyze the safety performance. The novel method and model could contribute to system-based safety research and promote systematic understanding of the safety performance of shield tunneling projects.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.