Prediction method of shield tunneling parameters in pebble stratum formed by weathered granite and quartzite

The pebble formation formed by weathered quartzite and granite is usually of high strength, strong permeability and poor self stability, which brings great difficulties to shield tunneling. It is necessary to adjust the tunneling parameters at any time to prevent the consequences of instability of the tunnel face, loss of stratum and surface settlement caused by the imbalance of the soil bin pressure. GA algorithm is embedded into PSO algorithm for parameter optimization, and grey theory is combined to establish the prediction model of soil chamber pressure based on grey least square support vector machine, which can solve the problem of difficult control of tunneling parameters in pebble stratum formed by weathered quartzite and granite. Research indicates: GA-PSO-GLSSVM chamber Earth pressure prediction model enhances the EPB chamber Earth pressure forecast accuracy in complicated strata by integrating the global search capability of the GA algorithm, the quick convergence of the PSO algorithm, and the anti-disturbance capability of the GM model. The GA-PSO-GLSSVM model has high goodness-of-fit and accuracy compared with other prediction models. This model can successfully prevent the a series of undesirable consequences such as destabilization of the palm surface, missing strata and settlement due to ground surface due to pressure imbalance in the soil chamber pressure. The research results can provide reference for EPB shield tunneling parameter control in Grade V surrounding rock

Coupled Field Equations for Saturated Soils and Its Application to Piezocone Penetration and Shield Tunneling.

An elasto-plastic coupled system of equations are formulated here in order to describe the time-dependent deformation of saturated cohesive soils. Formulation of these equations is based on the principle of virtual work and the theory of mixtures for inelastic porous media as proposed by Prevost (1980) and Kiousis and Voyiadjis (1988). The saturated soil is considered as a mixture consisting of two deformable media, the solid grains and the water. Each medium is regarded as a continuum and follows its own motion. The coupled equations are developed for large deformations with finite strains in an updated Lagrangian reference frame. The coupled behavior of the two phase material is implemented into the finite element program GAP/CTM (Geotechnical Analysis Program based on the Coupled Theory of Mixtures), which is developed by the author. This formulation is applied in the analysis of two geotechnical problems. The piezocone penetration and the shield tunneling in cohesive soils. The piezocone penetration in cohesive soils is numerically simulated and implemented into the finite element program (GAP/CTM). The continuous penetration of the cone is simulated by applying an incremental vertical movement of the cone tip boundary. The numerical simulation is done for two cases. In the first case, the interface friction between the soil and the piezocone penetrometer is neglected. In the second case, interface friction is assumed between the soil and the piezocone. Results obtained from the simulation using the proposed model are compared with those obtained from the miniature piezocone penetration tests (PCPT) for cohesive soil specimens conducted at the LSU calibration chamber. The resulting excess pore pressure distribution and its dissipation using the numerical model are compared with some available predicting methods. A two-dimensional computational model is developed in order to simulate the continuous advance of the Earth Pressure Balance (EPB) Shield during the tunneling process in cohesive soils. This model is implemented into the finite element program (GAP/CTM). The computational model is based on the plane strain transverse-longitudinal sections that can incorporate the three-dimensional deformation of the soil around and ahead of the shield face. The continuous shield advance is modeled using the remeshing technique. This model has been used to analyze the N-2 tunnel project constructed in 1981 in San Francisco, California

Predictive Models to Evaluate the Interaction Effect of Soil-Tunnel Interaction Parameters on Surface and Subsurface Settlement

Nowadays, the need for subway tunnels has increased considerably with urbanization and population growth in order to facilitate movements. In urban areas, subway tunnels are excavated in shallow depths under densely populated areas and soft ground. Its associated hazards include poor ground conditions and surface settlement induced by tunneling. Various sophisticated variables influence the settlement of the ground surface caused by tunneling. The shield machine's operational parameters are critical due to the complexity of shield-soil interactions, tunnel geometry, and local geological parameters. Since all elements appear to have some effect on tunneling-induced settlement, none stand out as particularly significant; it might be challenging to identify the most important ones. This paper presents a new model of an artificial neural network (ANN) based on the partial dependency approach (PDA) to optimize the lack of explainability of ANN models and evaluate the sensitivity of the model response to tunneling parameters for the prediction of ground surface and subsurface settlement. For this purpose, 239 and 104 points for monitoring surface and subsurface settlement, respectively, were obtained from line Y, the west bond of Crossrail tunnels in London. The parameters of the ground surface, the trough, and the tunnel boring machine (TBM) were used to categorize the 12 potential input parameters that could impact the maximum settlement induced by tunneling. An ANN model and a standard statistical model of multiple linear regression (MLR) were also used to show the capabilities of the ANN model based on PDA in displaying the parameter's interaction impact. Performance indicators such as the correlation coefficient (R2), root mean square error (RMSE), and t-test were generated to measure the prediction performance of the described models. According to the results, geotechnical engineers in general practice should attend closely to index properties to reduce the geotechnical risks related to tunneling-induced ground settlement. The results revealed that the interaction of two parameters that have different effects on the target parameter could change the overall impact of the entire model. Remarkably, the interaction between tunneling parameters was observed more precisely in the subsurface zone than in the surface zone. The comparison results also indicated that the proposed PDA-ANN model is more reliable than the ANN and MLR models in presenting the parameter interaction impact. It can be further applied to establish multivariate models that consider multiple parameters in a single model, better capturing the correlation among different parameters, leading to more realistic demand and reliable ground settlement assessments. This study will benefit underground excavation projects; the experts could make recommendations on the criteria for settlement control and controlling the tunneling parameters based on predicted results. Doi: 10.28991/CEJ-2022-08-11-05 Full Text: PD

Tunneling-induced ground movement and building damage prediction using hybrid artificial neural networks

The construction of tunnels in urban areas may cause ground displacement which distort and damage overlying buildings and services. Hence, it is a major concern to estimate tunneling-induced ground movements as well as to assess the building damage. Artificial neural networks (ANN), as flexible non-linear function approximations, have been widely used to analyze tunneling-induced ground movements. However, these methods are still subjected to some limitations that could decrease the accuracy and their applicability. The aim of this research is to develop hybrid particle swarm optimization (PSO) algorithm-based ANN to predict tunneling-induced ground movements and building damage. For that reason, an extensive database consisting of measured settlements from 123 settlement markers, geotechnical parameters, tunneling parameters and properties of 42 damaged buildings were collected from Karaj Urban Railway project in Iran. Based on observed data, the relationship between influential parameters on ground movements and maximum surface settlements were determined. A MATLAB code was prepared to implement hybrid PSO-based ANN models. Finally, an optimized hybrid PSO-based ANN model consisting of eight inputs, one hidden layer with 13 nodes and three outputs was developed to predict three-dimensional ground movements induced by tunneling. In order to assess the ability and accuracy of the proposed model, the predicted ground movements using proposed model were compared with the measured settlements. For a particular point, ground movements were obtained using finite element model by means of ABAQUS and the results were compared with proposed model. In addition, an optimized model consisting of seven inputs, one hidden layer with 21 nodes and one output was developed to predict building damage induced by ground movements due to tunneling. Finally, data from damaged buildings were used to assess the ability of the proposed model to predict the damage. As a conclusion, it can be suggested that the newly proposed PSO-based ANN models are able to predict three-dimensional tunneling-induced ground movements as well as building damage in tunneling projects with high degree of accuracy. These models eliminate the limitations of the current ground movement and building damage predicting methods

One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction

An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% R-2, the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s

Advanced Theoretical and Computational Methods for Complex Materials and Structures

The broad use of composite materials and shell structural members with complex geometries in technologies related to various branches of engineering has gained increased attention from scientists and engineers for the development of even more refined approaches and investigation of their mechanical behavior. It is well known that composite materials are able to provide higher values of strength stiffness, and thermal properties, together with conferring reduced weight, which can affect the mechanical behavior of beams, plates, and shells, in terms of static response, vibrations, and buckling loads. At the same time, enhanced structures made of composite materials can feature internal length scales and non-local behaviors, with great sensitivity to different staking sequences, ply orientations, agglomeration of nanoparticles, volume fractions of constituents, and porosity levels, among others. In addition to fiber-reinforced composites and laminates, increased attention has been paid in literature to the study of innovative components such as functionally graded materials (FGMs), carbon nanotubes (CNTs), graphene nanoplatelets, and smart constituents. Some examples of smart applications involve large stroke smart actuators, piezoelectric sensors, shape memory alloys, magnetostrictive and electrostrictive materials, as well as auxetic components and angle-tow laminates. These constituents can be included in the lamination schemes of smart structures to control and monitor the vibrational behavior or the static deflection of several composites. The development of advanced theoretical and computational models for composite materials and structures is a subject of active research and this is explored here for different complex systems, including their static, dynamic, and buckling responses; fracture mechanics at different scales; the adhesion, cohesion, and delamination of materials and interfaces

An integrated platform for design and numerical analysis of shield tunnelling processes on different levels of detail

Building and construction information modelling for decision making during the life cycle of infrastructure projects are vital tools for the analysis of complex, integrated, multi-disciplinary systems. The traditional design process is cumbersome and involves significant manual, time-consuming preparation and analysis as well as significant computational resources. To ensure a seamless workflow during the design and analysis and to minimise the computation time, we propose a novel concept of multi-level numerical simulations, enabling the modelling on different Levels of Detail (LoDs) for each physical component, process information, and analysis type. In this paper, we present SATBIM, an integrated platform for information modelling, structural analysis and visualisation of the mechanised tunnelling process for design support. Based on a multi-level integrated parametric Tunnel Information Model, numerical models for each component on different LoDs are developed, considering proper geometric as well as material representation, interfaces and the representation of the construction process. Our fully automatic modeller for arbitrary tunnel alignments provides a high degree of automation for the generation, the setup and the execution of the simulation model, connecting the multi-level information model with the open-source simulation software KRATOS. The software of SATBIM is organized in a modular way in order to offer high flexibility not only for further extensions, but also for adaptation to future improvements of the simulation software. The SATBIM platform enables practical, yet flexible and user-friendly generation of the tunnel structure for arbitrary alignments on different LoDs, supporting the design process and providing an insight into soil-structure interactions during construction

Integrated parametric multi-level information and numerical modelling of mechanised tunnelling projects

This paper presents a concept for parametric modelling of mechanized tunnelling within a state of the art design environment, as the basis for design assessments for different levels of details (LoDs). To this end, a parametric representation of each system component (soil with excavation, tunnel lining with grouting, Tunnel Boring Machine (TBM) and buildings) is developed in an information model for three LoDs (high, medium and low) and used for the automated generation of numerical models of the tunnel construction process and soil-structure interaction. The platform enables a flexible, user-friendly generation of the tunnel structure for arbitrary alignments based on predefined structural templates for each component, supporting the design process and at the same time providing an insight into the stability and safety of the design. This model, with selected optimal LoDs for each component, dependent on the objective of the analysis, is used for efficient design and process optimisation in mechanized tunnelling. Efficiency and accuracy are further demonstrated through an error-free exchange of information between Building Information Modelling (BIM) and the numerical simulation and with significantly reduced computational effort. The interoperability of the proposed multi-level framework is enabled through the use of an efficient multi-level representation context of the Industry Foundation Classes (IFC). The results reveal that this approach is a major step towards sensible modelling and numerical analysis of complex tunnelling project information at the early design stages