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

Stratum Displacement Law and Intelligent Optimization Control Based on Intelligent Fuzzy Control Theory During Shield Tunneling

The laws of Stratum displacement and optimal control are critical for shield operation. This article’s focus is made on the intelligent fuzzy control theory concentrating on earth pressure, total thrust, driving speed, cutter torque, grouting pressure and grouting volume as the main elements of the study. A model of intelligent fuzzy control theory based on the model of No. 9 Line of Guangzhu Rail transit, on the Tianma river shield section. The paper also analyzes stratum displacement law due to shield tunnelling, executes & analyses intelligent controls for optimization of parameters, combining the five two-dimensional structures of the double structure of fuzzy control system. According to the observations made on the model. The model is upto date and the control of all parameters develops stably. The parameter ranges should be controlled as follows: earth pressure, 0.19 ~ 0.22Mpa; total thrust, 1100 ~ 1350T; driving speed, 38 ~ 50mm / min; cutter torque, 1600 ~ 2300 KN • m; grouting pressure, 0.19 ~ 0.25Mpa and grouting volume, 30 ~ 50L/min. Keywords: Shield tunnel, intelligent fuzzy control, Stratum displacement, optimal control DOI: 10.7176/CER/13-6-01 Publication date:October 31st 202

Tunnel Settlement Prediction by Transfer Learning

Tunnel settlement has a significant impact on property security and personal safety. Accurate tunnel-settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point in the tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, a source model is constructed and trained by deep learning, then parameter transfer is used to transfer the knowledge gained from the source model to the target model, which has a small dataset. Based on this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the tunnel of Shanghai metro line 13 at Jinshajiang Road and proven to be effective. Artificial neural network and support vector machines were also tested for comparison. The results showed that the transfer-learning method provided the most accurate tunnel-settlement prediction

Ground movement associated with microtunneling

Microtunneling is a trenchless technology for construction of pipelines. Its process is a cyclic pipe jacking operation. Microtunneling has been typically used for gravity sewer systems in urban areas. Despite its good success record overall, several large ground settlement cases caused by microtunneling have been reported. Also, in contrast with large diameter urban tunneling, there are few research projects about the ground settlement caused by microtunneling. In this dissertation, the ground settlement caused by microtunneling is studied using a theoretical approach, empirical approach, numerical simulation approach, and artificial intelligence approach. In the theoretical approach, the equivalent ground loss and settlement caused by concentrated ground loss have been used to drive the ground settlement profile. In the empirical approach, the ground settlement caused by large diameter tunneling case histories is used. In the numerical approach, FLAC 3D software, a commercially available finite difference code, is used to simulate the ground settlement caused by microtunneling. In the artificial intelligence approach, a three-layer back propagation neural network is developed to predict the ground settlement caused by microtunneling using the numerical simulation results. It is found that the neural network developed as part of this thesis work provides a means of rapid prediction of the surface ground settlement curve based on the soil parameters, project geometry and estimated ground loss. This prediction matches FLAC3D results very well over the full range of parameters studied and has a reasonable correspondence to the field results with which it was compared

A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction

Ground settlement prediction during the process of mechanized tunneling is of paramount importance and remains a challenging research topic. Typically, two paradigms are existing: a physics-driven approach utilizing process-oriented computational simulation models for the tunnel-soil interaction and the settlement prediction, and a data-driven approach employing machine learning techniques to establish mappings between influencing factors and the ground settlement. To integrate the advantages of both approaches and to assimilate the data from different sources, we propose a multi-fidelity deep operator network (DeepONet) framework, leveraging the recently developed operator learning methods. The presented framework comprises of two components: a low-fidelity subnet that captures the fundamental ground settlement patterns obtained from finite element simulations, and a high-fidelity subnet that learns the nonlinear correlation between numerical models and real engineering monitoring data. A pre-processing strategy for causality is adopted to consider the spatio-temporal characteristics of the settlement during tunnel excavation. Transfer learning is utilized to reduce the training cost for the low-fidelity subnet. The results show that the proposed method can effectively capture the physical information provided by the numerical simulations and accurately fit measured data as well. Remarkably, even with very limited noisy monitoring data, the proposed model can achieve rapid, accurate, and robust predictions of the full-field ground settlement in real-time during mechanized tunnel excavation

FRACTAL SPACE BASED DIMENSIONLESS ANALYSIS OF THE SURFACE SETTLEMENT INDUCED BY THE SHIELD TUNNELING

The surface settlement during the tunneling process is becoming increasingly difficult to forecast as its surroundings become more and more erratic, and the maximal surface settlement raises risks posed suddenly by various uncertain factors. This paper proposes a novel approach to prediction of the surface settlement and analyzes the stability of tunnel construction. The dimensionless analysis and Buckingham’s π-theorem are adopted for this purpose, and some useful dimensionless quantities are found, which can be used to determine the surface settlement’s main properties. In this manner, the paper offers new ways of predicting surface settlement in various cases, and it sheds a new light on the tunnel’s design and safety monitoring

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

Earth pressure balance (EPB) shield tunneling in Bangkok : ground response and prediction of surface settlements using artificial neural networks

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Vita.Includes bibliographical references.Although Earth Pressure Balance (EPB) shields have been used for several decades, very little information exists about the actual mechanisms of shield-ground interaction. The ground response mechanism induced by EPB tunneling is difficult to understand, because this requires not only reliable ground deformation measurements in the field but also operational records of the shield. Numerous empirical and analytical relations between characteristics of traditional shields and surface and subsurface deformations exist; also 2-D and 3-D numerical analyses have been applied to such tunneling problems. However, very few approaches have been developed for EPB tunneling. This research makes use of the fact that in the Bangkok MRTA project, data on ground deformation and shield operation were collected. The tunnel sizes are practically identical and the subsurface conditions over long distances are comparable, which allow one to establish relationships between ground characteristics and EPB-operation on the one hand, and surface and subsurface deformations on the other hand. A computerized database, which records much of the information on a ring-by-ring (1.2 meter interval) basis, was developed for this purpose. After using the information to identify which ground- and EPB-characteristic have the greatest influence on ground movements, an approach based on Artificial Neural Networks (ANN) was used to develop predictive relations. Since the method has the ability to map input to output patterns, ANN enable one to map all influencing parameters to surface settlements.(cont.) Combining the extensive computerized database and the knowledge of what influences the surface settlements, ANN can become a useful predictive method. This research attempts to evaluate the potential as well as the limitations of ANN for predicting surface settlements caused by EPB shield tunneling and to develop optimal neural network models for this purpose. Specifically, this involves settlement predictions over the tunnel axes of single and twin tunnels; together with other interpretations, it is also possible to predict settlement troughs. Other shield effects such as lateral deformation and liner deformation of the first tunnel caused by the second tunnel are also evaluated.by Suchatvee Suwansawat.Sc.D

A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering

The paper proposes a novel computational method for real-time simulation and monitoring-based predictions during the construction of machine-driven tunnels to support decisions concerning the steering of tunnel boring machines (TBMs). The proposed technique combines the capacity of a process-oriented 3D simulation model for mechanized tunnelling to accurately describe the complex geological and mechanical interactions of the tunnelling process with the computational efficiency of surrogate (or meta) models based on artificial neural networks. The process-oriented 3D simulation model with updated model parameters based on acquired monitoring data during the advancement process is used in combination with surrogate models to determine optimal tunnel machine-related parameters such that tunnelling-induced settlements are kept below a tolerated level within the forthcoming process steps. The performance of the proposed strategy is applied to the Wehrhahn-line metro project in Düsseldorf, Germany and compared with a recently developed approach for real-time steering of TBMs, in which only surrogate models are used

LIQUID-SOLID COUPLING RESPONSE OF SURROUNDING ROCK MASS OF LARGE-DIAMETER RIVER-CROSSING SHIELD TUNNEL

The purpose is to investigate the response of seepage field, displacement field and stress field in the surrounding rock mass during dynamic tunneling in soft soil area. Relied on a large-diameter river-crossing shield tunnel project, considering driving force, shield tail grouting pressure, and the friction resistance between the shield shell and the soil, a three-dimensional fine tunnel model considering the liquid-solid coupling effect in the soil during dynamic tunneling was established by employing the finite difference method. The response characteristics of pore water pressure, displacement and stress in the surrounding rock mass were obtained. The results show that during shield tunneling and shield tail grouting, the pore water pressure in the range of 0.5 times the hole diameter around the tunnel decreases and increases respectively due to the liquid-solid coupling in the surrounding rock mass. When the shield tunneling moves away, the pore water pressure of the soil near the vault decreases, and the pore water pressure near the tunnel arch bottom increases. The impact range of shield tail grouting on the vertical settlement of the upper soil is about 0.5 times the hole diameter. The shield tail grouting can effectively reduce the vertical settlement of the top soil and slow down the vertical uplift of the bottom soil. During shield tunneling the vertical stress distribution of the soil above the vault of the working position and around the excavation surface is funnel-shaped, and the vertical stress around the excavated tunnel decreases