Safety Of Tunnel Construction Research Articles

Safety risk assessment of loess tunnel construction under complex environment based on game theory-cloud model

Due to the impact of the surrounding environment, the safety impact factors encountered during the construction of loess tunnels are complex and numerous, which causes frequent accidents, and there is a lack of safety risk assessment methods applicable to the construction of loess tunnels under complex environment. Based on the Luochuan tunnel project of the Xi’an–Yan’an High-Speed Railway, this paper analyzes the impact factors of loess tunnel construction risks, and 15 main impact factors involving subjective and objective factors are selected to establish the safety risk assessment system of loess tunnel construction under complex environment. To determine the weight of the impact factors, this paper introduces the combination weighting method based on game theory for the first time. Then, the risk assessment model of loess tunnel construction safety is established by using the conventional cloud model theory. Finally, the model is applied to the supporting project for verification. The results show that support and lining have the largest impact on tunnel construction safety, followed by construction management, surrounding rock grade, harmful engineering ground, monitoring measurement, forepoling, and construction method. The assessment result is consistent with the actual construction risk degree, which proves that the assessment result of the model is accurate and reliable, and the model has guiding significance for the safety risk assessment of loess tunnel construction under complex environment.

Experimental study on evolution law of dynamic characteristic parameters during the tunnel surrounding rock block collapse process

Tunnel surrounding rock block collapse is one of the most common geological disasters in the fractured rock tunnel. Due to its sudden characteristics, the traditional monitoring methods such as displacement monitoring cannot be effective, which seriously threatens the safety of tunnel construction. In this paper, the monitoring experiment research of the whole rock block collapse process is carried out, by introducing a number of time-domain and frequency-domain dynamic characteristic parameters such as Vibration Peak Value, Crest Factor, Natural Vibration Frequency, etc. The experiment results show that the evolution law of dynamic characteristic parameters such as the Vibration Peak Value, RMS Value, Crest Factor, Natural Vibration Frequency in the whole rock block collapse process can be clearly divided into three stages: Stability phase, Detachment phase, Accelerated destruction phase. Moreover, the strength and transfixion rate of the main structure plane have a great influence on the dynamic characteristic parameters of the block, but the dynamic characteristic parameters have no obvious relationship with the transfixion position of the main structure plane. In view of the tunnel engineering, this paper proposes a three-color early warning method for the tunnel surrounding rock block collapse based on dynamic characteristic parameters with Crest Factor and natural vibration frequency as the main monitoring indexes. The method is applied in the K97 + 860.1 section of Qingshan Tunnel in Jinan Ring Expressway, and the collapse disaster is warned 12 min in advance, which verifies the feasibility of this method. This research provides a new way for the monitoring and early warning method of tunnel surrounding rock block collapse disaster, and can provide important support for the active prevention and control of block collapse disaster.

Coupling Analysis of Tunnel Construction Safety Risks Based on N-K Model and SD Causality Diagram

Tunnel construction is characterized by its large scale, long periods and vulnerability to environmental impact, which pose great challenges to tunnel construction safety. In order to analyze the coupling mechanism of tunnel construction safety risks and assess these risks, we conducted a study on the coupling evaluation of these risks in order to improve tunnel construction safety risk management. By analyzing 150 accident cases related to tunnel construction safety, an N-K model (natural killing model) was constructed to quantify the risk level of each coupling form from four aspects—personnel risk factors, equipment risk factors, environmental risk factors and management risk factors—and the SD (system dynamics) causality diagram was used to construct risk element conduction paths and identify the key influencing factors of different coupling forms. The research results show that with the increase in risk coupling factors, the risk of tunnel construction safety accidents also increases; weak personnel safety awareness, aging and wear of equipment, poor operating environment and construction site management chaos are the key risk factors whose prevention needs to be focused on. The related research results can provide a new method for decision makers to assess tunnel construction safety risks and enrich the research on tunnel construction safety risk management.

Construction and Application of LSTM-Based Prediction Model for Tunnel Surrounding Rock Deformation

Tunnel surrounding rock deformation is a significant issue in tunnel construction and maintenance and has garnered attention from both domestic and international scholars. Traditional methods of predicting tunnel surrounding rock deformation involve fitting monitoring and measuring data, which is a laborious and resource-intensive process with low accuracy when predicting data with significant fluctuations. A deep learning approach can improve monitoring efficiency and accuracy while reducing labor costs. In this study, taking an actual tunnel project as an example, a long short-term memory (LSTM) network model was constructed based on the recurrent neural network algorithm with deep learning to model and analyze the tunnel monitoring and measurement data, and the model was used to analyze and predict the vault settlement of the tunnel. LSTM is a type of artificial neural network architecture that is commonly used in deep learning applications for sequence prediction tasks, such as natural language processing, speech recognition, and time-series forecasting. In predicting data with smaller fluctuations, the maximum error is 4.76 mm, the minimum error is 0.03 mm, the root mean square error is 2.64, and the coefficient of determination is 0.98. In predicting data with larger fluctuations, the maximum error is 8.32 mm, the minimum error is 0.13 mm, the root mean square error is 4.42, and the coefficient of determination is 0.88. The average error of the LSTM network model is 2.16 mm. With the growth of the prediction period, the prediction results become more and more stable and closer to the actual vault settlement, which provides a reliable reference for introducing the LSTM prediction method with deep learning to tunnel construction and promoting tunnel construction safety.

Response of acoustic emission and vibration monitoring data during rock block collapse in the tunnel: Small- and large-scale experiments study

Block collapse is a common disaster in the jointed rock mass of tunnels and usually involves two typical evolution stages: rock bridge breakage and rock block instability. The evolutionary characteristics of mutation induced by the gradual failure of rock mass are the mechanical basis for monitoring and assessing disasters. In this study, an acoustic emission (AE)-vibration joint monitoring method is proposed to fully investigate the two failure stages based on the disaster mechanism. It is critical to reveal the evolution law of AE and natural frequency information in block collapse. Small-scale shear tests on rock specimens are conducted to investigate the rock bridge breakage stage, while large-scale physical simulation tests are performed to investigate the block instability stage. During rock bridge breakage, AE exhibits a clearer signal response to the internal rock fracture than the natural frequency and captures brittle failure under low- and medium-stress conditions. The natural frequency exhibits a significant signal response under low-stress conditions during the block instability stage. Under medium- and high-stress conditions, the signal becomes less sensitive. Under low-stress conditions, the natural frequency of the block gradually decreases with increasing free surface, implying that the stability of the block declines. The natural frequency decreases with the decreasing stiffness of the block, which is a vital precursor to the early warning of block collapse disasters. Additionally, AE is the primary indicator in the rock bridge breakage stage, while natural frequency is the primary indicator in the rock block instability stage. From the perspective of technological innovation in underground tunneling, AE-Vibration joint monitoring prompts a new idea to evaluate the stability of key blocks in tunnel construction safety applications.

A novel hybrid model for missing deformation data imputation in shield tunneling monitoring data

Complete and high-quality deformation monitoring data are critical for shield tunnel construction safety and quality. In engineering practices, data missing frequently occurs during instrumentation, adversely impacting further analysis and decision making. Existing imputation methods either ignore the crucial interactions between different parameters during shield tunneling or focus on the global characteristics of deformation data while neglect their local difference. This paper proposes a novel hybrid model, MCCB, combining multi-view matrix completion algorithms, convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM) algorithms to impute missing deformation values in shield tunnel monitoring data. The performance of the proposed method is verified using bridge deformation data from a practical project in Beijing. Different missing patterns of the bridge deformation data are filled. The experiment results show that the proposed model can effectively learn the various characteristics of the deformation data and outperforms the four selected models and its two sub-models, and can be used to improve the accuracy of the deformation prediction through data imputation. The novelty of this study includes two aspects. First is that the complicated interaction between different parameters and local difference of the data are considered simultaneously. They have not been addressed before by existing studies. Second is that the innovative combination of matrix completion and deep learning algorithm for application in missing deformation values imputation. To our best knowledge, no research on engineering construction has implemented this technique before.

Predicting tunnel water inflow using a machine learning-based solution to improve tunnel construction safety

Water inflow is a typical and complicated geological hazard that may have a significant effect on both the building timeline and the safety of a tunnel under construction. Therefore, accurate water inflow estimation in tunneling is a key factor for the project's success. Such information is critical for the early conceptual and design phases, when key choices must be made. For this purpose, an optimized model based on the gene expression programming (GEP) method was proposed to estimate the water inflow in tunnels. An equation was generated for the optimized GEP model through the best fit of the predictions. Finally, by comparing the equation’s outputs with the actual ones and comparing its behavior with practice, its potential ability for estimating the water inflow of tunnels was approved. This model can reduce the uncertainties about tunnels and give machine learning development in tunnel planning.

Stability Analysis of Tunnel Surrounding Rock When TBM Passes through Fracture Zones with Different Deterioration Levels and Dip Angles

In fracture zones, tunneling with a double-shield Tunnel Boring Machine (TBM) presents significant challenges, including deformation overrun of the surrounding rock, TBM jamming, and excavation face collapse. To assure the tunnel construction safety and efficiency, it is necessary and crucial to conduct a stability analysis of the tunnel surrounding rock when a TBM passes through the fracture zones. The tunnels from Jiadingshan Road Station to Anshan Road Station in Qingdao Metro Line 8 are constructed by double-shield TBMs. It inevitably passes through fracture zones with different deterioration levels and dip angles. In this study, based on this construction section, numerical models of fracture zones with different deterioration levels and dip angles were developed to analyze the displacements of tunnel vaults, inverts, and haunches. In addition, the maximum shear stresses of the surrounding rock were analyzed. Finally, the displacement and shear stress variation patterns of the surrounding rock with different deterioration levels and dip angles were obtained. The findings reveal the stability behavior of tunnels under various fracture zones. They can serve as a valuable reference and theoretical foundation for future tunnel construction projects utilizing double-shield TBMs in areas with fracture zones.

3C-3D tunnel seismic reverse time migration imaging: A case study of Pearl River Delta Water Resources Allocation Project

In recent years, tunnel boring machines (TBMs) have been widely used because of their fast construction speed and economical benefits, and the need to obtain geological information ahead of the tunnel to ensure the TBM tunnel construction safety has also arisen. Faced with the complex geological environment, seismic forward-prospecting is the primary method for predicting the unfavorable geological structure in front of the tunnel face. As a crucial step in seismic forward-prospecting method, migration can image geological characteristics and help learn the distribution of unfavorable geology. To achieve all-round imaging of unfavorable geology, we suggested a three-component (3C) three-dimensional (3D) tunnel seismic reverse time migration imaging method. The acoustic wave equation is employed for the forward and backward wavefield extrapolation after the P/S-wave separation to eliminate the imaging artifacts from severe converted waves in tunnels. GPU parallelization is used to accelerate the calculation to further adapt to the rapid excavation of TBMs. The proposed method is validated in two typical numerical models, especially for 3C seismic data that provides all-round geological information to achieve enhanced imaging energy and artifact reduction, which demonstrates the significance of 3C seismic data to migration imaging. This case study was conducted in the Pearl River Delta Water Resources Allocation Project in Guangdong Province, China. The reliability of 3C3D tunnel seismic reverse time migration imaging in field tunnel engineering was investigated. Two consecutive detections were performed, and then the spatial positions of faults and fractures in front of the tunnel face were accurately judged by the combination of the two imaging results. Based on the detection results, the excavation rate was slowed down and the tunnel lining support was increased. The TBM then successfully passed through the risk areas. This case study can serve as a reference for other tunnels with similar concerns regarding the necessity of 3C seismic data and consecutive detections.

Improved Surrounding Rock Classification Method for the Middle Rock Pillar of a Small Clear-Distance Tunnel

In tunnel design and construction, proper and accurate classification of rock surrounding tunnels is needed to ensure tunnel construction safety, guarantee construction quality, and reduce project costs. With rapid urbanization, numerous small clear-distance tunnels have been constructed in dense urban road networks. Compared with ordinarily separated tunnels, the construction scale and difficulty of small clear-distance tunnels are greater, and the requirements for the classification of rock surrounding tunnels are accordingly higher. A small clear-distance tunnel in an urban super large and complex underground interchange hub of the Xiamen Haicang Evacuation Channel was selected as the background, and the classification method of the middle rock pillar in a small clear-distance tunnel is presented based on the general classification standard of surrounding rocks. Based on the geometric, physical, and mechanical factors of the middle rock pillar, six indices affecting the stability and quality of the middle rock pillar were selected, and the classification index system of the middle rock pillar was established from the two dimensions of the basic and auxiliary indices. The basic and auxiliary indices were scored using the scoring method, and the different grades of the middle rock pillar were divided according to different scores. The middle rock pillar classification standard was applied to the quality assessment of the middle rock pillar, which provided a basis for the on-site assessment of the quality of the middle rock pillar and proved the accuracy and superiority of the improved classification standard. The newly established classification standard can provide a reference for selecting the correct construction method and supporting structure type for small clear-distance tunnels.

Study of the disaster-causing mechanism and reinforcement measures for soft rock deformation and lining cracking

Underground engineering construction is facing increasingly complex geological conditions and engineering challenges, such as surrounding rock deformation and lining cracking, that seriously threaten the safety of tunnel construction and operation. Aiming at these problems, a pipeline tunnel crossing jointed expansive mudstone strata was taken as an example, and the disaster characteristics of surrounding rock and lining were analyzed through field investigation. The disaster-causing mechanism and corresponding control measures were studied through laboratory tests and numerical simulations, which were then applied to actual construction. Meanwhile, the deformation and stress response of the surrounding rock and tunnel structure were analyzed and investigated through monitoring and numerical data. The results showed that the vault settlement and horizontal convergence deformation of surrounding rock were reduced by 64.69 mm and 54.74 mm, respectively, under the improved construction scheme. The maximum surrounding rock stress was 430.26 kPa under the improved construction scheme, which was 18.15% lower than the original stress. The maximum axial force of the steel arch frame was 33.02 kN, ensuring the stability of the supporting structure and tunnel construction safety. Finally, the rationality and effectiveness of the reinforcement measures adopted were assessed.

Risk factors influencing tunnel construction safety: Structural equation model approach

At present, the global tunnel construction industry is developing rapidly, but construction accidents are also common. A large number of casualties and property losses are alarming people. It is urgent to pay attention to the causes of tunnel construction accidents, ensure the safety of construction sites, and reduce tunnel construction accidents. Through literature and case analysis, we have sorted out 35 typical tunnel causative factors for research and analysis, which are divided into 7 types. Based on the variable system, we prepared a measurement questionnaire, and 536 valid questionnaires were collected. The structural equation model (SEM) was used to study the relationship between these variables. The influence mechanism and interaction relationship between the variables are analyzed in depth in terms of influence intensity and path coefficient. The results showed that the following six latent variables significantly influence tunnel construction accidents: human factors, material factors, geological exploration design, technical management, safety management, and natural conditions. Natural conditions have the most significant impact, followed by human factors and safety management. Particular attention should be paid to education, training, and safety management in construction risk control. The structural model and research results are helpful to establish the cause theory of tunnel construction accidents, and guide the formulation of safety management policies for tunnel construction projects, reduce tunnel accidents and ensure construction safety.

Factors Affecting Road Tunnel Construction Accidents in China Based on Grounded Theory and DEMATEL.

Despite the continuous progress of tunnel construction technology and safety management technology, road tunnel construction safety still faces many challenges in China, such as how to ensure the effective management and safety control of people and materials, how to ensure the implementation of technology and program implementation, risk assessment of construction site environmental information, etc. Exploring the causes of tunnel construction accidents and understanding the properties of the factors and their interrelationships can effectively control the sources of risk and contribute to the safety control of tunnel construction. Therefore, we have collected 30 formal accident investigation reports from the government safety supervision and management department from 2005 to 2021, including detailed investigation and accident analysis. Based on grounded theory, a qualitative research method to generalize experience through direct observation, abstraction, and analysis of data, we use Nvivo11 software to analyze reports and obtain 6 selective codes, 16 spindle codes, and 43 open codes. In addition, we construct a theoretical model of tunnel construction accident influencing factors, which passed the saturation test. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) model is used to analyze the influencing mechanism and interaction relationships of these factors. The two dimensions of influence degree and centrality are used to determine the critical influencing factors of tunnel construction accidents in mountainous areas. They are security awareness and professionalism. According to the cause degree, the influencing factors are divided into cause and result factors. Finally, the basis and suggestions for reducing construction accidents are presented.

Amplification effect of near-field ground motion around deep tunnels based on finite fracturing seismic source model

Dynamic failure of rock masses around deep tunnels, such as fault-slip rockburst and seismic-induced collapse, can pose a significant threat to tunnel construction safety. One of the most significant factors that control the accuracy of its risk assessment is the estimation of the ground motion around a tunnel caused by seismicity events. In general, the characteristic parameters of ground motion are estimated in terms of empirical scaling laws. However, these scaling laws make it difficult to accurately estimate the near-field ground motion parameters because the roles of control factors, such as tunnel geometry, damage zone distribution, and seismic source parameters, are not considered. For this, the finite fracturing seismic source model (FFSSM) proposed in this study is used to simulate the near-field ground motion characteristics around deep tunnels. Then, the amplification effects of ground motion caused by the interaction between seismic waves and deep tunnels and corresponding control factors are studied. The control effects of four factors on the near-field ground motion amplification effect are analyzed, including the main seismic source wavelength, tunnel span, tunnel shape, and range of damage zones. An empirical formula for the maximum amplification factor ( α m ) of the near-field ground motion around deep tunnels is proposed, which consists of four control factors, i.e. the wavelength control factor ( F λ ), tunnel span factor ( F D ), tunnel shape factor ( F s ) and excavation damage factor ( F d ). This empirical formula provides an easy approach for accurately estimating the ground motion parameters in seismicity-prone regimes and the rock support design of deep tunnels under dynamic loads.

An Established Theory of Digital Twin Model for Tunnel Construction Safety Assessment

In traditional construction safety assessment, it is difficult to describe the safety status of different construction stages. To solve this problem, this paper proposes a digital twin modeling theory for construction safety assessment. Firstly, this paper analyzes the requirements of a digital twin model. Secondly, the required information is collected by IoT. Finally, the DT model is established based on the collected information. This DT model analyzes the collected information by ML, which aims to conducting the assessments of construction safety. To verify this method, this paper analyzes the vault settlement during tunnel construction. The analysis results show that the DT model can predict the settlement value with high accuracy. Moreover, the safety state is assessed dynamically based on the settlement value by DT.

A digital twin approach for tunnel construction safety early warning and management

Accidents tend to happen during tunnel construction due to the harsh environmental and geological conditions. Towards high safety risk during tunnel construction, a digital-twin based multi-information intelligent early warning and safety management platform is proposed in this study. At first, a communication line is built inside tunnel through multifunctional base stations. Multi-source information from tunnel construction could be collected and transmitted automatically. Then, dynamic tunnel construction information and surrounding rock conditions are virtual simulated. Meanwhile, four-level early warning and emergency response plan are built to provide judgement on the construction status. At last, application effects of the platform are validated in a deep buried tunnel project, Dongtianshan Tunnel in China, which has extreme weather, harmful gas, and bad geological conditions. A collapse accident of tunnel excavation face was successfully predicted by the platform. Early warning and appropriate emergency measures were taken in time, and the personnel safety was ensured.

Study on Large Deformation Prediction and Control Technology of Carbonaceous Slate Tunnel in Lixiang Railway

The construction of railway tunnel in carbonaceous slate environment is easy to cause rock mass disturbance, which leads to large deformation of surrounding rock and then threatens the safety of tunnel construction and operation. Therefore, based on the Wanlamu tunnel and Huajiaopo tunnel projects of Lixiang railway, combined with the regional geological conditions of the tunnel, and using the field monitoring technology and statistical principle, this paper analyzes the characteristics of tunnel surrounding rock pressure, secondary lining cracking, and initial support deformation under the conditions of different bedding slate. It is analyzed that the large deformation of carbonaceous slate surrounding rock is mainly related to the bedding, characteristics of surrounding rock, bias pressure, and the existence of high ground stress. The tunnel deformation is mainly manifested in peripheral convergence and vault settlement, and the peripheral convergence value is greater than the vault settlement value. In this study, the maximum daily convergence deformation of the tunnel peripheral convergence exceeds 117.8 mm, and the maximum convergence deformation can reach 951.7 mm, which is seriously affected by the slate bedding. According to statistics, more than 90% of the cross-section surrounding rock deformation of the tunnel with the slate bedding angle of 30° exceeds 600 mm. Then, based on BP neural network, the large deformation prediction model of railway tunnel under the carbon slate environment is constructed, and the accurate prediction of large deformation of carbon slate tunnel is realized, with the minimum prediction error of only 0.81%. It is pointed out that the main factors leading to the large deformation of the carbon slate tunnel are the lithology of the slate, the difficulty in predicting the deformation of the surrounding rock, and the failure of the initial support to close in time. The tunnel deformation can be effectively controlled by strengthening the geological work, optimizing the design parameters (enhancing the support stiffness, multiple support, and increasing the reserved deformation), and mining active reinforcement.

Mine Personnel Positioning System Based on Dual-Wavelength Phase Unwrapping

During the construction of coal mine laneway, due to the particularity of the geographical location and structure of the laneway, its accidents often occur, causing great trouble to the personal and property safety of the construction personnel. Therefore, according to the environmental characteristics of the coal mine tunnel and the positive correlation between the phase envelope range and the wavelength, this paper proposes a structural design of the personnel positioning system of the coal mine tunnel based on the dual wavelength phase unwrapping, which combines the coherent detection structure with the dual wavelength phase measurement method in digital holography. In order to reduce false positives in the position information, the synthetic wavelength is used to increase the spread range of the phase. The experiment shows that this structure can reduce the false positioning without reducing the positioning accuracy, and the average double wavelength phase demodulation can reduce the positioning false alarm by 82.5%, which greatly improves the accuracy of the prediction of the tunnel construction safety, brings great protection to the personal and property safety of the tunnel construction personnel, and improves the mining efficiency of the coal mine.

Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area

The detailed analysis of the initial ground stress distribution law is an important work for the safety of tunnel construction and operation. Especially, the high ground stress phenomenon in the deep-buried tunnel area is common, which has a great impact on the tunnel construction. Based on the on-site measured ground stress data, the analysis of the initial ground stress field by numerical simulation and multiple linear regression is mainly described in this study. Following the comparison and selection of three coefficient estimation methods for the regression equation, the best regression method is selected for inversion and verification. The distribution characteristic of the initial ground stress at different buried depths of the tunnel line is obtained. The inversion results of the initial ground stress in a tunnel area, in China, show that the lateral pressure coefficient gradually decreases with the buried depth increasing, while the overall lateral pressure coefficient is in the range of 1.0–2.0, showing a more significant horizontal tectonic. At the area where the tunnel passes through the fault, a small amount of horizontal tectonic stress is released. The ratio of horizontal principal stress to vertical principal stress is smaller than that on both sides, which is different from the distribution characteristic of lateral pressure coefficient without the impact of fault. It shows that faults have a great influence on ground stress. The lateral pressure coefficient in the area near the fault must be determined according to the on-site measured results.

A new method for measuring the adhesion strength of rock-concrete specimens based on the calibration interface of the modified flat-joint model

In tunnel engineering constructed by NATM, the adhesion strength between surrounding rock and lining is an important index to measure the safety of tunnel construction. However, the field measurement and laboratory test inevitably have the problem of low efficiency. In contrast, reasonable numerical simulation can be an alternative way to study rock-concrete adhesion strength. Therefore, in this paper, the influence factors of the adhesion strength of rock-concrete are studied by direct tensile laboratory test combined with particle flow code in three-dimension (PFC3D). The flat-joint model was used to reproduce the behavior of rock and concrete. The concept of effective elastic modulus of bi-material interface was introduced to derive the modified equation of interface normal stiffness. Then the modified flat-joint model was applied to PFC3D based on the Fish language and the interface parameters were calibrated. Compared with the results of laboratory tests, it is found that this method can accurately simulate the adhesion strength between rock and concrete, which can also restore the post-peak response of laboratory tests. Therefore, more parameter studies are considered which show that: the mechanical properties of rock have little influence on the adhesion strength of composite specimens. However, the physical properties of rock, such as porosity, have great influence on that, which can be seen from the crack propagation mode at the interface of composite specimens. Sandstone cemented better with concrete will retain the rate sensitivity of rock and concrete materials to the greatest extent. When the experimental strain rate increases from 0.06 s−1 to 0.9 s−1, the adhesion strength of sandstone-concrete specimens increases by 50 %, while that of granite-concrete specimens hardly changes. The research results can be helpful to better understand the adhesion performance of rock-concrete interface and have certain engineering significance for tunnel construction supported by shotcrete.