Application Strategies of Shotcrete Anchor Support Technology in Highway Bridge and Tunnel Engineering

This article analyzes the application strategies of shotcrete anchor support technology using a highway bridge-tunnel construction project as an example.The article covers various strategies,including support plan formulation,mortar shotcrete anchor construction,grid steel frame construction,steel mesh construction,and concrete support construction.This analysis aims to provide a guideline for those interested in applying this technology and improving the quality and safety of highway bridges and tunnels construction.

Applications of Pre-Geological Prediction in Tunnel Construction

Pre-geological prediction (PGP) is defined as the prediction of engineering geologic condition and hy-drogeological condition certain distance ahead of the working face. The purpose of this paper is to introduce mainlygeologic survey before and in excavation, to clarify their emphasis on PGP. At the same time, the technique is appliedto an engineering case, the longest highway tunnel in Gansu province. Data of geological survey of outside tunnels,sound wave detection, and geologic sketch for both tunnel face and sidewalls within the tunnel are analyzed. Afteranalyzing these data, long-term pre-geological prediction forecasting basic geological conditions of fault 4 such aslithology, scope, location, etc., and short-term and more accurate pre-geological prediction are reported.

A construction strategy for a tunnel with big deformation

By integrating literature reviews, site observa- tion, field monitoring, theoretical analysis, summarization, etc., a construction strategy was proposed and verified for tunneling with big deformation in this paper. The tunnel was in phyllite, shotcrete cracks and steel arch distortion were observed, and a big deformation with a maximum of 2.0 m was monitored during the initial stage of the construction. Through carefully examining the site observation and laboratory test results, a construction principle was established for the tunneling on the basic concept of maintaining the rock strength/stiffness and keeping the rock dry, by providing confinement pressure to the rock, reducing the rock exposure time, keeping water out of the tunnel, etc. To achieve the construction principle, a set of specific construction measures with 11 items was further proposed and applied to the construction. To check the effectiveness of the construction measures, field monitoring was carried out, which showed that the rock deformation was well controlled and the tunnel became stable. An allowable deformation was then determined using the Fenner formulae and the monitored data in order to guide further construction, which received a good result. From this study, it can be concluded that providing quick strong initial support and reserving core soil at the working faceare extremely important to control the rock deformation and keep the tunnel stable.

Probabilistic prediction of expected ground condition and construction time and costs in road tunnels

Ground condition and construction （excavation and support） time and costs are the key factors in decision-making during planning and design phases of a tunnel project. An innovative methodology for probabilistic estimation of ground condition and construction time and costs is proposed, which is an integration of the ground prediction approach based on Markov process, and the time and cost variance analysis based on Monte-Carlo （MC） simulation. The former provides the probabilistic description of ground classification along tunnel alignment according to the geological information revealed from geological profile and boreholes. The latter provides the probabilistic description of the expected construction time and costs for each operation according to the survey feedbacks from experts. Then an engineering application to Hamro tunnel is presented to demonstrate how the ground condition and the construction time and costs are estimated in a probabilistic way. In most items, in order to estimate the data needed for this methodology, a number of questionnaires are distributed among the tunneling experts and finally the mean values of the respondents are applied. These facilitate both the owners and the contractors to be aware of the risk that they should carry before construction, and are useful for both tendering and bidding.

Construction Technology of Large-deformation High Geostress Soft Rock Tunnel

In the process of tunnel construction,if large-deformation occurs in high geostress soft rock,it will likely cause geological disasters.This situation will not only seriously affect the smooth progress of tunnel construction,but also cause serious safety threat to the construction personnel.Therefore,with the continuous growth in the number and scale of tunnel construction in recent years,the construction technology for high geostress soft rock with large-deformation has begun to receive more and more attention from the society.Based on this,this paper takes an actual project as an example to analyze the specific application of the technology in order to improve the construction effect and avoid the damage caused by the large-deformation of the high geostress soft rock.

Comprehensive Management Strategy for Tunnel Construction Quality Inspection and Monitoring

With the increase in the number and scale of tunnel projects in China in recent years,the comprehensive management for quality inspection and monitoring measurement in the tunnel construction process is receiving more and more attention.Based on this,this paper analyzes the comprehensive management of quality inspection and monitoring during tunnel construction to ensure the quality of tunnel construction.

Research on the Causes and Coping Strategies of the Land Subsidence Caused by the Tunnel Construction Projects

In this paper, we conduct research on the causes and coping strategies of the land subsidence caused by the tunnel construction projects. We analyze the issues from the following of the perspectives. （1） Analysis method. To solve large scale system of the development of computer hardware and the numerical calculation method, we use the basic analysis to deal with it. （2） The empirical of methods. Ground motion is usually leads to the basic development of the inclined tunnel surface vertical displacement, the result of the movement process can turn to a settling tank. （3） Machine learning based approaches. In one of biggest difficulties when using neural network method is to obtain all possible parameters related to ground subsidence, we use the machine learning model to handle the challenge. In the final part, we show prospect for the future research, we will combine more numerical analysis tools to optimize the current methodology.

Research on Deformation Control Indicators and Standard of Urban Traffic Tunnel Construction Adjacent to High-rise Building

The biggest environmental problem caused by the construction of tunnels adjacent to high-rise buildings is the settlement of buildings.The paper analyzes the influence of tunnel excavation on the deformation of the superstructure and the deformation mode of the superstructure.It introduces the indicators and standards for the construction control of tunnel adjacent to the building at home and abroad.Combined with the Yuzhong tunnel project under construction in Chongqing,the main monitoring indicators and control standards of the Yuzhong Tunnel passing through the main buildings are given after comprehensive analysis and considerations,which provide a reference for the deformation control indicators of similar urban traffic tunnels adjacent to high-rise buildings.

Construction Safety Management and Construction Technology of Low-gas Tunnels

With the coordinated development of social economy and technology today,various advanced construction techniques and well-established management measures have begun to be widely used in coal-tunnel construction.However,in the construction process of low-gas tunnels,it will also cause a certain degree of adverse impact on the construction quality and safety due to the lack of technical experience and management experience to a certain extent.Based on this,this paper takes the actual tunnel project of a coal mine as an example to analyze the main construction technology and safety management measures of low-gas tunnels,so as to provide guarantee for the quality and safety of such tunnel construction.

Study on Ground Deformation during Shield Tunnel Construction

Through the systematic analysis of the ground settlement generated by the process of shield tunneling,the relationships between ground deformation and construction parameters are studied in this paper.Based on the assumption of linear small deformation,a mathematical model of the relationship between ground deformation and construction parameters is set up.The principle and method of optimization for estimating ground deformation is studied.The actual measured data are compared with the results of theoretical analysis in a case.Considering different ground formations in different construction sites with different adverse effects on surface and underground structures,the ground surface deformations caused by shield tunneling is an aimed topic in this paper.The contributions and research implications are the revealed relationships between the ground deformation and the shield tunneling parameters during construction.

Optimization of anchorage support parameters for soft rock tunnel based on displacement control theory

In the construction of a soft rock tunnel,it is critical to accurately estimate the pre-stressed anchor support parameters for surrounding rock reinforcement;otherwise,engineering disasters may occur.This paper presents a support parameter selection method that aims to allow deformation as a control objective,which was applied to the tunnel located in Muzailing Highway,Min County,Dingxi City,Gansu Province,China.Through theoretical analysis,we have identified five factors that influence pre-stressing anchorages.The selection of mechanical parameters for the rock mass was carried out using an inverse analysis method.Compared with the measured data,the maximum displacement error of the numerical simulation results was only 0.07 m.The length of anchor cable,circumferential spacing of anchor cable,longitudinal spacing,and pre-stress index are adopted as the input parameters for the support vector machine neural network model based on particle swarm optimization(PSO-LSSVM).Besides,the vault subsidence and the maximum deformation of surrounding rock are considered as output values(performance indices).The goodness of fit between the predicted values and the simulated values exceeds 0.9.Finally,all support parameters within the acceptable deformation range are calculated.The optimal support variables are derived by considering the construction cost and duration.The field application results show that it is feasible to construct the sample database utilizing the numerical simulation approach by taking the displacement as the control target and using the neural network to specify the appropriate support parameters.

Performance of the suspension method in large cross-section shallow-buried tunnels

Large cross-section tunnel construction induces ground surface settlements, potentially endangering both subterranean projects and nearby above-ground structures. A novel tunnel construction method, known as the suspension method,is introduced in this paper to mitigate surface settlement. The suspension method employs vertical tie rods to establish a structural connection between the initial tunnel support system and the surface steel beam, thereby exerting effective control settlements. To analyze the performance of the proposed method, systematic numerical simulations were conducted based on the practical engineering of Harbin Subway Line 3. The surface settlement and vault settlement characteristics during construction are investigated. The results show a gradual increment in both surface and vault settlement throughout the construction process, culminating in a stabilized state upon the completion of construction.In addition, compared to the double-side drift method and the Cross Diaphragm Method(CRD) method, the suspension method can obviously reduce the surface settlement and vault settlement. Moreover, the surface settlements and the axial force of tie rods were continuously monitored during the construction process at the trial tunnel block.These specific monitoring measurements are illustrated in comparison to numerical analysis results. The monitored results show great agreement with the numerical predictions, confirming the success of the project. This research can serve as a valuable practical reference for similar projects, offering insights and guidance for addressing ground surface settlements and enhancing construction safety in the domain of large cross-section tunneling.

Comparison of machine learning methods for ground settlement prediction with different tunneling datasets

This study integrates different machine learning(ML) methods and 5-fold cross-validation(CV) method to estimate the ground maximal surface settlement(MSS) induced by tunneling.We further investigate the applicability of artificial intelligent(AI) based prediction through a comparative study of two tunnelling datasets with different sizes and features.Four different ML approaches,including support vector machine(SVM),random forest(RF),back-propagation neural network(BPNN),and deep neural network(DNN),are utilized.Two techniques,i.e.particle swarm optimization(PSO) and grid search(GS)methods,are adopted for hyperparameter optimization.To assess the reliability and efficiency of the predictions,three performance evaluation indicators,including the mean absolute error(MAE),root mean square error(RMSE),and Pearson correlation coefficient(R),are calculated.Our results indicate that proposed models can accurately and efficiently predict the settlement,while the RF model outperforms the other three methods on both datasets.The difference in model performance on two datasets(Datasets A and B) reveals the importance of data quality and quantity.Sensitivity analysis indicates that Dataset A is more significantly affected by geological conditions,while geometric characteristics play a more dominant role on Dataset B.

Acoustic emission monitoring of rockbursts during TBM-excavated headrace tunneling at Jinping Ⅱ hydropower station

To better understand the mechanical properties of marble at Jinping II hydropower station, this paper examines the changes of brittle rocks in excavation damaged zones(EDZs) before and after excavation of tunnel with the tunnel boring machine(TBM). The paper attempts to employ the acoustic emission(AE) to study the AE characteristics and distribution of rockburst before and after TBM-excavated tunnel. It is known that the headrace tunnel #2, excavated by the drill-and-blast(D&B) method, is ahead of the headrace tunnel #3 that is excavated by TBM method. The experimental sub-tunnel #2–1, about 2000 m in depth and 13 m in diameter, between the two tunnels is scheduled. In the experimental sub-tunnel #2–1, a large number of experimental boreholes are arranged, and AE sensors are installed within 10 m apart from the wall of the headrace tunnel #3. By tracking the microseismic signals in rocks, the location, frequency, quantity, scope and intensity of the microseismic signals are basically identifed. It is observed that the AE signals mainly occur within 5 m around the rock wall, basically lasting for one day before tunnel excavation and a week after excavation. Monitoring results indicate that the rockburst signals are closely related to rock stress adjustment. The rock structure has a rapid self-adjustment capacity before and after a certain period of time during tunneling. The variations of rock stresses would last for a long time before reaching a fnal steady state. Based on this, the site-specifc support parameters for the deep tunnels can be accordingly optimized.

A Frost Heaving Prediction Approach for Ground Uplift Simulation Due to Freeze-Sealing Pipe Roof Method

Freeze-sealing pipe roof method is applied in the Gongbei tunnel,which causes the ground surface uplift induced by frost heave.A frost heaving prediction approach based on the coefficient of cold expansion is proposed to simulate the ground deformation of the Gongbei tunnel.The coefficient of cold expansion in the model and the frost heaving rate from the frost heave test under the hydration condition can achieve a good correspondence making the calculation result closer to the actual engineering.The ground surface uplift along the lateral and longitudinal direction are respectively analyzed and compared with the field measured data to validate the model.The results show that a good agreement between the frost heaving prediction model and the field measured data verifies the rationality and applicability of the proposed model.The maximum uplift of the Gongbei tunnel appears at the center of the model,gradually decreasing along with the lateral and longitudinal directions.The curve in the lateral direction presents a normal distribution due to the influence of the constraint of two sides,while the one along the lateral direction shapes like a parabola with the opening downward due to the temperature field distribution.The model provides a reference for frost heaving engineering calculation.

Application of transient electromagnetic method in tunnel exploration

Along with the economic development,large tunnels need to be constructed in many high mountains or complex structure regions,and the design and construction of detailed tunnel exploration become particularly important.Transient electromagnetic method( TEM) uses a non-grounded loop or an electrode to send a primary electromagnetic pulse into the ground,and observes spatial and temporal distribution of secondary electromagnetic field,which is widely applied in mineral resource exploration,engineering geology,etc.The application of TEM in the tunnel construction of Kaiyuan Temple is researched in this study.The smoke loop inversion is adopted for inverting the transient electromagnetic data.The drilling data and high density resistivity method data are combined for comparison.The obtained inversion result greatly coincides with the actual conditions.Therefore,the geophysical prospecting basis can be provided for the division of stratum,delineation of geological structure,and design and construction of tunnel pavement.By virtue of TEM,drilling data and high density resistivity method,the tunnel construction of Kaiyuan Temple has obtained satisfactory results.

Study on smoke propagation in tunnel construction of a hydropower station: A full-scale fire experiment

In this study,full-scale fire experiments were conducted in a hydropower station to investigate smoke propagation during tunnel construction.The flame height,smoke temperature and stratification,smoke descent and spread velocity were analyzed via measurements and on-site observations.The initial combustion stage was largely affected by ignition source during tunnel construction for diesel pool fire,and the average flame height in the fully developed stage could reach 1.4-2.1 m in experimental fire scenarios.The gradient of the smoke temperature evolution near the fire was the opposite for the upstream and downstream regions.The longitudinal temperature distribution was concentrated in a small range at the heights of the smoke layer,and gradually decreased by air entrainment as the height decreased,while further increasing in the lower half of the tunnel height in the near-fire region under heat radiation from the fire source.Moreover,distinct and stable smoke stratification formed during the fully developed combustion stage,and the smoke layer interface was at approximately half the tunnel height.Smoke descent was aggravated in the decay stage of combustion,and the fire risk remained high after the fully developed period.The smoke front spread velocity was empirically determined for the full-scale tunnel fire scenarios.Conclusions from full-scale experiments can support smoke control design and on-site fire emergency response plans for hydropower stations.

Investigation of carbon emission in slurry shield tunnel construction based on modified process analysis method

Underground engineering,including shield tunnel construction,is a significant contributor to carbon dioxide emissions in infrastructure engineering projects.To better predict and control the carbon emissions associated with shield tunnel construction,this paper presents a novel calculation method:the modified process analysis method based on inputoutput and process analysis methods.To evaluate the effectiveness of the proposed method,a specific shield tunnel construction project was selected as a case study.The modified process analysis method was used to analyze the various factors that influence carbon emissions during the project’s construction phase.In addition,a neural network approach was applied to validate the accuracy of the calculation using the LSTM and BP neural network.The results demonstrate that the proposed method not only combines the strengths of traditional methods but also offers high accuracy and acceptable error rates.Based on these findings,several measures to reduce carbon emissions during shield tunnel construction are suggested,providing valuable insights for reducing CO_(2) emissions associated with infrastructure engineering projects.This study highlights the importance of adopting innovative approaches to reduce carbon emissions and promotes the implementation of sustainable practices in the construction industry.Through the use of advanced analytical methods,such as the proposed modified process analysis method,we can effectively mitigate the environmental impact of construction activities and make significant contributions to the global effort to combat climate change.

Air flow and gas dispersion in the forced ventilation of a road tunnel during construction

Forced ventilation is typically used in the construction of tunnels since it is an economical method to provide high amounts of fresh air.Air velocity and pollutant concentration near the work face are determined by the ventilation arrangement.In the study,field measurement of air velocity was performed,and computational fluid dynamics(CFD)models were constructed to investigate airflow near the work face and predict the gas distribution in a gas tunnel construction.The effects of the distance between the air duct exit and the work face(L_(1))were evaluated by analyzing the flow field and pollutant concentration.The evaluation shows that the ventilation efficiency improves if L_(1) does not exceed 15 m in a road tunnel with full-face excavation.With respect to a road tunnel with benching excavation,the effects of bench length(L_(2))are analyzed,and the results of the analysis indicate that ventilation efficiency is optimal when L_(2)=5 m and L_(2)=10 m and the air-duct diameter corresponds to 1.6 m.The CFD results fit the field measurement significantly well,and the current ventilation system in the construction exhibits a relatively high efficiency.The findings of the study aid practitioners in optimizing ventilation efficiency.

Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

Tunnel Boring Machines(TBMs)are vital for tunnel and underground construction due to their high safety and efficiency.Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs.This study investigates the effectiveness of tree-based machine learning models,including Random Forest,Extremely Randomized Trees,Adaptive Boosting Machine,Gradient Boosting Machine,Extreme Gradient Boosting Machine(XGBoost),Light Gradient Boosting Machine,and CatBoost,in predicting the Penetration Rate(PR)of TBMs by considering rock mass and material characteristics.These techniques are able to provide a good relationship between input(s)and output parameters;hence,obtaining a high level of accuracy.To do that,a comprehensive database comprising various rock mass and material parameters,including Rock Mass Rating,Brazilian Tensile Strength,and Weathering Zone,was utilized for model development.The practical application of these models was assessed with a new dataset representing diverse rock mass and material properties.To evaluate model performance,ranking systems and Taylor diagrams were employed.CatBoost emerged as the most accurate model during training and testing,with R2 scores of 0.927 and 0.861,respectively.However,during validation,XGBoost demonstrated superior performance with an R2 of 0.713.Despite these variations,all tree-based models showed promising accuracy in predicting TBM performance,providing valuable insights for similar projects in the future.