Displacement Back Analysis Method Research Articles

Design of Tunnel Initial Support in Silty Clay Stratum Based on the Convergence-Confinement Method

The stress release ratio of the surrounding rock in tunnel excavation is one of the most important indicators that affect the stress distribution and displacement of the surrounding rock. To determine the variation law of the stress release ratio of the surrounding rock during excavation in silty clay stratum, the stress release law is determined based on the convergence–confinement method (CCM) and field test data. The stress release law of the surrounding rock under support is determined based on the displacement back analysis method. The permitted displacement safety factor of silty clay under different subgrade conditions and the optimal supporting time of the initial supporting structure are determined by comparing the stress release ratio with surrounding rock displacement. The results indicated that the stress release ratio of surrounding rock in the silty clay stratum is approximately 78–90% when the coordinate displacement of the supporting structure and surrounding rock is stable under the current excavation and support conditions. For the surrounding rock of subgrade V in the silty clay stratum, the safety factor of the permitted displacement in the tunnel vault is approximately 2.91, and the initial support should be carried out within 1 m behind the face advancing. For the surrounding rock of subgrade VI1, the safety factor of the permitted displacement is 1.40, and the initial support must be carried out 1 m ahead of the tunnel face. For the surrounding rock of grade VI2, the initial support must be carried out 4 m ahead of the tunnel face.

An Intelligent Optimization Back-Analysis Method for Geomechanical Parameters in Underground Engineering

The geomechanical parameters in underground engineering are usually difficult to determine, which can pose great obstacles in underground engineering. A novel displacement back-analysis method is proposed to determine the geomechanical parameters in underground engineering. In this method, the problem of geomechanical parameter determination is converted into an optimization problem, regarding the geomechanical parameters as the optimization parameters, and the error between the calculated results and the field measurement information as the optimization objective function. The grasshopper optimization algorithm (GOA), which offers excellent global optimization performance, and the Gaussian process regression (GPR) machine learning, offering powerful fitting ability, are combined to address the time-consuming numerical calculations. Furthermore, the proposed method is combined with the 3D numerical calculation software FLAC3D to form the GOA-GPR-FLAC3D method, which can be used in the displacement back-analysis of geomechanical parameters in underground engineering. The results of a case study show that the proposed method can greatly improve computational efficiency while ensuring high precision compared with the GOA. When applied to the Tai’an Pumped Storage Power Station, this method can obtain more accurate results compared with the GOA under the same evaluation times and is more suitable for the back-analysis of rock parameters in underground engineering.

A novel method for identifying geomechanical parameters of rock masses based on a PSO and improved GPR hybrid algorithm

In view of the shortcomings of existing artificial neural network (ANN) and support vector regression (SVR) in the application of three-dimensional displacement back analysis, Gaussian process regression (GPR) algorithm is introduced to make up for the shortcomings of existing intelligent inversion methods. In order to improve the generality of the standard GPR algorithm with single kernel function, an improved Gaussian process regression (IGPR) algorithm with combined kernel function is proposed by adding two single kernel functions. In addition, in the training process of IGPR model, the particle swarm optimization (PSO) is combined with the IGPR model (PSO-IGPR) to optimize the parameters of the IGPR model. After the IGPR model can accurately map the relationship between geomechanical parameters and rock mass deformation, the PSO algorithm is directly used to search the best geomechanical parameters to match the deformation calculated by igpr model with the measured deformation of rock mass. The application case of Beikou tunnel shows that the combined kernel function GPR has higher identification accuracy than the single kernel function GPR and SVR model, the IGPR model with automatic correlation determination (ARD) kernel function can obtain higher identification accuracy than the IGPR model with isotropic (ISO) kernel function, and the PSO-IGPR hybrid model based on ARD kernel function has the highest identification accuracy. Therefore, this paper proposes a displacement back analysis method of the PSO-IGPR hybrid algorithm based on ARD kernel function, which can be used to identify the geomechanical parameters of rock mass and solve other engineering problems.

A novel displacement back analysis method considering the displacement loss for underground rock mass engineering

Most back analysis methods for geotechnical engineering are based on the measured displacement. However, before the monitoring sections are assembled, the displacement—termed the displacement loss—has already been induced; this displacement is difficult to determine, and thus, it is not considered in the back analysis. In the present study, a novel displacement back analysis method considering the displacement loss is developed, that can obtain not only the reasonable mechanical parameters of rock masses but also the displacement loss. To reduce the computational cost of back analysis, a new hybrid optimization algorithm based on the Gaussian process (GP) and particle swarm optimization (PSO) is presented. The GP is used as an inexpensive fitness evaluation surrogate to predict the global optimum solution and accelerate the local search of PSO, which is employed to determine the best mechanical parameters for the model. Combined with FLAC3D numerical analysis, a novel back analysis method called GP-PSO-FLAC3D is proposed. The results of a case study demonstrate that this method can effectively predict more reasonable mechanical parameters and displacement loss using the monitored displacement. An engineering application in the auxiliary tunnel of the Jinping II hydropower station indicates that the elastic deformation of the surrounding rock increases rapidly after excavation, especially for deep tunnels, thereby resulting in a large displacement loss. The back analysis results for the main powerhouse of the Taian pumped storage power station indicate that the displacement loss also exists in engineering processes involving ordinary geostress conditions. Therefore, the displacement loss of a surrounding rock mass cannot be ignored in the stability evaluation or back analysis of underground engineering, especially for deep underground rock engineering.

Longitudinal deformation profile of a tunnel in weak rock mass by using the back analysis method

Analysis of the rock mass deformation behavior is a very important aspect of the safety assessment for tunnel construction in weak rock mass. In this paper, the deformation characteristics of a soft rock mass tunnel using three beaches construction method were investigated, which include the crown settlement and horizontal displacement and have 9 sections with 3 different construction schemes. The optimized construction schemes by decreasing the beaches length and changing the geologist of primary support were proposed. Then, applying the displacement back analysis method to calculate the rock mass parameters, double parameters were analyzed by using the golden section method. Results show that the tunnel deformations were affected by the elastic modulus E and the lateral pressure coefficient λ of rock mass, and the change of E has greater influence than λ on the tunnel deformation. The change of λ has greater influence on the crown settlement than that on the horizontal displacement. Furthermore, the regularity and characteristics of longitudinal deformation profile (LDP) in a weak rock mass tunnel was studied by utilizing the Fast Lagrangian Analysis of Continua (FLAC), and the LDP of the three long-beach construction scheme and the three short-beach construction scheme were compared. The results show that the complete displacements of tunnel under the three short-beach construction scheme condition by decreasing the lengths of the middle and lower benches are smaller than that under the three short-beach construction scheme condition, however the pre-deformation of the tunnel deformation under this two construction scheme conditions is nearly the same. The extrusion deformation at the tunnel face of the three short-beach construction scheme is larger than that of the three long-beach construction scheme. Therefore, increasing the area of the core soil is a feasible measure to control the extrusion deformation on the tunnel face. Finally, the tunnel optimized construction scheme was verified benefit the tunnel stability. The measures of decreasing the length of middle and lower bench and closing the invert early and immediately will benefit the tunnel stability.

A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China

Xigeda formation is a type of hundred-meter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design, and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua (FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loading-test pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neural-network-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.

Estimation of Fracture Stiffness, In Situ Stresses, and Elastic Parameters of Naturally Fractured Geothermal Reservoirs

AbstractKnowledge of fracture stiffness, in situ stresses, and elastic parameters is essential to the development of efficient well patterns and enhanced geothermal systems. In this paper, an artificial neural network (ANN)–genetic algorithm (GA)-based displacement back analysis is presented for estimation of these parameters. Firstly, the ANN model is developed to map the nonlinear relationship between the fracture stiffness, in situ stresses, elastic parameters, and borehole displacements. A two-dimensional discrete element model is used to conduct borehole stability analysis and provide training samples for the ANN model. The GA is used to estimate the fracture stiffness (kn, Ks), horizontal in situ stresses (σH, σh), and elastic parameters (E, v) based on the objective function that is established by combining the ANN model with monitoring displacements. Preliminary results of a numerical experiment show that the ANN-GA-based displacement back analysis method can effectively estimate the fracture stif...

Application of Displacement Back Analysis in Geometry Monitoring of Segmental Construction of Prestressed Concrete Continuous Girder Bridge

This paper apply displacement back analysis theory for geometry form monitoring and calculating work of segmental construction of prestressed concrete continuous girder bridge, making use of displacement back analysis method to optimize bridge structure parameters, ensuring accuracy of geometry form control with calculated results of every girder segment in entire construction. This method is used in the construction monitoring and supervision for Yuanyichang circular curved rigid frame bridge on Pingan-Adai highway, to make the closure accuracy and the bridge geometry form reaches the effect of aspiration. Through the result of the finished bridge, what we can obtain is that the displacement back analysis theory could fulfill the requirement of factual project and be widely used for geometry monitoring of segmental construction of prestressed concrete continuous girder bridge.

Time-dependent behavior of tunnel lining in weak rock mass based on displacement back analysis method

Weak rock mass behavior is an important and challenging consideration during construction and utilization of a tunnel. Tunnel surrounding ground deformation in weak rocks causes to gradual development of loading on the support system and threats the opening stability. In this research, time-dependent behavior of Shibli twin tunnels was investigated using laboratory testing, monitoring data, and finite difference numerical simulation approaches. The host rock of Shibli tunnels are mainly composed of gray to black Shale, Marl and calcareous Shale. Geological maps and reports demonstrate a heavily jointed condition in the host rock through two orogenic phases. The experiment was organized in following order to understand the behavior of the rock mass around the tunnels. At first, triaxial creep test were conducted on intact rock specimens. Then, the time-dependent behavior of the tunnel host rock was numerically simulated considering Burger-creep visco-plastic model (CVISC). Finally, displacement based direct back analysis using univariate optimization algorithm was applied. Also, the properties of the CVISC model and initial stress ratio were estimated. Numerical modeling was verified by its comparison with tunnel displacement monitoring results. The creep behavior of the rock mass was predicted during tunnel service life based on back analysis results. Results show that thrust force, bending moment, and the resulting axial stresses will gradually increase at the spring line of the final lining. After 55years of tunnel utilization the compressive strengths of lining concrete will not be stable against the induced-stresses by thrust force and bending moment, thus the tunnel inspection and rehabilitation are recommended.

Deformation Module Dynamic Inversion of Xietan Sewage Treatment Plant Slope when Exposed to Reservoir Water Level Fluctuation Based on Fluid-Solid Coupling Theory

This paper selects the Xietan sewage treatment plant slope as research object, used the fluid-solid coupling finite element analysis software (ABAQUS) and intelligent displacement back analysis method to dynamically inverse the deformation module of rock and soil of the slope when exposed to water level fluctuation. The results show that the calculation displacement values of measuring point which based on the inversion method is very close to that based on field measurement, which indicates the intelligent displacement back analysis method is suitable to dynamically inverse rock and soil physical and mechanical parameters of slope when exposed to reservoir water level fluctuation.

Intelligent Displacement Back Analysis Method of Three-Dimension Applied in Unsymmetrical Pressure Tunnel with Shallow Depth

Artificial neural network has been widely used in displacement back analysis, but it has the problems of large sample, over-fitting, local optimization and poor generalization performance, so it has the poor adaptability in the Geotechnical Engineering. Support Vector Machines algorithm has the advantages of small sample, global optimization and generalization performance. A direct optimization method based on genetic algorithm and the improved support vector regression algorithm (GA-SVR) is applied in order to identify multinomial parameters intelligently and forecast displacements fast and exactly, combined with an unsymmetrical pressure tunnel with shallow depth section of the left line of import in BEIKOU Tunnel on Zhangjiakou-Shijiazhuang highway. The application result shows the new type of intelligent displacement back analysis could obtain accurately the parameters of rock mechanics and initial stress in limited monitoring data and provide parameters for ahead-forecast of rock deformation.

Intelligent Simulation and Recognition of Metro Station Excavation Based on Differential Evolution and Finite Element

Differential Evolution (DE) is a new algorithm. Displacement back analysis method based on the algorithm can effectively solve the problems of rock mechanics parameters which are not accurate. Constitutive integration algorithm divided into explicit and implicit integration is the key points of finite element analysis, which affect the convergence and accuracy of the results. Return mapping algorithm avoiding directly solving the equivalent plastic strain is a kind of implicit integration algorithm, which would achieve rapid and accurate for the solution of constitutive equations. This article describes the theoretical framework based on elastic-plastic, von Mises yield criterion conditions, using C + + language to carry out plastic simulation of Dalian metro station CRD excavation and parameter identification based on differential evolution algorithm. The calculated stress, displacement and deformation can determine the surface subsidence and the development of plastic zone, the stability analysis to provide a reference for the construction.

Long term stability assessment of Siah Bisheh powerhouse cavern based on displacement back analysis method

Siah Bisheh pumped storage powerhouse cavern with complex geometry, changeable geological formations and diverse geotechnical properties of rocks, is under construction on the Chalus River at the north of Iran. Powerhouse cavern is located near the lower dam reservoir and its crown is more than 30 m down the lower dam maximum lake level. After impounding of lower dam, powerhouse region will be located under saturated condition. Therefore long term stability assessment of the powerhouse cavern under saturated condition is unavoidable. In this study, displacement based direct back analysis using univariate optimization algorithm were applied and geomechanical properties of rocks, stress ratio and joints parameters were identified. Numerical modeling results are in good agreement with measured displacements using extensometers which confirm the numerical modeling accuracy and back analysis results. Then ordinary analysis of powerhouse cavern under natural condition using back analysis results were carried out. Results of analysis shows that powerhouse cavern is stable under natural condition and existing support system has suitable efficiency and could effectively control displacements. Finally, powerhouse cavern long term stability under saturated condition was analyzed. Results of analysis shows that after lower dam impounding, pore water pressure and uplift pressure in discontinuities around powerhouse cavern will arose and tend to local failure of powerhouse cavern in region 2nd and 3rd instrumentation arrays. To obtain powerhouse long term stability, it is recommended to construct a cutoff curtain around powerhouse cavern.

Geomechanical parameters identification by particle swarm optimization and support vector machine

Back analysis is commonly used in identifying geomechanical parameters based on the monitored displacements. Conventional back analysis method is not capable of recognizing non-linear relationship involving displacements and mechanical parameters effectively. The new intelligent displacement back analysis method proposed in this paper is the combination of support vector machine, particle swarm optimization, and numerical analysis techniques. The non-linear relationship is efficiently represented by support vector machine. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, a global optimum search on the obtained SVMs by particle swarm optimization can lead to the geomechanical parameters identification effectively.

Estimating mechanical rock mass parameters relating to the Three Gorges Project permanent shiplock using an intelligent displacement back analysis method

Establishing the mechanical rock mass parameters is one of the important tasks for the highwall stability analysis of the permanent shiplock at the Three Gorges Project in China. Existing back analysis methods are not sufficient to provide the necessary accuracy and to recognize non-linear relations. The new displacement back analysis method proposed in this paper is a combination of a neural network, an evolutionary calculation, and numerical analysis techniques. The non-linear relation involving displacement and mechanical parameters is adequately recognized by the neural network techniques. The neural networks learn using an evolutionary technique, with samples created by orthogonal design and tested with new cases given by event design. With the neural network model established, the mechanical parameters are recognized using a genetic algorithm over a large search space in the global range. The predicted displacement occurring for each excavation step from January 1998 to the end of excavation and their cumulative values for 5 later excavation steps are closely characterized by the new analysis technique.