Displacement Back Analysis Research Articles

Intelligent information-based construction in tunnel engineering based on the GA and CCGPR coupled algorithm

Based on the construction of the Beikou tunnel, a genetic algorithm (GA) and combined covariance Gaussian process regression (CCGPR) coupled algorithm (GA-CCGPR) were introduced for information-based construction in tunnel engineering. In the initial monitoring period, GA-CCGPR algorithm is used to execute displacement back analysis and predict displacement of surrounding rock; after entering the long-term monitoring period, GA-CCGPR algorithm for prediction of surrounding rock displacement is established by training with the measured displacement data directly. One GA and support vector regression (SVR) coupled algorithm (GA-SVR) is introduced in this study for comparison with the GA-CCGPR algorithm. The application results of the Beikou tunnel show that the maximum relative error and maximum average relative error of displacement prediction for 3 continuous excavation steps based on the GA-CCGPR algorithm are 14.13% and 11.26% in the stage of inversion prediction, respectively. Correspondingly, these two indexes of GA-SVR algorithm reach as high as 34.78% and 23.52%, respectively. The displacement prediction results of the surrounding rock indicate that the maximum relative error and maximum average relative error of the GA-CCGPR algorithm are only 10.64% and 3.59% in long-term monitoring period, respectively; however, these two indexes based on the GA-SVR algorithm are 46.3% and 6.18%, respectively. In addition, the computational time of the SVR algorithm is 3–4 times higher than that of the CCGPR algorithm in both the sample training and parameter identification in initial monitoring period. Similarly, the time required for the SVR algorithm to complete the sample training process is 3–4 times that of the CCGPR algorithm in long-term monitoring period. Finally, an optimization method for the preliminary support parameters was proposed based on the GA and CCGPR coupled algorithm presented in this paper to form a complete information-based construction method for tunnel engineering. With the advantages of fast processing, simple operation and high accuracy, this method can be widely used in tunnel engineering and similar applications.

Fusion of Hydraulic Tomography and Displacement Back Analysis for Underground Cavern Stability Investigation

AbstractThis paper first elucidates the importance of hydraulic heterogeneity of a rock mass on the design of an underground water‐sealed storage cavern. It then introduces an information fusion approach that integrates hydraulic tomography, displacement back analysis, and a stochastic successive linear estimator to map spatially varying hydraulic conductivity (Ks), Young's modulus (E′), cohesion (c′), and internal friction angle (ϕ′). This algorithm yields statistically unbiased estimate of the Ks, E′, c′, and ϕ′ fields and their uncertainty due to insufficient samples of their heterogeneity. Afterward, this uncertainty is translated into the probability of failure distribution around the cavern for the risk assessment. Results show that heterogeneity of Ks plays an important role in the deformation and failure of the cavern. They also show that the proposed approach can reveal more detailed parameter distributions than kriging approach, based on a large number of samples of the parameters before excavations. Further, the predicted probability of failure distribution based on the proposed approach can effectively reflect the actual failure locations.

Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm

Fast and efficient determination of the mechanical parameters of surrounding rock masses is vitally important to the calculation and evaluation of the stability of surrounding rock masses in tunnel engineering. In this paper, a displacement back-analysis (DBA) model is proposed to identify the mechanical parameters based on support vector regression (SVR) optimized by multi-strategy artificial fish swarm algorithm (MAFSA). The MAFSA adopts the differential evolution strategy, the particle swarm optimization strategy, the adaptive step size and phased vision strategy on the basis of artificial fish swarm algorithm (AFSA) to enhance the global search capability and improve convergence speed and optimization accuracy. Then, the kernel width and the penalty parameter of SVR are optimized by MAFSA, forming into MAFSA-SVR. Meanwhile, the training and testing samples for MAFSA-SVR are constructed by orthogonal design and forward calculation by FLAC3D code. Finally, the DBA model is established based on MAFSA-SVR and applied to the mechanical parameter inversion of surrounding rock masses in the Heshi tunnel with the following conclusion: the relative errors of all the mechanical parameters are less than 8% between the inversed values of the DBA model based on MAFSA-SVR and the actual values. The method proposed in this paper could provide an efficient tool for the mechanical parameter inversion of the tunnel surrounding rock masses.

Sequential back analysis of spatial distribution of geomechanical properties around an unlined rock cavern

This paper develops a sequential displacement data collection and back analysis approach for mapping spatially distributed Young’s modulus (E) in a rock mass during the excavation of an unlined rock cavern (URC). Results show that this approach provides an unbiased estimate of the E field and its uncertainty. It also reveals a more detailed E distribution than kriging approach, which is based on samples of E values from boreholes before excavations. Further, predicted shear strain distribution and displacement of cavern periphery based on the estimates from this approach are more accurate than those based on the kriging estimate.

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.

Determination of the mechanical parameters of rock mass based on a GSI system and displacement back analysis

It is very important to obtain the mechanical paramerters of rock mass for excavation design, support design, slope design and stability analysis of the underground structure. In order to estimate the mechanical parameters of rock mass exactly, a new method of combining a geological strength index (GSI) system with intelligent displacment back analysis is proposed in this paper. Firstly, average spacing of joints (d) and rock mass block rating (RBR, a new quantitative factor), surface condition rating (SCR) and joint condition factor (J c) are obtained on in situ rock masses using the scanline method, and the GSI values of rock masses are obtained from a new quantitative GSI chart. A correction method of GSI value is newly introduced by considering the influence of joint orientation and groundwater on rock mass mechanical properties, and then value ranges of rock mass mechanical parameters are chosen by the Hoek–Brown failure criterion. Secondly, on the basis of the measurement result of vault settlements and horizontal convergence displacements of an in situ tunnel, optimal parameters are estimated by combination of genetic algorithm (GA) and numerical simulation analysis using FLAC3D. This method has been applied in a lead–zinc mine. By utilizing the improved GSI quantization, correction method and displacement back analysis, the mechanical parameters of the ore body, hanging wall and footwall rock mass were determined, so that reliable foundations were provided for mining design and stability analysis.

Uniqueness of displacement back analysis of a deep tunnel with arbitrary cross section in transversely isotropic rock

Uniqueness of displacement back analysis of a deep tunnel with arbitrary cross section in transversely isotropic rock

Displacement back analysis for underground engineering based on immunized continuous ant colony optimization

The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust.

Determination of horizontal in-situ stresses and natural fracture properties from wellbore deformation

Accurate and low-cost information on in-situ stresses and fracture properties is critical in reducing well costs and increasing well recoveries. In this paper, a hybrid model based on the displacement back analysis is proposed for determining the in-situ stress magnitudes and fracture properties at the wellbore scale using wellbore displacements. The new methodology is an integration of artificial neural network (ANN), genetic algorithm (GA), and numerical analysis. An ANN is used to map the non-linear relationship between the maximum and minimum horizontal in-situ stresses (σH, σh) and natural fracture properties (e.g., joint angle, θ, aperture, a1 and a2, and spacing, s) and the wellbore displacements. A forward modelling (UDEC) is used to compute wellbore displacements as a function of horizontal in-situ stresses and natural fracture properties, and to create the necessary training and testing samples for ANN. The set of unknown horizontal in-situ stresses and natural fracture properties at wellbore scale are searched in a global space using GA based on the objective function. Results of the numerical experiment show that the hybrid ANN-GA model based on the displacement back analysis can effectively recognise the horizontal in-situ stresses and natural fracture properties from wellbore deformation during drilling.

Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.

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.

Study and Application of Displacement Back Analysis Based on CACA-SVM

An new intelligent displacement back analysis, named as CACA-SVM, was proposed based on support vector machine (SVM) and Continuous Ant Colony Algorithm (CACA). On the one hand, CACA-SVM used SVM to build the nonlinear mapping relationship between them. On the other hand, CACA-SVM used the global optimization performance of CACA to search the optimal rock mechanics parameters in the global space. The nonlinear mapping relationship built by SVM can fit and forecast the measuring point displacements under different parameters with high accuracy, avoiding the complex numerical calculation. CACA can prevent object function from trapping in local optimum and improve precision of back analysis. Case study shows that the forecasting trend was in good agreement with the measured trend, which indicated that the model was suitable for solving the geotechnical engineering problem of nonlinearity and uncertainty and could well be applied to displacement back analysis.

Study and Application of Slope Displacement Back Analysis Based on SVM-CTS

A new method of displacement back analysis, named SVM-CTS, was proposed based on support vector machine (SVM) and continuous tabu search (CTS). On the one hand, SVM-CTS used SVM to build the nonlinear mapping relationship between the measuring point displacements and rock and soil mechanics parameters of positive analysis based on the study samples. On the other hand, SVM-CTS used the global optimization performance of CTS to catch the optimal rock and soil mechanics parameters in the global space. The nonlinear mapping relationship built by SVM can fit and forecast the measuring point displacements under different parameters with high accuracy. CTS can prevent object function from trapping in local optimum and improve precision of back analysis. Case study shows that SVM-CTS can be well applied to the displacement back analysis in geotechnical engineering.

A Novel Hybrid Algorithm with Marriage of Particle Swarm Optimization and Homotopy Optimization for Tunnel Parameter Inversion

The principle of Particle Swarm Optimization (PSO) and Homotopy Optimization (HO) is introduced. For the purpose of improving the local-searching efficiency of the PSO，HO-PSO is presented by combining the PSO with HO. New space homotopy curve produced by homotopy particle function directs the particle’s local optimization. For comparison，the methods of HO-PSO, PSO and FEM(Finite Element Method) are used to calculate the tunnel parameter inversion of the Shuibuya Project on the basis of the measured displacements. The result shows that HO-PSO takes smaller time compared with PSO and FEM in a same precision level, and the calculated values are in good agreement with the measured values，which also indicate that the HO-PSO can be well applied to the displacement back analysis in tunnel engineering.

Reservoir geomechanical parameters identification based on ground surface movements

Determination of geomechanical parameters of petroleum reservoir and surrounding rock is important for coupled reservoir–geomechanical modeling, borehole stability analysis and hydraulic fracturing design. A displacement back analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) combination is investigated in this paper to identify reservoir geomechanical parameters based on ground surface displacements. An ANN is used to map the nonlinear relationship between Young’s modulus, E, Poisson’s ratio, v, internal friction angle, Φ, cohesion, c and ground surface displacements. The necessary training and testing samples for ANN are created by using numerical analysis. GA is used to search the set of unknown reservoir geomechanical parameters. Results of the numerical experiment show that the displacement back analysis technique based on ANN–GA combination can effectively identify reservoir geomechanical parameters based on ground surface movements as a result of oil and gas production.

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.

Back Analysis for Mass Parameters of Tunnel Surrounding Rock

Tunnel engineering information construction has been widely used, and the back analysis is its core. As the common useful method, displacement back analysis is of special advantages. This paper introduces the calculative method based on the application in a railway tunnel. The result shows that strain softening model can be used to simulate the large deformation mechanism of surrounding rock.