Back Analysis Method Research Articles

3D probabilistic landslide run-out hazard evaluation for quantitative risk assessment purposes

The quantitative risk assessment of landslides requires the spatial impact probability of their run-out processes to conduct vulnerability assessments and prioritize mitigation measures. Hence, a general framework for three-dimensional (3D) probabilistic landslide run-out hazard evaluation is presented in this paper. In the proposed framework, a continuum mechanics concept-based dynamic numerical model, called Massflow, is used to conduct run-out analyses. A multi-observation Bayesian back analysis method with Markov chain Monte Carlo simulation is used to calibrate the uncertainties of the input parameters based on a past landslide event, which integrates the prior information, observation information, and model bias factor to yield posterior distributions of the input parameters. Then, the posterior distributions are used as inputs for a future event to estimate the exceedance probabilities of run-out intensities (i.e., maximum run-out height and velocity) on a 3D terrain and produce run-out hazard maps for quantitative landslide risk assessments purposes. To improve the computational efficiency of the proposed framework without losing accuracy, a multi-response Kriging-based surrogate model with a sequential adaptive sampling strategy is developed for both the back analysis and forward prediction. Finally, two sequential slides of landslide CJ#8 located in the Heifangtai terrain, Gansu, China are employed to test the performance of the proposed framework, with its advantages and potential drawbacks discussed in detail. Results show that the proposed framework can effectively calibrate the model input parameters to evaluate the run-out hazard of similar landslides, and the predicted results can be employed in landslide risk assessments and mitigations.

Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm

Under construction disturbance, the surrounding rock of a soft rock tunnel shows obvious aging characteristics. The creep characteristics of a rock mass under stress-seepage coupling greatly influence the long-term stability of a project. How to simply, quickly, and accurately determine the creep parameters of a rock mass under coupling conditions is significant to engineering structure design and construction. The optimal weights and thresholds of the BP neural network are sought through the immune algorithm to avoid the problem of slow convergence speed of the BP neural network and easy to fall into local optimum. Therefore, an intelligent back analysis method based on the IA-BP algorithm is established, which leads to the development of the corresponding intelligent back analysis program. The creep effect of the rock mass was simulated herein using the Drucker–Prager yield criterion and the time hardening creep law as the forward optimization method constitutive model. In addition, a sensitivity analysis of the parameters was performed to determine the optimal number of inversion parameters. By comparing and analyzing the residual between the inversion results of the IA-BP algorithm, PSO-BP algorithm, and the test values, the high precision of the IA-BP algorithm is proved. Taking the Lan Zhou-Hai Kou national expressway tunnel as an engineering example, a multiparameter creep inversion of the tunnel surrounding rock under the stress-seepage coupling condition was conducted using the inverse analysis method of the IA-BP algorithm. The results showed that the proposed IA-BP algorithm can effectively prevent the BP neural network from falling into a local minimum. Also, the algorithm is fast and accurate. The intelligent back analysis method based on the IA-BP algorithm is applied to the multifield coupling parameter back analysis, provides the basis and help for the structural design and construction of soft rock tunnel in water-rich stratum.

Reliability back analysis of a 3D wedge slope based on the nonlinear Barton-Bandis failure criterion

Due to the development of joints, wedge failure has become the main failure type of rock slopes. Obtaining accurate parameters is an important prerequisite in studying slope stability. However, these are difficult to obtain due to the restrictions of field conditions. Based on a real and simple wedge, the method of stability analysis and parameter acquisition of a wedge with the nonlinear Barton-Bandis failure criterion is discussed, and then this method is extended to the conventional form of a wedge. With minimal available data, a simple field test and the reliability back analysis method are combined to propose a specific method for determining the most likely combination of nonlinear strength parameters when wedge failure occurs. This approach can provide a simple and practical method for determining the nonlinear strength parameters for engineering applications. Thus, the small frequent collapse incidences in jointed rock slopes can be effectively utilized to provide a reference for the prevention and control of large-scale failure.

Probabilistic back analysis for improved reliability of geotechnical predictions considering parameters uncertainty, model bias, and observation error

The predicted performance of a geotechnical system may deviate from the in situ observation due to the uncertainties in the input geotechnical parameters, solution model, and observation error. A precise characterization of these uncertainties is a significant challenge primarily because of limited data availability. The Bayesian theory provides a means for updating these uncertainties by incorporating prior statistical information and observations. However, conventional Bayesian inference focuses on limited sources of uncertainties. This paper presents a probabilistic back analysis method for improved reliability of subsequent predictions that considers all the uncertainties. Three distinct features of this new method include: (1) multiple observations are incorporated into the Bayesian updating, (2) the statistical information of the uncertain variables is updated in a stage-by-stage manner, and (3) the posterior distributions of uncertain variables are derived with Markov Chain Monte Carlo (MCMC) simulation that is based on the Hamiltonian Monte Carlo (HMC) algorithm. Two case histories, including a braced excavation problem and a tunnel excavation problem, are analyzed to demonstrate the effectiveness of the new method. The advantages of this new back analysis method over the conventional Bayesian updating analyses are documented.

Development of slope deformation monitoring system based on tilt sensors with low-power wide area network technology and its application

A simple monitoring method for observing slope movement and deformation can be performed with tilt sensors. A more efficient monitoring method, using a type of wireless communication called LPWA (low-power wide area) network technology, has also recently become available. In the present study, a monitoring system was developed that combines tilt sensors and LPWA network technology for measuring the movement of slopes and sending the information through wireless communication. First, radio wave propagation experiments were conducted to find the proper location for the gateway device of the monitoring system. Then, in March 2018, the developed system that consists of tilt and water-level sensors and the wireless communication of LPWA was installed at an observation site. Since the installation, the data from the tilt sensors have been successfully collected through LPWA, and a slope failure was unexpectedly observed at the observation site during the Heavy Rain Event of July 2018, which caused severe disasters, particularly from western Japan to the Tokai region. Using the data from the tilt sensors, the rainfall data, and the groundwater level, the slope failure was analysed. A site investigation was conducted, and the shear strength parameter was examined using back analysis method. In conclusion, the developed system using sensors and LPWA has continued to efficiently monitor the movement of the targeted slope, but the collection of additional data will be required to increase the reliability of the system.

Nonlinear Regression Analysis for Deep Rock Mass Parameters of the Hoek-Brown Failure Criterion Based on the Differential Evolution

The Hoek-Brown (H-B) failure criterion is an empirical failure criterion. The estimates of Hoek-Brown criterion parameters, such as the geological strength index (GSI) and the disturbance coefficient (D), are usually subjective. This paper focused on modifying the initial estimates of GSI and D to improve the accuracy of parameters. The nonlinear regression model (NLRM) of the Hoek-Brown failure criterion was proposed to analyze the rock parameters by using the sensitivity analysis and the displacement equation of the surrounding rocks. Then, a reasonable back analysis method was developed by introducing the differential evolution (DE), which was used to accurately obtain the parameters of the Hoek-Brown failure criterion in practical engineering. This method was successfully used to analyze the stability of the roadway in a deep coal mine. The results showed that the NLRM can better reflect the relationship between GSI, D, μ and the displacement of roadways, and the back analysis results are consistent with the filed monitoring results. This method can provide a helpful reference for modifying the influence of empirical and subjective factors on H-B parameters selection, and improving the accuracy of Hoek-Brown criterion parameters in the similar engineering applications.

Study on Refined Back-Analysis Method for Stress Field Based on In Situ and Disturbed Stresses

Due to uncertainties in the geological evolution process, engineering geological conditions, and nonuniqueness of the solution for in situ stress regression analysis, stress field regression has always been a difficult issue in geotechnical engineering. In view of this problem, a refined back-analysis method for the stress field is proposed in this study based on in situ stress and disturbed stress. First, the in situ stress and disturbed stress in the unloading zone are measured by field tests. The process of geological evolution and excavation disturbance is numerically simulated. The theoretical values of in situ stress and disturbed stress are obtained. The nonlinear mapping relationship of the boundary condition and the stresses at the measuring points is established by the evolutionary neural network. The measured in situ stress and disturbed stress in the unloading zone are imported into the neural network to obtain the boundary conditions. Finally, the boundary condition obtained by back analysis is assigned to the numerical model for calculating the stress field at the project site. The method proposed in this study was applied to analyze the stress field of Mengku Iron Mine in Xinjiang, China. It was found that the accuracy of back-analysis results has been greatly improved because the contributions of both the in situ stress and the disturbed stress were considered in the proposed method. Meanwhile, according to the engineering analysis, the maximum horizontal principal stress in the open-pit slope was reduced by a certain degree, with a maximum reduction of 30%–40% after mining. However, the major principal stress at 0–350 m below the mine pit increased remarkably, with an increment of above 60% compared to the in situ stress. It indicated that excavation unloading has a great influence on the local stress field.

Back analysis of surrounding rock parameters of tunnel considering displacement loss and space effect

Displacement back analysis is often performed to estimate rock parameters in tunnel construction. However, most researchers use the final values of monitoring displacement without considering the loss involved and the space effect, causing substantial errors. In this study, a displacement-based back analysis method is developed for estimating rock parameters, and both the displacement loss and space effect are considered when selecting and processing the output data of the algorithm. The method involves the following steps: First, reasonable training samples are set through numerical simulation, sensitivity analysis, and orthogonal design. Next, an extreme learning machine (ELM) optimized using particle swarm optimization (PSO) is trained to replace the time-consuming numerical analysis. Finally, the PSO algorithm is again utilized to determine the optimal parameters, and the displacement loss is calculated. The results of a simulation case indicate that the proposed method is highly precise and can be generalized adequately. The prediction accuracy can be improved by selecting rock parameters with high sensitivities and the typical monitoring data. An engineering application in the Jigongshan Tunnel in Shenzhen, China, demonstrates that this method can offer a reliable reference in terms of rock parameters to fulfill practical engineering demands, and provide an alternative approach for ground stress estimation during tunneling.

Investigation of Failed Slopes Based on a New Method of Back Analysis in Rock Masses: A Case Study

Small variation in shear strength parameters results in remarkable changes in the safety factor (SF) of a rock slope. In this regard, rock mass strength of a failed slope can be estimated with reasonable accuracy using back analysis approach. The conventional back analysis (BA) procedure is based on trial and error approach on the mb and s parameters of Hoek–Brown failure criterion in conjunction with the Geological Strength Index (GSI) classification system. A new BA procedure presented in this paper, a functional relationship, that indicted all possible combinations of mb and s constants, was derived using stability analysis of a slope assuming that the SF of the slope is equal to unity. Then, another relationship is established between mb and s based on the magnitude of GSI. Intersection of the two relationships when plotting on a graph, defines a unique value for mb and s. Finally, effect of the constant a in the generalized Hoek–Brown failure criterion on the GSI value obtained from the method BA is discussed and its effect is considered in the BA approach. The most advantage of the proposed method is that a reliable GSI is obtained in a relatively short time. For better explanation of the new BA procedure, this method is used for the back analysis of a failed slope along the national railway of Iran. Comparing results obtained from the new BA approach and the traditional back analysis method well-specified which the new proposed BA approach increases the accuracy of parameter estimation.

Comparison of Strength Paremeters by Ring Shear Test and Back Analysis Method for Stability Evaluation of Reactivated Landslides

Comparison of Strength Paremeters by Ring Shear Test and Back Analysis Method for Stability Evaluation of Reactivated Landslides

The determination of mine waste dump material properties through back analysis

Determining the properties of mine waste dump material is very difficult, it is caused by the difference of dimension between the laboratory equipment and field material size distribution. This study aims to determine the material properties from mine waste dump material using back analysis method after failure was occurred. The back analysis method is based on a trial and error concept which focuses on failure geometry and it is conducted by varying the cohesion and friction angle until the safety factor was less than 1.0. The laboratory results showed a 66.2 kPa change as well as a 48.8°friction angle deviation in mine waste dump material properties, while the back analysis indicated 33.7 kPa and 27.4°. It means, there is a reduction in the value. Therefore, the increasing in these properties are expected to allow the redesign of heap stability.

Determination of geomaterial mechanical parameters based on back analysis and reduced-order model

The back analysis method is widely used in geotechnical and geological engineering to guide construction and assure safety through feedback analysis and dynamic design. In this study, a back analysis framework was developed by combining a reduced-order model (ROM), simplicial homology global optimization (SHGO), and a numerical model. The ROM was used to approximate the response of the geotechnical structure. Moreover, SHGO was regarded as an optimization method for investigating the mechanical and strength properties of geomaterials based on the ROM using the developed framework. The developed framework was then verified and illustrated by application to a circular rock tunnel. The determined parameters of the rock mass were in excellent agreement with the actual values, and the deformation law predicted by the developed method was consistent with the analytical solution in the tunnel. These results demonstrate that the developed method is feasible for geotechnical and geological engineering. Further, the new framework was applied to a practical slope project. The results indicate that the proposed method is practical, accurate, and convenient when used to determine the geomaterials mechanical properties based on monitored information. Thus, our method is a promising tool for back analysis in practical geotechnical and geological engineering applications.

A Back Propagation Neural Network-Based Method for Intelligent Decision-Making

A shortage or backlog of inventory can easily occur due to the backward forecasting method typically used, which will affect the normal flow of funds in pharmacies. This paper proposes a replenishment decision model with back propagation neural network multivariate regression analysis methods. With the regular pattern between sales and individual variables, supplemented with the safety stock empirical formula, an accurate replenishment quantity can be obtained. In the case analysis, this paper takes the sales situation of a pharmacy as an example and tests the accuracy and stability of the model. The results show that the model has good prediction accuracy which can be introduced into the intelligent pharmacy system and used in the replenishment of the intelligent pharmacy to prevent overstocking or a shortage of stock, thus improving the financial situation, reducing the manpower burden of typical retail pharmacy, and helping residents buy medicines.

Şev Stabilitesi Probleminin Geri Analizle Çözümü: Örnek Bir Vaka

Bu çalışmada bir şev stabilitesi problemi incelenmektedir. Şev üzerinde bir yol güzergâhı bulunmaktadır ve şevin göçme potansiyeli yüksektir. Bu çalışmanın amacı, şevde güvenlik analizinde kullanılan programların tutarlılığının incelenmesi ve şev hareketini önleyecek tasarımın belirlenmesidir. Bu amaçla, ilk olarak, şevin topoğrafik özellikleri belirlenmiş ve mevcut duruma ait model oluşturulmuştur. Şev modeline ait verileri elde etmek için arazi ve laboratuvar çalışmalarından faydalanılmıştır. Geri analiz yöntemi kullanılarak kayma yüzeyine ait parametreler elde edilmiştir. Mevcut durumu yansıtan model üzerinde, şevi güvenli hale getirecek tasarım oluşturulmuştur. Analizler; sonlu elemanlar yöntemini esas alan Plaxis 2D (2018) ve limit denge yöntemlerine dayanan Slide V.6.0. programları ile gerçekleştirilmiştir. Sonuç olarak, güvenlik analizi hesabında kullanılan programlardan uyumlu sonuçlar elde edilmiştir. Ele alınan şev stabilitesi probleminde, iyileştirme yöntemi olarak fore kazıkla destekleme uygun görülmüştür. Ek olarak drenaj yöntemleri ile yüzey sularının uzaklaştırılması önerilmiştir.

Method for constructing multi-dimensional feature map of malicious code

Malicious code is characterized by a large number of types, rapid increase in number, continuous update of transmission routes, and continuous enhancement of back analysis and back detection methods. Therefore, how to effectively detect and analyze malicious code has been a problem of great concern. This paper studies the features of binary file and disassembly file of malicious code, introduces the concept of information gain, and proposes a method to construct the multi-dimensional characteristic graph of malicious code. Finally, the convolutional neural network is used to classify the multi-dimensional feature graph of malicious code, which provides a new idea for the feature extraction of malicious code.

A Quantitative Evaluation Method Based on Back Analysis and the Double‐Strength Reduction Optimization Method for Tunnel Stability

Quantifying tunnel stability using the proposed combination of back analysis and the strength reduction method (SRM) is useful during construction. To feasibly and reliably obtain geotechnical parameters for the surrounding rock (which vary in different places), a real‐coded genetic algorithm is used in setting the initial parameters of the neural network to improve the prediction accuracy of the parameters via back analysis by reasonably selecting the selection operator, crossover operator, and mutation operator. After obtaining the parameters, the proposed SRM, i.e., the optimization double‐strength reduction method (ODSRM), which is based on the optimization method, is used to evaluate stability. By using this method, the cohesion and friction angle have different reduction factors that are more reasonable and accurate. The combined method is verified in an application to the Yu Liao Tunnel, where it is demonstrated that the combined method can use the measured displacements to obtain the safety factor. Compared with the traditional method, the proposed back analysis method can reduce errors in the predicted performance, and unlike the SRM, the ODSRM can avoid overestimating the safety factor with the same reduction factor. Finally, the presented methods can reduce the amount of calculation required and are convenient for evaluating tunnel stability with displacement.

Model Test and Back Analysis of Shield Tunnel Load Distribution in Soft Clay

Combined with the soft clay layer of Foshan metro, a back‐analysis method combining model test and numerical simulation is developed. First, the similitude criterion for the model used in this study was derived from similarity theory and elasticity mechanics equations. Artificial clay is prepared by mixing kaolin, bentonite, loess, and river sand in proportions of 4 : 2: 3 : 1. Gypsum, water, and borax are mixed in proportions of 1 : 0.7 : 0.015 to simulate the tunnel lining. The model tests were carried out based on four load modes: the combination of mode 1, vertical load distribution and horizontal load linear distribution, mode 2, vertical load distribution and horizontal load parabolic distribution, mode 3, vertical load parabolic distribution and horizontal load linear distribution, and mode 4, vertical and horizontal load parabolic distribution. Then, the calculation model corresponding to the four load modes is established using ABAQUS, and the measured data is back‐analyzed as the known quantity. The specific load values obtained were 359 kPa, 380 kPa, 361 kPa, and 368 kPa by the load‐internal force curve. The bending moment and axial force are calculated by substituting the back‐analysis load values back into the model and comparing the results with the measured values; it was found that the internal forces under the back‐calculation load still deviated by varying degrees. By using the comprehensive error function E to evaluate the advantages and disadvantages of the four distribution modes, the comprehensive errors are 4.3%, 1.7%, 6.5%, and 5.9%, respectively. That is, the error of load distribution of mode 2 (the combination of vertical load distribution and horizontal load parabolic distribution) is the lowest and is highly consistent with the measured value, which is the closest to the characteristics of the load pattern of the stratum.

From probabilistic back analyses to probabilistic run-out predictions of landslides: A case study of Heifangtai terrace, Gansu Province, China

The input parameters of dynamic numerical models for landslide run-out distance simulations cannot be measured accurately, which results in significant uncertainties in the predictions obtained using these models. To address this issue, a novel framework combining probabilistic back analyses and probabilistic predictions of landslide run-out distance is developed in this study, with the aim of ensuring the reliability of run-out predictions in risk assessments. First, a probabilistic back analysis method is applied to calibrate the input parameters and quantify their uncertainties; the calibrated parameters are then used for a probabilistic forward run-out prediction. Unlike the trial and error calibration approach, the back analysis method is implemented probabilistically by modeling the input parameters as random variables and efficiently improving their distributions through Markov chain Monte Carlo-based Bayesian inference using the observed run-out distance of a previous landslide event. These improved posterior distributions are employed to obtain the run-out distance exceedance probability curve of a potential landslide similar to the previous event; this curve can be used to estimate the probability of the run-out distance being exceeded at different locations along the landslide's path. By selecting different threshold values for the exceedance probabilities, a landslide hazard zoning map can be generated, serving as a valuable tool for landslide risk assessment and management. Moreover, a Kriging-based surrogate model is used to approximate the dynamic numerical model employed in this study, which significantly improves the computational efficiency of the proposed methodology. Finally, two sequential landslides that have occurred in the Heifangtai terrace of Gansu Province, China, are considered as a case study to demonstrate the performance of the proposed methodology.

A Novel Method for Analyzing the Factors Influencing Ground Settlement during Shield Tunnel Construction in Upper-Soft and Lower-Hard Fissured Rock Strata considering the Coupled Hydromechanical Properties

Focusing on the special stratum conditions of upper-soft and lower-hard fissured rock strata, this paper conducts quantitative research and analysis on the factors influencing the ground settlement caused by shield tunnel construction considering the coupled hydromechanical properties and presents a corresponding numerical simulation-artificial neural network-Bayesian network- (NS-ANN-BN-) based method. In this method, with a subway shield tunnel as the engineering context, three numerical models are established, in which the shield tunnel is located in soft strata, the shield tunnel is located in semisoft and semihard strata, or the shield tunnel is located in hard strata. According to the numerical simulation (NS) calculations, the ground settlement values under the three types of strata are 38.96 mm, 10.42 mm, and 3.13 mm, respectively. A radial basis function artificial neural network (RBF-ANN) is used to establish the nonlinear mapping relationship between the stratum parameters and the ground settlement, and the training samples and test samples are generated through NS to train the RBF-ANN. After the training is completed, the accuracy of the neural network meets the requirements. The elastic moduli of coarse sand~gravel sand k3 and moderately weathered rhyolite k4 and the cohesion of moderately weathered rhyolite c4 are selected as the key parameters. A large number of training cases are generated through the RBF-ANN, and the Bayesian network (BN) prior probability is calculated by self-learning. A BN model of ground settlement for shield tunnel construction in the upper-soft and lower-hard fissured rock strata is established. The BN back analysis method is used to quantitatively analyze the influencing factors of the ground settlement. The results show that when the tunnel is located in soft strata, the surface settlement is mainly affected by parameter k3. When the ground settlement increases considerably, the three parameters all have a strong influence. When the tunnel is located in semisoft and semihard strata, the influence of the three parameters on the ground settlement is weak. When the tunnel is located in hard strata, the ground settlement is mainly affected by parameter k4. When the ground settlement greatly increases, parameters k3 and c4 have less influence. When the tunnel is located in strata with different soft-hard ratios, the ground settlement is mainly affected by the elastic moduli of coarse sand~gravel sand and moderately weathered rhyolite. This method can provide a reference for the ground settlement analysis of shield tunnel construction in areas with similar fissured rock strata.

Back-analysis of the water retention curve of a GCL on the wetting path

A new approach is presented to obtain the water retention curve of a geosynthetic clay liner (GCL) on the wetting path (i.e. when it is hydrating). This approach is based on a back-analysis process using data from a hydration test. The back-analysis method was validated by comparing it to conventional experimental measurement techniques such as vapour equilibrium, chilled-mirror dew point and osmotic techniques as well as published data. This approach is shown to be a viable alternative to quickly identify the retention properties of a GCL on the wetting path.