Back Analysis Research Articles

3D large-scale numerical model of open-pit lake slope stability—case study of Lake Most

Almost all post-exploitation open pit mines in the world are shaped as a final water reservoir. One of the main hazards is the slope stability of lake banks. To develop a reliability methodology for assessing the long-term stability of flooded open-pit lake, a back analysis was conducted using 2D and 3D large-scale numerical models of Lake Most, which is one of the largest mining lakes in Europe (Czech Republic). The large-scale numerical model was built, based on the site observations, large scale LiDAR data, in situ characterisation tests, and statistical analysis of geotechnical data, on DTMs defining the complex geology of the site and on numerous piezometric levels to build the water table. Local and global safety factor (SF) were calculated using the strength reduction method. The results highlighted the reliability of the methodology to combine the geometric model with the geological model to create a large-scale numerical model, to identify local and potentially instable zones, and to highlight the role of a weak contact layer. The calculation of 3D SF has shown a very good correlation between the lowest SF and the ground movement observations noted by the Czech authorities.

A Heuristic Method to Evaluate the Effect of Soil Tillage on Slope Stability: A Pilot Case in Central Italy

Among the various predisposing factors of rainfall-induced shallow landslides, land use is constantly evolving, being linked to human activities. Between different land uses, improper agricultural practices can have a negative impact on slope stability. Indeed, unsustainable soil tillage can modify the mechanical properties of the soils, leading to a possible increase of the instability phenomena. However, the effects of soil tillage on slope stability are poorly investigated. To address this topic, the PG_TRIGRS model (a probabilistic, geostatistic-based extension of TRIGRS) was applied to a cultivated, landslide-prone area in central Italy, thoroughly studied and periodically monitored through systematic image analysis and field surveys. A heuristic approach was adopted to quantitatively evaluate the effect of soil tillage on the mechanical properties of the soil: after a first run of the model with unbiased parameters, the slope stability analysis was carried out assuming several percentages of reduction of the effective soil cohesion to mimic an increasing impact of soil tillage on the strength conditions. Then, a comparison between observed landslides and the spatial distribution of the probability of failure derived from the application of PG_TRIGRS was carried out. A back analysis with contingency matrix and skill scores was adopted to search for the best compromise between correct and incorrect model outcomes. The results show that soil tillage caused a 20 to 30% reduction in soil cohesion in the analyzed area.

Application of the Maximum Sensitivity Principle in the Optimization of Measuring Point Arrangement in Transversely Isotropic Rock Mass Engineering

The inverse analysis method based on in situ measurement information provides a new approach to determine the mechanical parameters of rock mass. The accurate on-site monitoring data is the prerequisite and basis for the success of displacement back analysis. To improve the accuracy of requirement parameters, a reasonable layout program should be developed to optimize the layout of the measuring position. In order to study the optimal arrangement of measuring points in anisotropic rock mass, the layout optimization of measuring points around a tunnel in the transversely isotropic rock mass is studied according to the maximum sensitivity criteria. The influence of the spatial location of the measuring points on the measurement of the tunnel deformation is obtained. The results indicate that the measuring point should be arranged close to the extreme point in the direction of the larger principal stress.

Monitoring-data mechanism-driven dynamic evaluation method for slope safety

Many high and steep slopes are formed in the construction of hydropower, highway, and railway projects. The long-term safety risk management and control problems are prominent. Slope deformation monitoring is one of the means to directly characterize the safety status of a slope, so it is of great significance to use slope deformation monitoring information to accomplish the dynamic evaluation of slope safety for safe engineering operations. In this study, the aging component of the slope displacement was separated using the empirical mode decomposition method. Combined with the response surface equation and the genetic optimization algorithm, a parameter back analysis method based on the aging displacement increment of the slope was proposed, and the determined calculation parameters can better reflect the creep characteristics of rock slope. Based on the results of the back analysis parameters, the most dangerous slip surface of the slope was determined using the strength reduction method, and the time series of the safety factor of the slope was obtained. Combining the dynamic coupling relationship between the slope displacement monitoring quantity and the slope stability, a surrogate model between the safety factor of the slope and the displacement aging component was constructed for the first time. A novel monitoring-data mechanism-driven dynamic evaluation method for slope safety was proposed considering the creep characteristics of the slope. The method was applied to an engineering slope example. The relative errors of the fitting and the prediction results of the dynamic evaluation model were less than 1%. The dynamic quantitative evaluation of the slope safety based on the displacement monitoring data was achieved. This provided an effective guarantee for slope safety control.

Probabilistic back analysis for rainfall-induced slope failure using MLS-SVR and Bayesian analysis

ABSTRACT Measurement and model uncertainties in soil parameters account for the difference between slope behaviour in the field and expected behaviour. The probabilistic back analysis is an effective approach to quantify these uncertainties in soil parameters. A new methodology for probabilistic back analysis is proposed to evaluate the uncertainties in soil parameters for observed data for slope. The proposed methodology implements multi-output least square support vector regression (MLS-SVR) to replicate the numerical model for slope under precipitation. This methodology also utilises a multi-objective genetic algorithm and Bayesian analysis to estimate updated statistics of soil parameters for observed data for slope. The rainfall-induced slope failure at Malin, Pune, India, in 2014 is used as a case study to validate the proposed methodology. The mean values of soil parameters are updated using multi-objective genetic algorithm for the expected values of safety factor. The uncertainties in soil parameters are estimated using Bayesian analysis. The updated statistics of input parameters suggest that matric suction governs the slope behaviour under rainfall precipitation. The results of the study suggest that continuous updating of the observations reduces the uncertainties involved in soil parameters. It is noted that the values of safety factor calculated using updated parameters are consistent with the slope failure observed in the field. Hence, results of the study can be used for the reliability-based design of slopes and the provision of remedial measures.

Displacement Back Analysis of Reservoir Landslide Based on Multi-Source Monitoring Data: A Case Study of the Cheyiping Landslide in the Lancang River Basin, China

Landslides that occur in the littoral zone of a reservoir can directly damage the hydraulic structures and threaten the lives and property around the reservoir. Due to the spatial variability and heterogeneities of rock mass, a limited amount of data obtained from laboratory and in situ tests cannot comprehensively characterize the mechanical properties of rock and soil masses. Therefore, displacement back analysis is often performed to determine the mechanical parameters of rock and soil masses. The spaceborne Interferometric synthetic aperture radar (InSAR) has proved to be a powerful tool for geodesy in the measurement of landslide movement. However, InSAR can only measure the surface motion of the landslide without the subsurface information. This study uses multi-source monitoring data in the landslide displacement back analysis, including surface InSAR and an internal borehole inclinometer. The identified material parameters and finite element simulation are used to predict the landslide deformation. The case study of the Cheyiping landslide located in the Lancang River basin demonstrates the necessity and feasibility of using multi-source monitoring data in landslide displacement back analysis. The Cheyiping landslide is currently in the creep deformation stage. The decrease in shear strength of rock masses due to the rheological deformation and the change in reservoir water level are the internal and external factors leading to excessive landslide deformation. The numerical modeling can accurately simulate the landslide movement using the identified material parameters. By combing multi-source monitoring data and numerical modeling, the reservoir landslide deformation analysis can help evaluate the landslide deformation state and stability, which is vital for reservoir risk mitigation and the sustainable development of hydropower resources.

Three-dimensional forward analysis and real-time design of deep tunneling based on digital in-situ testing

The mechanical behavior of a deep rock mass cannot be precisely evaluated with the traditional theory and model used for shallow tunneling. Moreover, the deep rock mass parameters are primarily determined from laboratory rock tests or field displacement-based back analysis. There is still a lack of a real-time, rapid, and dynamic forward analysis method for deep tunneling. In this study, three-dimensional (3D) numerical results based on the Mohr–Coulomb (MC), Drucker–Prager (DP), Hoek–Brown (HB), and generalized Zhang–Zhu (GZZ) strength criteria were compared and analyzed using physical tunnel models with different buried depths. Subsequently, practical models of shallow-, medium-, and deep-buried tunnels were established to investigate the stress and deformation evolution during tunneling, and a digital in-situ test-based forward analysis method was developed to dynamically update the rock mass parameters and perform subsequent 3D numerical simulations. (1) GZZ-based numerical results agreed well with the physical model tests; (2) large differences in the tunnel excavation responses (stress and deformation) between the 3D strength criterion (GZZ) and the 2D strength criteria (MC and HB) indicate the significant effect of the intermediate principal stress (σ2) on tunnel stability, and these differences are more significant in deep tunnels because of the widely distributed plastic zone; (3) steel arch distortion is closely related to the minimum principal stress (σ3) and σ2 of the surrounding rock near the tunnel face, and the effect of σ2 increases with the buried depth; however, steel arch distortion in the plane strain tunnel section can be equally attributed to both principal stresses (σ3 and σ2). This indicates that complex engineering problems cannot be accurately analyzed with the plane strain model, particularly in relation to deep tunnels; (4) overemphasis on the radial stiffness of the support system seems inappropriate, and more attention should be paid to the longitudinal and overall stiffness of the deep tunnel support. The digital in-situ test-based 3D forward method and design theory developed in this study may contribute further to the dynamic construction and design of deep and ultra-deep rock tunnels.

The Mandatory 4 Years Desludging Frequency of Underground Septic Tanks in Residential Areas: A Case Study in Sibu Sarawak, East Malaysia

Underground septic tanks are the most common and conventional primary black water treatment adopted by local households in Sarawak. To ensure its usefulness, periodic mandatory desludging or emptying of the various types of rateable holdings was enforced from 1998. The current mandatory desludging frequency is 4 years for residential households, although its effectiveness is still not clear, substantial annual finance and subsidies are being allocated by the Sarawak State Government. In this study, the underground septic tanks in Sibu located in residential areas in Sarawak, East Malaysia, were evaluated quantitatively based on the measured faecal sludge with a known retention time in respect to the regulated desludging frequency. Furthermore, various problems were encountered during the initial measurement carried out by sludge judge which typically used by the wastewater treatment and chemical plants. A modified PVC pipe was proposed to overcome these observed problems. This has greatly improved the measurement time and substantially reduced unwanted impediments for the contractor during desludging. The back analysis results showed that the recommended desludging frequency based on the ratio of total sludge and scum to the effective working volume in 30% and 50% thresholds for residential areas were 3.82 and 6.36 years, respectively. Furthermore, it was discovered that there was a potential to adjust the existing mandatory 4 years desludging frequency to a longer period for cost-effective consideration. This study is strictly on the physical quantity measurement of sludge and scum volumes for the known retention times. The complex influences of microbiological activities, micro-environmental factors and sizes or geometries of septic tanks for faecal sludge production were not considered.

Numerical Simulation of Non-Stationary Parameter Creep Large Deformation Mechanism of Deep Soft Rock Tunnel

The accelerated creep plays an important role in the disasters of soft-rock tunnels under high stress. However, most of previous studies only involved attenuation creep and uniform creep. Large deformation disasters of soft rock occurred during the tunneling process in the Qianzhou–Sanyangchuan Tunnel, Gusu Province, China. In the paper, we developed the nonlinear generalized Nishihara rheological model with non-stationary parameter creep (NGNRM) to simulate the accelerated creep behaviors of soft rocks under high stress, and implemented it in ABAQUS, to reveal the mechanism of large deformation of soft rock. We proposed the multi-objective back analysis method of surrounding rock mechanical parameters based on the eXtreme Gradient Boosting and the non-dominated sorting genetic algorithm-II. In addition, the orthogonal test design method was used to determine the main parameters affecting the displacement of the tunnel. Using the proposed method, we can evaluate the large deformation mechanism of deep soft rock tunnels, and scientifically determine when to reinforce to prevent a large deformation disaster of the tunnel.

Intelligent identification of soil and operation parameters in mechanised tunnelling by a hybrid model of artificial neural network-genetic algorithm (case study: Tabriz Metro Line 2)

ABSTRACT In this article, the ability of the artificial neural network-genetic algorithm (ANN-GA) to perform back analysis and predict maximum surface settlement in mechanised tunnelling is investigated. The required data of the ANN meta-model was generated using 150 three-dimensional finite-difference simulations. The global sensitivity analysis was performed on 19 parameters, including 17 geotechnical parameters of soil layers and 2 operational parameters of face pressure and grouting pressure. The predicted results using ANN were in good agreement with the numerical simulations so that R = 99% and rRMSE = 1.5% are obtained. Then, back analysis was performed using the ANN-GA hybrid algorithm and the geotechnical data of the monitoring point were updated using the maximum surface settlement monitored at this point. Also, for the geotechnical parameters considered in the design phase, using the same algorithm, the number of operational parameters required for optimal settlement was predicted.

Back Analysis of Surrounding Rock Parameters in Pingdingshan Mine Based on BP Neural Network Integrated Mind Evolutionary Algorithm

The mechanical parameters of surrounding rock are an essential basis for roadway excavation and support design. Aiming at the difficulty in obtaining the mechanical parameters of surrounding rock and large experimental errors, the optimized BP neural network model is proposed in this paper. The mind evolutionary algorithm can adequately search the optimal initial weights and thresholds, while the neural network has the advantage of strong nonlinear prediction ability. So, the optimized BP neural network model (MEA-BP model) takes advantage of the two models. It can not only avoid the local extreme value problem but also improve the accuracy and reliability of the prediction results. Based on the orthogonal test method and finite element analysis method, training samples and test samples are established. The nonlinear relationship between rock mechanical parameters and roadway deformation is established by the BP model and MEA-BP model, respectively. The importance analysis of the three input variables shows that the ∆D is the most important input variable, while ∆BC has the smallest impact. The comparison of prediction performance between the MEA-BP model and BP model demonstrates that the optimized initial weights and thresholds can improve the accuracy of prediction value. Finally, the MEA-BP model has been well applied to predicting the mechanical parameter for the surrounding rock in the Pingdingshan mine area, which proves the accuracy and reliability of the optimized model.

Numerical Investigation of Impact Characteristics of Rigid Retaining Wall for Blocking Landslides: A Case Study of the Shum Wan Road Landslide

Landslides pose a serious threat to the downstream infrastructure and personnel safety. For situations where the location of possible landslides is difficult to determine, or the potential landslides areas are widely distributed and scattered, passive protective structural measures need to be adopted to relieve the threat of landslides. Taking the Shum Wan Road landslide as an example, the discrete element method was used to reproduce and analyze the landslide movement. On this basis, this study conducts a further numerical investigation on the impact characteristics of the rigid retaining wall used to block landslides. The results show that the numerical back analysis can well represent the evolution of the landslide, and the numerical results are in good agreement with the actual situation, thus confirming its correctness and effectiveness. Under the condition of setting a rigid retaining wall, the landslide can be effectively blocked and form a final deposition behind the retaining wall. The impact on the rigid retaining wall varies with time, and the distribution of the impact in space is also uneven. Through the statistical analysis of the contact forces on the retaining wall, the distributions of static and peak stresses on the retaining wall can be obtained. Compared with the static stability, the spatial distribution of dynamic impact is more difficult to predict, especially the area of the peak impact has a significant influence on the design. Numerical investigation can efficiently and reliably provide abundant information of static stress and dynamic impact on the rigid retaining wall, which can provide important reference for the optimal design of the rigid retaining wall structure.

Prediction of hydraulic conductivity parameters of slurries from centrifuge consolidation settlements

Slurry materials such as mine tailings and dredged clays undergo large, self-weight consolidation settlements. Compressibility and hydraulic conductivity characteristics of such slurries are essential for the estimation of their settlements and settlement rates for the safe maintenance of the storage facilities. These two constitutive functions of slurry materials are frequently estimated from laborious large-strain consolidation tests. Centrifuge consolidations tests are receiving great attention, therefore, to circumvent the time-consuming consolidation tests in the 1 g environment. However, the inverse analysis of settlement–time data for the estimation of compressibility and hydraulic conductivity functions is computationally expensive due to the requirement of a large number of forward analyses and thus is not available yet for the centrifuge environment. In this work, for the first time, two existing back analysis procedures for 1 g column tests were explored for predicting the hydraulic conductivity parameters of several slurry materials from the centrifuge test data. These procedures require well-defined and time-invariant compressibility functions of the slurry materials. These methods need only time–settlement data from a single centrifuge test, and do not depend on the excess pore pressure profiles. A finite difference solution of the analytical model for finite strain consolidation behavior of samples in centrifuge environment was utilized for the forward analysis. Four simulated synthetic temporal settlement curves and four centrifuge consolidation test data from the literature were utilized for validation of the studied methods. The methods were simple, computationally inexpensive due to the requirement of only a smaller number of forward analyses, and effective in accurately predicting the hydraulic conductivity constitutive parameters.

Microstructure and mechanical properties of spark plasma sintered austenitic ODS steel

In this work, austenitic oxide dispersion strengthened (AODS) steel of composition Fe–16Cr–16Ni–1.5 W–0.21Ti–0.3Y2O3 (wt. %) was fabricated using two–stage ball milling followed by consolidation through spark plasma sintering (SPS). In the first–stage, mechanical alloying (MA) of ferritic powder and nano sized Y2O3 was carried out. This was followed by the addition of Ni in second–stage milling. SPS of the milled powder was carried out at 900, 950, 1000 and 1050 °C to explore the role of SPS temperature on density, microstructure as well as mechanical properties of the consolidated samples. A relative density of ∼ 99% was obtained for samples sintered at 950 and 1000 °C. The as–sintered samples were subsequently solution annealed at 1075 °C for 2 h and water quenched. X–ray diffraction studies confirmed the presence of austenite in the consolidated and solution annealed samples. Electron back scatter diffraction analysis of solution annealed samples sintered at all the temperatures revealed a bimodal microstructure. The average grain size of 1.07 ± 0.72 µm was obtained for solution annealed samples sintered at 1000 °C. Yield strength and elongation of the same was measured as 851 MPa and 18%, respectively at room temperature. These values are the best combination of strength to elongation achieved on AODS alloys processed using MA and SPS, which makes this AODS steel much promising for high temperature applications.

Shear failure process of rectangular tunnel: Physical experimental test and numerical back-analysis

Shear failure is a typical failure mode in underground engineering and has resulted in a large number of instability issues. To study the shear failure mechanism, physical shearing tests for rectangular tunnel of sandstone, including single rectangular tunnel (SRT) and double rectangular tunnels (DRT) specimens, were carried out under three normal stress states. During the shearing tests, the internal failure process and the external cracks evolution of tunnel specimens were recorded by video camera, and the acoustic emission (AE) events of specimens were also monitored for exposing the mesoscopic failure characteristics. In addition, the discrete element method was also applied to analyze the corresponding shear experiment of rectangular-tunnel specimens. The tested results showed that the shear strength, shear modulus of the samples were positively correlated with the normal stress. For example, the shear strength, and shear modulus of SRT increased from 5.18 MPa to 8.70 MPa, and 1.02GPa to 1.88GPa, respectively when the normal stress increased from 2.0 MPa to 5.0 MPa. More importantly, the shear failure of rectangular tunnel model can be generally divided into four stages: (i) Shearing compression stage characterized by loading contacting and compressing; (ii) Shearing micro-crack propagation stage dominated by inner particles ejection of tunnel, tensile crack initiation and shear crack propagation of specimen; (iii) Shearing fracture stage dominated by the specimen’s crack propagation, coalescence, penetration, and corresponding rock ejection; (iv) Shearing friction stage dominated by shear damage of tunnel specimen. Corresponding back analysis by numerical simulation and scanning electron microscope results indicated that the shear failure of rectangular-tunnel specimen can be classified as tensile-shear mixed failure mode. The research results provide a new cures for shear prevention and support design of rectangular tunnels in underground engineering.

Understanding the Sources of Ambient Fine Particulate Matter (PM2.5) in Jeddah, Saudi Arabia

Urban air pollution is rapidly becoming a major environmental problem of public concern in several developing countries of the world. Jeddah, the second-largest city in Saudi Arabia, is subject to high air pollution that has severe implications for the health of the exposed population. Fine particulate matter (PM2.5) samples were collected for 24 h daily, during a 1-year campaign from 2013 to 2014. This study presents a detailed investigation of PM2.5 mass, chemical composition, and sources covering all four seasons of the year. Samples were analyzed for black carbon (BC), trace elements (TEs), and water-soluble ionic species (IS). The chemical compositions were statistically examined, and the temporal and seasonal patterns were characterized using descriptive analysis, correlation matrices, and elemental enrichment factor (EF). Source apportionment and source locations were performed on PM2.5 samples using the positive matrix factorization (PMF) model, elemental enrichment factor, and air-mass back trajectory analysis. The 24-h mean PM2.5 and BC concentrations ranged from 33.9 ± 9.1–58.8 ± 25 µg/m3 and 1.8 ± 0.4–2.4 ± 0.6 µg/m3, respectively. Atmospheric PM2.5 concentrations were well above the 24-h WHO guideline of 15 µg/m3, with overall results showing significant temporal and seasonal variability. EF defined two broad categories of TEs: anthropogenic (Ni, V, Cu, Zn, Cl, Pb, S, Lu, and Br), and earth-crust derived (Al, Si, Mg, K, Ca, Ti, Cr, Mn, Fe, and Sr). The five identified factors resulting from PMF were (1) fossil-fuels/oil combustion (45.3%), (2) vehicular emissions (19.1%), (3) soil/dust resuspension (15.6%), (4) industrial mixed dust (13.5%), and (5) sea-spray (6.5%). This study highlights the importance of focusing control strategies, not only on reducing PM concentration but also on the reduction of components of the PM as well, to effectively protect human health and the environment.

Efficient back analysis of multiphysics processes of gas hydrate production through artificial intelligence

Natural gas hydrate, a crystalline solid existing under high-pressure and low-temperature conditions, has been regarded as a potential alternative energy resource. It is globally widespread and occurs mainly inside the pores of deepwater sediments and sediments under permafrost area. Hydrate production via well depressurization is deemed well-suited to existing technology, in which the pore pressure is lowered, the natural gas hydrate is dissociated into water and gas, and the water and gas are produced from well. This method triggers multiphysics processes such as fluid flow, heat transfer, energy adsorption, chemical reaction and sediment deformation, all of which are dependent on the amount of gas hydrates remaining in the pores. Therefore, modeling of hydrate production is computationally intensive and expensive. While back-analysis through observed production history is essential for better understanding of the reservoir characteristics and reliable prediction for future gas hydrate production, a large number of required simulations makes it impractical. This study employs Artificial Intelligence (AI) to achieve an efficient back-analysis of the gas hydrate production conducted at the offshore Nankai site, Japan, in 2013. The results show that the AI-based metamodel is capable of reproducing outputs of heavy computation of the multiphysics processes and thus performs back-analysis greatly efficiently. The efficient AI-based metamodel also makes it possible to carry out sensitivity analysis and it is found that the permeability and the preyield plasticity parameter are most influential to reservoir responses. The approach of this study can be applicable to other reservoirs and will reveal the ground truth in-situ properties and the most influential properties, contributing to better understanding of reservoir behavior for future gas hydrate production.

Fuzzy identification of bioactive components for different efficacies of rhubarb by the back propagation neural network association analysis of UPLC-Q-TOF/MSE and integrated effects

BackgroundRhei Radix et Rhizoma (rhubarb), as one of the typical representatives of multi-effect traditional Chinese medicines (TCMs), has been utilized in the treatment of various diseases due to its multicomponent nature. However, there are few systematic investigations for the corresponding effect of individual components in rhubarb. Hence, we aimed to develop a novel strategy to fuzzily identify bioactive components for different efficacies of rhubarb by the back propagation (BP) neural network association analysis of ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry for every data (UPLC-Q-TOF/MSE) and integrated effects.MethodsThrough applying the fuzzy chemical identification, most components of rhubarb were classified into different chemical groups. Meanwhile the integration effect values of different efficacies can be determined by animal experiment evaluation and multi-attribute comprehensive indexes. Then the BP neural network was employed for association analysis of components and different efficacies by correlating the component contents determined from UPLC-Q-TOF/MSE profiling and the integration effect values. Finally, the effect contribution of one type of components may be totaled to demonstrate the universal and individual characters for different efficacies of rhubarb.ResultsIt suggested that combined anthraquinones, flavanols and their polymers may be the universal character to the multi-functional properties of rhubarb. Other components contributed to the individuality of rhubarb efficacies, including stilbene glycosides, anthranones and their dimers, free anthraquinones, chromones, gallic acid and gallotannins, butyrylbenzenes and their glycosides.ConclusionsOur findings demonstrated that the bioactive components for different efficacies of rhubarb were not exactly the same and can be systematically differentiated by the network-oriented strategy. These efforts will advance our knowledge and understanding of the bioactive components in rhubarb and provide scientific evidence to support the expansion of its use in clinical applications and the further development of some products based on this medicinal herb.

Efficient probabilistic back analysis of spatially varying soil parameters based on monitored displacements

Efficient probabilistic back analysis of spatially varying soil parameters based on monitored displacements

Probabilistic hazard assessment of landslide-induced river damming

Landslide-induced river damming poses a considerable threat to the safety of humans and infrastructure. Prediction of landslide-induced river damming is of great significance for quantitative risk assessment and emergency response planning. However, owing to the large uncertainties embedded in the input parameters and dynamic numerical models, a reliable physically based prediction of landslide dam formation is still challenging. In this study, we proposed a probabilistic framework to predict rockslide-induced river damming and its associated barrier lake. Both parameter and model uncertainties were considered to reproduce the run-out process and the deposition behavior of the Baige landslide based on dynamic numerical simulation, while calibrating the input parameters through a sequential Bayesian back analysis. The first slide of the Baige landslide was considered to calibrate the input parameters with observations at several deposition depths. The proposed method was validated by predicting the second slide-induced river damming using the calibrated results from the first slide. Furthermore, the second slide with deposition depth observations was employed to update the input parameters again. Through the sequential Bayesian back analysis, the probability of the river damming induced by the potentially unstable rock masses was predicted, which yielded a hazard zonation map of the barrier lake exceeding various water levels. This hazard zonation map may be employed to guide quantitative risk assessment and corresponding emergency response plans for future landslide-induced upstream backwater-inundation.