Geohazards in the three Gorges Reservoir Area, China – Lessons learned from decades of research

Abstract The impoundment of the 660-km long reservoir behind the huge Three Gorges Dam, the world's largest hydropower station, increased regional seismicity and reactivated severe geohazards. Before the reservoir filling was initiated in 2003, the region had approximately two earthquakes per year with magnitudes between 3.0 and 4.9; after the full impoundment in 2008, approximately 14 earthquakes per year occurred with magnitudes between 3.0 and 5.4. In addition, hundreds of landslides were reactivated and are now in a state of intermittent creep. Many landslides exhibit step-like annual pattern of displacement in response to quasi-regular variations in seasonal rainfall and reservoir level. Additional problems include rock avalanches, impulse waves and debris flows. The seriousness of these events motivated numerous studies that resulted in 1) Better insight into the behavior and evolution mechanism of geohazards in the Three Gorges Reservoir Area (TGRA); 2) Implementation of monitoring and early-warning systems of geohazards; and 3) Design and construction of preventive countermeasures including lattice anchors, stabilizing piles, rock bolts, drainage canals and tunnels, and huge revetments. This paper reviews the hydro-geologic setting of TGRA geohazards, examines their occurrence and evolution in the past few decades, offers insight learned from extensive research on TGRA geohazards, and suggests topics for future research to address the remaining challenges

Landslide displacement forecasting using deep learning and monitoring data across selected sites

Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models’ remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms are the sole DL model studied in the extant comparisons. However, several other DL algorithms are suitable for time series forecasting tasks. In this paper, we assess, compare, and describe seven DL methods for forecasting future landslide displacement: multi-layer perception (MLP), LSTM, GRU, 1D convolutional neural network (1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture composed of 1D CNN and LSTM (Conv-LSTM). The investigation focuses on four landslides with different geographic locations, geological settings, time step dimensions, and measurement instruments. Two landslides are located in an artificial reservoir context, while the displacement of the other two is influenced just by rainfall. The results reveal that the MLP, GRU, and LSTM models can make reliable predictions in all four scenarios, while the Conv- LSTM model outperforms the others in the Baishuihe landslide, where the landslide is highly seasonal. No evident performance differences were found for landslides inside artificial reservoirs rather than outside. Furthermore, the research shows that MLP is better adapted to forecast the highest displacement peaks, while LSTM and GRU are better suited to model lower displacement peaks. We believe the findings of this research will serve as a precious aid when implementing a DL-based landslide early warning system (LEWS).SUPPORTO SCIENTIFICO PER L’OTTIMIZZAZIONE, IMPLEMENTAZIONE E GESTIONE DEL SISTEMA DI MONITORAGGIO CON AGGIORNAMENTO DELLE SOGLIE DI ALLERTAMENTO DEL FENOMENO FRANOSO DI SANT’ANDREA – PERAROLO DI CADORE (BL)” and the Spanish Grant “SARAI, PID2020-116540RB-C21,MCIN/AEI/10.13039/501100011033” and “RISKCOASTInSAR displacement data of the El Arrecife landslideGeohazard Exploitation Platform (GEP) of the European Space AgencyNoR Projects Sponsorship (Project ID: 63737

Slope Instability of the Earthen Levee in Boston, UK: Numerical Simulation and Sensor Data Analysis

The paper presents a slope stability analysis for a heterogeneous earthen levee in Boston, UK, which is prone to occasional slope failures under tidal loads. Dynamic behavior of the levee under tidal fluctuations was simulated using a finite element model of variably saturated linear elastic perfectly plastic soil. Hydraulic conductivities of the soil strata have been calibrated according to piezometers readings, in order to obtain correct range of hydraulic loads in tidal mode. Finite element simulation was complemented with series of limit equilibrium analyses. Stability analyses have shown that slope failure occurs with the development of a circular slip surface located in the soft clay layer. Both models (FEM and LEM) confirm that the least stable hydraulic condition is the combination of the minimum river levels at low tide with the maximal saturation of soil layers. FEM results indicate that in winter time the levee is almost at its limit state, at the margin of safety (strength reduction factor values are 1.03 and 1.04 for the low-tide and high-tide phases, respectively); these results agree with real-life observations. The stability analyses have been implemented as real-time components integrated into the UrbanFlood early warning system for flood protection

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA

Noise-Insensitive Prognostic Evaluation of Historic Masonry Structures

In recent years, a significant amount of research has been directed towards the development of prognostic methodologies to forecast the future health state of an engineering system assisting condition based maintenance. These prognostic methods, having furthered the maintenance practices for mechanical systems, have yet to be applied to historic masonry structures, many of which stand in an aged and degraded state. Implementation of prognostic methodologies to historic masonry structures can advance the planning of successful conservation and restoration efforts, ultimately prolonging the life of these heritage structures. This thesis presents a review of prognostic concepts and techniques available in the literature as applied to various engineering disciplines, and evaluates the well-established prognostic techniques for their applicability to historic masonry structures. Challenges of adapting the existing prognostic techniques to historic masonry are discussed, and the future direction in research, development, and application of prognostic methods to masonry structures is highlighted. One particular prognostic technique, known as support vector regression, has had successful applications due to its ability to compromise between fitting accuracy and generalizability (i.e. flatness) in the training of prediction models. Optimal tradeoff between these two aspects depends on the amount of extraneous noise in the measurements, which in civil engineering applications, is typically caused by loading conditions unaccounted for in the development of the prediction model. Such extraneous loading, often variable with time affects the optimal tradeoff. This thesis presents an approach for optimally weighing fitting accuracy and flatness of a support vector regression model in an iterative manner as new measurements become available. The proposed approach is demonstrated in prognostic evaluation of the structural condition of a historic masonry coastal fortification, Fort Sumter located in Charleston, SC. A finite element model is used to simulate responses of a casemate within the fort considering differential settlement of supports. Within the case study, the adaptive optimal weighting approach proved to have increased prediction accuracy over the non-weighted option

Capturing and characterising pre-failure strain on failing slopes

Effective management of slope hazards requires an understanding of the likely triggers, geometry, failure dynamics, mechanism and timing; of these the last two remain most problematic. Reducing the epistemic uncertainty of these elements is crucial, particularly for landslides that are not easily mitigated. The ‘inverse-velocity method’ utilises the linearity in inverse-strain-rate change through time in brittle materials to forecast the timing of final slope collapse. A significant body of published deformation data is available, yet to date there has been no attempt to collate a catalogue of landslide deformations from a large number of sites to examine emergent behaviour; notably variations in and controls on movement prior to failure. This thesis collates thirty-one examples of tertiary creep and related attributes from a broad literature search of over 6,000 peer-reviewed journals. Results show that tertiary creep operates over durations ranging from ~37 minutes to 3,171 days. Patterns of acceleration corroborated with published parameterisations of brittle failure; namely Voight’s (1989) model. Most examples (86%) were best-fit with hyperbolic curves, described by an α coefficient within the 1.7 and 2.2 range; indicative of deformation driven by crack growth. No significant relationships between slope and creep characteristics were found within the database of examples, however the lack of standard reporting of slope failures, particularly between industry documents and academic papers, limits the analysis. The database validates the ‘inverse-velocity method’ as a robust forecasting technique. Iterative a priori analysis of data has shown that slopes deforming in a brittle manner are more likely to predict slope collapse ‘too soon’ as a false positive prediction. Analysis has also shown that tertiary creep is typically delimited (87% of examples) within the first 25% of the total creep duration. Recommendations towards monitoring specifically highlight the need for instruments to deliver spatial accuracies to ~10mm, surface based capture and continuous measurement. Developing processing procedures for point cloud data derived from a permanent terrestrial laser scanning system is recommended as the best approach to small-scale deformation monitoring

Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis. A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system

A Novel Method for Landslide Displacement Prediction by Integrating Advanced Computational Intelligence Algorithms

Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability