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Soil aggregate stability and pore structure are key indicators of soil degradation. Waves generated by the water-level fluctuations could severely deteriorate soil aggregates, which eventually induce soil erosion and several other environmental issues such as sedimentation and flooding. However, due to limited availability of the hydrological alteration data, there is a limited understanding of soil aggregates, intra-aggregate pore dynamics, and their relationships under periodically flooded soils. The present study has relied on long-term hydrological alteration data (2006-2020) to explore the impacts of inundation and exposure on soil aggregates and pore structure variations. Soil samples from increasing elevations (155, 160, 163, 166, 169, and 172 m) in the water-level fluctuation zone of the Three Gorges Reservoir were exposed to wet-shaking stress and determined soil structural parameters. The overall inundation and exposure ratio (OvI/E) gradually decreased from 1.87 in the lowest to 0.27 in the highest elevation, respectively. Predominant distribution of macropores was recorded in lower elevations, while micropores were widely distributed in the upper elevations. The mean weight diameter (MWD) was significantly lower in the lower (2.4-3.7 mm) compared to upper (5.3-6.0 mm) elevations. The increase in MWD has increased the proportion of micropores (PoN < 50 mu m), with R-2 = 0.59. This could suggest that the decrease in flooding intensity can create favorable conditions for plant roots growth. The strong flooding stress in lower elevations (i.e., higher values of the OvI/E) accelerated the disintegration of soil aggregates and considerably increased the formation of macropores due to slaking and cracking. The findings of the present study emphasize the need to restore degraded soils in periodically submerged environments by implementing vegetation restoration measures. This could enhance and sustain aggregate stability, which was also proved to increase functional pores under hydrological alterations.

期刊论文 2025-06-19 DOI: 10.1002/ldr.5673 ISSN: 1085-3278

Landslides commonly evolve from slow, progressive movements to sudden catastrophic failures, with saturation and displacement rates playing significant roles in this transition. In this paper, we investigate the influence of saturation, displacement rate, and normal stress on the residual shear strength and creep behaviour of shear-zone soils from a reactivated slow-moving landslide in the Three Gorges Reservoir Region, China. Results reveal a critical transition from rate-strengthening to rate-weakening behaviour with increasing displacement rates, significantly influenced by the degree of saturation. This transition governs the observed patterns of slow movement punctuated by periods of accelerated creep, highlighting the potential for exceeding critical displacement rates to trigger catastrophic failure. Furthermore, partially saturated soils exhibited higher residual strength and greater resistance to creep failure compared to nearly and fully saturated soils, underscoring the contribution of matric suction to shear strength.

期刊论文 2025-06-05 DOI: 10.1016/j.enggeo.2025.108042 ISSN: 0013-7952

Slip zone soil, a crucial factor in landslide stability, is essential for understanding the initiation mechanisms and stability assessment of reservoir bank landslides. This study investigates the strength characteristics of slop zone soil under drying-wetting (D-W) cycles to inform research on reservoir bank landslides. As an illustration of this phenomenon, the Shilongmen landslide in the Three Gorges Reservoir serves as a case study. Taking into account the impact of both D-W cycles and the overlying load on the soil. the strength characteristics of the slip zone soil are investigated. Experimental results show that slip zone soil exhibits strain softening during D-W cycles, becoming more pronounced with more cycles. D-W cycles cause deterioration in shear strength and cohesion of slip zone soil, especially in the first four cycles, while the internal friction angle remains largely unchanged. The compaction effect of the overlying load mitigates the deterioration caused by D-W cycles. The findings reveal the weakening pattern of mechanical strength in slip zone soil under combined effects of overlying load and D-W cycles, offering valuable insights for studying mechanical properties of slip zone soil in reservoir bank landslides.

期刊论文 2025-05-01 DOI: 10.16285/j.rsm.2024.0885 ISSN: 1000-7598

Introduction Surface deformation in the Three Gorges Reservoir area poses significant threats to infrastructure and safety due to complex geological and hydrological factors. Despite existing studies, systematic exploration of long-term deformation characteristics and their driving mechanisms remains limited. This study combines SBAS-InSAR technology and machine learning to analyze and predict surface deformation in Fengjie County, Chongqing, China, between 2020 and 2022, focusing on riverside urban ground, riverside road slopes, and ancient landslides in the reservoir area.Methods SBAS-InSAR technology was applied to 36 Sentinel-1A images to monitor surface deformation, complemented by hydrological and meteorological data. Machine learning models-Random Forest (RF), Extremely Randomized Trees (ERT), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)-were evaluated using six metrics, including RMSE, R2, and SMAPE, to assess their predictive performance across diverse geological settings.Results Deformation rates for riverside urban ground, road slopes, and ancient landslides were -3.48 +/- 2.91 mm/yr, -5.19 +/- 3.62 mm/yr, and -6.02 +/- 4.55 mm/yr, respectively, with ancient landslides exhibiting the most pronounced deformation. A negative correlation was observed between reservoir water level decline and subsidence, highlighting the influence of seasonal hydrological adjustments. Urbanization and infrastructure development further exacerbated deformation processes. Among the models, LSTM demonstrated superior predictive accuracy but showed overestimation trends in ancient landslide areas.Discussion Reservoir water level adjustments emerged as a critical driver of subsidence, with rapid water level declines leading to increased pore pressure and soil compression. Seasonal effects were particularly evident, with higher subsidence rates during and after the rainy season. Human activities, including urbanization and road construction, significantly intensified deformation, disrupting natural geological conditions. Progressive slope failure linked to road expansion underscored the long-term impacts of engineering activities. For ancient landslides, accelerated deformation patterns were linked to prolonged drought and reservoir-induced hydrological changes. While LSTM models showed high accuracy, their limitations in complex geological settings highlight the need for hybrid approaches combining machine learning with physical models. Future research should emphasize developing integrated frameworks for long-term risk assessment and mitigation strategies in reservoir environments.Conclusions This study provides new insights into the complex surface dynamics in the Three Gorges Reservoir area, emphasizing the interplay of hydrological, geological, and anthropogenic factors. The findings highlight the need for adaptive management strategies and improved predictive models to mitigate subsidence risks.

期刊论文 2025-01-13 DOI: 10.3389/feart.2024.1503634

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

期刊论文 2024-10-01 DOI: 10.1016/j.jrmge.2024.01.003 ISSN: 1674-7755

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: longterm deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

期刊论文 2024-10-01 DOI: 10.1016/j.jrmge.2023.09.030 ISSN: 1674-7755

Wave erosion is the main erosion type in the water -level fluctuation zone (WLFZ) of the Three Gorges Reservoir Area (TGRA). Despite vegetation can effectively mitigate wave erosion in the WLFZ, its influence on the wave force and wave erosion remains unclear. Therefore, the wave experiments were conducted under 3 Cynodon dactylon coverage rates (0, 30% and 60%) and 9 wave conditions (3 wave heights of 4, 6 and 8 cm combined with 3 wave periods of 1, 2 and 3 s) to analyse the wave force (expressed as the wave pressure on the slope surface and the pore water pressure in the slope) and wave erosion rate, and the factors influencing wave erosion were identified. The results indicated that the wave pressure, pore water pressure and wave erosion rate increased by 19.14%-104.75%, 16.84%-65.04% and 23.33%-91.64%, respectively, as wave height increases. The wave pressure decreased by 1.50%-31.23% followed by an increase by 22.05% to 87.10% with the increase of wave period, whereas the pore water pressure and wave erosion rate decreased by 28.33%-53.59% and 20.46%- 63.59%, respectively. However, these quantities decreased by 2.10%-50.84%, 17.06%-40.23% and 17.28%- 82.18%, respectively, with the increase of Cynodon dactylon coverage rate. It was also discovered that the pore water pressure and Cynodon dactylon coverage rate attained the highest positive and negative correlation coefficients with the wave erosion rate, respectively. In addition, pore water pressure accumulation is the most critical influence factor on wave erosion, and Cynodon dactylon could effectively reduce the pore water pressure via its roots, thus improving the slope wave erosion resistance. This study could be useful to understand the mechanism of plants on controlling wave erosion and could provide a scientific reference for wave erosion control and the ecological construction in the WLFZ.

期刊论文 2024-05-01 DOI: 10.1016/j.ecoleng.2024.107233 ISSN: 0925-8574

Brown carbon (BrC) is known as a light-absorbing organic aerosol which affects the visibility and radiative forcing budget in the troposphere. The optical properties were studied for filter-based PM2.5 samples collected from the winter of 2015 to the summer of 2016 at one rural and three urban sites in the Three Gorges Reservoir (TGR) region, China. The average light absorption coefficient for BrC (beta(abs,405, BrC)) at 405 nm and its contributions to total aerosol light absorption during winter were 12.1 +/- 7.0 Mm(-1) and 23.8 +/- 9.1% respectively, higher than those during summer (1.7 +/- 0.8 Mm(-1) and 11.2 +/- 4.1%). Spatially, the average beta(abs,405, BrC) was higher at the urban sites (13.4 +/- 7.3 Mm(-1)) than that at the rural site (7.8 +/- 3.2 Mm(-1)). The average mass absorption efficiency of BrC at 405 nm (MAE(405),(BrC)) was 0.8 +/- 0.4 m(2) g(-1) during winter which was 2.7 times higher than that during summer (0.3 +/- 0.1 m(2) g(-1)). Furthermore, the absorption Angstrom exponents (AAE) at 405-980 nm (AAE(405-980)) were 1.1 +/- 0.1 in summer and 1.3 +/- 0.2 in winter respectively. Correlation analysis suggests that biomass burning (BB) played an important role in beta(abs,405, BrC) during winter. Additionally, the relatively high AAE(405_980) during winter was mainly due to the BrC from both BB and secondary organic aerosol. The fractional contribution of solar energy absorption by BrC relative to BC in the wavelengths of 405-445 nm was 23.9 +/- 7.8% in summer and 63.7 +/- 14.2% in winter, significantly higher than that in the range of 405-980 nm (11.9 +/- 3.4% and 29.9 +/- 6.1% respectively). Overall, this study contributes to the understanding of sources of BrC in the climate-sensitive TGR region of southwestern China.

期刊论文 2020-05-15 DOI: 10.1016/j.atmosenv.2020.117409 ISSN: 1352-2310
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