Dissolved organic matter (DOM) dynamics in large tropical rivers play a key role in aquatic carbon cycling, yet remain poorly documented in Vietnamese systems, particularly for chromophoric and fluorescent DOM (CDOM and FDOM). In this study, 68 surface water samples were collected along the main channel of the Red River (northern Vietnam), from upstream regions to the estuary, during the dry (March) and wet (July) periods of 2019. CDOM and FDOM characteristics were investigated using UV-visible absorbance and excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC). A three-component PARAFAC model identified two humic-like components (C1 and C3) and one tryptophan-like component (C2). Significant differences between the two sampling periods, representing contrasting hydrological conditions, and marked spatial variability were observed. CDOM and FDOM concentrations were significantly higher during the wet period, with CDOM absorption coefficient at 350 nm [a(350)] of 4.8±2.2 m-1 (wet) and 1.9±0.8 m-1 (dry), specific ultraviolet absorbance (SUVA254) of 10.0±2.6 L mg C-¹ m-¹ (wet) and 4.3±0.8 L mg C-¹ m-¹ (dry), C1 of 118±25 QSU (wet) and 66±29 QSU (dry), C3 of 74±10 QSU (wet) and 47±23 QSU (dry), and C2 of 418±85 QSU (wet) and 119±63 QSU (dry). These patterns reflected enhanced terrestrial inputs driven by monsoon runoff and soil leaching, resulting in more aromatic, higher-molecular-weight DOM. Along the river continuum, upstream waters were dominated by humic, terrestrially derived material (higher values of DOC, a(350), C1, C3). In contrast, the Red River Delta exhibited lower DOM concentrations and greater material processing, consistent with dilution and in-river processing. The higher relative contribution of tryptophan-like compounds (higher %C2, C2/C1, and C2/C3 ratios) in the delta during the dry period may reflect enhanced biological activity and/or possible anthropogenic inputs. These findings highlight the combined influence of monsoon hydrology, in-stream processes, and human pressures on DOM dynamics in a major Southeast Asian river.
Molybdenum (Mo) is classified as a critical mineral in Indonesia. It is widely used across various industries, including the nuclear sector, due to its distinctive properties and potential applications in nuclear medicine and nuclear fuels. The Padean granite in southern Sumatra has long been regarded as part of a Mesozoic, I-type, volcanic-arc–related granitoid suite, and no significant mineralization has previously been reported from the study area. However, the recent identification of molybdenite occurrences during regional exploration highlights the need for a systematic reassessment of granite's petrogenesis and mineralization potential. This study investigates the petrogenesis of the Cretaceous Padean granite by integrating field observations, petrographic analyses, and geochemistry to provide insights into the controls of magma evolution and the geodynamic setting of molybdenum ore formation. The granites comprise two distinct suites: I-type granites from mantle-related sources in volcanic-arc settings, and S-type granites formed through crustal anatexis during syn-collisional processes. Molybdenite occurs mainly as disseminations and as quartz ± muscovite veins, formed during late-magmatic to magmatic-hydrothermal stages. Molybdenite mineralization is preferentially associated with highly fractionated S-type granites. Mo concentrations range from 1639 to 1722 ppm in mineralized granitoids and from 7062 to 60,305 ppm in hydrothermal veins, representing enrichments by several orders of magnitude relative to barren samples. Advanced fractional crystallization, as indicated by strong Rb enrichment and Sr-Ba depletion, together with the assimilation of Mo-rich sedimentary components, as suggested by high Th/La ratios, controlled Mo concentration in late-stage magmatic-hydrothermal systems. These results indicated that molybdenite mineralization in the study area was primarily controlled by sediment-derived S-type magma generation in a volcanic-arc to syn-collisional setting, followed by advanced fractional crystallization and late-stage magmatic-hydrothermal fluid exsolution. This study highlights the critical role of magma differentiation and tectonic framework in controlling Mo fertility in Late Cretaceous granite-related systems of Sumatra.
Drought has intensified across the Mekong River Basin (MRB) due to climate variability and increasing human interventions, creating substantial risks for water resources and agriculture. This study applied the SWAT model to simulate hydrological processes and evaluate meteorological, agricultural, and hydrological droughts using the SPI, SSWI, and SRI indices. Model calibration (1982–2001) and validation (2002–2019) at seven hydrological stations produced satisfactory to very good performance, with R2 values ranging from 0.80 to 0.89 and 0.52 to 0.69, respectively. The model effectively captured seasonal flow dynamics, although uncertainties increased toward downstream regions influenced by reservoir operations and land-use change. Analysis of precipitation and soil moisture revealed strong spatial contrasts among sub-basins: upstream areas exhibited limited infiltration and rapid drainage. At the same time, midstream and downstream regions retained soil moisture longer due to favorable topography and soil characteristics. Multi-decadal drought evaluation reveals several prominent drought periods from 1970 to 2000, consistent with major large-scale climate anomalies. Correlation analysis shows stronger linkages among drought indices at longer accumulation periods, indicating tighter coupling of drought processes under prolonged dry conditions. Overall, integrating SWAT simulations with multi-index drought diagnostics provides a robust framework for characterizing meteorological, agricultural, and hydrological drought dynamics, supporting improved drought monitoring and water resource management in the MRB.
The Jailolo region, located on Halmahera Island, is characterized by intense seismic activity due to its proximity to two subduction zones and the Halmahera volcanic arc. To optimize the analysis of local earthquake activity in the region, we used an automated workflow that combines the deep-learning phase picker EQtransformer with the unsupervised machine learning association algorithm GaMMA to construct a high-resolution microseismic catalog. Phase arrivals were first identified using the EQTransformer and subsequently associated with each other using GaMMA, a Bayesian Gaussian Mixture Model with spatial clustering for earthquake event detection. This workflow produced 6,174 preliminary detections, of which 2,902 high-quality events were retained after relocation and filtering. The earthquakes predominantly occurred at depths shallower than 50 km, with an average local magnitude (ML) of 0.91. The obtained catalog revealed a large number of previously unreported small-magnitude earthquakes in the International Seismological Center (ISC) catalog. The completeness magnitude (Mc = 0.63) indicates a significant improvement in detection capability, enabling detailed analysis of microseismicity. The study highlights the effectiveness of automated machine-learning techniques in improving the resolution of seismicity patterns in a tectonically complex region such as Jailolo.
Land subsidence is one of the geotechnical phenomena that significantly affects infrastructure, construction, and sustainable urban development. Therefore, predicting and identifying areas susceptible to land subsidence has received increasing attention in recent studies. This study examines the application of deep learning models to spatial data analysis for predicting land subsidence susceptibility in the Hanoi area. Land subsidence data were obtained from satellite imagery and processed using a multi-temporal InSAR approach to determine surface deformation. They were then split into two datasets: 70% for training and 30% for validation. A total of 19 conditioning factors were used as input variables for the models, including aspect, slope, curvature, elevation, normalized difference vegetation index (NDVI), groundwater, engineering geology, hydrogeology, Holocene sediment thickness, land use/land cover (LULC), rainfall, topographic wetness index (TWI), and Landsat 8 spectral Bands 1–7. Four deep learning models, including CNN, LSTM, DRDN, and CNN-LSTM, were developed and compared to evaluate their predictive capability. The performance of the models was assessed using AUC, RMSE, MAE, and other evaluation metrics. The results show that all four models achieved good predictive performance, among which the DRDN model provided the best overall results, with AUC = 0.984, MAE = 0.064, and RMSE = 0.254 for the training dataset, while the corresponding values for the validation dataset were AUC = 0.957, MAE = 0.093, and RMSE = 0.305, indicating that the model has high accuracy and strong generalization capability in mapping land subsidence susceptibility in the study area. In addition, SHAP analysis revealed that Holocene sediment thickness and groundwater were the most important factors controlling land subsidence susceptibility in Hanoi.
This study aims to predict flood susceptibility and community adaptation capacity based on machine learning and socioeconomic data. Da Nang City was selected as the case study in this study. Five machine learning algorithms, Random Forest (RF), Adaboost (ADB), Bagging (BA), Gradient Boosting (GB), and XGBoost (XGB) were used to predict flood susceptibility, and 80 households were selected to assess the community’s adaptation capacity. The findings indicate that the RF model performed better than the other models, with an AUC score of 0.989, followed by ADB (0.987), BA (0.985), XGB (0.984), and GB (0.983). The eastern regions are affected by very high and high flooding, including Hai Chau, Thanh Khe, Ngu Hanh Son districts, and part of Son Tra. These regions have low elevations and high construction density. However, western mountainous areas, such as the Hoa Vang district and part of the Lien Chieu district, are in very low- and low-flood areas. The adaptive capacity of communities in Da Nang City is shaped by natural, physical, human, social, and financial resources.
Despite increasing evidence of groundwater salinization, the processes driving it and its geographic extent in the Abulug River Basin remain unexamined. Although complex methods exist, this study demonstrates that a simplified approach can yield meaningful insights into hydrological processes in the basin by combining findings from hydrochemical analysis, isotope techniques, and GIS mapping. Thirty-two sampling sites yielded 96 samples. Most of the samples exhibited similar behavior, except for a few. While the majority of the well samples in the research area are classified as Ca-Mg-HCO3, a recently formed groundwater with a short average residency period recharged by precipitation, a few samples exhibited the Na-Cl water type, which typically denotes salinization. Two river samples and seven well samples are within the mixing line. These samples are identified using the results from several ionic elements, ratios, and isotopic composition. Evolution diagrams, such as the Gibbs, Chadha, and Piper diagrams, suggested that seawater intrusion was not the only cause of salinization. The groundwater quality in the basin's coastal aquifer is deteriorating due to dominant salinization drivers, including seawater intrusion, paleo-saline groundwater, and rock-water interactions such as cation exchange, silicate weathering, and halite dissolution. It is recommended to prioritize policy implications for local managers, such as abstraction limits, integration of geophysical surveys, and sentinel network monitoring.
Coastal debris directly impacts people's health and the environment, so assessing it is crucial to industrialization, modernization, and economic development while conserving the environment for stable, long-term prosperity. Using drones to photograph debris on different coasts in Vietnam from 2023 to 2025, this study aims to develop deep learning (DL) models to assess coastal debris distribution. The UNet and PSPN using a ResNet34 backbone achieved better debris distribution identification with an input size of 64 × 64 pixels. The assessment indicated that the debris was primarily concentrated in littoral areas and embankments, and that it fluctuated with seasonal variations and collection efforts. The majority of debris was found to have been generated by fishing vessels, river systems, and marine trading activities, as evidenced by local surveys. Coastal debris, including nylon bags, was highly visible throughout the site. Current debris management is inefficient because manual periodic collection methods lack sufficient disposal solutions, and local groups struggle to collect trash. The research found that both local authorities and residents were deficient in exchanging environmental information effectively due to poor ecological awareness among the public. Therefore, the proposed framework should be regarded as a management recommendation rather than a field-tested operational system. This proposal can help increase environmental monitoring and engagement, enabling local authorities to work more effectively with their communities to implement effective debris management practices.
Sustainable urban development management in mountainous cities with complex basin topography is becoming a formidable challenge for urban planners amidst rapid urbanization. This study quantifies the land use/land cover (LULC) dynamics in Dien Bien Phu City from 2018 to 2025 using an integrated framework that combines multi-temporal Sentinel-2 imagery, Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM), and the Random Forest machine learning algorithm on the Google Earth Engine platform. The classification results achieved high reliability, with overall accuracy (OA) ranging from 94.2% to 98.8% and Kappa coefficients (Kappa) exceeding 0.96. Spatial analysis reveals a pronounced structural shift in land cover: built-up areas increased significantly from 8.8% in 2018 to 12.4% in 2025, while forest cover declined sharply from 35.1% to 26.7%. Notably, bare land accounted for a substantial proportion (14.1%) by 2025, potentially indicating land preparation activities and reflecting possible short-term fluctuations in plantation forest cover. The results further suggest that urban growth is transitioning from a historically monocentric pattern towards a more polycentric model, primarily expanding into southern and western corridors. By benchmarking these findings against the 2045 General Planning Vision, this study identifies potential conflict zones between urban expansion and ecological conservation in mountainous buffer areas. These findings provide a scientific basis for urban authorities to regulate land-use conversion, particularly in high-risk basin foothill zones, while simultaneously addressing the imbalance between infrastructure expansion and ecological preservation. In particular, the spatial delineation of urbanization pressures provides actionable guidance for establishing greenbelts and optimizing urban growth boundaries in accordance with the 2045 General Planning Vision, thereby enhancing the long-term climate resilience of basin-type cities under accelerating environmental change.
Research on Late Quaternary sequence stratigraphy of the northeastern shelf off the Mekong River Delta provides critical insights into the evolution of depositional systems in response to global sea-level changes. The integration of sediment stratigraphic frameworks with observations of recent sediment accumulation and transport helps predict the future development of the Mekong River Delta. In this study, seismic and sediment data collected from six cruises (2004−2015) on the inner-middle shelf were processed and interpreted. Two depositional sequences were identified from high-resolution seismic data, designated SQ1 and SQ2. The depositional sequence SQ2 consists of a transgressive systems tract (TST2) and a regressive systems tract (RST2) identified on the middle shelf. The depositional sequence SQ1, overlying SQ2, comprises a transgressive systems tract (TST1) and a highstand systems tract (HST1), developed during the post-LGM transgression after ~19.6 ka. Analysis of SQ1 and SQ2 shows that regressive sediments prevail over transgressive sediments throughout a full fourth-order sea-level cycle (120 ka). Under modern conditions of high relative sea-level rise, spatial variability in sediment accumulation rates from the subaqueous Mekong River Delta to the middle shelf is controlled by hydrodynamic forcing, continental shelf gradient, and variations in sediment transport distance from river mouths: < 2 cm/y on the topset; ~1 to > 10 cm/y on the foreset, with an overall tendency to decrease downslope; < 0.5 cm/y on the bottomset-middle shelf. Sediment accumulation rates are relatively low on the middle shelf. Furthermore, the distribution and characteristics of seabed surface sediments indicate that suspended sediment transport across the middle shelf from east to west along the paleo-Dong Nai River, under the influence of the southwest monsoon, reaches the deep sea. Regulation of sequence stratigraphy and modern sediment processes indicates that the present subaqueous Mekong River Delta has reached the limit of seaward progradation and is gradually shifting to retrogradation.
The Permian-Triassic igneous rocks of the Truong Son Fold Belt, on the northeastern margin of the Indochina block, formed during Paleotethyan subduction and the subsequent collision with the South China block. Systematic variations in zircon ages, geochemistry, and isotopic compositions are observed from northern Laos toward the Song Ma Suture zone, the collisional boundary with the South China block. In the Xiang Khuang-Muang Khoun (MK) area, 170–200 km from the suture, magmatic rocks (271–253 Ma) include gabbros and I-type granitoids with relatively higher εNd(t) values (-1.5 to -9) and lower 87Sr/86Sri (0.704–0.717). Toward the suture, in the Nam Phao-Kim Cuong area, granitoids dated at 260–251 Ma are predominantly S-type, highly peraluminous granites with intermediate isotopic εNdi values (-7.4 to -9) and 87Sr/86Sri values (0.7115–0.7285). In the Sam Neua (SN) area, closest to the suture, granitoids dated at 251–244 Ma are primarily I-type and minorly S-type, with highly enriched isotopic compositions (εNdi of -8.4 to -14; 87Sr/86Sri between 0.708 and 0.775). The trace-element chemistry of all granitoids indicates volcanic-arc affinities, though signatures also suggest intraplate and post-collisional influences. The association of granitoids with coeval gabbro-diorites in the MK area suggests binary mixing between mantle magmas and Mesoproterozoic crust-derived melts. In contrast, felsic magmas in the SN area likely reflect melting of diverse crustal components, including Paleoproterozoic continental crust of South China affinity. The suture zone-ward younging of magmatism is consistent with slab rollback during the final stages of continental collision.
In this paper, the main objective is to predict total bearing capacity (TBC) of pretensioned spun concrete piles (PSCP) using Machine Learning (ML) methods namely Reduced Error Pruning Tree (REPT), Gaussian Process (GP), Artificial Neural Networks (ANN) and two novel hybrid models including: Cascade Generalization based Gaussian Processes (CG-GP) and Cascade Generalization based Artificial Neural Networks (CG-ANN) based on data from 95 PSCP piles installed at the Hoa Binh 5 wind power plant project in Vietnam. For model development, field-estimated TBC values obtained from Pile Driving Analyzer (PDA) tests were used as the output parameter. The predictive capability of the models was validated using common statistical indicators, namely Mean Absolute Error (MAE), Coefficient of Determination (R2) and Root Mean Square Error (RMSE) with 70% of the data used for training and 30% for testing. The results indicated that the proposed hybrid CG-ANN model (R2 = 0.935, RMSE = 44.691 ton, MAE = 30.215 ton) outperformed all other models including CG-GP (R2 = 0.929, RMSE = 50.738 ton, MAE = 37.812 ton), Artificial Neural Networks-ANN (R2= 0.926, RMSE = 47.963 ton, MAE = 32.167 ton), REPT (R2= 0.776, RMSE = 75.350 ton, MAE = 53.115 ton) and GP (R2= 0.916, RMSE = 52.785 ton, MAE = 39.967 ton) in the correct prediction of the TBC of PSCP. The results demonstrate that the hybrid CG-ANN model can serve as an efficient and reliable tool for rapid, accurate estimation of PSCP bearing capacity, thereby helping reduce the time and cost associated with elaborate field testing.
This study investigated wave-induced suspended sediment dynamics and seabed morphological changes around the Truong Sa Island in the East Vietnam Sea. The analysis was conducted using the MIKE 21/3 Coupled Model FM, which integrates wave, hydrodynamic, sediment transport, and bed evolution modules to simulate seabed responses to both seasonal and interannual wave forcing during 2013-2015. The model was forced with ERAS reanalysis data and validated using observed tidal and bathymetric datasets from the Vietnam Academy of Science and Technology (VAST). Results revealed that seabed changes were most pronounced in the northeastern nearshore region of the Truong Sa Island. It was driven by the prevailing northeast-directed wave conditions, which also contributed to elevated suspended sediment concentrations (SSCs). Seasonally, winter exhibited more dynamic seabed changes and greater suspended sediment variability than summer. Under the most severe wave conditions in 2015, the simulated domain experienced a net seabed incision with an average depth of-0.54 m. Spatially, the nearshore areas were characterized by significant seabed incision and high SSCs, whereas offshore regions showed minimal seabed changes and low SSCs. Moreover, persistent sediment deposition was observed in the harbor southwest of the Truong Sa Island, implying the need for regular dredging to maintain operational capacity. The findings underscore the need for integrated management strategies that balance coastal development, marine ecosystem protection, and navigational safety.
The soil shrinkage curve (SC) and the shrinkage limit are important for assessing the impact of volume change as soil dries. The SC is used to calculate the degree of saturation from the soil-water characteristic curve (S-SWCC) and is subsequently used to estimate other unsaturated soil property functions, such as shear strength and hydraulic conductivity. The present research aims are to: (1) identify the most appropriate parameters for the estimation for the SC without conducting a SC test, (2) investigate the effect of the initial compaction degree, characterized by void ratio and dry density, on the SC, and (3) emphasize the role of SC as it relates to the unsaturated shear strength and permeability functions. To achieve these objectives, the SC and SWCC tests were conducted on a weathered soil sample collected from a natural landslide slope along Route 74 connecting A Luoi and Nam Dong in Hue, Vietnam. The findings show that the SC can be predicted using empirical equations, thereby negating the need for direct SC testing. Unsaturated soil functions can be estimated from the SC and S-SWCC.
This study evaluates the performance of multispectral optical sensors onboard PlanetScope (PS) and Sentinel-2 satellites in mapping burned areas resulting from a small forest fire that occurred on 21 March, 2025, in Nghiem Mountain, northern Vietnam. Cloud-free pre-and post-fire imagery acquired on the same dates (17 January and 12 May, 2025) were used to compute the difference Normalized Difference Vegetation Index (dNDVI) using Red and Near-Infrared surface reflectance. A threshold value (T = 0.10), selected after analyzing the dNDVI histograms, was applied to classify burned (dNDVI > T) and unburned regions (dNDVI <= T). Results showed a strong spatial correlation between dNDVI maps derived from both satellites (R = 0.97), although Sentinel-2 tends to yield slightly higher dNDVI values than PS satellites. The burned area estimated from PS was 20.622 ha, while Sentinel-2 produced a similar estimate of 20.225 ha, a difference of less than 2% and in close agreement with the official damage assessment report (=20 ha). Most discrepancies occurred along fire boundaries, where mixed pixels and spectral heterogeneity are expected. Our results demonstrate the effectiveness of Sentinel-2 and PS satellite imagery for mapping burned areas from small-scale fires, which is essential for forest management. Despite several limitations, including dependence on clear-sky conditions and the lack of a ground-based validation dataset, the proposed approach provides a timely and cost-effective solution for wildfire mapping at small scales, particularly important in remote regions.
The Dai Loc shear zone in central Vietnam contains granulite-facies rocks and is a key area for studying the Early Paleozoic metamorphic evolution of the Indochina Block. An integrated study of in-situ geochronology, trace element geochemistry, and microtextural analysis was conducted to decipher the metamorphic evolution of this highgrade unit. Monazites from the two granulite samples display three distinct chemical domains, whose trace element compositions closely correlate with garnet growth and breakdown. Yttrium- and heavy rare-earth element (HREE)rich monazite core domains are interpreted to have formed with limited garnet growth, recording a discrete growth episode during prograde metamorphism at -435 Ma. Y- and HREE-poor domains are linked to significant garnet growth during peak conditions at -420 Ma. The elevated Y+HREE concentrations in the outermost rim domains indicate their formation during garnet breakdown and likely date the retrograde metamorphism to -390 Ma. These U-Pb monazite ages align well with the U-Pb zircon ages from granulites and syn-metamorphic granitoids in the study area, reinforcing the inferred metamorphic timeline. The results of this petrochronological study highlight the importance of integrating petrology with trace element data from major and accessory phases to link geochronological data to metamorphic P-T paths.
Over the past few decades, urban expansion has accelerated worldwide. This process can increase future flood risks due to local changes in hydrological conditions and the increased exposure and vulnerability of communities in flood-prone areas. Therefore, assessing the impact of urban expansion on flood susceptibility is an important task that can support local authorities in urban planning and in mitigating flood impacts. The objective of this study was to assess the impact of urban expansion on flood susceptibility in Hanoi using machine learning models: Deep Neural Networks (DNN), Adaptive Boosting (ADB), Extreme Gradient Boosting (XGB), and Random Forest (RF). A total of 1058 flood points and 14 conditioning factors corresponding to 2014 and 2024 were used as input to the models. Statistical indices, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Area Under the Curve (AUC), and Coefficient of Determination (R2) were used to evaluate the performance of the proposed model. The results showed that the DNN model achieved the highest performance in assessing the impact of urban expansion on flood susceptibility (AUC=0.92), followed by XGB (0.91), ADB (0.86), and RF (0.82). During 2014-2024, urban expansion combined with the impacts of climate change has significantly increased the areas susceptible to flooding. In Hanoi, areas in the "high" and "very high" flood-susceptibility categories have been expanding continuously, accounting for about 25% of the total study area. In contrast, the "medium" group has a slight decreasing trend, while the "low" and "very low" areas have narrowed. This shows that urban expansion is increasing the area prone to flooding. The results of this study provide a solid scientific basis, supporting planners and policymakers in identifying limitations in current flood risk adaptation measures and in developing more appropriate spatial and temporal strategies to minimize flood impacts.
In recent years, the primary concern in the downstream region of the Mekong River system has been changes in river discharge due to climate change and the construction of upstream hydropower dams. This change might affect the salinity and productivity in the southern waters of the Mekong Delta. This study used measured water discharges, satellite ocean color, and a gridded salinity dataset from 2010 to 2020 to investigate these variations. The spatial and temporal distributions of sea surface salinity and chlorophyll a concentration have been discussed based on statistical analysis. It has been confirmed that the salinity in the Gulf of Thailand is consistently lower than that in the East Vietnam Sea. In the Mekong plume area, salinity is strongly correlated with variations in freshwater discharge. The main reasons for these distributions were discussed in this paper. It is noted that the time lag between freshwater discharges and salinity and chlorophyll a concentrations in the Mekong plume region was 1 month. In contrast, they were 5 months and 2 months in the Gulf of Thailand, respectively. It can be confirmed that Mekong River water did not play a significant role in the productivity of the Gulf of Thailand, but it is the main source of the lower salinity. In the offshore area of the Mekong plume, the impact of freshwater discharges was small in magnitude but quite apparent in phase, with a 2-month time lag between salinity and ocean color.
This experimental study evaluates the influence of soil consistency on the ultimate bearing capacity and elastic modulus of single geogrid-reinforced and unreinforced stone columns installed in soft-to-very soft clay. Laboratory model tests were conducted in the Ho Thuong Tin area of Hanoi, Vietnam, under three controlled soil conditions with liquidity indices (IL) of 0.78, 1.0, and 1.5. A total of six displacement-controlled load tests were performed in a unitcell setup, with and without a geogrid reinforcement layer at the column head. Results show that the inclusion of geogrid significantly enhanced both the ultimate bearing capacity and stiffness of the stone column: the bearing capacity increased by approximately 12-25%, and the elastic modulus (E50) increased by 19-27% relative to unreinforced columns. The study indicates that geogrid reinforcement at the column head provides a technically material-saving and straightforward alternative to complete encasement, with potential economic advantages particularly for soft clays where lateral confinement is limited. The findings provide new experimental data to inform the design of geogrid-reinforced stone columns under varying soil conditions.
This study highlights a novel ensemble approach integrating the JCHAIDStar model with various ensemble hazard susceptibility assessment and mapping of landslides and flash floods (LS-FF) in Ha Giang province, Vietnam. A total of 963 landslides and 106 flash flood events were used for model development and validation. Flash floods rapidly saturate soil, reducing its cohesion and destabilizing slopes, which leads to landslides. Conversely, landslides may block rivers, creating natural dams that fail abruptly, resulting in flash floods. In this study, a comprehensive dataset comprising 963 landslides, 106 flash floods, and thirteen conditioning factors related to topography, hydrology, geology, and meteorology was utilized. This dataset was split into training (70%) and test (30%) sets for model development and validation, with AUC used to evaluate performance; the Bag-JCHAIDStar model achieved the highest predictive accuracy (AUC = 0.985 for training and 0.951 for testing). The results demonstrated that ensemble-based JCHAIDStar models outperformed single benchmark models (LR and SVM). The generated susceptibility maps provided reliable spatial information for land-use planning and disaster risk mitigation.
