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Recurrent landslide events triggered by typhoons and tropical storms over Vietnam pose a longstanding threat to the nation’s population and infrastructure. Changes in hydroclimatic conditions, especially the growing intensity and frequency of storms, have elevated landslide susceptibility in many parts of the country. This research examines the spatio-temporal variations in landslide susceptibility across central Vietnam over several years, using multi-temporal landslide inventories from Typhoon Ketsana (2009), Tropical Storm Podul (2013), and Typhoon Molave (2020). Additionally, the research explores the impact of individual landslide causative factors on the probabilistic occurrences of landslides. The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. These changes may be early indicators of increasing landslide susceptibility in response to changing hydro-climatic conditions. The research found that annual average rainfall and topographic elevation are the two most important variables influencing landslide prediction, showing a nonlinear relationship with landslide probability. The landslide susceptibility models achieved high Area Under the Receiver Operating Characteristic Curve (AUC) (>95%), accuracy (>89%), and sensitivity (>90%) scores, signifying the robustness of the models. Additionally, the uncertainty of the models was quantified and spatially mapped. This multi-temporal analysis of landslide susceptibility is crucial for understanding the regional susceptibility trends and identifying areas with increasing, decreasing, and consistently high susceptibility to landslides. These insights are invaluable for prioritizing mitigation and risk reduction strategies in landslide-prone regions and guiding appropriate land use planning.

Vietnam, classified as a developing nation, encounters numerous challenges within its construction sector, including the scarcity of comprehensive and documented historical data regarding risks and a deficiency in embracing contemporary methodologies to mitigate the impact of risk factors on construction project objectives. This paper outlines initial findings from an ongoing research endeavor that centers on implementing Lean Construction (LC) techniques to enhance construction management practices specifically for marble floor finishing work within Vietnam. Therefore, this study aims to apply the construction lean principle combined with discrete-event simulation (DES) by using EZStrobe to simulate the marble floor finishing process in reality, from observing and collecting data of each activity in the actual process on the site. By building, running simulations, and resulting from real-world simulations, we'll understand the sources of waste, and then apply lean construction principles through methods such as just in time, reduce the batch size and resources priorities, and multi-skilled teams for the initial construction process. The study's lean modeling results has led to a 13% reduction in construction cycle time, a 141% improvement in process efficiency, a 268% enhancement in average productivity, and a 96% reduction in labor cost. The result has become the reference document resource for the managers and construction engineers to improve the performance of not only general finishing work but also marble floor finishing work.

Shallow landslides have posed significant threats to humans around the world. In order to reduce landslide disaster risk, the effectiveness of early warning systems and hazard zonation work needs to be improved. This research attempted to couple a landslide simulation model (LS-RAPID model) and a hydrological model (Rainfall-Runoff-Inundation (RRI) model) to exploit the advantages of each model for simulating and predicting landslide hazard (location and timing). The pilot area is a small catchment where a shallow landslide happened in July 2015 after 2 days of heavy rain. The landslide buried 3 houses and killed 8 people in Cao Thang Ward, Halong City, Vietnam. A soil sample was collected from the sliding surface and tested using an undrained ring-shear apparatus ICL-2 in the undrained condition. A thickness map of the potential sliding material was interpolated from the relationship between the depth of sandy soil layer and slope (based on 12 soil drill locations) and updated through field surveys in the study area. Different pore water pressure ratio scenarios were applied in the LS-RAPID model to simulate in 3D the initiation and motion of the rapid shallow landslide to create different hazard maps. The subsurface water level was monitored at two locations on the top of the shallow landslide. Based on the observed subsurface water and rainfall data, the RRI model was calibrated and then integrated with the LS-RAPID scenarios to generate Risk Index maps. The simulation results from the newly proposed coupled hydrological-geotechnical framework were compared with those from the observed landslide hazard and showed the reliability to predict the spatial and temporal occurrence of landslide hazard. This could be very useful for supporting decision-makers in rainfall-induced landslide hazard early warning and land use planning.

Landslide-induced waves are one of the most disastrous hazards that can post a great threat to human lives and properties. At about 4:00 pm, 5 November 2017, a landslide-induced tsunami-like wave suddenly occurred across the Truong river in Bac Tra My District, Quang Nam province, Vietnam. The water wave destroyed six houses at the opposite bank and caused one person dead and three others injured. This study seeks to investigate the initiation mechanism and process of the landslide and its impulse wave. First, we examined landslide characteristics through site investigations, unmanned aerial vehicle (UAV) surveys, and laboratory testing with a series of standard geotechnical tests on collected soil samples. Then, the initiation and motion of the rainfall-induced landslides were reproduced by the integrated landslide simulation model (LS-RAPID). Finally, a combined computer simulation of the landslide motion and its impulse wave was performed by using a landslide-induced tsunami simulation model (LS-Tsunami). In which, output data from the LS-RAPID was used as input parameters for LS-Tsunami. The analysis shows that the rainfall with very high intensity in a short-time period was the triggering factor of the landslide, which is common factor in the study area. The 12-, 24-, and 48-h accumulative precipitation prior to the landslide recorded to 530, 760, and 950 mm, respectively. In addition, the rainfall trigger presented a typical pattern of rainstorm events in a long duration. Simulation results show that the impulse wave was generated by the landslide mass rapidly entering the river, crossing the river, and directly causing the disastrous damage to the resident area opposite site of the fail slope. The landslide moved down at a maximum speed of 16.4 m/s when its body approached the water surface and generated a maximum wave height of 5 m. There is good agreement between the observed geomorphic evidences and water traces on the site and simulation results of the landslide and its impulse wave. The paper provides a good case study on the understanding of the mechanism and dynamic process of the whole event that significantly contribute to potential landslide hazard assessment and future disaster mitigation in the area.

In Vietnam, landslides frequently occur on cut slopes along the road system during the rainy season. An understanding of the contributing factors and triggering mechanisms is essential so that effective measures can be taken to stabilize cut slopes and mitigate impacts caused by landslides. This study uses as a research subject the largest deep-seated landslide triggered by heavy rainfall on July 21, 2018, and the subsequent sliding induced by 5-day continuous rainfall events on the Halong–Vandon expressway. We examined the causative factors, failure mechanisms, and characteristics of the landslides through detailed geological investigation, unmanned aerial vehicle (UAV) surveys, and analysis of data from geology, geomorphology, well-prepared documents of rainfall events, and the expressway project. Results show that the heavy rainfall was the triggering factor for both events while slope cutting was the main landslide causative factor. The slump-type landslides occurred on weathered limestone layers that were parallel to the dip-slope direction of the strata. Geological settings of highly fractured and weathered sandstone, siltstone, and limestone combined with the development of karst caves favored the buildup of groundwater levels in deep layers, thereby causing deep-seated landslides. The analysis shows that in addition to geological factors, the landslide occurrences resulted from anthropogenic effects including the improper design of the calculation method for safety factors in road construction and quarrying activities. Site evidence and UAV photos also reveal that the July 21 landslide body on the lower slope was reactivated to travel downward due to the dynamic effect of the subsequent sliding on July 31. Based on numerical analysis using the Plaxis 2D model, an estimated sliding surface similar to the actual plane was simulated for the entire slope. Furthermore, the study presents an appropriate solution that has been applied to slope stabilization.

Using unmanned aerial vehicles (UAVs) is an effective way to gather data from Internet of Things (IoT) devices. To reduce data gathering time and redundancy, thereby enabling the timely response of state-of-the-art systems, one can partition a network into clusters and perform aggregation within each cluster. Existing works solved the UAV trajectory planning problem, in which the energy consumption and/or flight time of the UAV is the minimization objective. The aggregation scheduling within each cluster was neglected, and they assumed that data must be ready when the UAV arrives at the cluster heads (CHs). This paper addresses the minimum time aggregation scheduling problem in duty-cycled networks with a single UAV. We propose an adaptive clustering method that takes into account the trajectory and speed of the UAV. The transmission schedule of IoT devices and the UAV departure times are jointly computed so that (1) the UAV flies continuously throughout the shortest path among the CHs to minimize the hovering time and energy consumption, and (2) data are aggregated at each CH right before the UAV arrival, to maximize the data freshness. Intensive simulation shows that the proposed scheme reduces up to 35% of the aggregation delay compared to other benchmarking methods.

Around hundred landslides were triggered by the Kumamoto earthquakes in April 2016, causing fatalities and serious damage to properties in Minamiaso village, Kumamoto Prefecture, Japan. The landslides included many rapid and long-runout landslides which were responsible for much of the damage. To understand the mechanism of these earthquake-triggered landslides, we carried out field investigations with an unmanned aerial vehicle to obtain DSM and took samples from two major landslides (Takanodai landslide and Aso-ohashi landslide) to measure parameters of the initiation and the motion of landslides. A series of ring-shear tests and computer simulations were conducted using a measured Kumamoto earthquake acceleration record from KNet station KMM005, 10 km west of Aso-ohashi landslide. The research results supported our assumed mechanism of sliding-surface liquefaction for the rapid and long-runout motion of these landslides.

At about 12:00 a.m., on October 13, 2020, a rapid rotational landslide induced by rainfall swept over Ranger Station-7 in Phong Xuan commune, Phong Dien district, Thua Thien Hue province, Vietnam, claiming the lives of 13 rescue team members. This paper presents an overview of the fatal landslide at Ranger Station-7. The analysis indicates that the entire landslide had a volume of approximately 81,550 m3. The observed topographical features indicate that the landslide mass consists of lower and upper blocks. The lower block of the landslide mass started sliding first; the movement was then followed by the slide of the upper block.

The abnormal water level rise on the west coast of Ca Mau Peninsula, Vietnam, has dramatically increased as extreme events have intensified in recent decades. To forecast it, it is necessary to understand the mechanism. However, its mechanism has yet to be studied. Based on the numerical simulation results, this study discusses abnormal water level change in a case study during the afternoon of August 2–3, 2019, and 11 July 2022. A coupled model of surge wave and tide (called SuWAT) was applied to simulate the tide, surge, and wind wave. SuWAT’s simulations were validated with the observational data of total water level and significant wave height, and the model could sufficiently reproduce the tidal, wind-induced surge and wave. The findings indicate that the unusual water level fluctuations along the western coast of the Ca Mau Peninsula resulted from a combination of high astronomical tides, storm surges, and waves. Notably, long-period swells substantially contributed to the overall water level, with peak measurements reaching 0.52 m on 3 August 2019, and 0.53 m on 11 July 2022. These surges and swell effects are mainly attributed to the study area’s prolonged and intense southwest monsoon.