Investigation of Natural and Human-Induced Landslides in Red Basaltic Soils

Abstract

Landslides are mass movements of rock, soil, or debris under the influence of gravity. These phenomena occur due to the loss of slope stability or imbalance of external loads. The intensity and consequences of landslides depend on various factors such as topography, geological structure, and precipitation regime. This study investigates the characteristics of rainfall-induced landslides in red basaltic soils on the basis of field investigations, geotechnical surveys, and slope stability modeling under anthropogenic triggers. The results indicate a close relationship between soil moisture and shear strength parameters, which significantly influence slope stability. A real-time observation system recorded groundwater level fluctuation in relation to surface runoff and precipitation rates. It is revealed that intense rainfall and low temperatures regulate soil moisture, resulting in a reduction of cohesion and shear strength parameters. These findings enhance the understanding of landslide mechanism in basaltic soil regions, which are highly sensitive to precipitation. The results also highlight that human activities play a significant role in triggering landslides. Therefore, a real-time monitoring system for rainfall, soil moisture, and groundwater is essential for early warning and supports the integration of smart technologies and Internet of Things (IoT) solutions in natural disaster management.

1. Introduction

Landslides are mass movements of rock, soil, or debris driven by gravitational forces, particularly in highland areas. They can be classified on the basis of movements (fall, topple, slide, spread, flow, or slope deformation), materials involved (rock, earth, debris, or mud), and the velocity of movement [1,2,3]. Landslides occur gradually, such as solifluction, soil creep, or slumping, while others happen rapidly, including mudflows, debris flows, and rockfalls. The speed and nature of movement is influenced by factors such as water content, slope angle, and grain size distribution [4,5,6,7].

Landslides are normally caused by natural factors, such as geological structures, weathering conditions, moisture, rainfall, or vegetation cover, resulting in severe consequences, posing significant threats to human lives and property [8,9,10,11,12,13]. Among these factors, rainfall is one of the biggest drivers that triggers slope instability [5,14]. Nowadays, landslides occur more frequently due to climate change (heavy rainfall and extreme weather conditions) and human activities, such as deforestation, excavation, building construction, or mining [15,16,17]. The frequency and consequence of landslides are dependent on the geological structures, rock weathering, groundwater table, soil composition, and natural conditions [18,19,20,21,22].

Basaltic soil, also known as “red basaltic soil or laterite”, is a weathering product of the basalt rock, a kind of fine-grained extruded rock [23]. Basalt rock contributes approximately 5% of the Earth’s crust, so its weathering and behavior are crucial in the global biogeochemical cycle [23,24]. Red basaltic soil consists of gibbsite, goethite, and kaolinite minerals, which are beneficial for numerous crops [25]. Recent studies revealed that basaltic soil exposed to high precipitation may reduce the ferric (Fe³⁺) and cation (Na⁺, Ca²⁺) or/and capture the CO₂ via silicate weathering that results in the poor strength of soil [23,26,27,28,29]. The colloidal properties of clay mineral in basaltic soil are sensitive to rainfall that may reduce the surface charging and reduce particle cohesion, leading to slope instability [22,25,30,31]. In contrast, biogeochemical processes may alter the soft red basaltic soil to the harder type, which is known as “lateric” [25,32]. Therefore, the intensity, frequency, and effects of rainfall on red basaltic soil behavior should be paid attention to regarding the mechanisms of rainfall-induced landslide hazards [33].

This study utilizes a comprehensive method to elucidate causes of landslide events occurring in the red basalt soil under high precipitation and human interventions such as slope cutting for road or building construction. A field observation, including site mapping, data collection, in situ analysis, and soil sampling, has been conducted to examine the geological structure, provide an in-depth understanding of the hydrological condition, and identify potential causes of the landslide [10,23,34]. During the investigation, undisturbed core samples were collected for laboratory analysis to determine the physical and mechanical properties of the soils [35]. A finite element model has been used to analyze the slope stability under various rainy conditions [36,37,38,39]. In addition, an advanced tilt sensor system was also installed to monitor the groundwater level and horizontal displacement of the soil layers in real-time [10]. Findings from this study elucidate the potential causes of mass movement events, which provide an insight into rainfall-induced and human-mediated landslide events in the red basalt soil area.

2. Materials and Methods

2.1. Field Investigation

Dalat City is located on the Langbian Plateau in the Central Highlands of Vietnam, at an elevation of approximately 1500 m above sea level. The topography is characterized by mountainous terrain in the northeast, plains in the southwest, and intervening valleys in the central region. Dalat experiences a subtropical highland climate with year-round temperate weather and average temperatures ranging from 14 to 23 °C (57 to 73 °F). The city has two distinct seasons: a rainy season from May to October and a dry season from November to April. The average annual rainfall of 2022, 2023, and 2024 were 2165, 2230, and 2050 mm, respectively. Dalat City has a dense network of rivers and streams, including the Dong Nai mainstream river and its tributaries. There are two soil groups: red feralite soil and humus alisols, both distributed at altitudes between 1000 and 2000 m [40]. The geological structure of Dalat comprises Precambrian basement rocks, Jurassic sedimentary, Late Mesozoic igneous rocks, and Cenozoic basalt formations [41,42,43].

Figure 1 illustrates the boundary of the survey area, where a field investigation was conducted from 29 April to 22 May 2017 to study soil characteristics and geological structures. A subsequent observation was conducted in July 2019 to examine hydrological parameters and perform a pumping test. There was total of 06 boreholes, namely HK01–HK06, which were drilled up to 15–20 m to explore the geological structure. The surface layer consists of 1–2 m of fill material, followed by a second layer dominated by lean clay with a reddish-brown to dark brown-white color. This layer is homogeneous and exhibits hard plastic consistency. The deeper layer comprises silty clay rich in silt content, with colors varying from dark yellow to reddish brown. During the investigation period, cumulative precipitation reached 268 mm, with a peak at 65 mm (Figure 2). Undisturbed core samples were collected for laboratory analysis to assess the physical properties and mechanical behavior of the soils [36].

2.2. Physical Properties Analysis

The sieve experiments were conducted in the laboratory to determine the contribution of various grain sizes contained in a core sample. The mechanical sieve was used to determine the coarse particle size (>75 $\mathsf{\mu }$m) and the hydrometer analysis was utilized for the finer size [44,45,46]. The whole nest of sieves is given a horizontal shaking for 10 min in a sieve shaker till the soil retained on each reaches a constant value. The contribution of different grain sizes regulates the physical properties of soil and its behavior under external loads. Meanwhile, permeability, shear strength, and moisture are reported to affect the landslide susceptibility negatively or positively [47,48].

2.3. Real-Time Observations System

A tilt sensor and strain gauge system were installed to monitor the groundwater table and horizontal displacement of the soil layers [10,41,42]. A total of 15 sections of strain gauges were installed in a drill hole (HK3 in Figure 1) every 1 m to observe the deformation behavior inside the cut slope. Figure 3 illustrates the schematic of the early warning system with the tilt sensor, pipe strain gauge, and water level gauge to detect any displacement of the slope. The gauge system was installed in a 60 mm diameter borehole drill to observe the horizon displacement up to 15 m at every meter. Positive or negative values are registered for unstable soil layer conditions [10]. Meanwhile, monitoring data is recorded every 10 min by the real-time data logger, NetLG-301NE^® (Osasi Co. Technos Inc., Kochi-si, Japan). A wireless transmitter is used to transfer monitoring data to satellite orbit, which could be received by the portable laptop computer or mobile devices. Signals from tilt sensors reveal the horizontal displacement of the soil structure [10].

2.4. Slope Stability Analysis

There are several factors that govern stability during rainfall including slope geometry, soil properties, rainfall intensity, soi moisture, and initial water table [51,52]. The objective of numerical model is to highlight the relative importance of some of these factors on the stability of unsaturated slopes. Linear relationship between soil shear strength, effective cohesion, air pore–air pressure, and friction angles, which is written as follows [51]:

(1)$\tau =c^{\prime }+\left({\sigma }_n-{\sigma }_u\right)tan{\varphi }^{\prime }+\left(u_a-u_w\right)tan{\varphi }^b$

where $\tau$ is the shear strength, $c^{\prime }$ is the effective cohesion, $\left({\sigma }_n-{\sigma }_u\right)$ is the net normal stress on the failure plane, ${\sigma }_n$ is the total normal stress, $u_a$ is the air pore–air pressure, $u_w$ is the pore–water pressure, $\left(u_a-u_w\right)$ is the matrix suction, ${\varphi }^{\prime }$ is the friction angle, and ${\varphi }^b$ is the angle linking the rate of increase in shear strength with increasing matric suction.

To determine the factor of safety, groundwater level, rainfall intensities, and soil properties were input to the numerical model. Observations of groundwater levels obtained from the tilt sensor system were also utilized in the model. Physical properties of the soil layers obtained from the sieve analysis were adopted for Mohr–Coulomb’s theory (Figure 4). Table 1 indicates the soil material input for the slope stability model. The model was set up with the Mohr–Coulomb material type and drainage behavior. Other input parameters, including unsaturated and saturated unit weight, initial void ratio, elastic modulus, Poisson’s ratio, cohesion, friction angle, and permeability, were determined from the field survey analysis. The rainfall rate obtained from the monitoring data from 14 May to 5 June 2022 was input to the model to analyze the factor of safety response from the precipitation [52,53,54,55,56]. Figure 4 illustrates the cross-section of the slope stability model.

3. Results

3.1. Investigation of the Landslide State

Figure 5 presents the average rainfall recorded in the calendar year of 2023, with cumulative total of 2230 mm and summary of relevant landslide events recorded. In the observation period, the number of rainy days is 188, which are primarily between May and November, with the highest daily reaching 82.5 mm. Three were major mass movement events occurred within 45 days of the rainny season. The first event occurred on 17 June on a plantation farm, burying approximately 50,000 m² of coffee crops. Six hours later, the situation escalated, resulting in the complete destruction of an additional 200,000 m² of coffee farms. On 29 June, a 30 m-high backfill slope failed, leading to two fatalities, three injuries, and damage to 12 houses. The most catastrophic event took place on 30 July, resulting in four fatalities and totally destroyed the section of National Road No. 20, a main route to Dalat City. This sudden landslide involved the failure of a 50 m hill slope of a plantation farm after two months of continuous rainfall. The high intensity and frequency of the rainfall altered the soil saturation condition, increased the groundwater level, and elevated the risk of slope instability.

3.2. Physical Characteristic of the Soil Structure

Table 2 indicates the physical characteristics in various depth layers of the soil structure obtained during the field survey. The water content in various layers decreased in the range of 41.47 to 33.01%, vertically. In addition, the void ratio was reduced by 10% in the top layer, whereas the cohesion increased from 14.7 to 20.7 kPa. Figure 6a illustrates the fine grain size composition (<75 μm) of the soil structures with a contribution of >70% in the core samples. On the other hand, the moisture is vertically dropped down in the range of 45–30% (Figure 6b). The fine-grain component with negative charge and high-water retention facilitates the soil structure [57]. Physical characteristics of the soil structure regulate the behavior of the slope to external factors.

3.3. Observation of Seasonal Water Table Variation and Soil Shear Strength Parameter Response

Figure 7 indicates the time series of groundwater table observed from 1 May 2022 to 1 January 2023. During the rainy season, the average rainfall varied from 20 to 50 mm and hit a peak at 80 mm, whereas the rainfall was below 20 mm in the dry season. The number of days of rainfall was four times that of the sunny days in this observed period. Intense rainfall has resulted in high accumulation of precipitation (>2000 mm), leading to high amplitude of groundwater table fluctuation. Rainfall mediates the groundwater level, soil moisture, and slope stability, affecting parameters such as erosion and mass flow movements [58,59,60,61]. In addition, intense precipitation facilitates the variation of groundwater and pore pressure, leading to slope stability and landslide occurrence [14,52,62,63].

Figure 8 indicates the relationship between water content and shear strength parameters of the soil. It was seen that the increase in water content reduced the cohesion (C) and friction angle ($\mathsf{\varphi }$). These correlations enhance the instability of the soil structure, which agrees with previous studies [64,65].

3.4. Rainfall-Mediated Slope Stability Analysis

Figure 9 indicates results rainfall-based factor of safety simulations using Plaxis^® 2D Edition V21 Update 1 (Bentley Systems^®, Exton, PA 19341, United States). The cumulative precipitation is 403 mm, with the highest rate at 81 mm. Results show that the factor of safety dropped from 1.135 to 1.09. It is noted that groundwater level varied from −4.31 to −4.65 m during this period. The fluctuation of groundwater table may result in soil moisture, which affects the shear strength of the soil and slope stability [52,61].

3.5. Observation of Horizontal Displacement of the Soil Structure

Figure 10 indicates horizontal displacement of the soil structure in various depth layers observed from the tilt system in the period of 00:00:00 1 May 2022 to 23:00:00 31 December 2022. The three-wired strain gauge system identified a signal in channel Nos. 5, 6, 13, and 15. In channels 5 and 6 (at −5 and −6 m depth), the strain sensors responded with a signal of ~8000 microstrains, whereas microstrains of 40–120 were recorded at channel Nos. 13 and 16, respectively. Other identified layers were stable during the observation period. Strain gauge signals provide an entropy value that can be used for detection and early warning of landslide events [66].

3.6. Human Impacts on the Slope Stability

Human activities are recognized as a significant driver for triggering landslide events [33]. In fact, several years ago, the number and frequency of fatal landslides increased significantly in Da Lat City; a relationship was found with human activities, such as building construction, deforestation, agricultural practices, or road construction, which are the main drivers that trigger mass movement. Figure 11a shows the stability analysis under external loads from the building construction. In this analysis, two cases of external load of the structure on the red soil layer are assumed at the top (Case 1) and the toe of the slope (Case 2) (see Figure 4). Varying external loads of the structure were applied in the numerical model to examine the response of the safety factor to the external load on the soil layer. Results show that the safety factors dropped from 1.022 to 0.89 and 0.92 for Case 1 and Case 2, respectively. In addition, Figure 11b indicates the behavior of the safety factor responded to external load with the highest drop of 13% in the case of a 160 kN/m² - structure pushing on the top of the slope.

4. Discussion

4.1. Groundwater Level Facilitates the Slope Stability

The groundwater table is a crucial factor in slope stability [53,67]. Figure 7 illustrates the close relationship between groundwater and rainfall. The water table increased in companion with the accumulative precipitation, which facilitates the soil moisture, resulting in the drop down of cohesion (c) and friction angle ($\mathsf{\varphi }$) that may result in decreasing the safety factor (see Figure 12). The higher shear strength parameter in soil structures will enhance the slope safety factor [68]. Figure 12c further indicates the highly correlated relationship between the variable safety factor and seasonal water table.

Fluctuation of groundwater level mediates the soil moisture, which plays a crucial role in slope stability [69]. The rise of groundwater results in frequent slope failure. In addition, higher soil moisture reduces the shear strength of the fine-grain slope due to the reduction in cohesion force of the silt–clay particles [68]. Therefore, dewatering is one of the suitable solutions for controlling the hydrological regime and reducing the slope instability and slope mass movement problems [63].

4.2. Human Activities Enhances the Loss Slope Stability of the Red Basaltic Soil

Human activities have been recognized as a significant driver of landslide occurrence, intensity, and frequency [33]. Man-made structures, road construction, or agricultural practices alter the natural equilibrium condition of the slope [70,71]. Scholars have revealed that the human-induced landslide contributes 71.7%, whereas naturally induced events obtained a percentage of 28.3% [70]. The investigation of 510 landslide events in the period of 2017–2023 in Lam Dong Province revealed a contribution of 91.3% of human activities-induced events. In addition, Figure 11a,b reveal the drop of safety factors in response to varying external loads applied on the top and toe of the slope with a decrease of 13% and 9.6%, respectively. The main activities related to recorded landslide events are deforestation, crop plantation, and road and building construction. Human triggers, specifically construction and deforestation, constitute more than three-quarters of all landslide causes [70].

4.3. Strain Gauge Sensor to Provide an Early Detect the Mass Movement

Early warning systems (EWSs) are becoming a popular landslide prevention measure. The integrated system includes Internet of Things (IoT) on-site sensors that automatically monitor and record parameters that cause landslides, such as slope displacement, hydrological and physical properties in the soil, and precipitation, using various tools [66]. Improvements in sensor technology and information transformation have opened a new horizon in multi-hazard mitigation and prevention. Signal recordings from strain gauge sensors offer valuable information to detect mass movement early. The strain gauge sensor continuously collects the data, transmits them to the users through a wireless system, and provides an alert on the susceptibility of the soil structure and slope stability.

5. Conclusions

This paper elucidates the influence of rainfall and human activities on landslide disaster in the red basaltic soil of the highland terrain, highlighting the following key findings:

-. High intensity and frequency of rainfall regulate the shear strength parameters of the red basaltic soil that result in the instability of the hillslope;

-. Fluctuations in groundwater levels contribute to increased soil moisture, which, in turn, reduces shear strength parameters and destabilizes slopes;

-. Human-induced activities exacerbate the frequency of mass movement events. Common triggers include slope cutting for construction and cultivation on steep, vulnerable slopes. These activities increase external loading and promote water entrapment within the soil, thereby reducing slope stability.

-. Tilt sensor systems provide real-time data on groundwater levels and horizontal displacement at various depths. This technology forms a crucial component of early warning systems for multi-hazard risk management. In addition, integrating smart technologies and Internet of Things (IoT) solutions is highly beneficial for effective natural disaster management.

Lastly, climate change—with increased precipitation and extreme weather events—poses an additional stressor to slope stability and hazard prevention efforts. Urbanization and agricultural activities further compound these risks. Therefore, the deployment of real-time monitoring systems is increasingly important. The early warning data they provide are vital for disaster prevention, mitigation, and safeguarding communities at risk.

Author Contributions

Conceptualization, H.S.N. and T.T.H.; methodology, T.T.H., T.L.K., and H.S.N.; writing—original draft preparation, T.T.H. and T.L.K.; writing—review and editing, H.S.N.; visualization, H.S.N. and T.T.H.; supervision, H.S.N.; project administration, T.L.K. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Acknowledgments

We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, for supporting this study.

Conflicts of Interest

Author Trung Tin Huynh was employed by the company Bach Khoa Ho Chi Minh City Science Technology Joint Stock. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Footnotes

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Figures and Tables

Figure 1 Survey area. In the (a), the yellow-highlighted area is the boundary of the “high risk” area from mass movement. The survey area is a central location with a density of houses and infrastructure. (b) presents the A-B cross-section of geological structure of DaLat zone comprises with the Upper Holocene (aQ_IV³) 1–10 m thick on the top. The Jurassic sedimentary of La nga Formation (J₂ln) with the sandstone, siltstone, shale, hornfels components is predominant in the study area. On the bottom, the Late Cretaceous igneous rocks, Ca Na Formation–Phase 2 (γK₂cn₂), is distributed below the La Nga Formation [41,42,43].

Figure 2 Average rainfall in Da Lat city in period of 2019–2024.

Figure 3 Schematic diagram of structure of unstable slope monitoring with groundwater gauge sensor; strain gauge to observe the angular displacement. The (−) signal indicated the negative slope movement whereas a (+) sinal indicated the positive movement of the slope. The monitoring data were collected in a power data logger, and transferred to mobile access devices by a wireless communication system [10,49,50].

Figure 4 Slope geometry and boundary conditions for stability analysis model.

Figure 5 Daily precipitation and associated landslides occurred in Dalat city in 2023. In the lower panel, photo no. (1) was the recorded landslide at a plantation farm; (2) described the landslide under building construction occurred in Da Lat city, and (3) was the landslide at National Road No. 20. The red dotted line was the conceivable slope curve of the landslide.

Figure 6 Physical parameters of the soil structure: (a) grain size distribution in various depth layers and (b) vertical moisture of the soil.

Figure 7 Observation of the ground water table. In (a), the green bar column indicated the annual rainfall whereas the red line was the accumulation rate of the precipitation. The observation groundwater level in the same period was indicated in (b).

Figure 8 Relationship of water content and shear strength parameters.

Figure 9 Factor of safety under accumulative precipitation. In this figure, the blue bar indicated the average precipitation, and blue dotted line indicated the cumulative precipitation. The red line indicated the factor of safety.

Figure 10 Observation of strains gauge sensor and displacement of the soil. The top panel (a) indicates the deformation of the pipe strain during the observation period; (b,c) detected the pipe strains at channel 5 and 6 (i.e., in depth of 5 to 6 meter below the surface) and channels 13 and 15 (i.e. in depth of 13 and 15 m below the surface).

Figure 11 Numerical analysis of the factors of safety and external loads from human activities. (a,b) indicates the variations and reduction percentage of factor of safety in various external loads applied on the top and toe of slope.

Figure 12 The response of factor of safety to groundwater fluctuation. (a) indicated the monthly variation of the groundwater level, and the response factor of safety is determined in (b). (c) illustrated the regression relationship of factors of safety and groundwater level fluctuations in red basaltic soil.

Table 1

Geophysical parameter inputs for the slope stability model.

Parameters	Unit	Layer 1	Layer 2	Layer 3	Layer 4	Layer 5
${\gamma }_{unsat}$	kN/m3	17.4	17.8	17.9	18.1	18.4
${\gamma }_{sat}$	kN/m3	17.7	18.3	18.3	18.5	18.8
$E^{\prime }$	kN/m2	3379	4049	4150	4529	5045
${\nu }^{\prime }$	-	0.2	0.2	0.2	0.2	0.2
$c^{\prime }$	kN/m2	15.0	18.0	17.9	19.1	21.6
${\phi }^{\prime }$	o	14.03	16.45	16.49	17.03	17.41
$k_x$	m/d	From grain size distribution
$k_y$	m/d

Table 2

Physical characteristics of the soil layers.

Layer/Depth(m)	Natural Gravity (g/cm3)	Water Content (%)	Void Ratio (%)	Liquid Limit (%)	Plastic Limit (%)	PlasticityIndex	Cohesion (kPa)	Friction Angle (°)
Layer 1 (−1.0)	1.74	41.47	54.78	45.71	34.09	11.62	14.7	14.1
Layer 2 (−3.0)	1.79	37.55	53.06	47.84	32.29	15.55	19.5	17.03
Layer 3 (−6.2)	1.77	37.27	53	47.08	33.01	14.07	18.1	17.06
Layer 4 (−12.2)	1.81	34.79	51.03	44.85	32.13	12.72	18	16.29
Layer 5 (−20.0)	1.83	33.01	49.42	43.89	32.81	11.08	20.7	17.31

Supplementary Materials

The following supporting information can be downloaded at: https://doi.org/10.6084/m9.figshare.28877864.v1.

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