Integration of Theoretical and Practical Parameters for the Geotechnical Characterization of a Small-Scale Landslide, Zaruma-Ecuador

2.1 Geographical and demographic context

This study is focused on Ecuador (Figure 1 (a)), specifically on a landslide located along the roadside in Sinsao parish (795851590 W-36629693 S) (Figure 1 (b)), situated in the foothills of the Andes Mountains at an elevation of 1,280 m above sea level (m.a.s.l.). The area has a humid forest climate, with an average annual precipitation of 1,900-2,000 mm [15] (Figure 1 (c)). According to the 2022 national census, the parish has a population of 1,594 inhabitants, with mining being the primary economic activity (40.83%), followed by agriculture, livestock, forestry, and fishing (12%) [16]. According to the latest Parish Land Use and Development Plan (PDOT, Spanish acronym), the study area experiences recurrent landslides during the rainy season [15]. In 2015, the National Risk Management Secretariat (SNGR, Spanish acronym) mapped 25 landslides along the Sinsao road after a single night of intense rainfall [17]. Additionally, in 2018, the area was officially designated as a high-risk landslide zone [10].

2.2 Geologic context

Sinsao parish is located within a geologically complex region characterized by high fracturing, altered volcano-sedimentary lithologies, and active tectonics. The area is part of the Zaruma-Portovelo metallogenic belt, which consists mainly of intrusive rocks, andesites, dacites, and colluvial-alluvial deposits [18].

Figure 2. Geologic map: a) Lithological features, b) Exposed soil observed in escarpments and through pits test

The geology in the study area belongs to the Saraguro group, consisting of calc-alkaline volcanic rocks of Oligocene age, the Portovelo unit (Figure 2(a)), bounded by the Piñas-Portovelo fault [19]. Upon weathering, this material has formed clayey, sandy, and sandy clayey soils, known as saprolites, with thicknesses ranging from 20 to 30 m, highly unstable (Figure 2(b)) [14, 20].

2.3 Methodological framework

This study integrated geotechnical evaluation techniques, including granulometry tests and Atterberg limits, with geophysical methods such as ERT, RS, and VES. The investigation was structured in three methodological phases (Figure 3). The first comprised ground reconnaissance and the collection of geotechnical and geophysical data through in situ tests and indirect techniques, complemented by the collection of geomechanical parameters and geological background collected in the scientific literature. The physical-mechanical and slope data were integrated and processed in SAGA GIS and Slide2 to estimate slope SF under different scenarios. Finally, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was carried out to identify key factors, followed by a mitigation strategies approach aligned with the identified level of stability.

Figure 3. Schematic representation of the research approach and phases in slope stability analysis

2.3.1 Phase I: Collection of geophysical, geotechnical, and bibliographic data

The recognition and delimitation of the landslide was carried out after a field visit, which verified the presence of escarpments at the top of the slope, moisture, cracks, and soil erosion at the base of the landslide (Figure 4).

Geomechanical considerations. Soil characterization was carried out using granulometry and Atterberg limit tests [21] at the ESPOL Geotechnical Laboratory, and the samples were classified according to the Unified Soil Classification System (USCS) [22]. Samples were taken from two test pits (1.20×1.55 m): the first at 0.70 m, 1.00 m, and 1.55 m depth; and the second at 0.70 m (Figure 4).

Figure 4. The map indicates the extent of a landslide, along with the locations of geophysical tests (ERT, VES, RS), test pits (C), and A-A' profile conducted within the affected area

Table 1. Keywords that make up the search equation in Scopus

Geomechanical parameters used to calculate SF were obtained through a systematic search of the Scopus database of mass-movement studies. Three keywords were used: mass movement, laboratory tests, and geomechanical parameters and their synonyms, combined with the Boolean operator "OR". The three variables were then combined with the Boolean operator "AND" to extract documents containing only the three keywords. The search was further limited to papers published in Q1 journals and geotechnical values obtained solely from laboratory tests. Only documents in English published between 2019 and 2024 were considered (Table 1). Each article was evaluated based on soil type according to the USCS classification and geotechnical conditions like those of the study area to ensure data reliability (clayey soil). Finally, 13 articles were selected from which values of cohesion (c), internal friction angle (ϕ), density (ρ), saturation (s), and unit weight (γ) were extracted for use in slope stability modeling (see Table S1 in Appendix).

Geoelectric and seismic refraction measures. To characterize the subsurface and identify potentially unstable zones, five field tests were performed (Figure 4). Two ERT profiles were obtained using the ABEM Terrameter LS 2 with a gradient field array [23], comprising 41 electrodes arranged in 200 m lines, reaching depths of investigation of 20-40 m.

The resistivity data were processed with Res2dinv (v4.10.20), using the Root Mean Square (RMS) method to refine the model, eliminate noisy measurements, and generate four high-quality interpolations, which were essential for the subsequent delimitation of the stratigraphy.

The RS method included a 140 m profile with 24 geophones recording seismic velocities. The data were processed using Reflexw software version 10.0, resulting in seismic tomography, where data quality was ensured by manually removing first-arrival noise on each strike. Two VES were measured using the Schlumberger configuration [24], with the Terrameter LS 2, with exploration lengths of up to 380 m and depths of investigation close to 70 m. The data were interpreted with IPI2win (v.3.0.1) to determine resistivities and thicknesses of subsurface layers comparing with the resistivities and thicknesses found in the ERT (Table 2).

Table 2. Resistivity and velocity ranges adapted to the case study

2.3.2 Phase II: Data integration and modeling the SF

SAGA GIS application for landslide zoning. To estimate the SF, SAGA GIS was used with the SafetyFactor tool, based on the infinite slope theory [29]. This model assumes that the fault plane is parallel to the ground surface and extends indefinitely, evaluating ground stability by calculating the ratio of resistive to sliding forces in each cell [30]. Using input data such as a slope raster, geomechanical soil properties from phase I (c, ϕ, ρ, s), and the depth of the fault plane determined through geophysical surveys within the study area, a continuous SF raster was produced. This raster was divided into five classes, which were then compared with the Ecuadorian Construction Standard (NEC, Spanish acronym) [31] to assess their stability. According to the NEC, a slope is considered stable if it has an SF of 1.5 under static conditions and normal groundwater levels, while under pseudo-static conditions, incorporating a seismic coefficient, the minimum acceptable SF is 1.05.

Slide2 application for stability analysis. Rocscience's Slide2 software was used for slope stability analysis, which estimates SF and the critical slip surface using limit equilibrium methods [32]. Using the topographic profile of the steepest slope surveyed in the field, geophysical data were incorporated to define the slope's stratigraphy and assign geotechnical properties (c, ϕ, γ) to each layer. The input data for each software used is detailed in Table 3.

Table 3. Input configuration for calculating the Safety Factor (SF) in SAGA and Slide2

The location of the water table was determined based on direct observations in test pits and interpreted geophysical data. With this information, two scenarios were developed: one under static conditions and another under pseudo-static conditions, incorporating the seismic design coefficients for the Zaruma area (horizontal coefficient (Zh): 0.30; vertical coefficient (Zv): 0.15) [33]. In both cases, the SF was calculated using the Bishop method [34], which involves dividing the slope into vertical slices and analyzing the force balance within each slice to determine the SF from the ratio of resistive to sliding forces along a circular failure surface, typical in clay soils or residual material [35].

2.3.3 Phase III: Participatory action plan

SWOT analysis is a common tool for assessing and identifying key factors in policy development and planning [36]. As the final phase of the study, a SWOT matrix was used to thoroughly evaluate the technical findings and their impact on the community. Based on criteria from geophysical, geotechnical, and geological experts, as well as the local and historical knowledge of community and political leaders—including the mayor, council members, and parish head—threats and weaknesses related to the landslide and population were identified.

This analysis helped recognize and leverage community strengths, transforming them into opportunities. Based on this strategic assessment, mitigation proposals were developed, tailored to the local context, to reduce vulnerability, guided by criteria of geotechnical stability, technical feasibility, and social acceptability. The focus was on prioritizing low-impact interventions that offer high community benefits.

Geophysical correlation (ERT, VES, and RS) revealed a geomorphological and hydrogeological environment highly susceptible to mass movement processes. Saturated layers (<30 Ω·m) exhibit lateral continuity toward the base of the slope, indicating critical areas with reduced shear strength where fault surfaces may form. The unstable unconsolidated soil (<65 Ω·m) present in the study area is estimated to have a thickness of between 5 and 20 m, below which there is a body of saprolite (>331 Ω·m) at a depth greater than 20 m at the bottom of the landslide, which indicates the presence of ancient colluvial deposits, on which the road stabilization was carried out.

The model developed in Slide2 enabled simulation of the internal behavior of the slope using multilayer geotechnical profiles derived from geophysical methods (ERT, VES, and RS) and geomechanical parameters. The SF obtained under static conditions was 0.99, below the acceptable limit for the NEC, and decreased to 0.56 when seismic coefficients were applied (Zh: 0.3, Zv: 0.15) (Figure 7). This decrease is justified by the incorporation of inertial forces that amplify destabilizing forces and reduce effective friction, especially in saturated fine soils where interstitial pressures increase [39, 40].

In contrast, the stability analysis indicates that the SF values fall below the minimum threshold established by the NEC for design and construction works. The model generated in SAGA GIS shows an SF of less than 1.5 in 25.6% of the area, highlighting critical areas where the terrain may move (even without additional loads or extreme events) at the top of the slope, particularly where active escarpments and NE-oriented cracks are present These SAGA GIS results have been used to estimate possible failure zones and landslide trend lines [41]. In this sense, triggering factors (such as high rainfall and earthquakes) could trigger slope failure. Such is the case of Tamban Chimbo in the Ecuadorian Andes, which shares climatic characteristics with Sinsao parish, where moderate rainfall over 11 days triggered a large landslide [42].

However, this analysis is limited to superficial parameters, which restricts its accuracy at the local and depth scales. This method is widely used to map landslide susceptibility by calculating SF, and studies in the mountainous terrain of the Cameron Highlands, Malaysia, demonstrate its feasibility, cost-effectiveness, and time savings [43].

Compared to the methodologies applied, Slide2 allowed the identification of saturated clay units that favor deep fault surfaces. Meanwhile, SAGA GIS identified unstable areas at the surface, with the upper part of the slope most unstable, which correlates with the presence of active escarpments and saturated clay evident in the Slide2 profile. However, when comparing both results under pseudo-static conditions, SF values of 0.56 and 0.3 were obtained in Slide2 (Figure 10(a)) and SAGA GIS (Figure 10(b)), respectively.

This difference suggests that SAGA GIS tends to generate more conservative scenarios, reflecting its simplified approach based on the infinite-slope model and generalized surface parameters [29]. Unlike Slide2, which estimates the critical surface using limit equilibrium methods [32]. Although this represents a technical limitation, it can also be beneficial in preventive contexts, as it enables preliminary zoning that is more susceptible to critical surface conditions (Figure 11). This refers to the case in Portofino (Italy), a mountainous region where these maps are used to predict future landslide occurrence reliably [44]. Additionally, stability modeling using Slide2 is significantly improved when geotechnical subsoil variability and its interaction with the hydrological and seismic regimes are incorporated [45].

The integration of geomechanical parameters derived from primary studies of similar soils is well-founded, as it feeds into the stability model in Slide2 and SAGA GIS. This offers the possibility of using this type of data in the preliminary analysis of slope stability [46]. At the same time, free databases containing spatial geological information were analyzed, enabling characterization of surface soil types using GIS [47]. This secondary characterization coincided with the laboratory results, which identified coarse sandy clay as the predominant material.

This research offers a methodological framework that can be replicated in Andean areas with logistical limitations. The integration of an exploration raster model and a deterministic limit-equilibrium model provides a diagnostic that strikes a balance between operational efficiency and technical depth. The combined application of these tools enables local capacities to be aligned with realistic, context-specific mitigation strategies based on a SWOT analysis, focusing not only on reducing risk but also on optimizing resource use for geotechnical studies in hard-to-reach areas.

Figure 10. Comparison of SF. a) Surfaces analyzed by Slide2 and measurements of the sliding area, b) SF along the profile and in affected areas

Figure 11. Integration of assessment tools for landslide-prone areas

Strategies such as revegetation have been proven effective in studies in Colombia, where increased root cohesion and decreased saturation [48] have been observed. Additionally, revegetation is the most economical and easy-to-manage method for reducing landslides in Italy [49]. These actions should be complemented by continuous monitoring of ground behavior, especially in areas where escarpment dynamics indicate active movement. Community involvement is essential for early detection of instability signs and for implementing conservation measures suited to local conditions. Protecting escarpments with natural or mixed structures is also a key strategy to reduce landslide recurrence, highlighting the need to integrate bioengineering solutions, participatory monitoring, and sustainable land management to ensure long-term slope stability.

The study was conducted under budgetary and logistical constraints inherent to an Andean context with scarce data and limited funding for technical studies. This justified the pragmatic approach of relying on a localized field characterization (two test pits and five geophysical points) and adopting geotechnical parameters derived from literature. This dependence on indirect data introduces uncertainty, primarily in two areas: the spatial extrapolation of parameters, where the limited sampling makes it difficult to map the actual variability of soil strength properties (cohesion and friction), affecting the model inputs; and the non-singularity of geophysical inversion, where the interpretation of resistivity profiles to define the stratigraphy of the Slide2 model could vary, impacting the geometry of the critical failure surface.

The direct consequence of these limitations is that the final SF values must be interpreted as a probabilistic range rather than exact deterministic values. This necessitates caution in concluding that the sector's risk and instability are high, as the results are sensitive to unsampled geometric and parametric variations. To mitigate this limitation, it is recommended to conduct further sensitivity analyses and increase the density of in-situ characterization and specific laboratory tests such as direct shear and permeability testing.

As a future projection, a more comprehensive review of scientific articles should be conducted to extract and generate a database as a tool to complement stability studies that lack access to laboratory tests. Develop a landslide susceptibility map for the entire Sinsao parish using SAGA GIS, integrating variables such as vegetation cover, drainage, and rainfall records to improve the tool's ability to detect landslides-prone areas. This zoning would enable the identification of critical areas for prioritizing geotechnical and stability studies using Slide2 and, consequently, defining priority intervention zones. Additionally, it is recommended to assess the impact of mining vibrations on slope stability, as this is a primary economic activity in the area [20, 50-52]. This information represents technical input for decision-makers, enabling them to develop a community EWS based on participatory monitoring, low-cost weather stations, and local communication networks, strengthening resilience to geodynamic threats in an area historically exposed to mass movement events.

This work is supported by ESPOL Polytechnic University research project "Registry of geological interest sites of Ecuador for sustainable development strategies" with institutional code CIPAT-004-2024.