Three-dimensional slope stability analysis based on irregular ellipsoid sliding surface.

The determination of the critical sliding surface is a crucial aspect of slope stability analysis. In reality, the environment in which slopes are located is often very complex, and the sliding surfaces have different shapes. Therefore, a three-dimensional stability study is proposed, using irregular ellipsoidal shapes as the sliding surface. By constructing the equation of the irregular ellipsoid and applying spatial geometric transformations, an irregular ellipsoid controlled by six parameters, (semi-axis length ( a ), width ( b ), height ( c ), rotation angle ( θ ), and spatial displacements ( t x and t z )), is obtained. The interpolation method is employed to apply the irregular ellipsoid to 3D slopes, thereby generating irregular ellipsoidal sliding surfaces. The slope stability coefficient is calculated by using the residual thrust method and genetic algorithm to determine the critical sliding surface. A comparative stability analysis is conducted between the standard ellipsoidal sliding surface and the irregular ellipsoidal sliding surface through a classic case study. The results show that the Type II irregular ellipsoidal sliding surface aligns well with the mining area. Finally, the research findings are applied to the 3D slope stability analysis of the Shengli West No.2 Open-Pit Coal Mine in China, validating the feasibility of the proposed method.

Study on the differences between Hoek-Brown parameters and equivalent Mohr-Coulomb parameters in the calculation slope critical acceleration and permanent displacement.

Mohr-Coulomb (MC) strength criterion has been widely used in many classical analytical expressions and numerical modeling due to its simple physical calculation, but the MC criterion is not suitable for describing the failure envelope of rock masses. In order to directly apply MC parameters to analytical expressions or numerical modeling in rock slope stability analysis, scholars established a criterion for converting Hoek-Brown (HB) parameters to equivalent MC parameters. However, the consistency of HB parameters and equivalent MC parameters in calculating critical acceleration of slope needs to be further explored and confirmed. Therefore, HB parameters are converted into equivalent MC parameters by considering the influence of slope angle (1# case and 2# case when slope angle is not considered and slope angle is considered respectively). Then, the lower-bound of finite element limit analysis is used for numerical modeling, and the results of calculating critical acceleration using HB parameters and equivalent MC parameters are compared, and the influence of related parameters on the calculation of critical acceleration is studied. Finally, the influence of different critical accelerations on the calculation of slope permanent displacement is further analyzed through numerical cases and engineering examples. The results show that: (1) In the 1# case, the critical acceleration obtained by the equivalent MC parameters are significantly larger than that obtained by the 2 #case and the HB parameters, and this difference becomes more obvious with the increase of slope angle. The critical acceleration obtained by the 2# case is very close to the HB parameters; (2) In the 1# case, slope height is inversely proportional to ΔAc (HB(Ac) - 1#(Ac)), and with the increase of slope height, ΔAc decreases, while in the 2# case, the difference of ΔAc (HB(Ac) - 2#(Ac)) is not significant; (3) In the 1# case, the sensitivity of the HB parameters to ΔAc is D > GSI > mi > σci, but in the 2# case, there is no sensitivity-related regularity; (4) The application of HB parameters and equivalent MC parameters in slope permanent displacement is studied through numerical cases and engineering examples, and the limitations of equivalent MC parameters in rock slope stability evaluation are revealed.

Efficient optimization parameter calibration method-based DEM simulation for compacted loess slope under dry-wet cycling.

Dry-wet cycles can cause significant deterioration of compacted loess and thus affect the safety of fill slopes. The discrete element method (DEM) can take into account the non-homogeneous, discontinuous, and anisotropic nature of the geotechnical medium, which is more capable of reflecting the mechanism and process of instability in slope stability analysis. Therefore, this paper proposes to use the DEM to analyze the stability of compacted loess slopes under dry-wet cycles. Firstly, to solve the complex calibration problem between macro and mesoscopic parameters in DEM models, an efficient parameter optimization method was proposed by introducing the chaotic particle swarm optimization with sigmoid-based acceleration coefficients algorithm (CPSOS). Secondly, during the parameter calibration, a new indicator, the bonding ratio (BR), was proposed to characterize the development of pores and cracks in compacted loess during dry-wet cycles, to reflect the impact of dry-wet action on the degradation of bonding between loess aggregates. Finally, according to the results of parameter calibration, the stability analysis model of compacted loess slope under dry-wet cycling was established. The results show that the proposed optimization calibration method can accurately reflect the trend of the stress-strain curve and strength of the actual test results under dry-wet cycles, and the BR also reflects the degradation effect of dry-wet cycles on compacted loess. The slope stability analysis shows that the DEM reflects the negative effect of dry-wet cycles on the safety factor of compacted loess slopes, as well as the trend of gradual stabilization with dry-wet cycles. The comparison with the finite element analysis results verified the accuracy of the discrete element slope stability analysis.

Infrared thermography reveals weathering hotspots at the Pozáry field laboratory.

Evaluating physical properties and mechanical parameters of rock slopes and their spatial variability is challenging, particularly at locations inaccessible for fieldwork. This obstacle can be bypassed by acquiring spatially-distributed field data indirectly. InfraRed Thermography (IRT) has emerged as a promising technology to statistically infer rock properties and inform slope stability models. Here, we explore the use of Cooling Rate Indices (CRIs) to quantify the thermal response of a granodiorite rock wall within the recently established Pozáry Test Site in Czechia. We observe distinct cooling patterns across different segments of the wall, compatible with the different degrees of weathering evaluated in the laboratory and suggested by IRT observations of cored samples. Our findings support previous examinations of the efficacy of this method and unveil correlations between cooling phases in the field and in the laboratory. We discuss the scale-dependency of the Informative Time Window (ITW) of the CRIs, noting that it may serve as a reference for conducting systematic IRT field surveys. We contend that our approach not only represents a viable and scientifically robust strategy for characterising rock slopes but also holds the potential for identifying unstable areas.

Stability evaluation of open-pit mine slope based on Bayesian optimization 1D-CNN.

As mechanized open-pit coal mining intensifies, assessing and predicting slope stability has become increasingly important. To address the limitations of traditional mechanical calculations, numerical simulations, and physical experiments, this paper identifies the key factors impacting slope stability in open-pit mines and develops a multi-parameter sample data set. The study employs hyperparameters optimized using a Bayesian algorithm, introduces additional convolutional layers, and combines the Adam optimizer with dropout techniques to enhance the feature extraction and performance of one-dimensional convolutional neural networks (1D-CNN). This leads to a Bayesian-optimized one-dimensional convolutional neural network (B-1D MCNN) model for predicting slope stability.The study evaluates the classification performance and accuracy of various models for slope stability, including BP neural networks, genetic algorithm-optimized convolutional neural networks, 1D-CNN, and B-1D MCNN, using accuracy, precision, and F1-score as metrics. The analysis also examines the influence of factor indicators and training set length on the model's output to assess its generalization capabilities.The research findings suggest that: (1) the B-1D MCNN model for evaluating slope stability demonstrates the capability to accurately depict the nonlinear correlation between influencing factors and slope stability. (2) Compared with other models, the B-1D MCNN model has shown enhancements of 10.96% to 27.85%, 10.26% to 28.55%, and 8.98% to 25.05% in terms of Accuracy, F1-Score, and Precision, respectively. (3) As the length of the training dataset increases, the performance of the model improves accordingly. (4) The B-1D MCNN model shows a generalization power of 87.5%.

A comparative study of environmental and economic assessment of vegetation-based slope stabilization with conventional methods.

The heavy rainfall induced by global warming has increased the risk of landslides. Eco-friendly approaches, such as employing vegetation, prove effective in satisfying the requirements of both engineering and environmental considerations in slope engineering. The research aims to comprehensively assess and compare the environmental, economic, and slope stability of new stabilization methods, including vegetation cover, in comparison to conventional approaches such as anchorage and nailing. The research initially explored the stability of slopes in various geometries, identifying areas prone to slope failure. Subsequently, slope stabilization designs were implemented using three methods: vegetation, nailing, and anchoring. To enable a comprehensive comparison from environmental and economic perspectives, both life cycle assessment and life cost assessment were conducted. According to the results, employing vegetation proves effective in stabilizing slopes at lower heights, particularly up to 8 m, leading to a negative carbon emission attributed to photosynthesis, reaching up to -249 kg CO2. In the mid-angle range (30°≤ θ ≤ 60°), anchoring emits less carbon dioxide than nailing due to fewer elements. As the slope angle is increased, the nailing method becomes preferable to the anchoring method due to its use of materials and equipment with lower carbon emissions. During slope stabilization through nailing and anchoring, cement and steel emerge as the primary contributors to carbon emissions. Vegetation stands out as the most cost-effective slope stabilization option, with costs potentially reduced by 250% compared to conventional methods. Based on this research, vegetation emerges as an eco-friendly and cost-effective alternative for slope stabilization in particular conditions where plants effectively ensure stability. Decisions regarding the use of anchoring or nailing can be made based on environmental and economic aspects, considering the slope geometry.

Engineering and environmental assessment of soilbag-based slope stabilisation for sustainable landslide mitigation in mountainous area.

Changes in land use significantly impact landslide occurrence, particularly in mountainous areas in northern Thailand, where human activities such as urbanization, deforestation, and slope modifications alter natural slope angles, increasing susceptibility to landslides. To address this issue, an appropriate method using soilbags has been widely used for slope stabilisation in northern Thailand, but their effectiveness and sustainability require assessment. This research highlights the need to evaluate the stability of the soilbag-based method. In this study, a case study was conducted in northern Thailand, focusing on an area characterised by high-risk landslide potential. This research focuses on numerical evaluation the slope stability of soilbag-reinforced structures and discusses environmental sustainability. The study includes site investigations using an unmanned aerial photogrammetric survey for slope geometry evaluation and employing the microtremor survey technique for subsurface investigation. Soil and soilbag material parameters are obtained from existing literatures. Modelling incorporates hydrological data, slope geometry, subsurface conditions, and material parameters. Afterwards, the pore-water pressure results and safety factors are analysed. Finally, the sustainability of soilbags is discussed based on the Sustainable Development Goals (SDGs). The results demonstrate that soilbags effectively mitigate pore-water pressures, improve stability, and align with several SDGs objectives. This study enhances understanding of soilbags in slope stabilisation and introduces a sustainable landslide mitigation approach for landslide-prone regions.

Stability of soil slope in Almaty covered with steel slag under the effect of rainfall.

The issue of rainfall-induced slope failure has attracted more attention from geotechnical engineers as a consequence of global warming. Current cumulative waste disposal has generated scientific interest in the utilization of waste materials in geotechnical design for climate change adaptation measures. Taking into consideration the effect of slope height and angle, steel slag-a waste product derived from the production of steel-was investigated as a slope cover against rainfall. To assess the stability of the slope and the infiltration of water into the soil, numerical analyses were conducted using both SEEP/W and SLOPE/W software in conjunction with rainfall conditions. Based on the findings, it can be concluded that increasing the slope's elevation and inclination will have an adverse effect on its safety factor. Steel slag can nevertheless be utilized for minimizing rainwater infiltration into the slope, as indicated by the pore-water pressure variations and graphs of the safety factor versus time. For a 20-m slope height, steel slag slopes have demonstrated a lower factor of safety difference in comparison to the initial slope without remediation. Regardless of slope angle and slope height, the safety factor reduces marginally during rainfall.

Stability analysis of rainfall-induced landslide considering air resistance delay effect and lateral seepage.

Accumulation landslides are prone to occur during the continuous infiltration of heavy rainfall, which seriously threatens the lives and property safety of local residents. In this paper, based on the Green-Ampt (GA) infiltration model, a new slope rainfall infiltration function is derived by combining the effect of air resistance and lateral seepage of saturated zone. Considering that when the soil layer continues to infiltrate after the saturation zone is formed, the air involvement cannot be discharged in time, which delays the infiltration process. Therefore, the influence of air resistance factor in soil pores is added. According to the infiltration characteristics of finite long slope, the lateral seepage of saturated zone is introduced, which makes up for the deficiency that GA model is only applicable to infinite long slope. Finally, based on the seepage characteristics of the previous analysis, the overall shear strength criterion is used to evaluate the stability of the slope. The results show that the safety factor decreases slowly with the increase of size and is inversely correlated with the slope angle and initial moisture content. The time of infiltration at the same depth increases with the increase of size and slope angle, and is inversely correlated with the initial moisture content, but is less affected by rainfall intensity. By comparing with the results of experimental data and other methods, the results of the proposed method are more consistent with the experimental results than other methods.

Hydro-mechanical effects of vegetation on slope stability: A review.

This study explores the complex interplay between vegetation and soil stability on slopes to enhance soil-bioengineering and slope stabilization techniques. We assess the multifaceted role of vegetation in soil stabilization, examining processes such as canopy interception, stemflow, and the effects of hydrological and mechanical changes induced by root systems and above-ground plant structures. Key underlying mechanisms and their effects on stability are reported, along with the evaluation of significant plant indicators from historical research. Our review revealed that plant coverage and root architecture are critical in reducing soil erosion, with plant roots increasing soil cohesion and reducing soil detachability. Above-ground vegetation provides a protective layer that decreases the kinetic energy of raindrops and allows for higher infiltration. The importance of species-specific root traits is emphasized as pragmatic determinants of erosion prevention. Additionally, the effects of root reinforcement on shallow landslides are dissected to highlight their dualistic nature. While root-soil interactions typically increase soil shear strength and enhance slope stability, it is crucial to discriminate among vegetation types such as trees, shrubs, and grasses due to their distinct root morphology, tensile strength, root area ratio, and depth. These differences critically affect their impact on slope stability, where, for instance, robust shrub roots may fortify soil to greater depths, whereas grass roots contribute significantly to topsoil shear strength. Grasses and herbaceous plants effectively controlled surface erosion, whereas shrubs mainly controlled shallow landslides. Therefore, it is vital to conduct a study that combines shrubs with grasses or herbaceous plants. Both above-ground and below-ground plant indicators, including root and shoot indicators, were crucial for improving slope stability. To accurately evaluate the impact of plant species on slope stability reinforcement, it is necessary to study the combination of hydro-mechanical coupling with both ground plant indicators under specific conditions.

Quasi-3D slope stability analysis of waste dump based on double wedge failure.

The double wedges sliding along the weak layer of the foundation can be observed on the slope of the waste dump and the sliding body is divided into the active wedge and passive wedge by the weak foundation and the failure surfaces of the waste dump. Because the conventional limit equilibrium slice method cannot reflect the polygonal slip surface of the slope of the waste dump with weak foundation, this study proposed a double wedge calculation method for the slope of the waste dump with weak foundation. The limit equilibrium analysis is performed on double wedges by considering the direction and values of the interaction force between double wedges to obtain the safety factor of the slope of the waste dump. Meanwhile, the quasi-3D double wedges stability analysis method of the waste dump slope with weak foundation is proposed by considering the influence of the geometry and sliding direction of the slope surface on the slope stability. The safety factor of the inverted dump slope is 0.82, the volume of the sliding body is 6.43 million m3, and the main sliding direction is 20° south by east. The shear strain rate cloud diagram of the section is 'y' type distribution, and the sliding body is divided into two independent blocks. The safety factor of the sliding body section obtained by the double wedge method is between 0.76 and 0.92, and the closer to the boundary of the sliding body, the greater the safety factor of the section. The quasi-three-dimensional safety factor obtained by theoretical analysis is 0.817. The results show that the calculation results of quasi-3D double wedge are basically consistent with the calculation results of strength reduction method, while the proposed method is simpler. It can be used as a quick method to evaluate slope stability in engineering practice.

Evaluation of rock stability challenges affecting the formation of sustainable pit lake towards mine closure following crown pillar extraction in open pit-underground mines.

The extraction of high-grade ore from the crown pillar (CP) in open pit-underground mines poses stability challenges and potential environmental risks. While an open pit has the potential to transition into a pit lake, the extraction of CP can induce failure in the surrounding walls, preventing the formation of the lake. There is also a concern that the backfilling material may not effectively confine toxic water within the pit, thereby risking contamination of the underground environment. To address these issues, a case study was conducted using FLAC3D and 3DEC models to evaluate the extent of failure caused by CP extraction. On-site observation, along with modelling, has revealed rock damage, including deformation stretching about 4 m from the pit wall and extending vertically from the pit floor to the ramp. The study identified three primary factors leading to pit wall failure or damage: steep pit slopes reaching approximately 70° near the pit floor, an underestimated CP thickness by about 4 m, and the concurrent extraction of ore from the pit wall alongside CP. Based on these findings, rehabilitation measures are suggested, including excavation of the deformed wall and cantilever, as well as partial pit backfilling. There is a substantial increase in the volume of backfill material as the extent of failure increases, which raise concerns about the decision-making process regarding CP extraction. Therefore, this article aims to raise environmental awareness and evaluate whether the benefits of ore extraction outweigh the considerations for pit wall support and the rehabilitation efforts during mine closure.

Study on large deformation of soil-rock mixed slope based on GPU accelerated material point method.

This study assesses the effect of stone content on the stability of soil-rock mixture slopes and the dynamics of ensuing large displacement landslides using a material point strength reduction method. This method evaluates structural stability by incrementally decreasing material strength parameters. The author created four distinct soil-rock mixture slope models with varying stone contents yet consistent stone size distributions through digital image processing. The initial conditions were established by linearly ramping up the gravity in fixed proportionate steps until the full value was attained. Stability was monitored until a sudden shift in displacement marked the onset of instability. Upon destabilization, the author employed the material point method to reconstruct the landslide dynamics. Due to the substantial computational requirements, the author developed a high-performance GPU-based framework for the material point method, prioritizing the parallelization of the MPM algorithm and the optimization of data structures and memory allocation to exploit GPU parallel processing capabilities. Our results demonstrate a clear positive correlation between stone content and slope stability; increasing stone content from 10 to 20% improved the safety factor from 1.9 to 2.4, and further increments to 30% and 40% ensured comprehensive stability. This study not only sheds light on slope stability and the mechanics of landslides but also underscores the effectiveness of GPU-accelerated methods in handling complex geotechnical simulations.

An ensemble framework-based approach for modeling stability of expansive soil slopes: fusion of machine learning algorithms and protection structure disease data.

Slope failures lead to catastrophic consequences in numerous countries, so accurate slope stability evaluation is critical in geological disaster prevention and control. In this study, the type and characteristics of slope protection structure disease were determined through the field investigation of an expansive soil area, and this information is incorporated into the numerical simulations and works to develop prediction models of slope stability. Four base machine learning (ML) methods are used to capture the relationship between protection structure diseases and factor of safety (FOS). Further, with the help of stacked generalization (SG), four ML models are combined, and the final SG model is used to predict the FOS. The results show that ML methods can effectively utilize this information and achieve excellent prediction results. The proposed SG model exhibits superior accuracy and robustness in predicting FOS compared to other ML methods. With FOS as the regression variable, the main feature contributions are slope height (37.05%) > slip distance of retaining wall (25.43%) > expansive force (18.03%) > slope gradient (12.00%); the coupling relationship among features is also captured by the proposed model. It is concluded that the SG method is particularly suitable for slope stability modeling under small sample conditions. Besides, the SG-based model effectively captures the impact of protection structure diseases on slope stability, enhances the interpretability of the ML model, and provides a reference for the maintenance and repair of the protection structure.

Predicting slope safety using an optimized machine learning model.

The hazards and consequences of slope collapse can be reduced by obtaining a reliable and accurate prediction of slope safety, hence, developing effective tools for foreseeing their occurrence is crucial. This research aims to develop a state-of-the-art hybrid machine learning approach to estimate the factor of safety (FOS) of earth slopes as precisely as possible. The current research's contribution to the body of knowledge is multifold. In the first step, a powerful optimization approach based on the artificial electric field algorithm (AEFA), namely the global-best artificial electric field algorithm (GBAEF), is developed and verified using a number of benchmark functions. The aim of the following step is to utilize the machine learning technique of support vector regression (SVR) to develop a predictive model to estimate the slope's safety factor (FOS). Finally, the proposed GBAEF is employed to enhance the performance of the SVR model by appropriately adjusting the hyper-parameters of the SVR model. The model implements 153 data sets, including six input parameters and one output parameter (FOS) collected from the literature. The outcomes show that implementing efficient optimization algorithms to adjust the hyper-parameters of the SVR model can greatly enhance prediction accuracy. A case study of earth slope from Chamoli District, Uttarakhand is used to compare the proposed hybrid model to traditional slope stability techniques. According to experimental findings, the new hybrid AI model has improved FOS prediction accuracy by about 7% when compared to other forecasting models. The outcomes also show that the SVR optimized with GBAEF performs wonderfully in the disciplines of training and testing, with a maximum R2 of 0.9633 and 0.9242, respectively, which depicts the significant connection between observed and anticipated FOS.

Stability risk assessment of slopes using logistic model tree based on updated case histories.

A new logistic model tree (LMT) model is developed to predict slope stability status based on an updated database including 627 slope stability cases with input parameters of unit weight, cohesion, angle of internal friction, slope angle, slope height and pore pressure ratio. The performance of the LMT model was assessed using statistical metrics, including accuracy (Acc), Matthews correlation coefficient (Mcc), area under the receiver operating characteristic curve (AUC) and F-score. The analysis of the Acc together with Mcc, AUC and F-score values for the slope stability suggests that the proposed LMT achieved better prediction results (Acc = 85.6%, Mcc = 0.713, AUC = 0.907, F-score for stable state = 0.967 and F-score for failed state = 0.923) as compared to other methods previously employed in the literature. Two case studies with ten slope stability events were used to verify the proposed LMT. It was found that the prediction results are completely consistent with the actual situation at the site. Finally, risk analysis was carried out, and the result also agrees with the actual conditions. Such probability results can be incorporated into risk analysis with the corresponding failure cost assessment later.

Nonlinear Strength Reduction Method of Rock Mass in Slope Stability Evaluation.

As the strength parameters of rock mass degrade differently during slope instability, different factors should be considered in the strength reduction method. Previous nonlinear reduction methods were essentially implemented based on the Mohr-Coulomb criterion, which was reported not to reflect the nonlinear performance of rock mass. To address this deficiency, in this study, the Hoek-Brown criterion was combined with a nonlinear reduction technique for slope stability evaluation. Firstly, based on the classical definition of safety factors, the relationships that should be satisfied by each parameter of the critical slope were derived. The critical curve of the slope regarding the Hoek-Brown constant mb and the uniaxial compressive strength of rock mass σcmass was then obtained. On the assumption that the slope parameter deterioration conforms to the shortest path theory, the reduction ratio of σcmass to mb was determined. The more objective k-means algorithm was employed to automatically search the potential sliding surface, on which the slope safety factor was calculated as the ratio of sliding resistance to sliding force. Finally, the slopes in published literature were adopted for verification, and the calculated safety factors were compared with those by other methods, which showed better efficacy.

Geophysical and numerical stability analysis of landslide incident.

Landslide is known for its precarious impact on environment, resources and human life. Recently, landslide has occurred in Lalisa village, Jimma Zone, Ethiopia which harshly caused damage to life and property. The incident resulted in perilous damage of about 27 ha of accessible land. This study hence particularly aimed at investigating the root cause of the incident and analyzing safety of the sliding slope so that the applicable remedial actions can be proposed. Geophysical analysis without soil structure disturbance was adopted to investigate the vertical soil profile, morphological stratification, location and alignment of discontinuity planes. Stability analysis by using Limit Equilibrium method was carried out for both normal and worst conditions to rate safety of the failing slope. Lithology of the site is characterized by highly weathered and fractured rock units exhibiting a significant variability over a little horizontal distance and depth. The stratigraphy also constitutes loose soil near the surface and saturated layer ranging from depth of 10 m to 25 m. The slope failure occurred at the site is of deep by its type that origin of its slip plane extends up to a depth of 12 m from the surface. Furthermore, factor of safety of the slope along the failed zone fell below 1.5 with the maximum value of 1.303 for the normal condition. The conducted investigation also indicated that the detachment and propagation of the sliding mass develops much faster with rise in soil moisture content whereas it categorically remains mild during dry seasons. Hence, the driving agent for the occurrence and propagation of the landslide incident was rainfall infiltration and the existence of weak saturated zone at the stated depth.

Rock Surface Strain In Situ Monitoring Affected by Temperature Changes at the Pozáry Field Lab (Czechia).

The evaluation of strain in rock masses is crucial information for slope stability studies. For this purpose, a monitoring system for analyzing surface strain using resistivity strain gauges has been tested. Strain is a function of stress, and it is known that stress affects the mechanical properties of geomaterials and can lead to the destabilization of rock slopes. However, stress is difficult to measure in situ. In industrial practice, resistivity strain gauges are used for strain measurement, allowing even small strain changes to be recorded. This setting of dataloggers is usually expensive and there is no accounting for the influence of exogenous factors. Here, the aim of applying resistivity strain gauges in different configurations to measure surface strain in natural conditions, and to determine how the results are affected by factors such as temperature and incoming solar radiation, has been pursued. Subsequently, these factors were mathematically estimated, and a data processing system was created to process the results of each configuration. Finally, the new strategy was evaluated to measure in situ strain by estimating the effect of temperature. The approach highlighted high theoretical accuracy, hence the ability to detect strain variations in field conditions. Therefore, by adjusting for the influence of temperature, it is potentially possible to measure the deformation trend more accurately, while maintaining a lower cost for the sensors.

Remotely piloted aircraft-based automated vertical surface survey.

Remotely Piloted Aircrafts (RPAs) are commonly used as a platform for collecting images which can be processed with Structure from Motion-Multi View Stereo (SfM-MVS) to generate 3D models. However, mobile applications for mapping planning are not designed for image acquisition of vertical surfaces, such as quarry walls or large cliffs, leaving the user to a manual flight operation, which does not ensure optimal overlap between images. Here we describe a workflow, based on the Litchi App, for automated RPA missions designed to acquire images of vertical surfaces or structures.•An easy-to-follow 8 steps method to survey vertical surfaces using a Remotely Piloted Aircraft.•It can be applied to outcrops, quarry walls, high cliffs and virtually any other type of vertical surface.•The workflow is flexible and can be adapted to a variety of target configurations and user-defined parameters.