土质边坡稳定性影响因素的研究

边坡稳定性涉及到诸多因素,引入人工神经网络预测边坡稳定性的方法--误差逆传播学习算法效果显著.边坡稳定性预测系统的输入信息包括岩土体参数、几何参数等,而输出信息则是网络预测的稳定系数和稳定状态.土质边坡主要以圆弧滑移破坏为主,通过人工神经网络预测的结果与实际监测结果的对比分析,证实了BP神经网络在评价土质边坡稳定性方面的效果显著;并在此基础上分析了土质边坡影响因素对边坡稳定性的影响程度.     

An extreme learning machine approach for slope stability evaluation and prediction

This paper presents slope stability evaluation and prediction with the approach of a fast robust neural network named the extreme learning machine (ELM). The circular failure mechanism of a slope is formulated based on its material, geometrical and environmental parameters such as the unit weight, the cohesion, the internal friction angle, the slope inclination, slope height and the pore water ratio. The ELM is proposed to evaluate the stability of slopes subjected to potential circular failures by means of prediction of the factor of safety (FS). Substantial slope cases collected worldwide are utilized to illustrate and assess the capability and predictability of the ELM on slope stability analysis. Based on the mean absolute percentage errors and the correlation coefficients between the original and predicted FS values, comparisons are demonstrated between the ELM and the generalized regression neural network (GRNN) as well as the prediction models generated from the genetic algorithms. Moreover, sensitivity analysis of the slope parameters and the ELM model parameters is carried out based on the two utilized evaluation functions. The time expense of the ELM on slope stability analysis is also investigated. The results prove that the ELM is advantageous to the GRNN and the genetic algorithm based models in the analysis of slope stability. Hence, the ELM can be a promising technique for approaching the problems in geotechnical engineering.     

Integrating intensity–duration-based rainfall threshold and antecedent rainfall-based probability estimate towards generating early warning for rainfall-induced landslides in parts of the Garhwal Himalaya,India

In order to generate early warning for landslides, it is necessary to address the spatial and temporal aspects of slope failure. The present study deals with the temporal dimension of slope failures taking into account the most widespread and frequent triggering factor, i.e. rainfall, along the National Highway-58 from Rishikesh to Mana in the Garhwal Himalaya, India. Using the post-processed three-hourly rainfall intensity and duration values from the Tropical Rainfall Measuring Mission-based Multi-satellite Precipitation Analysis and the time-tagged landslide records along this route, an intensity–duration (I–D)-based threshold has been derived as I?=?58.7D ^?1.12 for the rainfall-triggered landslides. The validation of the I–D threshold has shown 81.6 % accuracy for landslides which occurred in 2005 and 2006. From this result, it can be inferred that landslides in the study area can be initiated by continuous rainfall of over 12 h with about 4-mm/h intensity. Using the mean annual precipitation, a normalized intensity–duration relation of NI?=?0.0612D ^?1.17 has also been derived. In order to account for the influence of the antecedent rainfall in slope failure initiation, the daily, 3-day cumulative, and 15- and 30-day antecedent rainfall values associated with landslides had been subjected to binary logistic regression using landslide as the dichotomous dependent variable. The logistic regression retained the daily, 3-day cumulative and 30-day antecedent rainfall values as significant predictors influencing slope failure. This model has been validated through receiver operating characteristic curve analysis using a set of samples which had not been used in the model building; an accuracy of 95.1 % has been obtained. Cross-validation of I–D-based thresholding and antecedent rainfall-based probability estimation with slope failure initiation shows 81.9 % conformity between the two in correctly predicting slope stability. Using the I–D-based threshold and the antecedent rainfall-based regression model, early warning can be generated for moderate to high landslide-susceptible areas (which can be delineated using spatial integration of preconditioning factors). Temporal predictions where both the methods converge indicate higher chances of slope failures for areas predisposed to instability due to unfavourable geo-environmental and topographic parameters and qualify for enhanced slope failure warning. This method can be verified for further rainfall seasons and can also be refined progressively with finer resolutions (spatial and temporal) of rainfall intensity and multiple rain gauge stations covering a larger spatial extent.     

Modeling and mapping of solar radiation using geostatistical analysis methods in Iran

Because of economic and technical limitations, measuring solar energy received at ground level (R _s ) isn’t possible in all parts of the country, and in only 12% of synoptic stations is this parameter measured and recorded. Thus, it should be estimated and modeled spatially based on other climatic variables using mathematical methods. In this research, many attempts have been made to introduce an air temperature-based model for Rs estimation, and then, based on the output of the mentioned models, several geostatistical methods have been tested, and finally an elegant spatial model is proposed for (Rs) zoning in Iran. In this regard, the relationships between the measured amounts of monthly solar radiation and other climatic parameters, such as a monthly average, maximum and minimum temperature, precipitation, relative humidity, and the number of sunny hours during the period 1970–2010, are examined and modeled. It was revealed that based on the linear relationship between the monthly average air temperatures and solar radiation values recorded in each of the stations, that the best-fit linear model, with R ²  = 0.822, MAE = 1.81, RMSE = 2.51%, and MAPE = 10.08, can be introduced for Rs estimation. Then, using the outputs of the proposed model, the amounts of (R _s ) are estimated in another 171 meteorological stations (a total of 192 stations), and eight geostatistical methods (IDW, GPI, RBF, LPI, OK, SK, UK, and EBK) were investigated for zoning. Comparing the resulting variograms showed that in addition to proof of spatial correlation between solar radiation data, they can be applied for modeling changes in various directions. Analyzing the ratio of the nugget effect on the roof of the variograms showed that the Gaussian model with the lowest ratio (Co/Co + C = 0.883) and (R ²  = 0.972), could model the highest correlation between the data and, therefore, it was used for data interpolation. To select the best geostatistical model, R2, MAE, and RMSE were used. On this basis, it was found that the RBF method with R ²  = 0.904, MAE = 3.02, RMSE = 0.39% is the most effective. Also, the IDW method with R ²  = 0.90, MAE = 3.08, RMSE = 0.391%, compared to other methods is the most effective. In addition, for data validation, correlations between observed and estimated values of solar radiation were studied and found R ²  = 0.86.     

Evolution Mechanism and Rainfall Warning Criteria for Maijianwo Slope in Henan Province,China

In this study, two different research methods are applied to investigate the evolution mechanism and rainfall warning criteria for Maijianwo slope located in Henan Province, China. On the one hand, an indoor-model test is performed under artificial rainfall and based on similarity theory. A set of monitoring system is utilized to track the moisture content, deformation and cracks of critical points of the model during the test. On the other hand, the numerical simulation is carried out to provide an insight into the variation of unstable zone and factor of safety for the landslide with the increasing cumulative rainfall. Results indicate that the evolution process of Maijianwo slope is composed of three stages of initiating, accelerating and failure respectively, and stability of slope decreases gradually as cumulative rainfall increases. Based on the evolution mechanism of retrogressive landslide verified by both model test and numerical simulation, cracking time of critical positions on the slope prior to each stage were set as the initiating time of each stage and the cumulative rainfall associated with each initiating time (E₁ = 75 mm, E₂ = 180 mm) has been defined as the warning criteria for the Maijianwo slope. As the cumulative rainfall in Maijianwo slope reaches 75 and 180 mm, the landslide orange and red warning codes are issued, respectively. Otherwise, the slope is in a safe condition when the cumulative rainfall is less than 75 mm.     

Evaluating the susceptibility of landslide landforms in Japan using slope stability analysis: a case study of the 2016 Kumamoto earthquake

This study analyzed 267 landslide landforms (LLs) in the Kumamoto area of Japan from the database of about 0.4 million LLs for the whole of Japan identified from aerial photos by the National Research Institute for Earth Science and Disaster Resilience of Japan (NIED). Each LL in the inventory is composed of a scarp and a moving mass. Since landslides are prone to reactivation, it is important to evaluate the sliding-recurrence susceptibility of LLs. One possible approach to evaluate the susceptibility of LLs is slope stability analysis. A previous study found a good correlation (R ² = 0.99) between the safety factor (F _s ) and slope angle (α) of F _s  = 17.3α ^?0.843. We applied the equation to the analysis of F _s for 267 LLs in the area affected by the 2016 Kumamoto earthquake (M _j  = 7.3). The F _s was calculated for the following three cases of failure: scarps only, moving mass only, and scarps and moving mass together. Verification with the 2016 Kumamoto earthquake event shows that the most appropriate method for the evaluation of LLs is to consider the failure of scarps and moving mass together. In addition, by analyzing the relationship between the factors of slope of entire landslide and slope of scarp for LLs and comparing the results with the Aso-ohashi landslide, the largest landslide caused by the 2016 Kumamoto earthquake, we also found that morphometric analysis of LLs is useful for forecasting the travel distance of future landslides.     

Estimating leaf chlorophyll contents by combining multiple spectral indices with an artificial neural network

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive, but the actual dataset always based on a single or a few kinds of specific species, has a limitation and instability for a common use. To address this problem, a combination of multiple spectral indices and a model simulated dataset are proposed in this paper. Six spectral indices are selected, including Blue Green Index (BGI), Photochemical Reflectance Index (PRI_5), Triangle Vegetation Index (TVI), Chlorophyll Absorption Ratio Index (CARI), Carotenoid Reflectance Index (CRI) and the green peak reflectance (R₅₂₅). Both stepwise linear regression (SLR) and back-propagation artificial neural network (ANN) are used to combine the six spectral indices for the estimation of chlorophyll content (Cab). In addition, to overcome the limitation of actual dataset, a “big data” is applied by a within-leaf radiation transfer model (PROSPECT) to generate a large number of simulated samples with varying biochemical and biophysical parameters. 30% of the simulated dataset (SIM30) and an experimental dataset are used for validation. Compared with linear regression method, NN yields better result with R² = 0.96 and RMSE = 5.80ug.cm^?2 for Cab if validated by SIM30, while R² = 0.95 and RMSE = 6.39ug.cm^?2 for SLR. NN also gives satisfactory result with R² = 0.80 and RMSE = 5.93ug.cm^?2 for Cab if validated by LOPEX dataset, however, the SLR only gets 0.72 of R² and 12.20ug.cm^?2 of RMSE. The results indicate that integrating multiple spectral indices can improve the Cab estimating accuracy with a better stability in different kind of species and the model simulated dataset can make up the shortfall of actual measured dataset.     

On the efficient estimation of small failure probability in slopes

The random finite element method (RFEM) combines the random field theory and finite element method in the framework of Monte Carlo simulation. It has been applied to a wide range of geotechnical problems such as slope stability, bearing capacity and the consolidation of soft soils. When the RFEM was first developed, direct Monte Carlo simulation was used. If the probability of failure (p _f ) is small, the direct Monte Carlo simulation requires a large number of simulations. Subset simulation is one of most efficient variance reduction techniques for the simulation of small p _f . It has been recently proposed to use subset simulation instead of direct Monte Carlo simulation in RFEM. It is noted, however, that subset simulation requires calculation of the factor of safety (FS), while direct Monte Carlo requires only the examination of failure or non-failure. The search for the FS in RFEM could be a tedious task. For example, the search for the FS of slope stability by the strength reduction method (SRM) usually requires much more computational time than a failure or non-failure checking. In this paper, the subset simulation is combined with RFEM, but the need for the search of FS is eliminated. The value of yield function in an elastoplastic finite element analysis is used to measure the safety margin instead of the FS. Numerical experiments show that the proposed approach gives the same level of accuracy as the traditional subset simulation based on FS, but the computational time is significantly reduced. Although only examples of slope stability are given, the proposed approach will generally work for other types of geotechnical applications.     

3D modeling of stratified and irregularly jointed rock slope and its progressive failure

Little has been published on the three-dimensional (3D) simulation of the progressive failure of rock slopes, possibly because the process of failure involves a complex, nonlinear evolution from initiation, through propagation and crack. In addition, rock is typically anisotropic, which makes it difficult to identify and describe the slope constituents and failure processes accurately. Despite such difficulties, further study of the fracture process is just as important as analyzing stress fields in 3D rock slope failures. In this paper, the 3D realistic failure process analysis code using finite element programming, and an extended version of numerical centrifugal method, is used to simulate slopes failure with different dip angles. The numerical centrifugal analysis results in this paper are found that the critical failure surface develops along the weak structural surface when the slope dip angle β is below 30°; conversely, the failure surface is formed along the toe of circular sliding when β is above 30°. In addition, it is also found that whether or not including the irregularity of joint into modeling to analyze the 3D slope stability problem will lead to a significant difference in factors of safety, it can reach 8.41 % at the same slope angle. Furthermore, the acoustic emission analyzing reveals deformed location characters of rock slope during the failure processes. With such capabilities, the approach contributes significantly to the in-depth study of the mechanisms of rock slope instability process.     

Relationship between hydrogeological parameters for data-scarce regions: the case of the Araripe sedimentary basin, Brazil

This paper applies and validates a method for generating spatially distributed hydraulic conductivity (k) based on the specific capacity (Q _s) for data-scarce regions. This method has been applied to the Araripe sedimentary basin, Brazil, and consists of four steps: (1) selection of (32) wells for which both k and Q _s data are available; (2) estimation of k as a function of Q _s for the (128) wells for which only specific capacity data are available; (3) spatial distribution of k using the kriging geostatistical tool; (4) validation of the method, using (17) representative wells with k measured data. The equation relating k and Q _s showed a statistically significant linear relationship (R = 0.93), from which a database has been generated using kriging with the spherical model. The results showed a calibration coefficient of Nash and Sutcliffe (NS) of 0.54 and moderate spatial dependence ratio of 69 %. The validation process provided only a moderate efficiency (NS = 0.22), possibly due to the geological complexity of the focus system. Despite its limitations, the method indicates the possibility of application of ordinary kriging to generate reliable data from auxiliary variables, especially for the water management of data-scarce areas.     

How does the water–rock interaction of marly rocks affect its mechanical properties in the Three Gorges reservoir area,China?

Marly rock is a sensitive rock group of landslides in the Three Gorges reservoir area, China. It is composed predominantly of carbonate and clay minerals, water–rock interaction (WRI) of which could activate landslides in the reservoir area. To study the mechanism by which WRI affects the mechanical properties of marly rock, samples were collected from two boreholes (depth 301.78 and 307.14 m) and slope surface. Then, laboratory tests were designed to study the quantitative relationship between mineral contents and mechanical properties of intact rock, to analyze both change process of mineral composition and microstructure under short-term and long-term WRI. Finally, the change in mechanical properties and its effect on slope stability are suggested. This study indicates that the uniaxial compressive strength and Poisson ratio can be estimated by linear regression equations: (1) σ _c = 8.959 × (C/Q) ? 0.744 × CM + 58.516; (2) μ = 0.014 × (C/Q) ? 0.001 × CM + 0.234. The chemical reactions of WRI mainly included dissolution and ion exchange. On the slope surface, dissolution mainly acts on calcite, illite, dolomite, feldspar and other minerals dissolved in water. Underground, both chemical reactions of dissolution and ion exchange easily approach equilibrium with long-term seepage. Small-size minerals and micropores damage the stable microstructure of marly rock. These changes of minerals and microstructure can trigger shallow slope failure and develop deep creep deformation along some crash zones in the reservoir shoreline.     

Prediction of pore-water pressure response to rainfall using support vector regression

Nonlinear complex behavior of pore-water pressure responses to rainfall was modelled using support vector regression (SVR). Pore-water pressure can rise to disturbing levels that may result in slope failure during or after rainfall. Traditionally, monitoring slope pore-water pressure responses to rainfall is tedious and expensive, in that the slope must be instrumented with necessary monitors. Data on rainfall and corresponding responses of pore-water pressure were collected from such a monitoring program at a slope site in Malaysia and used to develop SVR models to predict pore-water pressure fluctuations. Three models, based on their different input configurations, were developed. SVR optimum meta-parameters were obtained using k-fold cross validation and a grid search. Model type 3 was adjudged the best among the models and was used to predict three other points on the slope. For each point, lag intervals of 30 min, 1 h and 2 h were used to make the predictions. The SVR model predictions were compared with predictions made by an artificial neural network model; overall, the SVR model showed slightly better results. Uncertainty quantification analysis was also performed for further model assessment. The uncertainty components were found to be low and tolerable, with d-factor of 0.14 and 74 % of observed data falling within the 95 % confidence bound. The study demonstrated that the SVR model is effective in providing an accurate and quick means of obtaining pore-water pressure response, which may be vital in systems where response information is urgently needed.     

Stability assessment of slide zones in Lesser Himalayan part of Yamunotri pilgrimage route,Uttarakhand, India

The present study deals with the slope stability analysis and geotechnical assessment of a part of pilgrimage route to one of the holy shrines of India, i.e. Yamunotri. The route also embraces a proposed site for 204 m high concrete gravity dam across River Yamuna near Lakhwar village with the aim of generation of 300 MW power. Several slide zones were identified and based on the discontinuity orientation, structural features and debris materials, they are recognised as planar, wedge or circular failure types. The morphological dimensions, structural data, orientations and geotechnical parameters of circular failure slides within weathered quartzites, phyllites and shales were evaluated by extensive field work and by laboratory tests for their stability analysis. Internal mechanisms, cohesion and angle of internal friction that resist shear stress in slope materials, obtained from direct shear test are showing minor variation due to relatively consistent grain size distribution and mineralogical composition. General slope is about 40°–43° with sparse vegetation. Materials in and around slide zones are sands with appreciable amount of fines falling in SP–SM category as per Unified Soil Classification System (USCS), except Niste B slide which has clean sands lying in SP group. Factor of safety, computed by requisite parameters of strength, soil and slope properties in circular failure charts, varies from 1.02 to 1.23 in dry conditions while it reduces below unity with increasing saturation, representing stable conditions in dry conditions but with seepage and saturation along the cracks and discontinuities during rainfall make them unstable. Presence of steep slopes, proximity to stream channels and significant weathered and jointed area are causative factors in the route with rainfall and road widening as major triggers initiating the failure.     

Satellite-based rainfall estimation and discharge measurement of Middle Indus River,Pakistan

The impacts of floods and droughts are intensified by climate change, lack of preparedness, and coordination. The average rainfall in study area is ranging from 200 to 400 mm per year. Rain gauge generally provides very accurate measurement of point rain rates and the amounts of rainfall but due to scarcity of the gauge locations provides very general information of the area on regional scale. Recognizing these practical limitations, it is essential to use remote sensing techniques for measuring the quantity of rainfall in the Middle Indus. In this research, Tropical Rainfall Measuring Mission (TRMM) estimation can be used as a proxy for the magnitude of rainfall estimates from classical methods (rain gauge), quantity, and its spatial distribution for Middle Indus river basin. In order to use TRMM satellite data for discharge measurement, its accuracy is determined by statistically comparing it with in situ gauged data on daily and monthly bases. The daily R ² value (0.42) is significantly lower than monthly R ² value (0.82), probably due to the time of summation of TRMM 3-hourly precipitation data into daily estimates. Daily TRMM data from 2003 to 2012 was used as input forcing in Soil and Water Assessment Tool (SWAT) hydrological model along with other input parameters. The calibration and validation results of SWAT model give R ² = 0.72 and 0.73 and Nash-Sutcliffe coefficient of efficiency = 0.69 and 0.65, respectively. Daily and monthly comparison graphs are generated on the basis of model discharge output and observed data.     

An RES-Based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting

Most blasting operations are associated with various forms of energy loss, emerging as environmental side effects of rock blasting, such as flyrock, vibration, airblast, and backbreak. Backbreak is an adverse phenomenon in rock blasting operations, which imposes risk and increases operation expenses because of safety reduction due to the instability of walls, poor fragmentation, and uneven burden in subsequent blasts. In this paper, based on the basic concepts of a rock engineering systems (RES) approach, a new model for the prediction of backbreak and the risk associated with a blast is presented. The newly suggested model involves 16 effective parameters on backbreak due to blasting, while retaining simplicity as well. The data for 30 blasts, carried out at Sungun copper mine, western Iran, were used to predict backbreak and the level of risk corresponding to each blast by the RES-based model. The results obtained were compared with the backbreak measured for each blast, which showed that the level of risk achieved is in consistence with the backbreak measured. The maximum level of risk [vulnerability index (VI) = 60] was associated with blast No. 2, for which the corresponding average backbreak was the highest achieved (9.25 m). Also, for blasts with levels of risk under 40, the minimum average backbreaks (<4 m) were observed. Furthermore, to evaluate the model performance for backbreak prediction, the coefficient of correlation (R ²) and root mean square error (RMSE) of the model were calculated (R ² = 0.8; RMSE = 1.07), indicating the good performance of the model.     

Comparison of univariate and multivariate geographically weighted regression for estimating air temperature over Iran

Station recording air temperature (T_a) has limited spatial coverage, especially in unpopulated areas. Since temperature can change greatly both spatially and temporally, stations data are often inadequate for meteorology and subsequently climatology studies. Time series of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (T_s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM), albedo from Era-Interim and meteorological data from 2006 to 2015, were used to estimate daily mean air temperature over Iran. Geographically weighted regression was applied to compare univariate and multivariate model accuracy. In the first model, which only interfered with land surface temperature (LST), the results indicate a weak performance with coefficient of determination up to 91% and RMSE of 1.08 to 2.9 °C. The mean accuracy of a four-variable model (which used LST, elevation, slope, NDVI) slightly increased (6.6% of the univariate model accuracy) when compared to univariate model. RMSE dropped by 19% of the first model. By addition albedo in the third model, the coefficient of determination increased significantly. This increase was 32% of the univariate model and 23.75% of the 4-variable model accuracy. The statistical comparison between the three models revealed that there is significant improvement in air estimation by applying the geographically weighted regression (GWR) method with interfering LST, NDVI, elevation, slope, and albedo with mean absolute RMSE of 0.62 °C and mean absolute R² of 0.99. In order to better illustrate the third model, t values were spatially mapped at 0.05 level.     

Risk assessment and prediction of safety factor for circular failure slope using rock engineering systems

Circular failure is generally observed in the slope of soil, highly jointed rock mass, mine dump and weak rock. Accurate estimation of the safety factor (SF) of slopes and their performance is not an easy task. In this research, based on rock engineering systems (RES), a new approach for the estimation of the SF is presented. The introduced model involves six effective parameters on SF [unit weight (γ), pore pressure ratio (r _u), height (H), angle of internal friction (φ), cohesion (C) and slope angle (\(\beta\))], while retaining simplicity as well. In the case of SF prediction, all the datasets were divided randomly to training and testing datasets for proposing the RES model. For comparison purposes, nonlinear multiple regression models were also employed for estimating SF. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R ²) and mean square error. The obtained results of this study indicated that the RES is a reliable method to predict SF with a higher degree of accuracy in comparison with nonlinear multiple regression models.     

Estimating transmissivity using empirical and geostatistical methods in the volcanic aquifers of Upper Awash Basin, central Ethiopia

Transmissivity (T) is a basic hydraulic parameter of an aquifer that is utilized in most groundwater flow equations to understand the flow dynamics and is generally estimated from pumping tests. However, the cost of performing a large number of aquifer tests is expensive and time consuming. The fact that specific capacity (S _c) is correlated with hydraulic flow properties of aquifers simplifies parameter estimation mainly because specific capacity values are more abundant in groundwater databases than values of transmissivity and they offer another approach to estimate hydraulic parameters of aquifers. In this study, an empirical relation is derived using 214 pairs of transmissivity and specific capacity values that are obtained from pumping tests conducted on water wells penetrating the complex volcanic aquifers of Upper Awash Basin, central Ethiopia. Linear and logarithmic regression functions have been performed and it is found that the logarithmic relationship predicting transmissivity from specific capacity data has a better correlation (R = 0.97) than the linear relationship (R = 0.79). The two parameters are log-normally distributed, in which the logarithmic relation is also better statistically justified than the linear relation. Geostatistical estimations of the transmissivity were made using different inputs and methods. Measured and supplemented transmissivity data obtained from estimates using the derived empirical relation were krigged and cokrigged, spherical and exponential models were fitted to the experimental variograms. The cross-validation results showed that the best estimation is provided using the kriging procedure, the transmissivity field represented by the measured transmissivity data and the experimental variogram fitted with the exponential model. Based on the geostatistical approach, the transmissivity map of the aquifer is produced, which will be used for groundwater flow modeling of the study area that will follow this analysis.     

Site scale modeling of slow-moving landslides,a 3D viscoplastic finite element modeling approach

This paper presents an advanced 3D numerical methodology to reproduce the kinematics of slow active landslides, more precisely, to reproduce the nearly constant strain rate (secondary creep) and the acceleration/deceleration of the moving mass due to hydrological changes. For this purpose, finite element analyses are performed in a large area covering a long time-span (12 years), in order to exhibit different interacting slope movements. First, we perform a stability analysis using the shear strength reduction (SSR) technique with a Mohr-Coulomb failure criteria. It is done in order to compute factors of safety (FS) and to identify two different scenarios, the first one being stable (FS > 1) and the second one being unstable (FS < 1). In the studied test case, the Portalet landslide (Central Spanish Pyrenees), the first scenario corresponds to an initial stable configuration of the slope and the second one to an unstable excavated configuration. Second, taking the first scenario as an initial condition, a time-dependent analysis is performed using a coupled formulation to model solid skeleton and pore fluids interaction, and a simplified ground water model that takes into account daily rainfall intensity. In this case, a viscoplastic constitutive model based on Perzyna’s theory is applied to reproduce soil viscous behavior and the delayed creep deformation due to the excavation. The fluidity parameter is calibrated to reproduce displacements measured by the monitoring systems. Our results demonstrate that 3D analyses are preferable to 2D ones for reproducing in a more realistic way the slide behavior. After calibration, the proposed model is able to simulate successfully short- and medium-term predictions during stages of primary and secondary creep.     

Application of genetic algorithm technique to inverse modeling of tide–aquifer interaction

Modeling of tide–aquifer interaction plays a vital role in the management of coastal aquifer systems. A novel and robust methodology is presented in this paper for estimating aquifer parameters of coastal aquifers from tide–aquifer interaction data using tide–aquifer interaction model and genetic algorithm (GA). Two stand-alone computer programs were developed to optimize hydraulic diffusivities of unconfined and confined coastal aquifers at multiple sites using GA technique and tide–aquifer interaction model and considering two approaches (‘lumped tidal component approach’ and ‘multi-tidal component approach’). Five sets of real-world tide–aquifer interaction data at two sites of an unconfined aquifer and one set of tide–aquifer interaction data at three sites of a confined aquifer were used to demonstrate the efficacy of the methodology. The analysis of the GA-based inverse modeling results indicated that the ‘multi-tidal component approach’ yields more accurate and reliable hydraulic diffusivities for the unconfined aquifer (RMSE = 0.0129–0.0521 m, NSE = 0.70–0.97, and d1 = 0.91–0.99) as well as for the confined aquifer (RMSE = 0.0204–0.0545 m, NSE = 0.95–0.97, and d1 = 0.99) compared with the ‘lumped tidal component approach’. A comparative evaluation of data-size revealed that the short-duration datasets of the unconfined aquifer provide more reliable estimate of hydraulic diffusivity than the long-duration datasets. Further, it was found that the spring and neap tidal data yield unreasonable values of hydraulic diffusivity with considerably high values of RMSE and very low values of r ², NSE, and d1, thereby suggesting that spring and neap tidal data are not suitable for aquifer parameter estimation. Overall, it is concluded that the GA-based tide–aquifer interaction model following ‘multi-tidal component approach’ is the most efficient tool for estimating aquifer parameters of unconfined and confined aquifers from tide–aquifer interaction data. The developed methodology is also applicable to other coastal basins of the world irrespective of hydrogeological settings.