A hybrid computational approach to formulate soil deformation moduli obtained from PLT

In this study, new empirical equations were developed to predict the soil deformation moduli utilizing a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed models relate secant (E_s), unloading (E_u) and reloading (E_r) moduli obtained from plate load–settlement curves to the basic soil physical properties. Several models with different combinations of the influencing parameters were developed and checked to select the best GP/SA models. The database used for developing the models was established upon a series of plate load tests (PLT) conducted on different soil types at various depths. The validity of the models was tested using parts of the test results that were not included in the analysis. The validation of the models was further verified using several statistical criteria. A traditional GP analysis was performed to benchmark the GP/SA models. The contributions of the parameters affecting E_s, E_u and E_r were analyzed through a sensitivity analysis. The proposed models are able to estimate the soil deformation moduli with an acceptable degree of accuracy. The E_s prediction model has a remarkably better performance than the models developed for predicting E_u and E_r. The simplified formulations for E_s, E_u and E_r provide significantly better results than the GP-based models and empirical models found in the literature.     

Prediction of pressuremeter modulus and limit pressure of clayey soils by simple and non-linear multiple regression techniques: a case study from Mersin, Turkey

The Standard Penetration Test (SPT) is one of the most frequently applied tests during the geotechnical investigation of soils. Due to its usefulness, the development of empirical equations to predict mechanical and compressibility of soil parameters from the SPT blow count has been an attractive subject for geotechnical engineers and engineering geologists. The purpose of this study is to perform regression analyses between the SPT blow counts and the pressuremeter test parameters obtained from a geotechnical investigation performed in a Mersin (Turkey) city sewerage project. In accordance with this purpose, new empirical equations between pressuremeter modulus (E _M) and corrected SPT blow counts (N ₆₀) and between limit pressure (P _L) and corrected SPT blow counts (N ₆₀) are developed in the study. When developing the empirical equations, in addition to the SPT blow counts, the role of moisture content and the plasticity index of soils on the pressuremeter parameters are also assessed. A series of simple and nonlinear multiple regression analyses are performed. As a result of the analyses, several empirical equations are developed. It is shown that the empirical equations between N ₆₀ and E _M, and N ₆₀ and P _L developed in this study are statistically acceptable. An assessment of the prediction performances of some existing empirical equations, depending on the new data, is also performed in the study. However, the prediction equations proposed in this study and the previous studies are developed using a limited number of data. For this reason, a cross-check should be applied before using these empirical equations for design purposes.     

Simple and multiple regression models for relationship between electrical resistivity and various soil properties for soil characterization

Precise determination of engineering properties of soil is essential for proper design and successful construction of any structure. The conventional methods for determination of engineering properties are invasive, costly and time-consuming. Electrical resistivity survey is an attractive tool for delineate subsurface properties without soil disturbance. Reliable correlations between electrical resistivity and other soil properties will enable us to characterize the subsurface soil without borehole sampling. This paper presents the correlations of electrical resistivity with various properties of soil. Soil investigations, field electrical resistivity survey and laboratory electrical resistivity measurements were conducted. The results from electrical resistivity tests (field and laboratory) and laboratory tests were analyzed together to understand the interrelation between electrical resistivity and various soil properties. The test results were evaluated using simple and multiple regression analysis. From the data analysis, significant quantitative and qualitative correlations have been obtained between resistivity and moisture content, friction angle and plasticity index. Weaker correlations have been observed for cohesion, unit weight of soil and effective size (D ₁₀).     

Multivariate Adaptive Regression Spline (Mars) for Prediction of Elastic Modulus of Jointed Rock Mass

This article presents multivariate adaptive regression spline (MARS) for determination of elastic modulus (E_j) of jointed rock mass. MARS is a technique to estimate general functions of high-dimensional arguments given sparse data. It is a nonlinear and non-parametric regression methodology. The input variables of model are joint frequency (J_n), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ₃) and elastic modulus (E_i) of intact rock. The developed MARS gives an equation for determination of E_j of jointed rock mass. The results from the developed MARS model have been compared with those of artificial neural networks (ANNs) using average absolute error. The developed MARS gives a robust model for determination of E_j of jointed rock mass.     

Uncertainties in downscaled relative humidity for a semi-arid region in India

Monthly scenarios of relative humidity (R _H) were obtained for the Malaprabha river basin in India using a statistical downscaling technique. Large-scale atmospheric variables (air temperature and specific humidity at 925 mb, surface air temperature and latent heat flux) were chosen as predictors. The predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis dataset for the period 1978–2000, and (2) simulations of the third generation Canadian Coupled Global Climate Model for the period 1978–2100. The objective of this study was to investigate the uncertainties in regional scenarios developed for R _H due to the choice of emission scenarios (A1B, A2, B1 and COMMIT) and the predictors selected. Multi-linear regression with stepwise screening is the downscaling technique used in this study. To study the uncertainty in the regional scenarios of R _H, due to the selected predictors, eight sets of predictors were chosen and a downscaling model was developed for each set. Performance of the downscaling models in the baseline period (1978–2000) was studied using three measures (1) Nash–Sutcliffe error estimate (E _f), (2) mean absolute error (MAE), and (3) product moment correlation (P). Results show that the performances vary between 0.59 and 0.68, 0.42 and 0.50 and 0.77 and 0.82 for E _f, MAE and P. Cumulative distribution functions were prepared from the regional scenarios of R _H developed for combinations of predictors and emission scenarios. Results show a variation of 1 to 6% R _H in the scenarios developed for combination of predictor sets for baseline period. For a future period (2001–2100), a variation of 6 to 15% R _H was observed for the combination of emission scenarios and predictors. The variation was highest for A2 scenario and least for COMMIT and B1 scenario.     

Empirical modeling of plate load test moduli of soil via gene expression programming

New empirical models were developed to predict the soil deformation moduli using gene expression programming (GEP). The principal soil deformation parameters formulated were secant (E_s) and reloading (E_r) moduli. The proposed models relate E_s and E_r obtained from plate load-settlement curves to the basic soil physical properties. The best GEP models were selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon a series of plate load tests conducted on different soil types at depths of 1–24 m. To verify the applicability of the derived models, they were employed to estimate the soil moduli of a part of test results that were not included in the analysis. The external validation of the models was further verified using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting E_s and E_r. The proposed models give precise estimates of the soil deformation moduli. The E_s prediction model provides considerably better results in comparison with the model developed for E_r. The simplified formulation for E_s significantly outperforms the empirical equations found in the literature. The derived models can reliably be employed for pre-design purposes.     

http://www.sciencedirect.com/science/article/pii/S1674987115000456

This article adopts three artificial intelligence techniques, Gaussian Process Regression(GPR), Least Square Support Vector Machine(LSSVM) and Extreme Learning Machine(ELM), for prediction of rock depth(d) at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques.Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth.     

土壤与地下水污染的地球物理地球化学勘查

列举了土壤、地下水中常见的有机和无机污染组分.进入地下水中的绝大部分污染物与介质发生物理化学反应后,各种金属、非金属离子、固体溶解物、盐类在地下潜水面附近逐渐浓集,导电性增强,电阻率明显降低,对电磁波的反射能力增强.经实际检测发现,某垃圾填埋场被垃圾渗漏液污染的土壤视电阻率在10 Ω·m左右,垃圾渗出液的实测电阻率在0.40 Ω·m左右.比较高密度电阻率法、瞬变电磁法、探地雷达法的效果后发现,高密度电阻率法的效果显著.荧光光谱和吸附乙烷、游离甲烷对指示储油气设施泄漏有显著效果,实际应用中,检测游离甲烷可快速圈定污染范围.     

Load transfer analysis of rock-socketed drilled shafts by coupled soil resistance

The load distribution and deformation of rock-socketed drilled shafts subjected to axial loads are evaluated by a load transfer method. The emphasis is on quantifying the effect of coupled soil resistance in rock-socketed drilled shafts using 2D elasto-plastic finite element analysis. Slippage and shear-load transfer behavior at the pile–soil interface are investigated by using a user-subroutine interface model (FRIC). It is shown that the coupled soil resistance acts as pile-toe settlement as the shaft resistance is increased to its ultimate limit state. Based on the results obtained, the coupling effect is closely related to the ratio of the pile diameter to soil modulus (D/E_s) and the ratio of total shaft resistance against total applied load (R_s/Q). Through comparison with field case studies, the 2D numerical analysis reasonably estimated load transfer of pile and coupling effect, and thus represents a significant improvement in the prediction of load deflections of drilled shafts.     

Application of statistical learning algorithms to ultimate bearing capacity of shallow foundation on cohesionless soil

This study employs two statistical learning algorithms (Support Vector Machine (SVM) and Relevance Vector Machine (RVM)) for the determination of ultimate bearing capacity (q_u) of shallow foundation on cohesionless soil. SVM is firmly based on the theory of statistical learning, uses regression technique by introducing varepsilon‐insensitive loss function. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. It also gives variance of predicted data. The inputs of models are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ) and angle of shearing resistance (?). Equations have been developed for the determination of q_u of shallow foundation on cohesionless soil based on the SVM and RVM models. Sensitivity analysis has also been carried out to determine the effect of each input parameter. This study shows that the developed SVM and RVM are robust models for the prediction of q_u of shallow foundation on cohesionless soil. Copyright © 2010 John Wiley & Sons, Ltd.     

The behaviour of an elastic non-homogeneous half-space. Part I-line and point loads

In the first part of this paper solutions are developed for the response of a non-homogeneous half-space subjected to either a surface point load or a surface line load. The non-homogeneity considered is a variation in Young's modulus (E) with depth (z) which takes the form E=m_EZ^α where m_E is a constant and α is referred to as the non-homogeneity parameter. The variation of these solutions as the non-homogeneity parameter α varies between the limits of zero (homogeneous soil) to unity (Gibson soil) gives some fresh insight into both these limiting cases.     

Estimation of Elastic Modulus of Intact Rocks by Artificial Neural Network

The modulus of elasticity of intact rock (E _i) is an important rock property that is used as an input parameter in the design stage of engineering projects such as dams, slopes, foundations, tunnel constructions and mining excavations. However, it is sometimes difficult to determine the modulus of elasticity in laboratory tests because high-quality cores are required. For this reason, various methods for predicting E _i have been popular research topics in recently published literature. In this study, the relationships between the uniaxial compressive strength, unit weight (γ) and E _i for different types of rocks were analyzed, employing an artificial neural network and 195 data obtained from laboratory tests carried out on cores obtained from drilling holes within the area of three metro lines in Istanbul, Turkey. Software was developed in Java language using Weka class libraries for the study. To determine the prediction capacity of the proposed technique, the root-mean-square error and the root relative squared error indices were calculated as 0.191 and 92.587, respectively. Both coefficients indicate that the prediction capacity of the study is high for practical use.     

Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).     

不同勘探方法在丰水期和枯水期岩溶塌陷探测效果分析研究

岩溶塌陷的发生具有突发性和隐蔽性,采取有效方法准确识别潜在致塌位置对科学合理防治岩溶塌陷具有重要意义。以武汉市典型地区为实例,对比分析了丰水期和枯水期地质雷达、高密度电阻率法、静力触探3种勘探方法对岩溶塌陷的探测效果。结果表明:在该类地质条件下,地质雷达在工作频率≤100 MHz时,对4 m以内的扰动土有明显的响应,40 MHz工作频率的探测深度枯水期大于丰水期,100 MHz工作频率的扰动土响应特征枯水期比丰水期明显。高密度电阻率法在丰水期对地层结构的刻画更为精细准确,适用于浅中层岩土体结构探测。静力触探在丰水期和枯水期无显著差异,适用于10 m以内土体探测,尤其适用于圈定塌陷区边界范围。研究结果可为水动力条件季节性变化大的地区其岩溶塌陷探测方法的选择提供参考,为潜在塌陷点的准确识别提供技术支撑。     

Significance of Dar-Zarrouk parameters in the exploration of quality affected coastal aquifer systems

The interpreted Earth subsurface resistivity layer parameters of 55 vertical geoelectrical soundings are analyzed over a fan shaped area of 1,700 km² from Pipli-Astrang-Bhramgiri, Orissa, India. In this study, Dar-Zarrouk (D-Z) parameters, namely the longitudinal conductance (S), transverse resistance (T) and longitudinal resistivity (R _s ) are analyzed and we encountered the resistivity regime of the clay layers, saline and fresh water bearing formations. The significance of these parameters in establishing an easily decipherable vision about the occurrence and distribution of fresh and saline water aquifers, while dealing with complicated situations of intermixing of the resistivity ranges of saline and fresh water aquifers has been illustrated. The results show that the Dar-Zarrouk (D-Z) parameters provide a useful and confident solution in delineating the saline and fresh water aquifers. The behavior of the D-Z parameters S, T and R _s , and its patterns in space over large areas with respect to the occurrence of saline water and fresh water aquifer systems in the deltaic coastal aquifer system has been demonstrated.     

Intelligent methods for predicting nuclear magnetic resonance of porosity and permeability by conventional well-logs: a case study of Saharan field

In the well-log data processing, the principal advantage of the nuclear magnetic resonance (NMR) method is the measurement of fluid volume and pore size distribution without resorting to parameters such as rock resistivity. Preliminary processing of the well-log data allowed first to have the petrophysical parameters and then to evaluate the performances of the transverse relaxation time T ₂ NMR. Petrophysical parameters such as the porosity of the formation as well as the effective permeability can be estimated without having recourse the fluid type. The well-log data of five wells were completed during the construction of intelligent models in the Saharan oil field Oued Mya Basin in order to assess the reliability of the developed models. Data processing of NMR combined with conventional well data was performed by artificial intelligence. First, the support vector regression method was applied to a sandy clay reservoir with a model based on the prediction of porosity and permeability. NMR parameters estimated using intelligent systems, i.e., fuzzy logic (FL) model, back propagation neural network (BP-NN), and support vector machine, with conventional well-log data are combined with those of NMR, resulting in a good estimation of porosity and permeability. The results obtained during the processing are then compared to the FL and NN regression models performed by the regression method during the validation stage. They show that the correlation coefficients R ² estimated vary between 0.959 and 0.964, corresponding to the root mean square error values of 0.20 and 0.15.     

Bayesian hierarchical models for soil CO<Subscript>2</Subscript> flux and leak detection at geologic sequestration sites

Proper characterizations of background soil CO₂ respiration rates are critical for interpreting CO₂ leakage monitoring results at geologic sequestration sites. In this paper, a method is developed for determining temperature-dependent critical values of soil CO₂ flux for preliminary leak detection inference. The method is illustrated using surface CO₂ flux measurements obtained from the AmeriFlux network fit with alternative models for the soil CO₂ flux versus soil temperature relationship. The models are fit first to determine pooled parameter estimates across the sites, then using a Bayesian hierarchical method to obtain both global and site-specific parameter estimates. Model comparisons are made using the deviance information criterion (DIC), which considers both goodness of fit and model complexity. The hierarchical models consistently outperform the corresponding pooled models, demonstrating the need for site-specific data and estimates when determining relationships for background soil respiration. A hierarchical model that relates the square root of the CO₂ flux to a quadratic function of soil temperature is found to provide the best fit for the AmeriFlux sites among the models tested. This model also yields effective prediction intervals, consistent with the upper envelope of the flux data across the modeled sites and temperature ranges. Calculation of upper prediction intervals using the proposed method can provide a basis for setting critical values in CO₂ leak detection monitoring at sequestration sites.     

Simplified DMT-based methods for evaluating liquefaction resistance of soils

This paper presents simplified dilatometer test (DMT)-based methods for evaluation of liquefaction resistance of soils, which is expressed in terms of cyclic resistance ratio (CRR). Two DMT parameters, horizontal stress index (K_D) and dilatometer modulus (E_D), are used as an index for assessing liquefaction resistance of soils. Specifically, CRR–K_D and CRR–E_D boundary curves are established based on the existing boundary curves that have already been developed based on standard penetration test (SPT) and cone penetration test (CPT). One key element in the development of CRR–K_D and CRR–E_D boundary curves is the correlations between K_D (or E_D) and the blow count (N) in the SPT or cone tip resistance (q_c) from the CPT. In this study, these correlations are established through regression analysis of the test results of SPT, CPT, and DMT conducted side-by-side at each of five sites selected. The validity of the developed CRR–K_D and CRR–E_D curves for evaluating liquefaction resistance is examined with published liquefaction case histories. The results of the study show that the developed DMT-based models are quite promising as a tool for evaluating liquefaction resistance of soils.     

Combining geophysical methods to estimate the spatial distribution of soils affected by water saturation

Geophysical methods have already shown their interest for the continuous characterisation of soils over landscapes, rapidly and, non-intrusively. But in bottomland areas, difficulties are encountered in relating geophysical properties to soil spatial distribution due to large variations in the depth, texture and/or water content of soils. Indeed, respective variations of these parameters can result in ambiguous geophysical responses. For example, a decrease in soil water content, which causes an increase in electrical resistivity, may be offset by an increase in soil clay content, inducing a decrease in resistivity. The objective of this study was to improve the continuous characterisation of soils affected by an excess of water by using a combination of geophysical techniques. Three techniques, the radio-magnetotelluric (RMT), the ground penetrating radar (GPR) and the electrostatic quadrupole (ESQP) were implemented along eight representative transects where soils were extensively described. The soil cover shows a succession from downslope to upslope consisting in fibric Fluvisols, gleyic Fluvisols, and Albefluvisols. None of the geophysical methods allows us to distinguish all soil limits and to estimate the geometry of soil horizons. The ESQP discriminates Fluvisols from Albefluvisols, whereas the RMT above all reveals differences in soil material thickness, which do not permit to discriminate between these soils. In complement, the GPR allows the estimation of the geometry of organic horizons and anthropic structures, such as ditches. Finally, the combination of these three techniques allows us to assess the main features of soil spatial distribution in bottomlands. To cite this article: V. Chaplot et al., C. R. Geoscience 336 (2004).     

Reliability Analysis of Quick Sand Condition

First order reliability method (FORM) is generally used for reliability analysis in geotechnical engineering. This article adopts generalized regression neural network (GRNN) based FORM, Gaussian process regression (GPR) based FORM and multivariate adaptive regression spline (MARS) based FORM for reliability analysis of quick sand condition. GRNN is related to the radial basis function (RBF) network. GPR is developed based on probabilistic framework. MARS is a nonparametric regression technique. A comparative study has been carried out between the developed models. The performance of GPR based FORM and MARS based FORM match well with the FORM. This article gives the alternative methods for reliability analysis of quick sand condition.