Prediction Of Hydraulic Conductivity Research Articles

Development and Application of a New Exponential Model for Hydraulic Conductivity with Depth of Rock Mass

Hydraulic conductivity generally decreases with depth in the Earth’s crust. The hydraulic conductivity–depth relationship has been assessed through mathematical models, enabling predictions of hydraulic conductivity in depths beyond the reach of direct measurements. However, it is observed that beyond a certain depth, hydraulic conductivity tends to stabilize; this phenomenon cannot be effectively characterized by the previous models. Thus, these models may make inaccurate predictions at deeper depths. In this work, we introduce an innovative exponential model to effectively assess the conductivity–depth relationship, particularly addressing the stabilization at greater depths. This model, in comparison with an earlier power-like model, has been applied to a globally sourced dataset encompassing a range of lithologies and geological structures. Results reveal that the proposed exponential model outperforms the power-like model in correctly representing the stabilized conductivity, and it well captures the fast stabilization effect of multiple datasets. Further, the proposed model has been utilized to analyze three distinct groups of datasets, revealing how lithology, geological stabilization, and faults impact the conductivity–depth relationship. The hydraulic conductivity decays to the residual hydraulic conductivity in the order (fast to slow): metamorphic rocks, sandstones, igneous rock, mudstones. The mean hydraulic conductivity in stable regions is roughly an order of magnitude lower than unstable regions. The faults showcase a dual role in both promoting and inhibiting hydraulic conductivity. The new exponential model has been successfully applied to a dataset from a specific engineering site to make predictions, demonstrating its practical usage. In the future, this model may serve as a potential tool for groundwater management, geothermal energy collection, pollutant transport, and other engineering projects.

Prediction of absolute unsaturated hydraulic conductivity – comparison of four different capillary bundle models

Abstract. To model water, solute, and energy transport in porous media, it is essential to have accurate information about the soil hydraulic properties (SHPs), i.e., the water retention curve (WRC) and the soil hydraulic conductivity curve (HCC). It is important to have reliable data to parameterize these models, but equally critical is the selection of appropriate SHP models. While various expressions for the WRC are frequently compared, the capillary conductivity model proposed by Mualem (1976a) is widely used but rarely compared to alternatives. The objective of this study was to compare four different capillary bundle models in terms of their ability to accurately predict the HCC without scaling the conductivity function by a measured conductivity value. The four capillary bundle models include two simple models proposed by Burdine (1953) and Alexander and Skaggs (1986), which assume a bundle of parallel capillaries with tortuous flow paths, and two more sophisticated models based on statistical cut-and-random-rejoin approaches, namely those proposed by Childs and Collis-George (1950) and the aforementioned model of Mualem (1976a). To examine how the choice of the WRC parameterization affects the adequacy of different capillary bundle models, we utilized four different capillary saturation models in combination with each of the conductivity prediction models, resulting in 16 SHP model schemes. All schemes were calibrated using 12 carefully selected data sets that provided water retention and hydraulic conductivity data over a wide saturation range. Subsequently, the calibrated models were tested and rated by their ability to predict the hydraulic conductivity of 23 independent data sets of soils with varying textures. The statistical cut-and-random-rejoin models, particularly the Mualem (1976a) model, outperformed the simpler capillary bundle models in terms of predictive accuracy. This was independent of the specific WRC model used. Our findings suggest that the widespread use of the Mualem model is justified.

Variations, controls and predictions of soil saturated hydraulic conductivity under different land use types in the alpine region of Tibet, China

Understanding the variations and controls of soil saturated hydraulic conductivity (Ks) under different land use types in the alpine region of Tibet is fundamental for predicting Ks in high-altitude regions where sample points are difficult to obtain and is important for establishing the regional soil hydrological model. In this study, soils samples under farmland, forestland, and grassland were collected from an east-west transect in southeastern Tibet, China. Basic soil properties and Ks were measured to analyze the influencing factors of Ks under different land use types and to establish suitable pedotransfer functions (PTFs) for predicting Ks in the alpine region. The average value of Ks followed the decreasing order of grassland (2.33 cm h−1) > farmland (2.12 cm h−1) > forestland (1.59 cm h−1). Ks of grassland (0.25–9.19 cm h−1) decreased gradually with increasing soil depth. Ks of farmland (0.11–8.82 cm h−1) increased initially and then decreased with increasing soil depth, whereas Ks of forestland (0.12–4.46 cm h−1) exhibited the opposite trend. There were significant differences in the main controlling factors of Ks under different land use types. In farmland, soil bulk density (BD), non-capillary porosity (Pn), clay and gravel content (GC) were the main drivers of Ks (P < 0.05). The dominant influencing factors of Ks in forestland were BD and total porosity (Pt) (P < 0.05), while Pn and GC were the main factors controlling Ks in grassland (P < 0.05). Compared with the multiple linear stepwise regression models and published PTFs, the BP neural network transfer model had better prediction accuracy for Ks in the alpine region. Our results not only provide hydraulic parameters for regional hydrological modeling and but also lay a foundation to construct Ks prediction models for other alpine regions in the world.

Prediction of hydraulic conductivity of sodium bentonite GCLs by machine learning approaches

Six machine learning methods (linear regression, logistic regression, extreme gradient boosting (XGBoost), support vector machine, K-nearest neighbours and artificial neural network) were used to predict/classify the hydraulic conductivity of conventional sodium bentonite (Na-B) geosynthetic clay liners (GCLs) to saline solutions or leachates. Data were collected from the literature and randomly divided into two groups – that is, 80% of the data were used to train machine learning models and the rest, 20%, were applied to evaluate model performance. Features that are known to affect the hydraulic conductivity of Na-B GCLs (e.g. mass per unit area of GCLs, monovalent and divalent cations, ionic strength (I), relative abundance of monovalent to divalent cations (RMD), swell index and effective stress) were employed to predict/classify the hydraulic conductivity of Na-B GCLs. Comparative analyses were conducted with seven subsets corresponding to the combination of different features, and the best model was determined through cross-validation. The results showed that XGBoost consistently had the best performance among all methods over all subsets of features for both regression and classification analyses. Subset 4, using the swell index, I, RMD, I 2 × RMD, monovalent cations, divalent cations, effective stress and mass per unit area as features, outperformed all other six subsets in both regression analysis (R 2 = 0.826) and classification analysis (accuracy = 0.887) in the out-of-sample tests.

A Comparison of Saturated Hydraulic Conductivity (Ksat) Estimations from Pedotransfer Functions (PTFs) and Field Observations in Riparian Seasonal Wetlands

Accurate saturated hydraulic conductivity (Ksat) predictions are critical for precise water flow estimations. Pedotransfer functions (PTFs) have been used to estimate Ksat based on soil structural and textural properties. However, PTF accuracy must be validated with observed Ksat values to improve confidence in model predictions. A study was conducted in the seasonal wetlands of a representative mixed land-use watershed in West Virginia (WV), USA. The observed data included soil characteristics and observed piezometric Ksat using slug tests. Soil texture was predominantly sandy, and the observed average Ksat ranged from 35.90 to 169.64 m/d. The average bulk dry density (bdry) increased, while porosity and volumetric water content decreased significantly with a depth to 45 cm (p &lt; 0.05). The degree of saturation varied significantly between monitoring sites (p &lt; 0.05). A Pearson correlation matrix and Principal Component Analysis (PCA) revealed that Ksat was more connected to soil textural properties, specifically clay. Single parameter PTFs that estimated Ksat as a function of clay content performed better (ME = −90.19 m/d, RMSE = 102.87 m/d) than the PTFs that used silt or sand percentages (ME= −96.86 m/d, RMSE = 108.77). However, all five PTFs predicted Ksat with low accuracy (RMSE &gt; 100 m/d), emphasizing the need to calibrate existing PTFs with observed data or develop site-specific PTFs. These results provide valuable insights into Ksat estimation in riparian wetlands of mixed land-use watersheds and are a helpful reference for land managers and future work.

Molecular dynamics simulation of thermal, hydraulic, and mechanical properties of bentonite clay at 298 to 373 K

Bentonite, a fine-grained geologic material rich in smectite clay, is considered for use in the isolation of high-level radioactive waste (HLRW) because of its low hydraulic permeability, high swelling pressure, and geochemical stability. A complicating factor in this application is that heat released by nuclear waste can trigger complex coupled thermal-hydraulic-mechanical-chemical (THMC) phenomena within the barrier. Prediction of these phenomena using large-scale simulators, which typically examine problems on scales of 10−2 to 104 m, is inhibited by insufficient knowledge of the material properties of bentonite and their dependence on temperature. Here, these properties were evaluated using replica-exchange molecular dynamics (REMD) simulations of a clay assemblage containing 27 Na-smectite nanoparticles with full atomistic-level resolution solvated using 187,131 water molecules. The simulations yielded predictions of heat capacity, thermal conductivity, thermal expansivity, hydraulic conductivity, and water and ion diffusivity at temperatures of 298 to 373 K. Results showed that temperature modulates the capacity of clay barriers to transfer heat, fluids, and chemical species to different degrees. Material properties of hydrated smectite predicted on scales of tens of nanometers and nanoseconds were consistent with the properties of bentonite measured on scales of centimeters and days.

Representation of the influence of soil structure on hydraulic conductivity prediction

The significant impact of soil structure on soil hydraulic properties and then on the associated water and solute transport is well recognized. However, existing soil hydraulic models that account for the effect of soil structure often at the cost of overparameterization, preventing further application in simulation of water and solute transport on large scales. In this study, we developed a new model that considers the effects of soil structure near saturation in prediction of hydraulic conductivity while introducing no new free parameters. Testing with 52 soil samples that include different soil types showed that the new model considerably improved the prediction of hydraulic conductivity compared to an earlier version of the model that did not take into account the effects of soil structure. The reported root-mean-square error (RMSE) of the new model is 0.78 cm d−1, much lower than the 0.90 cm d−1 when the earlier version of model is used. When the new model was used for fitting observations, it performed very well, with an RMSE of only 0.23 cm d−1 remained for 52 soil samples. This new soil hydraulic model provides a simple and practical way to incorporate the influence of soil structure near saturation in water and solute transport simulations.

Prediction of hydraulic conductivity of porous granular media by establishment of random forest algorithm

The present study investigates the possibility of using random forest (RF) algorithm for prediction of hydraulic conductivity of coarse-sized granular material. Meanwhile, the conventional soft computing methods named artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) are considered to assess the robustness of the established approach. The characterized properties related to the particle size distribution as well as the air void content are utilized to predict the hydraulic conductivity coefficient. In addition, owing to the direct effect of the applied hydraulic head on the measured values of water flow velocity, the hydraulic gradient is also characterized as an influential parameter. The statistical comparison between the prediction capability of the RF and the results of ANN and ANFIS models corroborates the robustness of the developed procedure based on the machine learning algorithm to indirectly predict the hydraulic conductivity of the provided porous media. The results of parametric analysis mainly demonstrate that the air void content among aggregates is by far the most influential factor affecting the permeability of granular media made of coarse-sized aggregate.

Prediction of the soil saturated hydraulic conductivity in a mining area based on CT scanning technology

In an open-cast mining area, large-scale machinery compaction causes severe soil degradation. Exploring the soil hydraulic characteristics at different bulk densities is of tremendous significance for soil restoration and soil hydrological balance in the reclamation area of the destructed land. Saturated hydraulic conductivity (Ks) is one of the important parameters to characterize soil infiltration capacity. The soil compaction in the mining area significantly affects soil saturated hydraulic conductivity, and its field measurement is very difficult due to the severe soil compaction. Precise prediction of soil saturated hydraulic conductivity is a prerequisite for understanding water movement and solute transport processes. In this study, soil samples taken from the dump site of the Antaibao open-pit coal mine in the Pingshuo mining area of China were prepared in laboratory according to different bulk densities (1.25, 1.3, 1.35, 1.4, 1.45, 1.5, 1.55, 1.6, 1.65, 1.7, 1.75, and 1.8 g cm−3), and the soil saturated water conductivity was measured in laboratory using constant head method. Moreover, the CT scanning technology was used to obtain soil macropore structure, and the fractal dimension of soil macropores was calculated, and the soil saturated water conductivity was predicted based on the fractal dimension. The number of macropores and macroporosity decreased with the increase in the bulk density, and the soil compaction resulted in poor connectivity of the soil macropores. The trend of the soil saturated water conductivity predicted using the CT scanning technique was consistent with that of the measured values in the laboratory under different bulk densities. With the increase of bulk density, the difference between the predicted value and the measured value increased slightly. Overall, a good prediction effect was achieved (R2 > 0.84). The prediction of the soil hydraulic characteristics of compacted soil in mining area based on CT scanning technology was feasible, and it can be applied to the study of practical problems in soil science.

Prediction of hydraulic conductivity based on the soil grain size using supervised committee machine artificial intelligence

Prediction of hydraulic conductivity based on the soil grain size using supervised committee machine artificial intelligence

Hydraulic behaviour of sand-biochar mixtures in water and wastewater treatment applications

• Influence of biochar on hydraulic behaviour of sand-biochar systems were investigated. • Inclusion of dual porosity system provided more accurate prediction against experimental data. • By creating 3D models of biochar-sand system, the interparticle porosity and tortuosity were calculated. • Excluding intraparticle porosity improves the accuracy of hydraulic conductivity prediction calculations. Interest in using biochar in environmental engineering applications has substantially increased with strong evidence emerging for its applicability in water and wastewater treatment, the containment of hazardous gases, carbon dioxide sequestration and the filtration of microplastics. Understanding the hydraulic behaviour of sand-biochar mixtures is critical for designing biochar-based water and wastewater filter systems. We present an investigation on the impact of the biochar microstructure on the hydraulic conductivity of sand-biochar mixtures, focusing on where there are gaps in existing knowledge on how the biochar affects the hydraulic properties. A dual-porosity concept for sand-biochar mixtures is introduced and included in four existing constitutive models for the hydraulic conductivity of soils. We have experimentally investigated the hydraulic properties of sand-biochar mixtures for uniformly graded sand with three representative grain sizes, and one type of hardwood biochar in the UK market. Based on a series of constant-head permeability tests, the hydraulic conductivity of the mixtures with biochar content from 0 to 50% has been measured. We used randomly generated spherical 3D models representing the same particle distribution of sand biochar systems to characterise the biochar's intraparticle porosity and tortuosity. By employing the dual-porosity concept and effective porosity instead of the total porosity, the accuracy of the four models' predictions was considerably improved when compared with the experimental measurements. We show that by excluding biochar's intraparticle porosity, the hydraulic conductivity prediction accuracy can be significantly improved, thus providing a more accurate prediction of the hydraulic behaviour of the soil-biochar filters.

Semi-empirical modelling of hydraulic conductivity of clayey soils exposed to deionized and saline environments

Clay liners are widely used as porous membrane barriers to control solute transport and to prevent the leakage of leachate both in horizontal and vertical flow scenarios, such as the isolated base and ramps of sanitary landfills. Despite the primary importance of saturated hydraulic conductivity in a reliable simulation of fluid flow through clay barriers, there is no model to predict hydraulic conductivity of clayey soils permeated with saline aqueous solutions because most of the current models were developed for pure water. Therefore, the main motivation behind this study is to derive semi-empirical models for simulating the hydraulic conductivity of clayey soils in the presence of arbitrary solute concentrations in addition to deionized water. In order to achieve this goal, a relatively comprehensive dataset of 842 measured hydraulic conductivities was retrieved from the experimental literature, where almost 44% of them are related to certain solute concentrations. Afterwards, two modelling approaches were introduced; the first one is a modified form of Mbonimpa et al.'s (2002) model, in which the constants are adjusted to take into consideration the variations in liquid limit due to a change in solute concentration. A modification term was added to the model for the sake of accuracy. In the second approach, a new form of solute concentration-dependent hydraulic conductivity function was proposed, where special attention was given to void ratio and adaptive liquid limit as effective parameters. The results revealed that hydraulic conductivity predictions could be erroneous if the influence of solute concentrations in permeating fluid is ignored. An error analysis was conducted to examine the models' applicability and deviations. A blind independent set of data, including 132 data points, was also used to verify models. On the other hand, both newly proposed models could predict the hydraulic conductivity for a variety of soils, salt species, and concentrations well. Therefore, the proposed modelling approaches are somehow unique by considering the salinity of the pore fluid in addition to deionized water. More importantly, both models are comprised of easy-to-measure parameters with clear physics-based implications.

A modified Kozeny–Carman equation for predicting saturated hydraulic conductivity of compacted bentonite in confined condition

Kozeny–Carman (KC) equation is a well-known relation between hydraulic conductivity and pore properties in porous material. The applications of KC equation to predicting saturated hydraulic conductivities of sands and non-expansive soils are well documented. However, KC equation is incapable of predicting saturated hydraulic conductivity of expansive soil (e.g. bentonite) well. Based on a new dual-pore system, this study modified KC equation for improving the prediction of saturated hydraulic conductivities of bentonites. In this study, an assumption that inter-layer space (micropore) has limited effect on fluid flow performance of compacted bentonite was adopted. The critical parameters including total porosity and total tortuosity in conventional KC equation were replaced by macroporosity and tortuosity of macropore, respectively. Macroporosity and microporosity were calculated by basal spacing of compacted bentonite, which was estimated by assuming that specific surface area is changeable during saturation process. A comprehensive comparison of bentonite's saturated hydraulic conductivity predictions, including modified KC equation proposed in this study, conventional KC equation, and prediction method based on diffuse double layer (DDL) theory, was carried out. It was found that the predicted saturated hydraulic conductivity of bentonites calculated using modified KC equation fitted the experimental data better than others to a certain extent.

Development of a New Pedotransfer Function Addressing Limitations in Soil Hydraulic Models and Observations

AbstractThe most applied pedotransfer functions (PTFs) often suffer from two main limitations: (a) the soil hydraulic models (SHMs) only account for capillary forces and/or the models show an unrealistic decrease near saturation for fine‐textured soils; (b) the observations of soil hydraulic properties (SHPs) used to generate the PTF generally do not cover very dry conditions. In this paper, we first present a simple method for predicting SHPs in the dry range from soil texture information. Together with measurements that cover only a relatively high matric potential range, the method yielded a good prediction of the complete SHPs from saturation to oven dryness. With this method, we extended a public dataset to cover dry conditions, and then applied it to develop a new PTF for a SHM that accounts for both capillary and adsorption forces and overcomes the unrealistic decrease near saturation for fine‐textured soils. A comparison with other PTF that was developed for the capillary‐based soil hydraulic model showed that the new PTF provided the most accurate predictions of SHPs. It reduced the root‐mean‐square‐error value from 0.055 to 0.045 cm3 cm−3 in predicting water content and from 0.84 to 0.66 log10 (cm d−1) in predicting hydraulic conductivity. We further applied this method to extend an existing capillary‐based PTF to dry conditions. The results showed an improved performance, with reported RMSE reduced from 0.058 (original) to 0.056 (extended) cm3 cm−3 and from 1.43 (original) to 1.20 (extended) log10 (cm d−1) for prediction of water content and hydraulic conductivity, respectively.

Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity

Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water moves through the soil. On the other hand, its measurement is difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for its estimation. Despite significant development over the years, the PTFs showed poor performance in predicting Ks. Using Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTF), i.e. a combination of GA with Multilayer Perceptron (MLP-GA) and Support Vector Machine (SVM-GA), were proposed in this study. We compared the performances of four machine learning algorithms for different sets of predictors. The predictor combination containing sand, clay, Field Capacity, and Wilting Point showed the highest accuracy for all the ML-PTFs. Among the ML-PTFs, the SVM-GA algorithm outperformed the rest of the PTFs. It was noticed that the SVM-GA PTF demonstrated higher efficiency than the MLP-GA algorithm. The reference model for hydraulic conductivity prediction was selected as the SVM-GA PTF paired with the K-5 predictor variables. The proposed PTFs were compared with 160 models from past literature. It was found that the algorithms advocated were an improvement over these PTFs. The current model would help in efficient spatio-temporal measurement of hydraulic conductivity using pre-available databases.

Improved Prediction of Hydraulic Conductivity With a Soil Water Retention Curve That Accounts for Both Capillary and Adsorption Forces

AbstractHydraulic conductivity curves (HCCs) are important inputs in land surface modeling. The general way for predicting an HCC from a soil water retention curve (SWRC) requires an additional input of the saturated hydraulic conductivity. However, the macro effect near saturation often results in difficulty and poor performance when predicting the conductivity. In this paper, we introduce a novel method for predicting the HCC fully from the SWRC, requiring no additional parameters. This is achieved by applying an estimated conductivity (from the SWRC) in the dry range as a new matching point, in addition to modifying an existing HCC model that accounts for both capillary and adsorption forces. Testing with a total of 159 soil samples indicated that the new model substantially improves the prediction of the HCC in compared with the model with the input of the saturated hydraulic conductivity, with the R2 increased from 0.48 to 0.76 and the root‐mean‐square error value reduced from 1.60 to 0.81 cm d−1. The abrupt drop near saturation of the HCC model for soils with small n values close to 1.0, which is a parameter used in shaping the SWRC, was also overcome by forcing the water content be saturated above a fixed potential of −1 cm.

Prediction of saturated hydraulic conductivity of sandy soil using Sauter mean diameter of soil particles

AbstractSaturated hydraulic conductivity of soil, Ksat, is a critical parameter for mathematical modelling of groundwater and soil water flow. Current empirical Ksat models, such as Kozeny–Carman model, Hazen models, or empirical approaches based on soil pedotranfer functions (PTFs) rely on the specific surface area, certain characteristic particle size(s), for example, d5, d10, d15, d50, etc., or the fractionation of sand, silt and clay. In this study, we proposed an alternative modelling framework to estimate Ksat from void ratio and arithmetic mean diameter of soil particles. We suggested to describe the particle size distribution (PSD) using a continuous mathematical distribution function, for example, the lognormal distribution function, and to obtain the Sauter mean diameter (dS) by calculating the moments of the PSD function. The proposed empirical equations were tested against the measured Ksat values for 54 sand samples with available measured PSD and void ratio as reported in Toumpanou et al. and validated using 49 sandy soil samples obtained from the Unsaturated Soil Database (UNSODA). We found that the dS was more relevant to Ksat than the number mean diameter (dN) and mass mean diameter (dM) (the RMSE was 0.486 for Ksat ‐ dN; 0.187 for Ksat ‐ dS; and 0.212 for Ksat ‐ dM, respectively). We also found that the proposed equation with dS had superior performance over the Hazen equation and PTF‐based models. Our results indicated that the Ksat calculated using dS, which was either estimated from discrete experimental data or calculated from a continuous lognormal PSD function, best matched with the measured values for coarse‐textured soils. Future studies should focus on developing empirical relationships for soil water retention curves and other soil properties based on continuous PSD functions.Highlights A modelling framework to estimate Ksat from the void ratio and arithmetic mean diameter is proposed. Sauter mean diameter, dS, can be obtained by calculating the moments of the PSD function. Ksat model using dS performs better than that using dN, dM, d5, d10, or d50 or PTF‐based models.

Prediction of hydraulic conductivity of porous media using a statistical grain-size model

Abstract Hydraulic conductivity (K) estimation of porous media is of great significance in contaminant movement and groundwater investigations. The present study examines the influence of effective grain size (d10) and standard deviation (σ) on the K value of borehole soil samples using 5.08, 10.16, and 15.24 cm diameter permeameters. A statistical grain size model was developed and the feasibility of seven empirical equations was evaluated with the measured K values. The K of soil samples increases with the increase in the d10 grain size and decreases with the increase in the σ value. Evaluation of K using empirical equations establishes that the Hazen equation shows relatively good agreement with the measured K values. The study substantiates the efficacy of the developed model as the Kmodel and Kmeasured based R2 (determination coefficient), MAE (mean absolute error), and RMSE (root mean square error) values are (0.982, 0.007, and 0.008), (0.972, 0.005, and 0.007), (0.953, 0.004, and 0.005) for 5.08, 10.16, and 15.24 cm diameter permeameters respectively. The developed model was validated by assessing its efficiency in the prediction of K values for independent soil samples. The developed model-based K accedes to the precise computation of the aquifer yield and groundwater recharge.

Inverse Modeling of Grout Curtain Hydraulic Conductivity Evolution Considering the Calcium Leaching Effect

The calcium leaching effect inevitably increases the grout curtain hydraulic conductivity. It is diffucult to sample and obtain the leaching-related calculation parameters for deep-buried grout curtains. This study introduced the inversion method into the calcium leaching analysis to get proper leaching-related calculation parameters and accurate results. An inverse analysis model was proposed using the genetic algorithm (GA) and finite element analysis technology to solve the calcium leaching problems. The objective function is constructed using the hydraulic head and leakage quantity time-series measurements, which improves the uniqueness and reliability of the inverse results. The proposed method was applied to the inverse analysis of the hydraulic conductivity evolution of the grout curtain in a concrete dam foundation. The predicted water heads and leakage quantity are consistent with the monitored data, indicating the rationality of this simulation. The grout curtain hydraulic conductivity prediction in 100 years is also presented. The results illustrate the feasibility of the proposed method for determining leaching-related parameters and the hydraulic conductivity prediction in the leaching process.

Unsaturated Hydraulic Conductivity Prediction Using Artificial Intelligence and Multiple Linear Regression Models in Biochar Amended Sandy Clay Loam Soil

Unsaturated Hydraulic Conductivity Prediction Using Artificial Intelligence and Multiple Linear Regression Models in Biochar Amended Sandy Clay Loam Soil