Natural Water Content Research Articles

Prediction of resilient modulus with pre-post experimental data of undisturbed subgrade soils using machine learning algorithms

The resilient modulus (MR) of subgrade, which shows relationship between stress and unit deformation of a pavement systems under traffic loads, is a design parameter of the pavement structure. Although a cyclic triaxial test apparatus can be used to directly determine the MR of the subgrade in the laboratory, utilizing prediction models based on easily obtainable soil parameters, is a more efficient method when taking time and cost considerations into account. A comprehensive laboratory testing program is designed to create MR prediction models using machine learning (ML) algorithms. 70 undisturbed soil samples are subjected to MR tests, as well as physical and engineering soil properties tests (water content, field density, specific gravity, gradation, consistency limits, unconfined compressive strength, swell pressure, swell percentage). Soil samples are drilled from a highway that has been in operation for over five years.First, a linear model like MLR is used in the study. Next, nonlinear regression models like RF, GBM, LightGBM, CatBoost, and XGBoost algorithms are used. Research findings showed that nonlinear regression models outperformed linear regression models in predicting the MR (R2 > 0.85), with the XGBoost algorithm yielding the best accuracy (R2 = 0.90). Apart from the primary effects such as confining pressure (σ3) and deviatoric stress (σd), it was found that unconfined compressive strength (qu), natural water content (wn), and swelling percentage (SR) are significant parameters in the prediction of MR among all parameters.

Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays

Accurate prediction of the compression index (cc) is essential for geotechnical infrastructure design, especially in clay-rich coastal regions. Traditional methods for determining cc are often time-consuming and inconsistent due to regional variability. This study presents an explainable ensemble learning framework for predicting the cc of clays. Using a comprehensive dataset of 1080 global samples, four key geotechnical input variables—liquid limit (LL), plasticity index (PI), initial void ratio (e0), and natural water content w—were leveraged for accurate cc prediction. Missing data were addressed with K-Nearest Neighbors (KNN) imputation, effectively filling data gaps while preserving the dataset’s distribution characteristics. Ensemble learning techniques, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and a Stacking model, were applied. Among these, the Stacking model demonstrated the highest predictive performance with a Root Mean Squared Error (RMSE) of 0.061, a Mean Absolute Error (MAE) of 0.043, and a Coefficient of Determination (R2) value of 0.848 on the test set. Model interpretability was ensured through SHapley Additive exPlanations (SHAP), with e0 identified as the most influential predictor. The proposed framework significantly improves both prediction accuracy and interpretability, offering a valuable tool to enhance geotechnical design efficiency in coastal and clay-rich environments.

Compaction and strength control method using natural water content on embankment soil from soft mud-rock ground

Compaction and strength control method using natural water content on embankment soil from soft mud-rock ground

Accurate Prediction of Compression Index of Normally Consolidated Soils Using Artificial Neural Networks

The compression index (Cc) serves as a crucial parameter in predicting consolidation settlement in fine-grained soils, representing the slope of the void ratio logarithmic effective stress curve obtained from oedometer tests. However, traditional consolidation testing methods are notably time-consuming, typically spanning a 15-day period for preparation, execution, and parameter calculation, leading to significant delays in civil engineering projects. Therefore, there is an urgent need for effective methodologies to determine consolidation parameters within a shorter timeframe. Although various empirical formulas have been proposed over the years to correlate compressibility with soil parameters, none have reliably predicted the Cc across different datasets. In this study, to overcome this challenge, an alternative approach using artificial neural network (ANN) methodology to predict the compression index of fine-grained soils based on index properties is proposed. For this purpose, an ANN was trained and validated using a dataset consisting of 560 high and low- plasticity soil samples obtained from construction sites in various regions of Turkey over the last forty years, as well as soil borings in Istanbul. The modeling of artificial neural networks was performed using the Regression Learner program, which integrates with the Matlab 2023a software package and offers a user-friendly graphical interface for AI model development without coding. The data set, which was structured as a matrix with dimensions of 458 × 6, included input parameters such as the natural water content, liquid limit, plastic limit, plastic index and initial void ratio, as well as information on the compression index, which was the output variable. The developed ANN model showed an outstanding predictive performance when predicting the output of the test data, achieving an outstanding R2 score of 0.81. This underlines the potential of ANN methodologies to efficiently extract important data with fewer experiments and in less time, and offers promising applications in the field of geotechnical engineering.

Effect of leachate on the geotechnical properties of soils at Gbagede Dumpsite Ilorin, Kwara State Nigeria

The increasing request for space for private buildings was brought about by the utilization of the previous dumpsites. If the issue of leachate infiltration into the soil isn't legitimately controlled it'll lead to future harm in construction works. The objectives are to compare the geotechnical properties of soil of the contaminated and uncontaminated regions region of the dump site and evaluate if the effect of leachate on the geotechnical properties of soil changes with depth. Laboratory soil tests were conducted on the soil samples obtained and compared the effect of these leachates at the dump site. These methods are Natural water content, Bulk Density, Specific Gravity, Shear strength, and Consolidation tests. The soil samples were obtained from the contaminated region, and the uncontaminated region (i.e. at 100 m away from the dumpsite). All soil samples were obtained at depths 0.5m, 1.0m, and 1.5m below the ground level, to know the effect of leachate on the soil at the dumpsite and also to know if the effects of leachate changes with depth as it goes down the soil. The results obtained show that samples at 0.5m and 1m depth have been affected by leachates but the effects are not so significant at 1.5m depth, thereby making the soil at depths 0.5m and 1m unfit for construction purposes. This result was useful to check the land requirement in urban areas and guide the geotechnical engineers when designing and constructing foundations for buildings and other related structures on these types of soils.

P34 Evaluating the adaptation of the amputee skin owing to prosthesis use: a study using in vivo Raman spectroscopy

Abstract Introduction and aims Lower limb prosthesis has proven to be an extremely successful intervention for amputees. However, to achieve adequate function, the device places significant friction and load on the skin, which leads to substantial skin breakdown. Current rehabilitation practices require skin to be ‘trained’ to perform a load-bearing role. Despite this, there are very high injury statistics for skin on the residual limb in the literature, we hypothesize that rehabilitation practices to adapt amputee skin are limited. Methods To interrogate this, we used in vivo Raman spectroscopy to evaluate the lower limbs of 13 unilateral lower limb amputees. This enabled us to assess how key constituents of the stratum corneum (SC) such as natural moisturizing factor (NMF), water and lipid content vary with depth. We also analysed sebum content to quantify cytokines present and performed mechanical characterization of the lower limb to investigate stiffness and friction. Results We found that the Raman spectra of the amputated limb show reduced NMF, lipid content and water content at the top of the SC when compared with the intact limb (P < 0.05). In addition, we found that amputee skin contains higher levels of inflammatory cytokines compared with control skin. We also recorded that skin on the residuum is four times softer than the intact limb, while friction is much higher at 0.5 on the residuum skin, compared with 0.3 on the intact limb. Conclusions Here, we show the adaptations of amputee skin in response to rehabilitation to be minimal. Contrary to the dogma that rehabilitation trains skin to bear load, we found that skin barrier function in amputee skin is impaired, as indicated by altered water and lipid profile in addition to higher baseline inflammation.

Correlations between physical properties and compression index of fine-grained soils based on a large dataset

A comprehensive statistical analysis of the correlations between the physical properties and compression index Cc of fine-grained soils, based on a large dataset from the soil testing results of a railway project in the eastern coastal area of Malaysia, is presented in this paper. More than 1000 soil samples covering a wide range of soil types, with physical properties, such as natural water content, initial void ratio, dry density, liquid limit, plasticity index, and mechanical property-compression index, were analyzed using linear and nonlinear regressions. A very high correlation was observed between the natural moisture content, initial void ratio, dry density, and compression index. A strong correlation was found between the plasticity, liquid, and compression indices. Lower and upper boundaries, confining most of the previous correlations, were found between the natural water content and compression index. A comparison of all the correlations showed the natural water content and initial void ratio as the two best properties for predicting the compression index of fine-grained soils.

Predicting soil parameters in Maroua 1st, Cameroon, using correlations of geophysical and geotechnical data

This study aims to predict specific soil parameters based on quantitative and qualitative correlations between electrical and geotechnical data. A total of 21 geotechnical boreholes followed by sampling, 21 light dynamic penetrometer tests, and 76 vertical electrical sounding surveys were carried out in Maroua 1st. The electrical resistivity data of the soil layers were obtained through 1D and 2D inversions of the ERT surveys. The geotechnical data were obtained from field tests and laboratory experiments. The statistical analysis of the entire dataset indicates that most of the samples are clay formations, as demonstrated by the means of variables and low variance coefficients, enabling qualitative identification. The distribution analysis of the parameters confirms the complexity of the physical characteristics of soils and the measurement procedures for each parameter. The Spearman's rank correlation matrix indicates that these observations can be used to propose predictive models for free swelling, allowable stress, natural water content, and electrical resistivity. Correlations between free swelling (εg) and other variables are not significant, as the degree of significance is lower than the fixed limit, except for models εg - W (R2 = 0.94), εg - ρ (Ώ.m) (R2 = 0.803), and εg - σ adm (R2 = 0.757). Grouping variables in multiple linear regression enables the development of a mathematical model. The predicted values of εg from this model demonstrate good agreement with the measured values for a validity domain of 0–16 %. The soil's natural water content has a strong correlation (R2 = 0.80 and rs = – 0.87) with electrical resistivity, meaning that an increase in water content results in a decrease in electrical resistivity. The permissible stress σadm has a strong correlation (R2 = 0.89 and rs = 0.85) with electrical resistivity. This indicates that changes in soil electrical resistivity are associated with soil stiffness.

Deep Learning Approach on Prediction of Soil Consolidation Characteristics

Artificial neural network models, crucial for accurate predictions, should be meticulously designed for specific problems using deep learning-based algorithms. In this study, we compare four distinct deep learning-based artificial neural network architectures to evaluate their performance in predicting soil consolidation characteristics. The consolidation features of fine-grained soil have a significant impact on the stability of structures, particularly in terms of long-term stability. Precise prediction of soil consolidation under planned structures is vital for effective foundation design. The compression index (Cc) is an important parameter used in predicting consolidation settlement in soils. Therefore, this study examines the use of deep learning techniques, which are types of artificial neural network algorithms with deep layers, in predicting compression index (Cc) in geotechnical engineering. Four neural network models with different architectures and hyperparameters were modeled and evaluated using performance metrics such as mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). The dataset contains 916 samples with variables such as natural water content (w), liquid limit (LL), plasticity index (PI), and compression index (Cc). This approach allows the results of soil consolidation tests to be seen more quickly at less cost, although predictively. The findings demonstrate that deep learning models are an effective tool in predicting consolidation of fine-grained soil and offering significant opportunities for applications in geotechnical engineering. This study contributes to a more accurate prediction of soil consolidation, which is critical for the long-term stability of structural designs.

A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

As large cities are continually being developed around coastal areas, structural damage due to the consolidation settlement of soft ground is becoming more of a problem. Estimating consolidation settlement requires calculating an accurate compressive index through consolidation tests. However, these tests are time-consuming, and there is a risk of the test results becoming compromised while preparing and testing the specimens. Therefore, predicting the compression index based on the results of relatively simple physical property tests enables more reliable and accurate predictions of consolidation settlement by calculating the compression index at multiple points. In this context, this study collected geotechnical data from the soft ground of Korea’s south coast. The collected data were used to construct a dataset for developing a compression index prediction model, and significant influencing factors were identified through Pearson correlation analysis. Simple and multiple linear regression analysis was performed using these factors to derive regression equations, and compression index prediction models were developed by applying machine learning algorithms. The results of deriving the significance of the influencing factors from the developed compression index prediction model showed that natural water content was the most significant factor in predicting the compression index. By collecting a significant amount of high-quality data and using the compression index prediction model and the model construction process proposed in this study, more accurate predictions of the compressive index will be possible in the future.

Use of near-infrared spectroscopy for the online monitoring of natural gas composition (hydrocarbons, water and CO2 content) at high pressure

The natural gas production from Brazilian pre-salt fields imposed new challenges for petrochemical industry. Actual treatment facilities are not adequate for this new scenario and studies have been conducted to apply new adsorbents materials and membranes for water and CO2 removal from natural gas at high pressures. To better develop such investigations, sensors for on-line monitoring of natural gas properties like CO2 and water content are important, since their presence affects the quality of the final product. Near infrared (NIR) spectroscopy associated to chemometric models (partial least squares) were employed for on-line monitoring of representative natural gas systems (methane/CO2, methane/water and methane/CO2/water) and a real natural gas at temperature and pressure ranges from 20 to 60 °C and 10 to 200 bar, respectively, water content up to gas saturation and CO2 content up to 50 wt%. Water solubility values used as reference for NIR Spectrometer calibration in model systems were taken from the literature and for real natural gas calculated with Cubic Plus Association (CPA) Equation of State (EoS), while CO2 content was experimentally controlled aiming to calibrate the chemometric models in the full range of pressure and temperature. Several strategies were adopted for the chemometric model’s development to obtain the best correlation between NIR spectra and experimental data. Results indicate good correlation in both calibration and validation steps attaining linear correlation coefficients (R2) higher than 0.96 for all systems investigated. The proposed methodology is a potential tool for on-line monitoring of natural gas composition, including CO2 and water content, at high-pressures and can be applied at petrochemical industries or in laboratories, dispensing sampling or any sample preparation.

A super-learner machine learning model for a global prediction of compression index in clays

Settlement of structures is determined by the stiffness of the soil where they are built. Compression index (cc) quantifies the compressibility of the soil and is a key parameter in the design of geotechnical structures. To predict the value of cc in clay soils, a global database of more than 1000 data points was collected and analysed. Liquid limit, plasticity index, natural water content, and initial void ratio were considered as predictor variables. A super-learner machine learning model was developed to predict cc from these variables. The model demonstrated a reasonable predictive performance and was subsequently integrated into an online tool. Additionally, four symbolic regression expressions were obtained to relate cc with some of the input variables when not all data are available, providing simple and practical alternatives for cc, estimation. This study provided two major contributions: (1) the non-local nature of the data expands the scope and generalizability of the findings, and (2) the availability of the proposed algorithm through an online application ensures its accessibility for geotechnical engineers, enhancing the work's practical applicability and intrinsic value.

Effect of nano Fe3O4-(CMC-Na)-PFS coagulant on coagulation–ultrafiltration process for treatment of algae -rich water

A novel composite flocculant (PCNF) was prepared using nano Fe3O4 as the condensed core, sodium carboxymethyl cellulose (CMC-Na) as the binder, and polyferric sulfate (PFS). The results indicate that the mass ratio of Fe3O4 to PFS is the key factor determining the flocculation effect of PCNF. When the mass ratio of Fe3O4 to PFS is 1:3, the Zeta potential value is approximately − 1.5 mV, indicating better removal of algae through adsorption electroneutralization. The resulting floc, characterized by negative charge and a loose structure, forms a filter cake layer that is easily backwashed on the surface of the ultrafiltration membrane. This floc also creates strong electrostatic repulsion with the membrane surface. Experimental results demonstrate that the specific flux decreased to 0.69, the total relative pollution was 0.33, and the proportion of irreversible pollution was only 34.65%, all of which were superior to traditional flocculant like PACl. Through microinterface mechanism analysis, it is concluded that the new PCNF flocculant, in combination with algal matter, forms a floc structure critical for mitigating ultrafiltration membrane pollution caused by high algal content in natural surface water.

Influence of time on the small strain shear modulus of an allophanic volcanic ash

The small strain shear modulus is an important parameter in the assessment of soil dynamics problems. Studies on the small strain shear stiffness of volcanic ash remain rare probably because globally they cover just under 1% of the land surface. However, on a regional scale, this figure may be consequential as in the case of Japan, where about one third of its total land surface is covered by andosols. In this research, we aimed at understanding the influence of confinement time, a non-negligible parameter, contingent on the soil type, which needs to be accounted for when assessing the shear modulus. Bender element tests were conducted on allophanic volcanic ash, kuroboku soil sampled from the southern island of Kyushu in Japan. The allophanic ashes present all the characteristics of a non-textbook soil, notably high natural water content, high liquid and plastic limits and high void ratios. From the micrographic images, it was observed that the soil structure consisted of different types of porous particles (allophane, imogolite, volcanic glass and so on) at different internal spatial scales. Strong electrostatic bonding between the allophane particles means that in normal conditions the soil material exist as aggregates. The consequence is that the end of the consolidation stage is reached within a few minutes. Thus, the threshold demarcating the onset of the creep stage is different compared to sedimentary materials or other clayey soils. Based on the test results, empirical equations for predicting the time-dependent behaviour of the shear modulus were proposed.

Determining parameters of peat soil for finite-element analysis using the Cam Clay model

A practical procedure for determining soil parameters when analysing the deformation of peat by finite-element analysis (FEA) using the Cam Clay model is presented. FEA has not been frequently used to predict the deformation of peat soil, even though it can be a powerful prediction tool. The main reason for this is that a practical method for determining parameters has not been established. In this paper, the remarkable correlation between the soil parameters required for FEA using the Cam Clay model and the natural water content (wn) and ignition loss (Li) of peat and organic clay is presented. Then, based on this correlation, a flow chart for determining the soil parameters of peat and organic clay is proposed. Using this chart, it is possible to determine soil parameters directly from triaxial compression, oedometer and other detailed tests or to estimate them from wn, Li and other physical indexes. A trial embankment on peat soil in Hokkaido, Japan, was also created. FEA was performed on the trial embankment using the proposed procedure for determining the soil parameters. The results of the FEA corresponded to the measured settlement behaviour in the trial embankment with an accuracy in the range −4% to +8%. The FEA results also modelled the measured behaviour of pore water pressure and lateral and vertical displacements in the trial embankment very well.

Development of facile field-test paper for rapid determination of iron contamination in natural water of acid sulfate soils

Abstract The iron concentration in natural water of acid sulfate soils is usually determined by complicated methods of SMEWW 3111B:2017 or ISO 6332: 1988 E in the laboratory for acidified water samples at pH ∼2–3. Therefore, the rapid field determination of iron content in natural water is a very useful method because it limits the error and reduces the experimental time for analysis. In this study, we proposed, developed, and tested a rapid colorimetric analysis method based on the chemical reaction of ferric and anion thiocyanate (SCN−) ions for an iron concentration range of 0–100 mg/L with a linear equation of ΔE = 0.5955x + 2.0909 and high correlation coefficient (R2) of 0.9864. The results from 10 water samples by this method were very close to those by flame absorption spectroscopy with a difference of less than 5%. Besides the biggest advantage of rapid site testing, this method also gives quite high accuracy compared to the molecular absorption method of ISO 6332:1988 E. In addition, this new method can quantify Fe3+, Fe2+, and total iron in water, suggesting a very useful and potential method for determining the iron content of natural water from acid sulfate soils.

Initial assessment of soil liquefaction potential for selected sites in Hilla city, Babylon Governorate, Iraq

The current research focused on the use of some geotechnical characteristics for the initial assessment of soil liquefaction at sites in the city of Hilla, Babylon Governorate. Five sites were chosen in the city centre and relatively close to the Shatt Al-Hilla. Geotechnical properties data were used to separate soils that can be classified as liquefiable from those that are not. The criteria used in this study were of two types: the Second Chinese Criteria, which is based on the natural water content and liquid limit; and the Modified Chinese Criteria, which depends on the clay content and liquid limit for the liquefaction potential of the soil. The results of this study showed that in the Second Chinese Criteria the risk of soil increases with an increase in the natural water content and a decrease in the liquid limit; however, in the Modified Chinese Criteria, the liquefaction potential increases with a decrease in the clay content to less than 10% and in the liquid limit to less than or equal to 32%.

The effect of giving cabbage litter on population growth of the african night crawder earthworm (Eudrilus eugeniae)

Cabbage (Brassica oleracea) is one of the vegetable wastes that is often produced by the Amahami market, Bima City. With its easy-to-wilt nature and high water content, cabbage causes it to rot quickly, which will impact environmental problems. Processing market waste certainly requires sustainable management. One model of waste management is to use it as feed. This research aims to determine the effect of giving cabbage waste on the increase in the population of earthworms (Eudrilus eugeniae). The research was carried out in Bima City, Ule Village, Asakota District, Tolotongga Environment. The type of this research is experimental. The research design was a Completely Randomized Design (CRD) with four treatments and ten replications. The parameter observed was the increase in the number of earthworms, analyzed using One Way Anova and followed by Duncan's Multiple Range Test (DMRT). The results of the research showed that giving cabbage waste (Brassica oleracea) as feed had a significant effect (0.009<0.05) on increasing the population of earthworms (Eudrilus eugeniae). The treatment with the highest results was P1 (50 grams of cabbage), with an average number of worms of 367.30.

Assessment of the water content of the soil and utilization of the energy balance model to promote environmentally friendly development of green, low-carbon industries

Abstract The use of energy balance models to study the water content of soil can effectively solve some problems in agricultural production, ecosystem management, and environmental management. The application of the energy balance model requires a large amount of data, and its application in agricultural production is proposed mainly for the prediction and evaluation of the water content of the soil. Various water content values of the soil were determined using an energy balance model and an oven-drying method. The natural water content of the sandy soil determined by oven drying was between 30% and 40% for the sample soil from the same batch, with the second sandy soil sample having the highest value of 39.2% and the 15th sandy soil sample having the lowest value of 30.3%. The natural water content of sandy soils determined using an energy balance model was between 30% and 35%, among which the natural water content of the fifth sandy soil sample was the lowest at 30.12% and the natural water content of the third sandy soil sample was the highest at 34.46%.

Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control

Abstract Currently, information processing of tunnel engineering has mainly adopted conventional mathematical statistics-based methods. Moreover, some nonlinear processing methods are implemented to derive more insights, even though the degree of research is not deep enough. In the research, the rock mechanics test is carried out by drilling a method and taking samples in situ according to the construction technology of tunnels in complex geological conditions and implementing computer information-based methods. Also, rock mechanics tests are carried out in the excavation test area of the flat tunnel. Based on the tests using physical properties, such as deformation, tensile, uniaxial compression, triaxial compression, and longitudinal wave velocity, the physical and mechanical characteristics of the surrounding rock in the tunnel area are comprehensively evaluated, and the stability of the tunnel rock mass is assessed to devise convenient conditions for the subsequent research of the complex geological tunnels based on green excavation. The particle density of sandy mudstone, the bulk density, the porosity, and the natural water content are represented by 2.67 ± 0.61 g/cm3, 2.56 ± 1.42 g/cm3, 7.45%, and 2.86%, respectively, in terms of physical characteristics. These indicate that the sandy mudstone structure is relatively loose, with relatively large pores, micro-fractures, and a high degree of natural water content. The representative deformation test curve of the rock block shows that the ratio of deformation modulus to the compressive strength of the rock block is 650 on average, and Poisson’s ratio ranges from 0.21 to 0.38. These show that sandy mudstone has deformation properties after compression. The tensile strength of sandy mudstone, the shear strength, and c are represented by 1.25 ± 0.23 MPa, f = 1.32, and = 2.35 MPa, respectively. The stated test results can provide a scientific basis for selecting engineering design and its construction parameters in similar areas. In addition, the measurement results show that the surrounding rock will gradually increase, and the tunnel space will gradually become shorter with the increase of buried depth when the gravity stress field occurs. The linear elastic displacement of soft rock is smaller than that of elastic–plastic analysis, and the deeper the tunnel is buried, the larger the displacement difference would be. Therefore, establishing a stable and orderly monitoring and detection system could fully understand the intrinsic law between surrounding rock stress release and surrounding rock pressure and obtain accurate monitoring and measured data to evaluate the grading management standard of a tunnel at the ultimate displacement. In a word, this research provides a feasible idea to study the decision process of green excavation and deformation control technology of tunnels in complex strata.