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The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

This paper aims to analyze soil electrical properties based on fractional calculus theory due to the fact that the frequency dependence of soil electrical parameters at high frequencies exhibits a fractional effect. In addition, for the fractional-order formulation, this paper aims to provide a more accurate numerical algorithm for solving the fractional differential equations.

This paper analyzes the frequency-dependence of soil electrical properties based on fractional calculus theory. A collocation method based on the Puiseux series is proposed to solve fractional differential equations.

The algorithm proposed in this paper can be used to solve fractional differential equations of arbitrary order, especially for 0.5th-order equations, obtaining accurate numerical solutions. Calculating the impact response of the grounding electrode based on the fractional calculus theory can obtain a more accurate result.

This paper proposes an algorithm for solving fractional differential equations of arbitrary order, especially for 0.5th-order equations. Using fractional calculus theory to analyze the frequency-dependent effect of soil electrical properties, provides a new idea for ground-related transient calculation.

This paper aims to focus on the inclusion of the frequency behavior of grounding system effect on surge arrester (SA) model parameters’ estimation.

The grounding system impedance and its frequency behavior are the factors that have influence on the SA performance. Up to now, the grounding system impedance effect and the frequency behavior of the soil parameters have not been studied for the estimation of the parameters of the SA frequency-dependent model. In this paper, the grounding system’s influence on the SA dynamic model has been simulated for rod- and counterpoise-shaped electrodes. Particle swarm optimization with a grey wolf optimization algorithm has been implemented as an optimization algorithm to adjust the parameters of the SA dynamic model.

The results show that the frequency behavior of the grounding impedance and soil electrical parameters can impress the optimum parameters of the SA frequency-dependent model and should be considered for more reliable results. Also, the results evidence that the proposed optimization method provides more accurate results compared to other optimization methods.

To the best of the authors’ knowledge, this work is one of the first attempts to investigate the effect of frequency grounding system on SA frequency-dependent model parameters.

The purpose of this paper is to investigate the simultaneous effects of ionization and dispersion of soil on the impulse behavior of grounding electrodes under first and subsequent stroke currents.

A recently introduced technique called improved multi-conductor transmission line (MTL) is simplified for grounding electrodes buried in both-affected soils.

The simulation results show that including the two effects simultaneously in highly resistive soils under high-valued subsequent stroke current is recommended. Otherwise, simultaneous effects can be disregard.

To the best of the authors’ knowledge, there is no research on sensitivity analyses for the simultaneous inclusion of the two effects on the effective length and the induced voltage on the soil surface. To this end, the simplified MTL is applied to the grounding electrodes. The simulation results show that the computational efficiency in comparison with previous methods is, first, considerably increased. Second, the simultaneous effects result in decreasing the soil surface voltage with respect to situations where either ionization or dispersion is taken into account (single-affected soils). In other words, the performance of grounding systems is improved. Third, the effective length in both-affected soil is has a middle value with respect to the single-affected soil. Such findings practically and financially are of importance.

The purpose of this paper is to investigate the ionization and dispersion effects in combination with the inhomogeneity of soil simultaneously on the effective lengths of counterpoise wires.

Improved multi-conductor transmission line model is used for computing effective length of counterpoise wires considering all aspects of soils.

The simulation results show that the ionization and dispersion effects simultaneously results in placing the effective length between situations where only one effect is considered. Also, predicting formulae for effective length of counterpoise wires considering all effects are proposed.

A sensitivity analysis on the effective lengths of counterpoise wires considering all aspects of soils is carried out.

This paper sets out to detect and characterize electric fields in the ground (such as stray current fields) using a tandem time/frequency method of signal analysis.

Results were obtained from investigations performed in the presence of a generated electric field with controlled variable characteristics, and in the presence of an electric field generated by a tramline. The analysis of measurement registers was performed using Short‐Time Fourier Transformation. The results were presented in the form of spectrograms, which illustrate changes in the spectral power density of the measured signal versus time.

Tandem time/frequency analysis reveals the random or deterministic character of the electric field, enabling its complete time/frequency characteristics to be obtained. Such information is inaccessible using exclusively the frequency analysis methods that utilize classical Fourier transformations. Moreover, an analysis of the spectral power density distribution of the signals in three directions on the ground surface makes it possible to define the localization of the field source.

Analysis methods for electric fields in the ground should be adapted to the evaluation of non‐stationary signals because the stray currents are of this type. Such a possibility is given by combined analysis in the domains of time and frequency. This method can be used as complementary to applied measurement techniques of stray current interference.

The method of electric field detection and characterization, as related to stray currents, previously has not been presented in the literature. This method of signal analysis may be adopted for other investigations that are reliant on the registration of voltages or potentials characterized by arbitrary frequencies.

This paper finds the returns from soil conservation practices and examines whether the welfare implications of adopting the conservation practices are heterogeneous across the farming groups in Indian agriculture.

The study uses an endogenous switching regression (ESR) method on the data collected from the 77th round of National Sample Survey (2019–21) to quantify the returns from adopting soil conservation practices.

It finds that farmers adopting soil health conservation practices would have reduced their crop yield by 13% if they did not implement them. Similarly, smallholders who have not adopted soil health management practices would have increased crop yield by 16% if they had adopted the practices. The authors also observed that the returns from adopting soil health management practices vary across farming groups, where marginal and large farms tend to gain higher yields. Finally, the authors find that regardless of farm size, smallholders who did not adopt soil health management practices would benefit from adopting these with increased crop yields of 29%–31%.

More data could have been better for drawing policy implications, since the number of soil card users are relatively less.

This research work uses nationally representative data, which is first in nature on this very aspect.

The corrosivity and competence of soils within Uburu and Okposi areas of the Southern Benue Trough, Nigeria, were evaluated using the electrical resistivity method. This paper aims to provide information that will aid pre-design of subsurface iron/steel pipe networks for distribution of pipe-borne water and construction of subsurface structures for agricultural and environmental purposes.

In total, 22 vertical electrical soundings (VES) in the Schlumberger configuration were acquired with Allied Ohmega™ Terrameter with a maximum half current (AB/2) electrode spacing of 200 m. Layer parameters were determined using partial curve matching techniques, using the Schlumberger master curves, while processing and modelling were done with the IPI2win™ software. The VES results were interpreted qualitatively and quantitatively to obtain various curve types and layer parameters, respectively, which were used to categorize the area into different competence and corrosivity zones. The first layer isoresistivity and competence maps were used to delineate four zones (A,B,C and D) with varying apparent resistivities and competences.

Incompetent soils with resistivity values ranging from 24.3-88.7 Om are found in Zone A. The soils in Zone A are mainly expansive clays which swell on absorption of water. Zone B contains moderately competent soils with resistivity values ranging from 273-308.6 Om, while Zones C and D are underlain by sandstones and contain competent to highly competent soils with resistivity values ranging from 511-750 Om and 835-1,525 Om, respectively. Zone E contains highly corrosive (24.3 Om) to mildly corrosive (102 Om) soils; Zone F contains soils that are essentially non-corrosive with resistivity values ranging from 271-1,525 Om, while the corrosivity of soils within Zone G varies from corrosive to mildly corrosive, with resistivity values ranging from 44.3-114 Om.

Some of the areas are not accessible because of community restrictions.

These findings are essentially very significant and should be taken into consideration when materials that are susceptible to corrosion are being considered for engineering, agricultural and environmental purposes in the area.

The findings will aid water resource planners and developers on how to protect metal pipes from corrosion, when used for water reticulation and agricultural purposes.

This paper fulfils an identified need to study the corrosivity of soils in the study area with a view to providing adequate protection to metal objects when being considered for water reticulation for domestic and agricultural purposes in the area.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

The purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.

This paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).

By applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highest R² value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highest R² with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.

As land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.