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Development of military vehicles capable of surviving shallow-buried explosive blast is seldom done using full-scale prototype testing because of the associated prohibitively high cost, the destructive nature of testing, and the requirements for large-scale experimental-test facilities and a large crew of engineers committed to the task. Instead, tests of small-scale models are generally employed and the model-based results are scaled up to the full-size vehicle. In these scale-up efforts, various dimensional analyses are used whose establishment and validation requires major experimental testing efforts and different-scale models. The paper aims to discuss these issues.

In the present work, a critical assessment is carried out of some of the most important past efforts aimed at developing the basic dimensional analysis formulation for the problem of impulse loading experienced by target structures (e.g. vehicle hull) due to detonation of explosive charges buried to different depths in sand/soil (of different consistency, porosity, and saturation levels).

It was found that the analysis can be substantially simplified if only the physical parameters associated with first-order effects are retained and if some of the sand/soil parameters are replaced with their counterparts which better reflect the role of soil (via the effects of soil compaction in the region surrounding the explosive and via the effects of sand-overburden stretching and acceleration before the associated sand bubble bursts and venting of the gaseous detonation products takes place). Once the dimensional analysis is reformulated, a variety of experimental results pertaining to the total blast impulse under different soil conditions, charge configurations, charge deployment strategies, and vehicle ground clearances are used to establish the underlying functional relations.

The present work clearly established that due to the non-dimensional nature of the quantities formulated, the established relations can be utilized across different length scales, i.e. although they are obtained using mainly the small-scale model results, they can be applied at the full vehicle length scale.

The purpose of this paper is to explore the variation law between the clay microstructure and macro external force by using soil scanning electron microscope (SEM) images.

First, SEM images of clay were pre-processed by MATLAB, and quantitative statistical parameters such as directional probability entropy, fractal dimension and shape factor are extracted. Second, the distribution force model was proposed, considering that the microscopic parameters of soil particles were independent of each other, and the distribution coefficient was determined according to the analytic hierarchy process (AHP). Then, the fitted formula of quantitative statistical parameters based on the distribution force model was obtained by taking the macroscopic distribution force as independent variable and the microscopic parameters of soil particles as dependent variable. Finally, the correctness of corresponding fitting formula was verified.

The results showed that the change of external consolidation pressure has great influence on the directional probability entropy and fractal dimension, while the shape factor reflecting the regular degree of soil particle shape is less sensitive to the consolidation pressure. The fitting formula has high accuracy, and mostly the R value can reach more than 0.9. All the data have passed the test, which proves that the distribution force model proposed in this paper is rational.

The model can be used to connect the macroscopic stress of soil with the micro-structure deformation of soil particles through mathematical formula, which can provide reference for engineering practice.

An extensive knowledge of soil characteristics and an awareness of the type and amount of pollutants present are essential to evaluate the utilization potential of soil resources for various land uses. On the other hand, for management and decision‐making purpose, there is a need to develop concise indices in order to adequately express the overall quality of soil resources. The aim of this study was to introduce a flexible soil quality index system, mainly based on fertility and the presence of pollutants.

A tree‐structured approach was adopted, leading to a concise final index from various intermediate sub‐indices. More specifically, a number of physical‐chemical‐biological parameters lead to an agronomic quality index (AQI). Another group of parameters referring to polluting substances of various origins are combined to give a multifunctional quality index (MQI). AQI and MQI are then coupled into an overall general quality index. The proposed model was implemented by using a set of data concerning various specific sites throughout Italy.

The proposed methodology proved useful in providing valuable and quick information about the overall soil quality performance and its main sources, and in helping to balance the contrasting needs of obtaining concise quantitative information, on the one hand, and of minimizing the inevitable loss of information inherent in every process of synthesis, on the other hand.

This paper presents an innovative quality index structure for the environmental and multifunctional management of soil, whose main value is related to its flexibility (e.g. different number and kind of parameters, various levels of aggregation) which makes it applicable to various contexts.

Traditional laboratory measurements of soil water diffusivity (D) and soil water retention curve (SWRC) are always time-consuming and labor-intensive. Therefore, this paper aims to present a simple and robust test method for determining D and SWRC without reducing accuracy.

In this study, a D model of unsaturated soil was established based on Gardner–Russo model and then a combination of Gardner–Russo model with one-dimensional horizontal absorption method to obtain n and a parameters of Gardner–Russo model. One-dimensional horizontal absorption experiments on loam, silt loam and sandy clay loam were conducted to obtain the relationships between measured infiltration rate and cumulative infiltration with wetting front distance. Based on the obtained relationships, the measured infiltration data from the one-dimensional horizontal absorption tests were used to calculate n and a parameters and further constructing D and SWRC.

Both the calculated D and SWRC inversed from the infiltration data were in good agreement with the measured ones that obtained from the traditional horizontal absorption method and the centrifuge method, respectively. Error analysis indicated that only the infiltration data are enough to reliably synchronously determine D and SWRC.

A simple and robust method is proposed for synchronous determination of soil water diffusivity and water retention curve.

This paper aims to construct a simplified distributed hydrological model based on the surveyed watershed soil properties database.

The new established model requires fewer parameters to be adjusted than needed by former hydrological models. However, the achieved stream-flow simulation results are similar and comparable to the classic hydrological models, such as the Xinanjiang model and the TOPMODEL.

Good results show that the discharge and the top surface soil moisture can be simultaneously simulated, and that is the exclusive character of this new model. The stream-flow simulation results from two moderate hydrological watershed models show that the daily stream-flow simulation achieved the classic hydrological results shown in the TOPMODEL and Xinanjiang model. The soil moisture validation results show that the modeled watershed scale surface soil moisture has general agreement with the obtained measurements, with a root-mean-square error (RMSE) value of 0.04 (m³/m³) for one of the one-measurement sites and an averaged RMSE of 0.08 (m³/m³) over all measurements.

In this paper, a new simplified distributed hydrological model was constructed.

In the literature, several empirical methods can be found to predict the occurrence of nonlinear soil liquefaction in soil layers. These methods are limited to the seismic conditions and the parameters used in developing the model. This paper seeks to present General Regression Neural Network (GRNN) model that addresses the collective knowledge built in simplified procedure.

The GRNN model incorporates the soil and seismic parameters of the region. It was developed in four phases; identification, collection, implementation, and verification. The data used consisted of 3,895 case records, mostly from the cone penetration test (CPT) results produced from the two major earthquakes that took place in Turkey and Taiwan in 1999. The case records were divided randomly into training, testing and validation datasets. Soil liquefaction decision in terms of seismic demand and seismic capacity is determined by the stress‐based method and strain‐based method, and further tested with the well‐known Chinese criteria.

The results produced by the proposed GRNN model explore effectively the complex relationship between the soil and seismic input parameters and further forecast the liquefaction potential with an overall success ratio of 94 percent. Liquefaction decisions were further validated by the SPT, confirming the viability of the SPT‐to‐CPT data conversion, which is the main limitation of most of the simplified methods.

The proposed GRNN model provides a viable tool to geotechnical engineers to predict seismic condition in sites susceptible to liquefaction. The model can be constantly updated when new data are available, which will improve its predictability.

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.

The dynamic response of the nuclear power plants (NPPs) with pile foundation reinforcement have not yet been systemically investigated in detail. Thus, there is an urgent need to improve evaluation methods for nonlithological foundation reinforcements, as this issue is bound to become an unavoidable task.

A nonlinear seismic wave input method is adopted to consider both a nonlinear viscoelastic artificial boundary and the nonlinear properties of the overburden layer soil. Subsequently, the effects of certain vital parameters on the structural response are analyzed.

A suitable range for the size of the overburden foundation is suggested. Then, when piles are used to reinforce the overburden foundation, the peak frequencies in the floor response spectra (FRS) in the horizontal direction becomes higher (38%). Finally, the Poisson ratio of the foundation soil has a significant influence on the FRS peak frequency in the vertical direction (reduce 35%–48%).

The quantifiable results are performed to demonstrate the seismic responses with respect to key design parameters, including foundational dimensions, the Poisson Ratio of the soil and the depth of the foundation. The results can help guide the development of seismic safety requirements for NPPs.

The purpose of this paper is to show impacts of household wastes handling on some physicochemical parameters of surface water, soil, sediments and borehole water samples in Port Harcourt. Waste generated from the households are indiscriminately dumped on roads, stream channels, bush lands and open spaces thus defacing the landscape of the city, flooding and spreading vector-borne diseases. As a result there is unsustainable and wasteful utilisation of resources which gives rise to pollution of the environment. The research determines the pollution profile of some dumpsites, surface water bodies that act as recipients of household waste from result of analysis of physicochemical parameters.

Major dumpsites in the city were selected for sampling. Three sampling stations at upstream, discharge point and downstream were selected for water and sediments. Water samples were collected in one-litre plastic containers, and then labelled and transported to laboratory for analysis. Sediment samples were collected at a depth of 5-10 m under water and preservation, handling and analysis were based on standard principles and procedures. Soil samples were dug with auger at both top (0-15 cm) and bottom (15-30 cm) in radial coordinates. The soil and sediment samples were first digested using the wet oxidation method before analysis with atomic absorption spectrophotometry.

The physicochemical analysis of surface water samples showed that conductivity, salinity, turbidity, sulphate, phosphate, magnesium, total hardness and lead exceeded the World Health Organization and Nigerian Industrial Standard limits for drinking water qualities at the sampled areas. The same parameters in addition to pH and manganese also exceeded the limits for sediment samples. The soil pH showed acidity at the sampling stations for both top and bottom depths. Conductivity, sulphate, iron, manganese, phosphate and lead also exceeded the FMEnv Guidelines and standards for soil quality. The continuous discharge of household wastes on water and soil resulted in elevated levels of some measured parameters.

The research was limited by funds. The cost of sampling and analysis was enormous and limited the study to parameters that available funds could carry. This factor also stretched the period of study. The non-availability of sponsorship made it difficult to extend the research to soil particle size distribution and the underground water movement of the study areas. Another limitation was logistics as the study covered vast areas of the metropolis demanding steady means of mobility that was not available. Epileptic supply of power delayed the timely delivery of result of analysis.

The results of the analysed physicochemical parameters of water, sediments, soil and borehole water samples indicated spatial variations in their values at the study area, with higher values at the discharge points and downstream than the upstream, while for the borehole water analysis, the effect was more pronounced at stations with appreciable quantity of degradable household wastes. The levels of some physicochemical variables exceeded standard limits as a result of continuous discharge of household wastes .The implication is that household waste handling influenced the physicochemical variables negatively and constant monitoring serves as a useful tool of abatement.

This work is original and has not been published before in any book or journal article locally or internationally. The research related waste handling lifestyles with concentrations of measurable parameters, which is an additional work to what other authors have done. The value is that data generated will be a reference material to other researchers, city planners, government agencies and institutions that are involved with environmental management. Furthermore it has added a new dimension to the discipline of waste management in terms of scope and contribution to knowledge.

The major component of agriculture production includes the type of seed, soil, climatic conditions, irrigation pattern, fertilizer, weed control, and technology used. Soil is one of the prime elements in modern times for agriculture. Soil is also one of the primary and important factors for crop production. The available soil nutrient status and external applications of fertilizers decide the growth of crop productivity (Annoymous, 2017). The upcoming research question that needs to be addressed is What is the application of soil data on soil health management for sustaining agriculture? Driven by the need, the aim of the present study is (a) to explore the soil parameters of a district, (b) compare the values with the standards, and (c) pave a way for mapping the crops with suitability of soil health. This study will not only be beneficial for the district to take appropriate steps to improve the soil health but also would help in understanding the causal relationship among soil health parameters, cropping pattern, and crop productivity.