scholarly journals Neural Network Based Estimation of Service Life of Different Metal Culverts in Arkansas

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Zahid Hossain ◽  
MdAriful Hasan ◽  
Rouzbeh Ghabchi
Keyword(s):  
Neural Network ◽  
Survey Data ◽  
Water Environment ◽  
The United States ◽  
Soil Survey ◽  
United States Department ◽  
Geochemical Properties ◽  
Laboratory Test Results ◽  
Different Types ◽  
Metal Pipes

The Arkansas Department of Transportation (ARDOT) uses different types of metal culverts and cross-drains. Service lives of these culverts are largely influenced by the corrosion of the metals used in these culverts. Corrosion of metallic parts in any soil-water environment is governed by geochemical and electrochemical properties of the soils and waters. Many transportation agencies including ARDOT primarily focus on investigating the physical and mechanical properties of soils rather than their chemical aspects. The main objective of this study is to analyze the geotechnical and geochemical properties of soils in Arkansas to estimate the service lives of different metal pipes in different conditions. Soil resistivity values were predicted after analyzing the United States Department of Agriculture (USDA) soil survey data using neural network (NN) models. The developed NN models were trained and verified by using laboratory test results of soil samples collected from ARDOT, and survey data were obtained from the USDA. The service lives of metal culverts were then estimated based on the predicted soil properties and water quality parameters extracted from the data acquired from the Arkansas Department of Environmental Quality (ADEQ). Finally, Geographic Information System-based corrosion risk maps of three different types of metal pipes were developed based on their estimated service lives. The developed maps will help ARDOT engineers to assess the corrosion potential of the metal pipes before starting the new construction and repair projects and will allow using proper culvert materials to maximize their life spans.

Deep Learning-based Soil Property Prediction for Remediation of Radioactive Contamination in Agriculture

2021 ◽  
Author(s):  
Franck Albinet ◽  
Gerd Dercon ◽  
Tetsuya Eguchi
Keyword(s):  
Deep Learning ◽  
Soil Properties ◽  
Radioactive Contamination ◽  
The United States ◽  
Soil Survey ◽  
United States Department ◽  
Least Squares Regression ◽  
Transfer Factors ◽  
Wide Range ◽  
Plant Transfer

<p>The Joint IAEA/FAO Division of Nuclear Techniques in Food and Agriculture, through its Soil and Water Management & Crop Nutrition Laboratory (SWMCNL), launched in October 2019, a new Coordinated Research Project (D15019) called “Monitoring and Predicting Radionuclide Uptake and Dynamics for Optimizing Remediation of Radioactive Contamination in Agriculture''. Within this context, the high-throughput characterization of soil properties in general and the estimation of soil-to-plant transfer factors of radionuclides are of critical importance.</p><p>For several decades, soil researchers have been successfully using near and mid-infrared spectroscopy (MIRS) techniques to estimate a wide range of soil physical, chemical and biological properties such as carbon (C), Cation Exchange Capacities (CEC), among others. However, models developed were often limited in scope as only small and region-specific MIR spectra libraries of soils were accessible.</p><p>This situation of data scarcity is changing radically today with the availability of large and growing library of MIR-scanned soil samples maintained by the National Soil Survey Center (NSSC) Kellogg Soil Survey Laboratory (KSSL) from the United States Department of Agriculture (USDA-NRCS) and the Global Soil Laboratory Network (GLOSOLAN) initiative of the Food Agency Organization (FAO). As a result, the unprecedented volume of data now available allows soil science researchers to increasingly shift their focus from traditional modeling techniques such as PLSR (Partial Least Squares Regression) to classes of modeling approaches, such as Ensemble Learning or Deep Learning, that have proven to outperform PLSR on most soil properties prediction in a large data regime.</p><p>As part of our research, the opportunity to train higher capacity models on the KSSL large dataset (all soil taxonomic orders included ~ 50K samples) makes it possible to reach a quality of prediction for exchangeable potassium so far unsurpassed with a Residual Prediction Deviation (RPD) around 3. Potassium is known for its difficulty of being predicted but remains extremely important in the context of remediation of radioactive contamination after a nuclear accident. Potassium can help reduce the uptake of radiocaesium by crops, as it competes with radiocaesium in soil-to-plant transfer.</p><p>To ensure informed decision making, we also guarantee that (i) individual predictions uncertainty is estimated (using Monte Carlo Dropout) and (ii) individual predictions can be interpreted (i.e. how much specific MIRS wavenumber regions contribute to the prediction) using methods such as Shapley Additive exPlanations (SHAP) values.</p><p>SWMCNL is now a member of the GLOSOLAN network, which helps enhance the usability of MIRS for soil monitoring worldwide. SWMCNL is further developing training packages on the use of traditional and advanced mathematical techniques to process MIRS data for predicting soil properties. This training package has been tested in October 2020 with thirteen staff members of the FAO/IAEA Laboratories in Seibersdorf, Austria.</p>

Development of a GIS Database of Corrosion Hazards for Use in Pipeline Integrity Assessments

2008 ◽  
Author(s):  
David L. Slayter ◽  
Christopher S. Hitchcock
Keyword(s):  
The United States ◽  
Soil Survey ◽  
National Database ◽  
Soil Conditions ◽  
Pipeline System ◽  
United States Department ◽  
Soil Corrosion ◽  
Gis Database ◽  
Geologic Hazards ◽  
The U.S

Geologic hazards pose a significant threat to pipeline integrity. As an existing pipeline system ages, targeted analysis and positioning of maintenance resources become increasingly important to remediating problem pipeline sections and to ensure timely response to system failures. A geographic information system (GIS) now is commonly used to model pipeline systems. Significant geologic hazards can be mapped and effectively managed in a GIS database as a way to assess risk and to target pipeline remediation resources. In particular, the potential for soil corrosion is a significant threat to pipelines. In the U.S., digital soil maps from the United States Department of Agriculture, Natural Resources Conservation Service (USDA NRCS) have been compiled into the Soil Survey Geographic (SSURGO) database. Numerous soil attributes are stored in the database allowing for a detailed examination of soil characteristics. SSURGO data is largely consistent in quality and geographic extent across the U.S. and is the best available database for a national assessment of soil corrosion potential. We describe the development of a national database for the collection of locations of known corrosion from pipeline system managers. This database can be compared to soil conditions, as noted in SSURGO or other supporting soil data, for the development of a model of soil parameters that may indicate the future potential for buried pipeline corrosion. This paper outlines the need for such a database, significant design considerations and the proposed process for model development.

scholarly journals Integrated characterization of subsurface media from locations up- and down-gradient of a uranium-contaminated aquifer

2019 ◽  
Author(s):  
Ji-Won Moon ◽  
Charles J. Paradis ◽  
Dominique C. Joyner ◽  
Frederick von Netzer ◽  
Erica L. Majumder ◽  
...  
Keyword(s):  
Trace Elements ◽  
Clay Minerals ◽  
Pore Water ◽  
The United States ◽  
Department Of Energy ◽  
United States Department ◽  
Sediment Structure ◽  
Core Sediments ◽  
Oak Ridge ◽  
Geochemical Properties

AbstractThe processing of sediment to accurately characterize the spatially-resolved depth profiles of geophysical and geochemical properties along with signatures of microbial density and activity remains a challenge especially in complex contaminated environments. To provide site assessment for a larger study, we processed cores from two sediment boreholes from background and contaminated core sediments and surrounding groundwater from the ENIGMA Field Research Site at the United States Department of Energy (DOE) Oak Ridge Reservation (ORR). We compared fresh core sediments by depth to capture the changes in sediment structure, sediment minerals, biomass, and pore water geochemistry in terms of major and trace elements including contaminants, cations, anions, and organic acids. Soil porewater samples were matched to groundwater level, flow rate, and preferential flows and compared to homogenized groundwater-only samples from neighboring monitoring wells. This environmental systems approach provided detailed site-specific biogeochemical information from the various properties of subsurface media to reveal the influences of solid, liquid, and gas phases. Groundwater analysis of nearby wells only revealed high sulfate and nitrate concentrations while the same analysis using sediment pore water samples with depth was able to suggest areas high in sulfate- and nitrate- reducing bacteria based on their decreased concentration and production of reduced by-products that could not be seen in the groundwater samples. Positive correlations among porewater content, total organic carbon, trace metals and clay minerals revealed a more complicated relationship among contaminant, sediment texture, groundwater table, and biomass. This suggested that groundwater predominantly flowed through preferential paths with high flux and little mixing with water in the interstices of sediment particles, which could impact microbial activity. The abundant clay minerals with high surface area and high water-holding capacity of micro-pores of the fine clay rich layer suggest suppression of nutrient supply to microbes from the surface. The fluctuating capillary interface had high concentrations of Fe and Mn-oxides combined with trace elements including U, Th, Sr, Ba, Cu, and Co. This suggests the mobility of highly toxic elements, sediment structure, and biogeochemical factors are all linked together to impact microbial communities, emphasizing that solid interfaces play an important role in determining the abundance of bacteria in the sediments.

scholarly journals Synthesis of an Expert System for Assessing the Security of Computer Networks Based on a Fuzzy Neural Network

2020 ◽  
Vol 9 (7) ◽  
pp. 935-939
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Computer Networks ◽  
Fuzzy Neural Network ◽  
Computer Systems ◽  
The United States ◽  
Fuzzy Neural Networks ◽  
Practical Significance ◽  
United States Department ◽  
Fuzzy Neural

The aim of the article is to substantiate the principles of synthesis of an expert system for assessing the security of computer networks based on a fuzzy neural network, and this is an urgent scientific and technical task. Requirements for the operative security assessment of computer networks for data protection are analyzed. It was shown that data security should be provided by the network administrator or persons who need to use special decision support systems in assessing the security of computer networks. To solve this problem, factors that characterize the security of electronic systems, including computer systems, have been identified; the use of fuzzy neural networks is proposed as a mathematical apparatus for constructing an expert system; a technique for the synthesis of a fuzzy neural network for assessing the security of computer networks has been developed; an appropriate fuzzy neural network has been created and tested for adequacy; the prospects of the proposed methodology for creating an expert system for assessing the security of computer systems have been established. The scientific and practical significance of developing such a system lies in the fact that a fuzzy neural network is configured on a specific object in order to quickly determine one of the seven levels of security of computer networks that are used in the United States Department of Defense.

In-Field and Soil-Related Factors that Affect the Presence and Prediction of Glyphosate-Resistant Horseweed (Conyza canadensis) Populations Collected from Indiana Soybean Fields

Weed Science ◽  
2009 ◽  
Vol 57 (3) ◽  
pp. 281-289 ◽  
Author(s):  
Vince M. Davis ◽  
Kevin D. Gibson ◽  
Valerie A. Mock ◽  
William G. Johnson
Keyword(s):  
Survey Data ◽  
Crop Residues ◽  
Field Survey ◽  
Glyphosate Resistance ◽  
The United States ◽  
Soil Survey ◽  
Classification And Regression Tree ◽  
Related Factors ◽  
Relative Significance ◽  
Late Season

Glyphosate-resistant (GR) crops have been rapidly adopted in the United States and the evolution of GR weeds throughout the world has also been on the rise. With experience, weed scientists and crop advisers develop “intuition” on the basis of field history and current in-field conditions for predicting whether escaped weed biotypes may be herbicide resistant. However, there are no previous reports on the association of in-field crop management factors with the prediction of herbicide resistance. By using in-field survey data, we tested the accuracy of predicting glyphosate resistance in late-season horseweed escapes. We hypothesized that glyphosate resistance in late-season horseweed populations found in soybean fields could be predicted using in-field knowledge of crop residues and the appearance and distribution of weeds in the field. Field survey data were collected to determine the distribution and frequency of GR horseweed populations in Indiana soybean fields during September and October of 2003, 2004, and 2005. After the in-field survey, soil properties for sampled field locations were also collected from the U.S. Department of Agriculture Natural Resources Conservation Service Web Soil Survey. GR horseweed predictions used in-field presence of crop residues and the appearance, abundance, and distribution of weeds in the field. The significance of independent data factors were determined by chi-square statistics. The interactions and relative significance of multiple factors were modeled using classification and regression tree analysis. Our results indicated that the most important factor for predicting GR populations was the identification of an altered plant phenotype after injury from POST glyphosate. This was followed by crop rotation, field distribution, and the presence of other escaped weed species in the field in a model with a classification rate of 0.68.

scholarly journals Natural language indexing for pedoinformatics

2021 ◽  
Author(s):  
John Furey ◽  
Austin Davis ◽  
Jennifer Seiter-Moser
Keyword(s):  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Information Structure ◽  
The United States ◽  
Classification Systems ◽  
Soil Survey ◽  
United States Department ◽  
Soil Taxonomy ◽  
Reference Base

The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.

Scanning electron microscopy of asbestos-containing material where the asbestos fibers are considered locked in a binder

1993 ◽  
Vol 51 ◽  
pp. 472-473
Author(s):  
J. R. Millette ◽  
R. S. Brown
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Environmental Protection Agency ◽  
Building Materials ◽  
The United States ◽  
Protection Agency ◽  
Asbestos Fibers ◽  
Different Types ◽  
Binder Material ◽  
Scanning Electron

The United States Environmental Protection Agency (EPA) has labeled as “friable” those building materials that are likely to readily release fibers. Friable materials when dry, can easily be crumbled, pulverized, or reduced to powder using hand pressure. Other asbestos containing building materials (ACBM) where the asbestos fibers are in a matrix of cement or bituminous or resinous binders are considered non-friable. However, when subjected to sanding, grinding, cutting or other forms of abrasion, these non-friable materials are to be treated as friable asbestos material. There has been a hypothesis that all raw asbestos fibers are encapsulated in solvents and binders and are not released as individual fibers if the material is cut or abraded. Examination of a number of different types of non-friable materials under the SEM show that after cutting or abrasion, tuffs or bundles of fibers are evident on the surfaces of the materials. When these tuffs or bundles are examined, they are shown to contain asbestos fibers which are free from binder material. These free fibers may be released into the air upon further cutting or abrasion.

Demonstrating Nutrient Cost Gradients: A Brooklyn Case Study

2014 ◽  
Vol 84 (5-6) ◽  
pp. 244-251 ◽  
Author(s):  
Robert J. Karp ◽  
Gary Wong ◽  
Marguerite Orsi
Keyword(s):  
Low Income ◽  
Energy Content ◽  
Food Selection ◽  
The United States ◽  
United States Department ◽  
Micronutrient Deficiencies ◽  
Caloric Density ◽  
Low Income Families ◽  
The Cost ◽  
The Impact

Abstract. Introduction: Foods dense in micronutrients are generally more expensive than those with higher energy content. These cost-differentials may put low-income families at risk of diminished micronutrient intake. Objectives: We sought to determine differences in the cost for iron, folate, and choline in foods available for purchase in a low-income community when assessed for energy content and serving size. Methods: Sixty-nine foods listed in the menu plans provided by the United States Department of Agriculture (USDA) for low-income families were considered, in 10 domains. The cost and micronutrient content for-energy and per-serving of these foods were determined for the three micronutrients. Exact Kruskal-Wallis tests were used for comparisons of energy costs; Spearman rho tests for comparisons of micronutrient content. Ninety families were interviewed in a pediatric clinic to assess the impact of food cost on food selection. Results: Significant differences between domains were shown for energy density with both cost-for-energy (p < 0.001) and cost-per-serving (p < 0.05) comparisons. All three micronutrient contents were significantly correlated with cost-for-energy (p < 0.01). Both iron and choline contents were significantly correlated with cost-per-serving (p < 0.05). Of the 90 families, 38 (42 %) worried about food costs; 40 (44 %) had chosen foods of high caloric density in response to that fear, and 29 of 40 families experiencing both worry and making such food selection. Conclusion: Adjustments to USDA meal plans using cost-for-energy analysis showed differentials for both energy and micronutrients. These differentials were reduced using cost-per-serving analysis, but were not eliminated. A substantial proportion of low-income families are vulnerable to micronutrient deficiencies.

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