scholarly journals Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 2261
Author(s):  
Grzegorz Straż ◽  
Artur Borowiec
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Laboratory Tests ◽  
Load Capacity ◽  
Unit Weight ◽  
Organic Soils ◽  
High Quality Research ◽  
Difficult Time ◽  
Interpretation Criteria ◽  
Artificial Neural

The estimation of the unit weight of soil is carried out using laboratory methods; however, it requires high-quality research material in the form of samples with undisturbed structures, the acquisition of which, especially in the case of organic soils, is extremely difficult, time-consuming and expensive. This paper presents a proposal to use artificial neural networks to estimate the unit weight of local organic soils as leading parameters in the process of checking the load capacity of subsoil, under a direct foundation in drained conditions, in accordance with current standards guidelines. The initial recognition of the subsoil, and the locating of organic soils at the Theological and Pastoral Institute in Rzeszow, was carried out using a mechanical cone penetration test (CPTM), using various interpretation criteria, and then, material for laboratory tests was obtained. The analysis of the usefulness of the artificial intelligence method, in this case, was based on data from laboratory tests. Standard multi-layer backpropagation networks were used to predict the soil unit weight based on two leading variables: the organic content LOIT and the natural water content w. The applied neural model provided reliable prediction results, comparable to the standard regression methods.

Artificial neural networks in diagnosis of thyroid function from in vitro laboratory tests

1993 ◽  
Vol 39 (11) ◽  
pp. 2248-2253 ◽  
Author(s):  
P K Sharpe ◽  
H E Solberg ◽  
K Rootwelt ◽  
M Yearworth
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thyroid Function ◽  
Laboratory Tests ◽  
Extreme Values ◽  
Real Life ◽  
Training Data ◽  
Data Set ◽  
Artificial Neural

Abstract We studied the potential benefit of using artificial neural networks (ANNs) for the diagnosis of thyroid function. We examined two types of ANN architecture and assessed their robustness in the face of diagnostic noise. The thyroid function data we used had previously been studied by multivariate statistical methods and a variety of pattern-recognition techniques. The total data set comprised 392 cases that had been classified according to both thyroid function and 19 clinical categories. All cases had a complete set of results of six laboratory tests (total thyroxine, free thyroxine, triiodothyronine, triiodothyronine uptake test, thyrotropin, and thyroxine-binding globulin). This data set was divided into subsets used for training the networks and for testing their performance; the test subsets contained various proportions of cases with diagnostic noise to mimic real-life diagnostic situations. The networks studied were a multilayer perceptron trained by back-propagation, and a learning vector quantization network. The training data subsets were selected according to two strategies: either training data based on cases with extreme values for the laboratory tests with randomly selected nonextreme cases added, or training cases from very pure functional groups. Both network architectures were efficient irrespective of the type of training data. The correct allocation of cases in test data subsets was 96.4-99.7% when extreme values were used for training and 92.7-98.8% when only pure cases were used.

scholarly journals Artificial Neural Networks in Agriculture

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 497
Author(s):  
Sebastian Kujawa ◽  
Gniewko Niedbała
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Transport Processes ◽  
High Quality Research ◽  
Wide Range ◽  
Brain Structure And Function ◽  
Artificial Neural ◽  
And Function

Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the basis of a wide range of independent variables, verification of diseases and pests, intelligent weed control, and classification of the quality of harvested crops. Artificial intelligence methods support decision-making systems in agriculture, help optimize storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management. The inclusion of machine learning methods in the “life cycle of a farm” requires handling large amounts of data collected during the entire growing season and having the appropriate software. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.

scholarly journals Redes neurais artificiais para previsão de capacidade de carga em estacas do tipo hélice contínua / Artificial neural networks for load capacity prediction in continuous flight auger piles

2021 ◽  
Vol 7 (12) ◽  
pp. 112577-112597
Author(s):  
Iago Irteson Pegado Moreira ◽  
Emerson Cordeiro Morais ◽  
Raykleison Igor dos Reis Moraes ◽  
Alex de Jesus Zissou ◽  
Pedro Silvestre da Silva Campos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Load Capacity ◽  
Artificial Neural ◽  
Capacity Prediction

scholarly journals MODELIZACIÓN DE LA RESISTENCIA A LA COMPRESIÓN DEL CONCRETO MEDIANTE REDES NEURONALES ARTIFICIALES

2017 ◽  
Vol 23 (2) ◽  
pp. 11
Author(s):  
Leoncio Luis Acuña Pinaud ◽  
Ana Victoria Torre Carrillo ◽  
Isabel Moromi Nakata ◽  
Pedro Celino Espinoza Haro ◽  
Francisco García Fernández
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Compression Strength ◽  
Production Control ◽  
Load Capacity ◽  
High Capacity ◽  
Physical And Mechanical Properties ◽  
Structural Element ◽  
Artificial Neural

El uso del concreto como elemento estructural va aumentando año tras año. Sin embargo, este producto requiere de unos estrictos controles de calidad sobre sus propiedades mecánicas para el uso como elemento estructural. Este tipo de control implica la existencia de equipos de ensayo con una capacidad de carga de hasta 3.000KN. Sería de gran utilidad para el control de producción la utilización de un método alternativo de gran fiabilidad, que permitiera conocer las propiedades mecánicas a partir de otras propiedades físicas y mecánicas más fáciles de obtener. La alta capacidad de las redes neuronales artificiales (ANN) para modelar los más diversos procesos industriales, las convierte en una herramienta de gran utilidad en el ámbito de la industria del concreto. En este estudio se ha desarrollado una red neuronal para obtener la resistencia a compresión del concreto y se ha modelado dicha propiedad a partir de la composición del concreto y de sus parámetros de fabricación. La red neuronal diseñada, un perceptrón multicapa, ha permitido obtener la resistencia a compresión del concreto con un coeficiente de correlación de 0,97. Esto demuestra la capacidad de las redes neuronales artificiales para obtener la resistencia a compresión del concreto. Palabras clave.-Concreto, Resistencia a compresión, Redes neuronales artificiales. ABSTRACTThe use of concrete as a structural element increases year by year. However, this product needs very stringent control of its mechanical properties in order to be uses as structural element. This type of control requires to have very large testing equipment with a load capacity of up to 3.000KN. Production control would benefit greatly from the use of a highly reliable alternative method that would enable the mechanical properties to be found through more easily obtained physical and mechanical properties. The high capacity of artificial neural networks (ANN) to model a broad range of industrial processes makes them a very useful instrument in the concrete industry. In this study, one neural network was developed to obtain the properties of compressive strength. This property was then modeled though the composition of concrete and manufacturing parameters. The network designed, a multilayer perceptron, allowed the compression strength to be obtained with a regression coefficient of 0,97. This demonstrates the effectiveness of ANN for obtaining the mechanical properties of compression strength of concrete. Keywords.-Concrete, Compression strength, Artificial neural networks.

scholarly journals Artificial neural network model for predicting load capacity of driven piles

2021 ◽  
Vol 10 (1) ◽  
pp. e12210111526
Author(s):  
Alcineide Dutra Pessoa ◽  
Gean Carlos Lopes de Sousa ◽  
Rodrigo da Cruz de Araujo ◽  
Gérson Jacques Miranda dos Anjos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Load Capacity ◽  
Pearson Correlation ◽  
Driven Piles ◽  
Load Tests ◽  
Artificial Neural

In geotechnics, several models, empirical or not, have been proposed for the calculation of load capacity in deep foundations. These models run mainly through physical assumptions and construction of approximations by mathematical models. Artificial Neural Networks (ANN), in addition to other applications, are excellent computational mechanisms that, based on biological neural learning, can perform predictions and approximations of functions. In this work, an application of artificial neural networks is presented. The objective here is to propose a mathematical model based on artificial intelligence focused on Artificial Neural Network (ANN) learning capable of predicting the load capacity for driven piles. The results obtained through the neural network were compared with actual values of load capacities obtained in the field through load tests. For this quantitative comparison, the following metrics have been chosen: Pearson correlation coefficient and mean squared error. The database used to carry out the project consisted of 233 load tests, carried out in diverse cities and different countries, for which load capacity, hammer weight, hammer drop height, pile length, pile diameter and pile penetration per blow values ​​were available. These values have been used as input values in a multilayer perceptron neural network to estimate the load capacities of the respective piles. It has been found that the proposed neural model presented, in general, correlation with field values above 90%, reaching 96% in the best result.

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