Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests

Beskopylny, Alexey; Lyapin, A. A.; Anysz, Hubert; Meskhi, Besarion; Veremeenko, Andrey; Mozgovoy, Andrey

doi:10.3390/ma13112445

Cited by 43 publications

(26 citation statements)

References 48 publications

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“…The main goal of the article is to find an optimal strategy for GC being in a conflict situation with a client, based on the historical data (real cases) regarding similar investments completed in the last six years. Considering the available data it was decided to use classification and regression trees based on their classification properties [26][27][28][29]. The second tool applied for calculations was artificial neural networks (multilayer perception MLP type).…”

Section: Supporting Toolsmentioning

confidence: 99%

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

2021

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A high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will of the parties involved results in completing a construction object. The cost increase, over the expected level, may cause settlements between parties difficult and lead to disputes that often finish in a court. Such decision of taking a client to a court may influence the future relations with a client, the trademark of the GC, as well as, its finance. To ascertain the correctness of the decision of this kind, the machine learning tools as decision trees (DT) and artificial neural networks (ANN) are applied to predict the result of a dispute. The dataset of about 10 projects completed by an undisclosed contractor is analyzed. Based on that, a much bigger database is simulated for automated classifications onto the following two classes: a dispute won or lost. The accuracy of over 93% is achieved, and the reasoning based on results from DT and ANN is presented and analyzed. The novelty of the article is the usage of in-company data as the independent variables what makes the model tailored for a specific GC. Secondly, the calculation of the risk of wrong decisions based on machine learning tools predictions is introduced and discussed.

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Section: Supporting Toolsmentioning

confidence: 99%

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

2021

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“…Algorithms for artificial intelligence and creation of a neural network were developed in the articles by Beskopylny et al [ 28 ] and Lyapin [ 29 ] based on direct algorithms for solving (Lyapin et al [ 30 ]). The data of the response of the time domain of the multilevel structure to the diagnostic shock load are used as input information.…”

Section: Introductionmentioning

confidence: 99%

Structural Monitoring of Underground Structures in Multi-Layer Media by Dynamic Methods

Lyapin

Beskopylny

Meskhi

2020

Sensors

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The actual problem of structural monitoring and modeling of dynamic response from buried building is considered in the framework of arbitrary dynamic load. The results can be used for designing underground transport constructions, crossings, buried reservoirs and foundations. In existing methods, the system of sensors that register the response to a dynamic action does not allow for effective interpretation of the signal without understanding the dynamic features and resonance phenomena. The analytical and numerical solution of the problem of the dynamics of a buried object in a layered medium is considered. A multilayer half-space is a set of rigidly interconnected layers characterized by elastic properties. At a distance, an arbitrary dynamic load acts on the half-space, which causes oscillations in the embedded structure, and the sensor system registers the response. The problem of assessing the dynamic stress-strain state (DSSS) is solved using Fourier transforms with the principle of limiting absorption. As an example, the behavior of an embedded massive structure of an underground pedestrian crossing under the influence of a dynamic surface source on a multilayer medium is considered, as well as instrumental support of the sensor system. The solution in the form of stress, strain and displacement fields is obtained and compared with the experimental data. The frequency-dependent characteristics of the system are determined and the possibility of determining the DSSS by a shock pulse is shown.

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“…Analysis of literature sources in this area [9,10,11,12,13,14,15], allows us to conclude that it is advisable to use a neural network algorithm to determine the properties of materials in the process of shock indentation. The aim of the study is static processing of data obtained during the experiment, investigation of the dependencies of the material characteristics of metals, selection of optimal parameters of the neural network.…”

Section: Introductionmentioning

confidence: 99%

Analysis of neural network results based on experimental data during indentation

2020

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The article is devoted to the development of machine learning methods for classes of technical problems, including determining the properties of materials. According to the authors, the neural network approximation algorithm is able to take into account the behavior of materials in various experimental conditions. The article provides illustrative examples of how a neural network with a single hidden layer can approximate a function of several variables with a given accuracy. As part of the study, a number of experimental measurements were made. The structure of the neural network and its main components are described.

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Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests

Cited by 43 publications

References 48 publications

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

Structural Monitoring of Underground Structures in Multi-Layer Media by Dynamic Methods

Analysis of neural network results based on experimental data during indentation

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