Soft computing based prediction of friction angle of clay

2020 ◽  
Vol 2 (104) ◽  
pp. 58-68
Author(s):  
R.K. Dutta ◽  
T. Gnananandarao ◽  
S. Ladol
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Friction Angle ◽  
Liquid Limit ◽  
Sand Content ◽  
Plastic Limit ◽  
Silt Content ◽  
Soft Computing Techniques

Purpose: This article uses soft computing-based techniques to elaborate a study on the prediction of the friction angle of clay. Design/methodology/approach: A total of 30 data points were collected from the literature to predict the friction angle of the clay. To achieve the friction angle, the independent parameters sand content, silt content, plastic limit and liquid limit were used in the soft computing techniques such as artificial neural networks, M5P model tree and multi regression analysis. Findings: The major findings from this study are that the artificial neural networks are predicting the friction angle of the clay accurately than the M5P model and multi regression analysis. The sensitivity analysis reveals that the clay content is the major influencing independent parameter to predict the friction angle of the clay followed by sand content, liquid limit and plastic limit. Research limitations/implications: The proposed expressions can used to predict the friction angle of the clay accurately but can be further improved using large data for a wider range of applications. Practical implications: The proposed equations can be used to calculate the friction angle of the clay based on sand content, silt content, plastic limit and liquid limit. Originality/value: There is no such expression available in the literature based on soft computing techniques to calculate the friction angle of the clay.

A Soft Computing Overview

2010 ◽  
pp. 1-11
Author(s):  
Marcos Gestal ◽  
Daniel Rivero
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Computing Methods ◽  
Testing System ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Computation Algorithms ◽  
Brain Behavior ◽  
The Brain

Nature has proved to be the best testing system, where we can analyze the effectiveness of any method of solving problems. It provides one of the most complex problems to be resolved: the survival. Analyzing how the species behave to achieve that survival, soft computing methods try to mimic this behavior to provide meaningful solutions to diverse problems. This chapter offers an introduction the fundamentals that the different soft computing techniques translate from Nature. It includes an approach of the brain behavior (Artificial Neural Networks) or the evolution ideas taken from Darwin’ laws (Evolutionary Computation algorithms).

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

Intelligent Biometric System Using Soft Computing Tools

2011 ◽  
pp. 259-276
Author(s):  
Anupam Shukla ◽  
Ritu Tiwari ◽  
Chandra Prakash Rathore
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Fuzzy System ◽  
Person Identification ◽  
Security Applications ◽  
Neuro Fuzzy ◽  
Biometric Systems ◽  
Physical Attributes ◽  
Novel Concept

Biometric Systems verify the identity of a claimant based on the person’s physical attributes, such as voice, face or fingerprints. Its application areas include security applications, forensic work, law enforcement applications etc. This work presents a novel concept of applying Soft Computing Tools, namely Artificial Neural Networks and Neuro-Fuzzy System, for person identification using speech and facial features. The work is divided in four cases, which are Person Identification using speech biometrics, facial biometrics, fusion of speech and facial biometrics and finally fusion of optimized speech and facial biometrics.

scholarly journals Prediction of Consolidation Characteristics from Index Properties

2018 ◽  
Vol 65 ◽  
pp. 06004
Author(s):  
Kok Shien Ng ◽  
Yee Ming Chew ◽  
Nur Izzati Ahmad Lazim
Keyword(s):  
Regression Analysis ◽  
Cohesive Soil ◽  
Liquid Limit ◽  
Index Properties ◽  
Compression Index ◽  
Plastic Limit ◽  
Considerable Time ◽  
Coefficient Of Consolidation ◽  
Good Relationship ◽  
Two Parameters

Compression index and coefficient of consolidation are two most important parameters in obtaining the consolidation characteristics of cohesive soil. Considerable time and effort are required to obtain these parameters from the oedometer test. Therefore, this study aims to correlate these two parameters with the index properties. Five remoulded samples are tested for their physical properties as well as their consolidation characteristics. The results show good relationship was obtained for the liquid limit and the compression index while the coefficient of consolidation is best correlated with the plastic limit. Multiple regression analysis was performed to improve the prediction. Liquid limit is best coupled with specific gravity to estimate the compression index while plastic limit and plastic index can be used to best predict the coefficient of consolidation.

Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samia Chebira ◽  
Noureddine Bourmada ◽  
Abdelali Boughaba ◽  
Mebarek Djebabra
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Real Time ◽  
Diagnostic Model ◽  
Operating Pressure ◽  
Content Type ◽  
Soft Computing Techniques ◽  
Blowout Preventer ◽  
Artificial Neural

PurposeThe increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.Design/methodology/approachThe starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.FindingsThe performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.Originality/valueThe performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

scholarly journals Towards Robustness in Neural Network Based Fault Diagnosis

2008 ◽  
Vol 18 (4) ◽  
pp. 443-454 ◽  
Author(s):  
Krzysztof Patan ◽  
Marcin Witczak ◽  
Józef Korbicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Analytical Techniques ◽  
Industrial Applications ◽  
Nonlinear Behaviour ◽  
Design Problems ◽  
Modelling Uncertainty ◽  
Soft Computing Techniques

Towards Robustness in Neural Network Based Fault DiagnosisChallenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.

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