scholarly journals Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 486
Author(s):  
Manish Kumar ◽  
Abidhan Bardhan ◽  
Pijush Samui ◽  
Jong Wan Hu ◽  
Mosbeh R. Kaloop
Keyword(s):  
Comparative Study ◽  
Bearing Capacity ◽  
Reliability Analysis ◽  
Soft Computing ◽  
Pile Foundation ◽  
Group Method ◽  
Anfis Model ◽  
Inference System ◽  
Reliability Indices ◽  
Soft Computing Techniques

Uncertainty and variability are inherent to pile design and consequently, there have been considerable researches in quantifying the reliability or probability of failure of structures. This paper aims at examining and comparing the applicability and adaptability of Minimax Probability Machine Regression (MPMR), Emotional Neural Network (ENN), Group Method of Data Handling (GMDH), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the reliability analysis of pile embedded in cohesionless soil and proposes an AI-based prediction method for bearing capacity of pile foundation. To ascertain the homogeneity and distribution of the datasets, Mann–Whitney U (M–W) and Anderson–Darling (AD) tests are carried out, respectively. The performance of the developed soft computing models is ascertained using various statistical parameters. A comparative study is implemented among reliability indices of the proposed models by employing First Order Second Moment Method (FOSM). The results of FOSM showed that the ANFIS approach outperformed other models for reliability analysis of bearing capacity of pile and ENN is the worst performing model. The value of R2 for all the developed models is close to 1. The best RMSE value is achieved for the training phase of the ANFIS model (0 in training and 2.13 in testing) and the poorest for the ENN (2.03 in training and 31.24 in testing) model. Based on the experimental results of reliability indices, the developed ANFIS model is found to be very close to that computed from the original data.

scholarly journals Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Elhadi K. Mustafa ◽  
Yungang Co ◽  
Guoxiang Liu ◽  
Mosbeh R. Kaloop ◽  
Ashraf A. Beshr ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Soft Computing ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Multivariate Adaptive Regression Splines ◽  
Coefficient Of Determination ◽  
Anfis Model ◽  
Inference System ◽  
Beijing Area ◽  
Soft Computing Techniques

The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

scholarly journals Prediction of Ultimate Bearing Capacity of Cohesionless Soils Using Soft Computing Techniques

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
S. Adarsh ◽  
R. Dhanya ◽  
G. Krishna ◽  
R. Merlin ◽  
J. Tina
Keyword(s):  
Bearing Capacity ◽  
Soft Computing ◽  
Ultimate Bearing Capacity ◽  
Support Vector ◽  
Width Ratio ◽  
Cohesionless Soils ◽  
Inference System ◽  
Theoretical Approaches ◽  
Soft Computing Techniques ◽  
Gp Model

This study examines the potential of two soft computing techniques, namely, support vector machines (SVMs) and genetic programming (GP), to predict ultimate bearing capacity of cohesionless soils beneath shallow foundations. The width of footing (), depth of footing (), the length-to-width ratio () of footings, density of soil ( or ), angle of internal friction (), and so forth were used as model input parameters to predict ultimate bearing capacity (). The results of present models were compared with those obtained by three theoretical approaches, artificial neural networks (ANNs), and fuzzy inference system (FIS) reported in the literature. The statistical evaluation of results shows that the presently applied paradigms are better than the theoretical approaches and are competing well with the other soft computing techniques. The performance evaluation of GP model results based on multiple error criteria confirms that GP is very efficient in accurate prediction of ultimate bearing capacity cohesionless soils when compared with other models considered in this study.

Soft-Computing Control of Ball and Beam System

2018 ◽  
Vol 9 (4) ◽  
pp. 1-21 ◽  
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Learning Ability ◽  
Gaussian Shape ◽  
Inference System ◽  
Neural Controllers ◽  
Beam System ◽  
Soft Computing Techniques ◽  
Ball And Beam System

This article derives a mathematical model and compares different soft-computing techniques for control of a highly dynamic ball and beam system. The techniques which were incorporated for control of proposed system were fuzzy logic, proportional-integral-derivative (PID), adaptive neuro fuzzy inference system (ANFIS) and neural networks. Initially, a fuzzy controller has been developed using seven gaussian shape membership functions. The article illustrates briefly both learning ability and parameter estimation properties of ANFIS and neural controllers. The results of PID controller were collected and used for training of ANFIS and Neural controllers. A Matlab simulink model of a ball and beam system has been derived for simulating and comparing different controllers. The performances of controllers were measured and compared in terms of settling time and steady state error. Simulation results proved the superiority of ANFIS over other control techniques.

scholarly journals DYNAMICAL CONTACT PARAMETER IDENTIFICATION OF SPINDLE-HOLDER-TOOL ASSEMBLIES USING SOFT COMPUTING TECHNIQUES

2020 ◽  
pp. 565
Author(s):  
Đorđe Čiča ◽  
Milan Zeljković ◽  
Saša Tešić
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Contact Dynamics ◽  
Accurate Identification ◽  
Inference System ◽  
Contact Parameter ◽  
Soft Computing Techniques ◽  
Cutting Processes ◽  
Contact Parameters ◽  
Tool Assembly

In industry, the capability to predict the tool point frequency response function (FRF) is an essential matter in order to ensure the stability of cutting processes. Fast and accurate identification of contact parameters in spindle-holder-tool assemblies is very important issue in machining dynamics analysis. This work is an attempt to illustrate the utility of soft computing techniques in identification and prediction contact parameters of spindle-holder-tool assemblies. In this paper, three soft computing techniques, namely, genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) were used for identification of contact dynamics in spindle-holder-tool assemblies. In order to verify the proposed identification approaches, numerical and experimental analysis of the spindle-holder-tool assembly was carried out and the results are presented. Finally, a model based on the adaptive neural fuzzy inference system (ANFIS) was used to predict the dynamical contact parameters at the holder-tool interface of a spindle-holder-tool assembly. Accuracy and performance of the ANFIS model has been found to be satisfactory while validated with experimental results.

Cracked Rotor Diagnostics Using Soft Computing

2007 ◽  
Author(s):  
B. Samanta ◽  
C. Nataraj
Keyword(s):  
Soft Computing ◽  
Transient Response ◽  
Fuzzy Inference ◽  
Cracked Rotor ◽  
Inference System ◽  
Time Frequency ◽  
Wavelet Energy ◽  
Soft Computing Techniques ◽  
Detection And Diagnosis ◽  
Run Up

A study is presented for detection and diagnostics of cracked rotors using soft computing techniques like adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and genetic algorithms (GA). A simple model for a cracked rotor is used to simulate its transient response during startup for different levels of cracks. The transient response is processed through continuous wavelet transform (CWT) to extract time-frequency features for the normal and cracked conditions of the rotor. Several features including the wavelet energy distributions and the grey moment vectors (GMV) of the CWT scalograms are used as inputs for diagnosis of crack level. The parameters of the classifiers, ANFIS and ANN, along with the features from wavelet energy distribution and grey moment vectors are selected using GA maximizing the diagnostic success. The classifiers are trained with a subset of the data with known crack levels and tested using the other set of data (testing data), not used in training. The procedure is illustrated using the simulation data of a simple de Laval rotor with a ‘breathing’ crack for different crack levels during run-up through its critical speed. A comparison of diagnostic performance for the classifiers is presented. Results show the effectiveness of the proposed approach in detection and diagnosis of cracked rotors.

Advanced soft computing techniques for predicting soil compression coefficient in engineering project: a comparative study

2019 ◽  
Vol 36 (4) ◽  
pp. 1405-1416 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Pijush Samui ◽  
Deepak Kumar ◽  
Anshuman Singh ◽  
Nhat-Duc Hoang ◽  
...  
Keyword(s):  
Comparative Study ◽  
Soft Computing ◽  
Engineering Project ◽  
Compression Coefficient ◽  
Soft Computing Techniques ◽  
Soil Compression
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