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.

scholarly journals Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

2021 ◽  
Vol 11 (21) ◽  
pp. 10317
Author(s):  
Mahmood Ahmad ◽  
Feezan Ahmad ◽  
Piotr Wróblewski ◽  
Ramez A. Al-Mansob ◽  
Piotr Olczak ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Bearing Capacity ◽  
Gaussian Process Regression ◽  
Ultimate Bearing Capacity ◽  
Shallow Foundations ◽  
Cohesionless Soil ◽  
Unit Weight ◽  
Cohesionless Soils ◽  
Computing Technique ◽  
Theoretical Approaches

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

Prediction of bearing capacity of H shaped skirted footings on sand using soft computing techniques

2020 ◽  
Vol 2 (103) ◽  
pp. 62-74
Author(s):  
T. Gnananandarao ◽  
V.N. Khatri ◽  
R.K. Dutta
Keyword(s):  
Bearing Capacity ◽  
Soft Computing ◽  
Field Data ◽  
Friction Angle ◽  
Ultimate Bearing Capacity ◽  
Statistical Parameters ◽  
Model Tree ◽  
Soft Computing Techniques ◽  
M5p Model Tree ◽  
Model Equations

Purpose: The present study aims to apply soft computing techniques, Artificial Neural Network (ANN) and M5P model tree, to predict the ultimate bearing capacity of the H plan shaped skirted footing on the sand Design/methodology/approach: A total of 162 laboratory test data for the regular plan shaped (square, circular, rectangular, and strip (up to L/B = 2.5) skirted footing were collected from the literature to develop the soft computing-based models. These models were later modified for the H Plan shaped skirted footing with the introduction of the multiplication factor. The input variables chosen for the regular plan shaped footings were skirt depth to width of the footing ratio (Ds/B), friction angle of the sand (􀉭), the ratio of the interface friction angle-to-friction angle of sand (δ/􀉭), and length-to-width (L/B) ratio of the footing. The output is the bearing capacity ratio (BCR, a ratio of the bearing capacity of the skirted footing to the bearing capacity of un-skirted footing). Findings: Sensitivity analysis was carried out to see the impact of the individual variable on the BCR). The sensitivity results reveal that the skirt depth to width of the footing ratio is the primary variable affecting the BCR. Finally, the performance of the developed soft computing models was assessed using six statistical parameters. The results from the statistical parameters reveal that model developed using ANN was performing superior to the one prepared using M5P model tree technique for the prediction of the ultimate bearing capacity of H plan shaped skirted footing on sand. Research limitations/implications: The model equations are developed with experimental laboratory data. Hence, these equations need further improvement by using field data. However, until now there no field data have been available to include in the present data set. Practical implications: These proposed model equations can be used to predict the bearing capacity of the H-shaped footing with the help of Ds/B, 􀉭, δ/􀉭 and L/B without performing the laboratory experiments. Originality/value: There is no such model equation that was developed so far for the H-shaped skirted footings. Hence, an attempt was made in this article to predict the bearing capacity of the H-shaped footing by using available experimental data with the help of soft computing techniques.

scholarly journals Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu
Keyword(s):  
Support Vector Machine ◽  
Soft Computing ◽  
Prediction Models ◽  
Base Isolation ◽  
Least Square ◽  
Support Vector ◽  
Isolation System ◽  
Inference System ◽  
Comparison Results ◽  
Soft Computing Techniques

Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF) buildings with lead-rubber bearing (LRB) isolation system under ground motion effects. These techniques include least square support vector machine (LSSVM), wavelet neural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) along with wavelet denoising. The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.

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.

Prediction of The Uniaxial Compressive Strength of Rocks Materials

2018 ◽  
pp. 31-96 ◽  
Author(s):  
Nurcihan Ceryan ◽  
Nuray Korkmaz Can
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
P Wave ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Artifical Neural Network ◽  
P Wave Velocity ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Direct And Indirect Methods

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

scholarly journals A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

2020 ◽  
Vol 10 (2) ◽  
pp. 635 ◽  
Author(s):  
Yingli LV ◽  
Qui-Thao Le ◽  
Hoang-Bac Bui ◽  
Xuan-Nam Bui ◽  
Hoang Nguyen ◽  
...  
Keyword(s):  
Soft Computing ◽  
Mean Squared Error ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting ◽  
Soft Computing Techniques ◽  
Residuals Analysis ◽  
Computing Models

In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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 Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

2017 ◽  
Vol 32 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Basant Yadav ◽  
Sudheer Ch ◽  
Shashi Mathur ◽  
Jan Adamowski
Keyword(s):  
Soft Computing ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Support Vector ◽  
Complex Nature ◽  
Data Set ◽  
Groundwater Levels ◽  
Soft Computing Techniques ◽  
Learning Machine ◽  
The Comparative Study

Abstract Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

scholarly journals A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast

2017 ◽  
Vol 26 (4) ◽  
pp. 641-655 ◽  
Author(s):  
Ashutosh Sharma ◽  
Manish Kumar Goyal
Keyword(s):  
Support Vector Regression ◽  
Soft Computing ◽  
Bayesian Regression ◽  
Monthly Rainfall ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Efficiency Coefficient ◽  
Wavelet Regression ◽  
Rainfall Forecast ◽  
Soft Computing Techniques

AbstractRainfall, being one of the most important components of the hydrological cycle, plays an extremely important role in agriculture-based economies like India. This paper presents a comparison between three soft computing techniques, namely Bayesian regression (BR), support vector regression (SVR), and wavelet regression (WR), for monthly rainfall forecast in Assam, India. A WR model is a combination of discrete wavelet transform and linear regression. Monthly rainfall data for 102 years from 1901 to 2002 at 21 stations were used for this study. The performances of different models were evaluated based on the mean absolute error, root mean square error, correlation coefficient, and Nash-Sutcliffe efficiency coefficient. Based on model statistics, WR was found to be the most accurate followed by SVR and BR. The efficiencies for the BR, SVR, and WR models were found to be 32.8%, 52.9%, and 64.03%, respectively. From the spatial analysis of model performances, it was found that the models performed best for the upper Assam region followed by lower, southern, and middle regions, respectively.

scholarly journals Comparison of six different soft computing methods in modeling evaporation in different climates

2016 ◽  
Author(s):  
Lunche Wang ◽  
Ozgur Kisi ◽  
Mohammad Zounemat-Kermani ◽  
Yiqun Gan
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Least Square ◽  
Support Vector ◽  
Climatic Data ◽  
Regression Methods ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Fuzzy Genetic ◽  
Different Climates

Abstract. Evaporation plays important roles in regional water resources management,terrestrial ecological process and regional climate change. This study investigated the abilities of six different soft computing methods, Multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at eight stations in different climates, air temperature (Ta), solar radiation (Rg), sunshine hours (Hs), relative humidity (RH) and wind speed (Ws) during 1961–2000 are used for model development and validation. The first part of applications focused on testing and comparing the model accuracies using different local input combinations. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations, while GRNN model performed better in Tibetan Plateau. The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. Generalized models were also developed and tested with data of eight stations. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ, CQ and HK stations.

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