Determination of Work Zone Capacity Using ELM, MPMR and GPR

2016 ◽  
pp. 93-111 ◽  
Author(s):  
Sangeeta Roy ◽  
J. Jagan ◽  
Pijush Samui
Keyword(s):  
Data Distribution ◽  
Gaussian Process Regression ◽  
Parametric Model ◽  
Work Zone ◽  
Work Intensity ◽  
Lane Width ◽  
Minimax Probability Machine Regression ◽  
Minimax Probability Machine ◽  
Learning Machine

This article examines the capability of Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Gaussian Process Regression (GPR) for determination of Work Zone Capacity. Number of lanes, number of open lanes, work zone layout, length, lane width, percentage trucks, grade, speed, work intensity, darkness factor, and proximity of ramps have been adopted as inputs of ELM, MPMR and GPR. ELM has excellent generalization performance, rapid training speed and little human intervention. MPMR is developed based on the concept of minimax probability machine classification. It does not assume any data distribution. GPR is a probabilistic, and non-parametric model. In GPR, different kinds of prior knowledge can be applied. This article describes a comparative study between the ELM, MPMR and GPR models.

Determination of Bearing Capacity of Shallow Foundation Using Soft Computing

2016 ◽  
pp. 292-328
Author(s):  
Jagan J. ◽  
Swaptik Chowdhury ◽  
Pratik Goyal ◽  
Pijush Samui ◽  
Yıldırım Dalkiliç
Keyword(s):  
Bearing Capacity ◽  
Gaussian Process Regression ◽  
Ultimate Bearing Capacity ◽  
Shallow Foundation ◽  
Successful Implementation ◽  
Important Criterion ◽  
Cohesionless Soils ◽  
Minimax Probability Machine Regression ◽  
Learning Machine

The ultimate bearing capacity is an important criterion for the successful implementation of any geotechnical projects. This chapter studies the feasibility of employing Gaussian process regression (GPR), Extreme learning machine (ELM) and Minimax probability machine regression (MPMR) for prediction of ultimate bearing capacity of shallow foundation based on cohesionless soils. The developed models have been compared on the basis of coefficient of relation (R) values (GPR= 0.9625, ELM= 0.938, MPMR= 0.9625). The results show that MPMR is more efficient tool but the models of GPR and ELM also gives satisfactory results.

Determination of Bearing Capacity of Shallow Foundation Using Soft Computing

2016 ◽  
pp. 1590-1626
Author(s):  
Jagan J. ◽  
Swaptik Chowdhury ◽  
Pratik Goyal ◽  
Pijush Samui ◽  
Yıldırım Dalkiliç
Keyword(s):  
Bearing Capacity ◽  
Gaussian Process Regression ◽  
Ultimate Bearing Capacity ◽  
Shallow Foundation ◽  
Successful Implementation ◽  
Important Criterion ◽  
Cohesionless Soils ◽  
Minimax Probability Machine Regression ◽  
Learning Machine

The ultimate bearing capacity is an important criterion for the successful implementation of any geotechnical projects. This chapter studies the feasibility of employing Gaussian process regression (GPR), Extreme learning machine (ELM) and Minimax probability machine regression (MPMR) for prediction of ultimate bearing capacity of shallow foundation based on cohesionless soils. The developed models have been compared on the basis of coefficient of relation (R) values (GPR= 0.9625, ELM= 0.938, MPMR= 0.9625). The results show that MPMR is more efficient tool but the models of GPR and ELM also gives satisfactory results.

scholarly journals Reliability Analysis of Slope Using MPMR, GRNN and GPR

2016 ◽  
pp. 712-726
Author(s):  
Dhivya Subburaman ◽  
Jagan J. ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Reliability Analysis ◽  
Moment Method ◽  
Gaussian Process Regression ◽  
The Other ◽  
Generalized Regression Neural Network ◽  
First Order Second Moment ◽  
Regression Techniques ◽  
Minimax Probability Machine Regression ◽  
Second Moment Method

First Order Second Moment Method (FOSM) is generally for determination of reliability of slope. This article adopts Minimax Probability Machine Regression (MPMR), Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) for reliability analysis of slope by using FOSM. In this study, an example of soil slope is given regarding how the proposed GPR-based FOSM, MPMR-based FOSM and GRNN-based FOSM analysis can be carried out. GPR, GRNN and MPMR have been used as regression techniques. A comparative study has been carried out between the developed GPR, MPMR and GRNN models. The results show that MPMR gives better performance than the other models.

Reliability Analysis of Slope Using MPMR, GRNN and GPR

2016 ◽  
pp. 208-224
Author(s):  
Dhivya Subburaman ◽  
Jagan J. ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Reliability Analysis ◽  
Moment Method ◽  
Gaussian Process Regression ◽  
The Other ◽  
Generalized Regression Neural Network ◽  
First Order Second Moment ◽  
Regression Techniques ◽  
Minimax Probability Machine Regression ◽  
Second Moment Method

First Order Second Moment Method (FOSM) is generally for determination of reliability of slope. This article adopts Minimax Probability Machine Regression (MPMR), Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) for reliability analysis of slope by using FOSM. In this study, an example of soil slope is given regarding how the proposed GPR-based FOSM, MPMR-based FOSM and GRNN-based FOSM analysis can be carried out. GPR, GRNN and MPMR have been used as regression techniques. A comparative study has been carried out between the developed GPR, MPMR and GRNN models. The results show that MPMR gives better performance than the other models.

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