The Experts below are selected from a list of 407769 Experts worldwide ranked by ideXlab platform
Dikai Liu - One of the best experts on this subject based on the ideXlab platform.
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Comparative study of artificial neural networks and Multiple Regression Analysis for predicting hoisting times of tower cranes
Building and Environment, 2001Co-Authors: Arthur W. T. Leung, Chi Ming Tam, Dikai LiuAbstract:Abstract This paper aims to develop a quantitative model for predicting the hoisting times of tower cranes for public housing construction using artificial neural network and Multiple Regression Analysis. Firstly, based on data collected from crane operators and site managers in seven construction sites, the basic factors affecting the hoisting times for tower cranes are identified. Then, artificial neural networks (ANN) and the Multiple Regression Analysis (MRA) are used to model the hoisting time, and from the results, the neural network model and the Multiple Regression model of hoisting time are established. The modeling methods and procedures are explained. These two kinds of models are then verified by data obtained from an independent site, and the predictive behaviors of the two kinds of models are compared and analyzed. Furthermore, the predictive behaviors of the neural network model are also investigated by a sensitivity Analysis. Finally, the modeling methods, predictive behaviors and the advantages of each model are discussed.
Rajaa Alqudah - One of the best experts on this subject based on the ideXlab platform.
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Modeling Blanking Process Using Multiple Regression Analysis and Artificial Neural Networks
Journal of Materials Engineering and Performance, 2012Co-Authors: Emad S. Al-momani, Ahmad T. Mayyas, Ibrahim Rawabdeh, Rajaa AlqudahAbstract:The design of blanking processes requires the availability of a procedure able to deal with both tooling and mechanical properties of the workpiece material (blank thickness, hardness, ductility, etc.). This research presents the development and comparison of two models to predict the quality of the blanked edge represented by burrs height, the first model is an artificial neural network (ANN) based, while the second model is a Multiple Regression Analysis (MRA) based. Finite Element modeling of the blanking process was used to generate the data for both models. Both ANN and MRA are able to give good prediction results, however, ANN still more accurate because it deals efficiently with hidden nonlinear relations when compared to MRA. The comparison between experimental and model results shows that average absolute relative error in the case of ANN was
Jeong-ick Lee - One of the best experts on this subject based on the ideXlab platform.
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A comparison in a back-bead prediction of gas metal arc welding using Multiple Regression Analysis and artificial neural network
Optics and Lasers in Engineering, 2000Co-Authors: Jeong-ick LeeAbstract:This research was done on the basis of prediction that there is a relationship between welding parameters and geometry of the back-bead in arc welding which is a gap. Multiple Regression Analysis and artificial neural network were used as methods for predicting the geometry of the back-bead. The Multiple Regression Analysis and the artificial neural network were formed, and the Analysis data or verification data which were used in the formation process of the Multiple Regression, and the training data or test data which were used in the formation process of the artificial neural network, were used to perform the prediction of the back-bead. Through this research, it was found that the error rate predicted by the artificial neural network was smaller than that predicted by the Multiple Regression Analysis, in terms of the width and depth of the back-bead. It was also found that between the two predictions, the prediction of the width of the back-bead was superior to the prediction of the depth in both methods.
Arthur W. T. Leung - One of the best experts on this subject based on the ideXlab platform.
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Comparative study of artificial neural networks and Multiple Regression Analysis for predicting hoisting times of tower cranes
Building and Environment, 2001Co-Authors: Arthur W. T. Leung, Chi Ming Tam, Dikai LiuAbstract:Abstract This paper aims to develop a quantitative model for predicting the hoisting times of tower cranes for public housing construction using artificial neural network and Multiple Regression Analysis. Firstly, based on data collected from crane operators and site managers in seven construction sites, the basic factors affecting the hoisting times for tower cranes are identified. Then, artificial neural networks (ANN) and the Multiple Regression Analysis (MRA) are used to model the hoisting time, and from the results, the neural network model and the Multiple Regression model of hoisting time are established. The modeling methods and procedures are explained. These two kinds of models are then verified by data obtained from an independent site, and the predictive behaviors of the two kinds of models are compared and analyzed. Furthermore, the predictive behaviors of the neural network model are also investigated by a sensitivity Analysis. Finally, the modeling methods, predictive behaviors and the advantages of each model are discussed.
Emad S. Al-momani - One of the best experts on this subject based on the ideXlab platform.
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Modeling Blanking Process Using Multiple Regression Analysis and Artificial Neural Networks
Journal of Materials Engineering and Performance, 2012Co-Authors: Emad S. Al-momani, Ahmad T. Mayyas, Ibrahim Rawabdeh, Rajaa AlqudahAbstract:The design of blanking processes requires the availability of a procedure able to deal with both tooling and mechanical properties of the workpiece material (blank thickness, hardness, ductility, etc.). This research presents the development and comparison of two models to predict the quality of the blanked edge represented by burrs height, the first model is an artificial neural network (ANN) based, while the second model is a Multiple Regression Analysis (MRA) based. Finite Element modeling of the blanking process was used to generate the data for both models. Both ANN and MRA are able to give good prediction results, however, ANN still more accurate because it deals efficiently with hidden nonlinear relations when compared to MRA. The comparison between experimental and model results shows that average absolute relative error in the case of ANN was
