Cost Prediction by Machine Learning: Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings - Couverture souple

Dogan Erdogmus, Sevgi Zeynep

 
9783639151749: Cost Prediction by Machine Learning: Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings
Couverture souple
ISBN 10 : 3639151747 ISBN 13 : 9783639151749
Editeur : VDM Verlag, 2009

Synopsis

It is desirable to predict construction costs in the early design stage to make sure that target costs are met. The book investigates the possibility of predicting the cost of construction early in the design phase by using machine learning techniques. Therefore, artificial neural network (ANN) and case based reasoning (CBR) prediction models were developed in a spreadsheet-based format. An investigation of the impacts of weight generation methods on the ANN and CBR models was conducted. The performance of the ANN model was enhanced by experimenting with the weight generation methods of simplex optimization, back propagation training, and genetic algorithms while the CBR model was augmented by feature counting, gradient descent, genetic algorithms, decision tree methods of binary-dtree, info-top and info-dtree. Cost data belonging to the superstructure of low-rise residential buildings were used to test these models. Both approaches were found to be capable of providing high prediction accuracy. A comparison of the ANN and CBR models was made in terms of prediction accuracy, preprocessing effort, explanatory value, improvement potentials and ease of use.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

It is desirable to predict construction costs in the early design stage to make sure that target costs are met. The book investigates the possibility of predicting the cost of construction early in the design phase by using machine learning techniques. Therefore, artificial neural network (ANN) and case based reasoning (CBR) prediction models were developed in a spreadsheet-based format. An investigation of the impacts of weight generation methods on the ANN and CBR models was conducted. The performance of the ANN model was enhanced by experimenting with the weight generation methods of simplex optimization, back propagation training, and genetic algorithms while the CBR model was augmented by feature counting, gradient descent, genetic algorithms, decision tree methods of binary-dtree, info-top and info-dtree. Cost data belonging to the superstructure of low-rise residential buildings were used to test these models. Both approaches were found to be capable of providing high prediction accuracy. A comparison of the ANN and CBR models was made in terms of prediction accuracy, preprocessing effort, explanatory value, improvement potentials and ease of use.

Biographie de l'auteur

S. Zeynep Dogan, PhD: Studied Construction Management at ¿zmir Institute of Technology and Illinois Institute of Technology at Chicago. Assistant Professor at IZTECH, ¿zmir, Turkey.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
3639151747
ISBN 13 
9783639151749
Reliure
Broché
Nombre de pages
124
Coordonnées du fabricant
non disponible
Personne responsable
non disponible