Cost Prediction by Machine Learning: Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings - Softcover
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.
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About the Author
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.
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Cost Prediction by Machine Learning
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VDM Verlag Dr. Müller, 2009
ISBN 10: 3639151747
ISBN 13: 9783639151749
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Dogan Erdogmus Sevgi ZeynepS. Zeynep Dogan, PhD: Studied Construction Management at Izmir nInstitute of Technology and Illinois Institute of Technology at nChicago. Assistant Professor at IZTECH, Izmir, Turkey.It is desirable to . Seller Inventory # 4962078
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Cost Prediction by Machine Learning : Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783639151749
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Cost Prediction by Machine Learning | Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings
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Taschenbuch. Condition: Neu. Cost Prediction by Machine Learning | Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings | Sevgi Zeynep Dogan Erdogmus | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639151749 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 101543217
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Cost Prediction by Machine Learning: Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings
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