VDM Verlag Cost Prediction by Machine Learning: Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings

Product Description 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. 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.