Cost Prediction by Machine Learning
Using Machine Learning Techniques for Early Cost Prediction of Structural Systems of Buildings
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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 simple...
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.
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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