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Paper w78-1999-2476:
Cost estimation of high performance concrete (hpc) high-rise commercial buildings by neural networks

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Fang C F, Froese T

Cost estimation of high performance concrete (hpc) high-rise commercial buildings by neural networks

Abstract:Neural network approach is applied to establish relationships between the quantities/cost of the concrete/formwork, which is required for the structural elements of tall buildings using high performance concrete (HPC), and the design variables. Hybrid and hierarchical strategies are proposed for the cost estimation, where the feed-forward networks are adopted. After training, the neural networks are utilized to predict automatically the quantities/cost of HPC wall-frame structures in tall commercial buildings. Verifications are conducted with respect to various sets of the design parameters and a comprehensive discussion is given.

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Series:w78:1999 (browse)
Cluster:papers of the same cluster (result of machine made clusters)
Class:class.analysis (0.077873) class.economic (0.017519) class.communication (0.013234)
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Permission to reproduce these papers has been graciously provided by the Research Press of the National Research Council of Canada. The support of the editors, particularly Dr. Dana Vanier, is gratefully appreciated.

 

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