Application of an artificial neural network based sensitivity analysis technique in concreting productivity study

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26161

Title:	Application of an artificial neural network based sensitivity analysis technique in concreting productivity study
Authors:	Lu, M Yeung, DS
Issue Date:	2003
Source:	2003 International Conference on Machine Learning and Cybernetics, 2-5 November 2003, v. 2, p. 1207-1212
Abstract:	As follow up effort, the present research takes advantage of the concreting productivity database as established by Lu and Anson (2004), and in particular, applies an ANN based sensitivity analysis technique to process the productivity data for the concreting operations using the stationary pump and reveal quantitative information on the significance of independent input factors. Definition of the partial derivative based sensitivity measure for a trained ANN model is distinguished for continuous, discrete, and binary variables respectively in order to factor in the effect on the model's sensitivity of the unit of measure and the relevant range of input factors of various natures, which could be considerably different in practical engineering applications. It is found that dropping the least sensitive factor identified from NN modeling brings about slight tradeoff of the prediction accuracy, while the sensitivity of the other independent factors remains relatively stable.
Keywords:	Artificial intelligence Backpropagation Construction Neural nets Productivity Sensitivity analysis
Publisher:	IEEE
ISBN:	0-7803-8131-9
DOI:	10.1109/ICMLC.2003.1259670
Appears in Collections:	Conference Paper

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