Neural Network Method of Estimating Construction Technology Acceptability
Publication: Journal of Construction Engineering and Management
Volume 121, Issue 1
Abstract
A neural network (NN) based approach is proposed for predicting the adoption potential or acceptability of a new construction technology. The acceptability of a technology for a target operation is defined as the proportion of users that choose to use the technology in comparison to a conventional (base) technology. All existing alternative technologies for the considered operation are collected as samples for study. The performance characteristics of each sample technology are stored in a vector comprising eigenvalues determined by using the analytical hierarchy process (AHP) method, and its acceptability is determined using a poll. The obtained performance-acceptability pairs are used to train a neural network using the back-propagation algorithm. The trained network can then be used to predict the acceptability of a new technology in question, given its performance attributes. Possible information sources for training set construction and possible applications of the approach are discussed. An example estimate of the adoption prospect of a new concrete distribution system for concrete placement on a mid-rise building project is provided. Tests of the presented NN approach with simulated data show a promising result, especially when the poll size used is sufficiently large.
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References
1.
AbouRizk, S., Mandalapu, S., and Skibniewski, M.(1994). “Analysis and evaluation of alternative technologies.”J. Mgmt. in Engrg., ASCE, 10(3), 65–71.
2.
Al-Tabtabai, H., and Diekmann, J. E. (1992). “Judgmental forecasting in construction projects.”Constr. Mgmt. and Economics, Vol. 10, 19–30.
3.
Chao, L.-C., and Skibniewski, M. J.(1994). “Estimating construction productivity: neural-network-based approach.”J. Computing in Civ. Engrg., ASCE, 8(2), 234–251.
4.
Lin, C.-T., and Lee, C. S. G.(1991). “Neural network based fuzzy logic control and decision system.”Trans. on Computers, IEEE, 40(12), 1320–1336.
5.
Moselhi, O., Hegazy, T., and Fazio, P.(1991). “Neural networks as tools in construction.”J. Constr. Engrg. and Mgmt., 117(4), 606–625.
6.
Pao, Y.-H. (1989). Adaptive pattern recognition and neural networks . Addison-Wesley Publishing Co., Reading, Mass.
7.
Peurifoy, R. L., and Ledbetter, W. B. (1985). Construction planning, equipment and methods . McGraw-Hill Book Co., New York, N.Y.
8.
Saaty, T. L. (1980). The analytical hierarchy process: planning, priority setting, resource allocation . McGraw-Hill Book Co., New York, N.Y.
9.
Skibniewski, M. J., and Chao, L.-C.(1992). “Evaluation of advanced construction technology with AHP method.”J. Constr. Engrg. and Mgmt., ASCE, 118(3), 557–593.
10.
Yeh, Y.-C., Kuo, Y.-H., and Hsu, D.-S.(1993). “Building KBES for diagnosing PC piles with artificial neural network.”J. Computing in Civ. Engrg., ASCE, 7(1), 71–93.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 121 • Issue 1 • March 1995
Pages: 130 - 142
Copyright
Copyright © 1995 American Society of Civil Engineers.
History
Published online: Mar 1, 1995
Published in print: Mar 1995
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