ESTIMATING LABOR PRODUCTIVITY USING PROBABILITY INFERENCE NEURAL NETWORK
This paper discusses the derivation of a probabilistic neural network classification model and its application in the construction industry. The probability inference neural network (PINN) model is based on the same concepts as those of the learning vector quantization method combined with a probabilistic approach. The classification and prediction networks are combined in an integrated network, which required the development of a different training and recall algorithm. The topology and algorithm of the developed model was presented and explained in detail. Portable computer software was developed to implement the training, testing, and recall for PINN. The PINN was tested on real historical productivity data at a local construction company and compared with the classic feedforward back-propagation neural network model. This showed marked improvement in performance and accuracy. In addition, the effectiveness of PINN for estimating labor production rates in the context of the application domain was validated through sensitivity analysis.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/08873801
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Supplemental Notes:
- This work was funded by a number of construction companies in Alberta and the Natural Science and Engineering Research Council of Canada under grant number IRC-195558/96.
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Corporate Authors:
American Society of Civil Engineers
1801 Alexander Bell Drive
Reston, VA United States 20191-4400 -
Authors:
- Lu, M
- AbouRizk, S M
- Hermann, U H
- Publication Date: 2000-10
Language
- English
Media Info
- Features: Appendices; Figures; References; Tables;
- Pagination: p. 241-248
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Serial:
- Journal of Computing in Civil Engineering
- Volume: 14
- Issue Number: 4
- Publisher: American Society of Civil Engineers
- ISSN: 0887-3801
Subject/Index Terms
- TRT Terms: Algorithms; Backpropagation; Construction industry; Estimating; Feedforward control; Labor productivity; Machine learning; Mathematical models; Neural networks; Probability theory; Sensitivity analysis; Software
- Subject Areas: Administration and Management; Construction; Data and Information Technology; Highways; Pipelines; I10: Economics and Administration; I50: Construction and Supervision of Construction;
Filing Info
- Accession Number: 00800602
- Record Type: Publication
- Contract Numbers: CMS 9457549, IRC-195558/96
- Files: TRIS
- Created Date: Oct 12 2000 12:00AM