Please use this identifier to cite or link to this item:
Title: Sensitivity analysis on a construction operations simulation model using neural networks
Authors: Lu, M
Chan, WH
Yeung, DS
Keywords: Construction
Digital simulation
Neural nets
Resource allocation
Sensitivity analysis
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 18-21 August 2005, Guangzhou, China, v. 7, p. 4173-4178 How to cite?
Abstract: This paper addresses how to perform sensitivity analysis on simulation models for large, complex, resource-constrained, and technology-driven construction operations, with particular focus on how to quantify the effects of each input factor upon the output measures of performance on a precast viaduct construction operations simulation model. We first briefly reviewed existing techniques for sensitivity analysis on simulation models and identified their respective limitations. Then we introduced and applied a neural network (NN)-based technique to facilitate sensitivity analysis on construction operations simulation models. The technique defined input sensitivity in undistorted, practically accurate terms and permitted relating a set of input factors to multiple outputs. In the case study on precast viaduct construction operations, we investigated the effects of four relevant factors - related to tractor resource provision, precast segment delivery logistics, and site layout - upon the average cycle time as required for erecting one span of the viaduct. It is concluded that a valid simulation complemented with the NN-based sensitivity analysis contributes to gaining insights and deriving new knowledge on the real system, which ultimately leads to improved cost-effectiveness and enhanced efficiency on the real system.
ISBN: 0-7803-9091-1
DOI: 10.1109/ICMLC.2005.1527669
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Citations as of Aug 14, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.