Chapter
Apr 26, 2012
Application of Multivariate Statistical Process Control Technique to Monitor and Control Construction Processes
Authors: Anfas Thowfeek UK, [email protected], Nashwan Dawood [email protected], and Ramesh Marasini [email protected]Author Affiliations
Publication: Computing in Civil Engineering (2007)
Abstract
In order to evaluate the status of the project at any particular point of time, identify problems and enable project managers to make the correct decisions and control the project effective performance measurement and analysis systems should be used on construction sites. This paper presents application of multivariate statistical process control (MSPC) techniques to monitor the construction site variables. Multivariate statistical process control (MSPC) has found growing applications in recent years for controlling processes in chemical plants and other manufacturing processes. The MSPC combines a large number of variables into few independent variables, which then can be monitored and any process deviations from the normal operating conditions can be identified with corrective actions suggested. Utilising MSPC technique construction process variables can be modelled using historical data and establishes patterns of correlated variables. The current on site variables can be analyse and report to project management in order to make effective future decisions. The statistics of external variables and internal variables influencing construction site operations were identified using a real life case study. The results of modelling the variables and conducting experiments with MSPC are analysed and discussed in this paper.
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© 2007 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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MPhil. Student, CCIR, School of Science and Technology (SST), University of Teesside, UK, Middlesbrough, TS1 3BA. E-mail: UK, [email protected]
Professor of Construction IT, CCIR, School of Science and Technology (SST), University of Teesside. E-mail: [email protected]
Research Fellow, CCIR, School of Science and Technology (SST), University of Teesside. E-mail: [email protected]
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