| Construction productivity, which is measured by output per unit of resource input, plays a key role in the success of a construction project. High productivity leads to lower unit cost to carry out a task or operation. Analyzing construction productivity, however, is a challenging task because of the nature of construction field conditions which contain complex resource flows that lack the organized production lines of a manufacturing facility in a controlled and weather protected environment. To study a construction operation, construction engineers typically spend days or weeks just to collect the data needed to conduct basic analysis. Often when data become available, the site condition has changed and the improvement ideas obtained from productivity analysis are already obsolete. Timely productivity monitoring can provide construction engineers with insightful information so that corrective measures can be applied immediately to control on-going construction.;In the field of data acquisition, the development of technologies for location awareness provides significant potential for improving the manual processes of collecting construction field data and, as a result, construction decisions can be made in a timely manner which can improve productivity, saving time and money for a construction project. Due to the complexity of construction operations in the field, location awareness technology alone cannot solve the field productivity puzzle readily. A critical breakthrough of transferring the location and time data into meaningful productivity information is needed which requires the investigation of resource interdependencies within a construction operation.;This research goes beyond applying location awareness technology to collect construction field data and focuses on integrating the quantatitive positioning and time data with operational reasoning rules to automatically generate qualitative operation information to support timely productivity analysis and decision making. The results of the research include a rule-based position-to-operation (P2O) model and its resulting time-lapse resource utilization. |
