AUTOMATIC DATA COLLECTION TO IMPROVE READY-MIXED CONCRETE DELIVERY PERFORMANCE

  • Pan Hao (School of Civil and Environmental Engineering, University of New South Wales) ;
  • Sangwon Han (School of Civil and Environmental Engineering, University of New South Wales)
  • Published : 2011.02.16

Abstract

Optimizing truck dispatching-intervals is imperative in ready mixed concrete (RMC) delivery process. Intervals shorter than optimal may induce queuing of idle trucks at a construction site, resulting in a long delivery cycle time. On the other hand, intervals longer than optimal can trigger work discontinuity due to a lack of available trucks where required. Therefore, the RMC delivery process should be systematically scheduled in order to minimize the occurrence of waiting trucks as well as guarantee work continuity. However, it is challenging to find optimal intervals, particularly in urban areas, due to variations in both traffic conditions and concrete placement rates at the site. Truck dispatching intervals are usually determined based on the concrete plant managers' intuitive judgments, without sufficient and reliable information regarding traffic and site conditions. Accordingly, the RMC delivery process often experiences inefficiency and/or work discontinuity. Automatic data collection (ADC) techniques (e.g., RFID or GPS) can be effective tools to assist plant managers in finding optimal dispatching intervals, thereby enhancing delivery performance. However, quantitative evidence of the extent of performance improvement has rarely been reported to data, and this is a central reason for a general reluctance within the industry to embrace these techniques, despite their potential benefits. To address this issue, this research reports on the development of a discrete event simulation model and its application to a large-scale building project in Abu Dhabi. The simulation results indicate that ADC techniques can reduce the truck idle time at site by 57% and also enhance the pouring continuity in the RMC delivery process.

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Acknowledgement

The data studied in this paper was provided by Reza Sabetnia. The authors appreciate his assistance sincerely.