Tracking Hauling Trucks for Cut-Fill Earthmoving Operations

Hauling trucks are important part of equipment fleets for large earthmoving operations such as those encountered in dams and highway construction projects. This paper presents an automated methodology for tracking and estimating productivity of hauling trucks fleet operations in near-real-time. Recent advancement in automated site data acquisition technologies made their use in tracking and monitoring of construction operations feasible. However, these technologies fail to track hauling truck fleets due to the change in the cut and fill locations from one cycle to another; making tracking and progress reporting difficult and inaccurate. In addition, there is very little work done utilizing data sensed directly from equipment, for example sensing when truck dumping bed is raised during the dumping process. The technologies deployed in the developed method are Radio Frequency Identification (RFID) and equipment control sensors. Low cost passive RFID tags are attached to hauling trucks and fixed RFID readers are attached to loaders or excavators. The read range of the used RFID tag is centimeters, to be activated only when a loader with an attached RFID reader is loading a truck. On the other hand, control sensor is connected to the truck control system and operated by the motion of its movable bed. The function of control sensors is to record the signal time when the truck operator gives order to the truck control system to raise or lower truck bed. The captured data is then transferred wirelessly from the RFID reader and control sensor to a computer housed in one of the temporary offices onsite and subsequently to the main server in the contractor’s head office. Fusing the data captured from RFID reader and control sensor is used to identify loading, travel, dumping and return time that constitute the hauling truck cycle time. The collected data is analyzed and processed automatically, without human intervention, to calculate the productivity of the hauling truck and to report it directly to onsite personnel. Relational database is developed to support the implementation of the proposed method. The developed database is used to process the data captured by the RFID and the control sensor to calculate the productivity achieved in cutfill operations in near-real-time. The developed methodology is expected to facilitate early detection of discrepancies between actual and planned performances.