PERFORMANCE EVALUATION OF THE UT AUTOMATED ROAD MAINTENANCE MACHINE

This final report mainly focuses on evaluating the overall performance of the University of Texas' (UT's) Automated Road Maintenance Machine (ARMM). First, the ARMM's man-machine-balanced control loop was further developed then rigorously tested and evaluated based on (1) accuracy, (2) time, and (3) quality of the resultant seal. For the efficiency evaluation, thirty pavement crack images, which included longitudinal, transverse, and block cracking, were collected from a UT research campus, and field trials were completed at five locations (Austin, San Antonio, Dallas, Corpus Christi, and Travis County) in the state of Texas. Several additional significant improvements were made to the ARMM during the course of this project. Second, the ARMM's productivity was estimated based on (1) the results of the efficiency evaluation of the man-machine control loop, (2) observations made during the series of field trials, and (3) a productivity model. The mathematical model that predicts the productivity of the ARMM under various work conditions was developed as a means for job estimating and for rating the performance of the ARMM. The ARMM's estimated productivity was then compared with the typical productivity rate associated with conventional crack sealing methods. Evaluation results from the field trials and implementation recommendations are also made in this final project report. It was concluded that the introduction of automated methods to the pavement crack-sealing process will improve productivity and quality and can reduce costs and safety risks. The latter is a direct result of reducing normal crew sizes of seven to eight workers to only three to four workers. The reduction in crew size and the increase in productivity of the sealing process translate directly into significant potential cost savings. By automatically recording work completed, the ARMM should help improve project controls; by its ability to work at night, the ARMM should reduce road user costs.