Abstract: Techniques to automate various aspects related to construction projects, including but not limited to, creation of schedules, verification of construction progress, estimation of costs, updating of schedules, creation and estimation of construction status, use of information generated by construction equipment, and the use of granular information to continually update the same. Data analysis can be used to refine the cost per task and calculate a cost to complete for a construction project. The cost to complete can be viewed at any time and it will be made more accurate by having the cost per task refined from historical data. Additionally, a rate of production statistic can be generated to compare workers across tasks and see if the implicit task durations used to today can be refined. A learning algorithm can be created by using reinforced learning to figure out the next best step if a project task is delayed or put on hold.