In today’s world the cost of construction has gone up due to increased demand  that exacerbates the shortage of construction materials. These shortages have forced construction personnel to find creative ways to increase reusability of materials, especially forming and shoring materials.  Shoring is key to holding the fresh concrete for horizontal structural members. The concrete inside the  shoring will continuously harden, and there will come a time to remove the  shoring from the structural member and carry its own weight. Removing shoring on time is crucial for the progress of a construction project. The longer the shoring stays on the structural member the more it affects the sequence of tasks that come after it. Moreover, According to ACI shoring systems are very costly and based on the kind of system its cost can increase to 60% of the total construction project.

My research focuses on the utilization of highly efficient machine learning models to solve construction engineering problems. For instance, the challenge of estimating the removal time of formwork and shoring has considerable effect on the overall cost and duration of construction work. In today’s construction practice it is customary to take field-cured samples and test them in laboratories to determine the concrete strength which then can be used to make decisions on whether to commence stripping of shoring. This process is not only time-consuming, but the samples taken may not represent the actual concrete structure. This leads to cost overruns and longer construction schedules. 

I am interested in curating machine learning models to develop tools that can facilitate the construction process by cutting the time it takes to build and decreasing the overall cost associated with it.