Automating lookahead planning using site appearance and space utilization
It addresses construction planning inefficiencies for project managers by integrating site spatial constraints, though it appears incremental in extending traditional scheduling techniques.
This study tackled the problem of automating lookahead planning in construction by using material appearances and space utilization to predict task completion rates, with results showing it can assist in developing automated plans in a sample project involving finishing works.
This study proposes a method to automate the development of lookahead planning. The proposed method uses construction material conditions (i.e., appearances) and site space utilization to predict task completion rates. A Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) model was trained using a segment of a construction project timeline to estimate completion rates of tasks and propose data-aware lookahead plans. The proposed method was evaluated in a sample construction project involving finishing works such as plastering, painting, and installing electrical fixtures. The results show that the proposed method can assist with developing automated lookahead plans. In doing so, this study links construction planning with actual events at the construction site. It extends the traditional scheduling techniques and integrates a broader spectrum of site spatial constraints into lookahead planning.