Automating lookahead planning using site appearance and space
  utilization
Eyob Mengiste Borja Garcia de Soto Timo Hartmann
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.