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A Deep Learning Algorithms to Generate Activity Sequences Using Historical As-built Schedule Data

Project schedule development requires having knowledge about the project’s activities and the proper sequence of them. In traditional practice, arranging project activities in a feasible sequential order heavily relies on the project scheduler’s practical experience. However, personal experience is limited and prone to include human errors. In this paper, a Deep Learning model is employed to be trained on historical project schedules to predict sequential activities. The proposed model uses a Bidirectional Long Short-Term Memory Recurrent Neural Networks that learns the activity predecessors in the forward direction and the activity successors in the backward direction. The model receives one or more activities and predicts subsequent and precedent activities in a sequential order that have the highest likelihood of occurrence in the historical data. The model is compared with a Sequential Pattern Mining technique that identifies the most probable sequential patterns of activities. The two methods are applied to as-built highway project schedules obtained from a highway agency in the U.S to compare the performance of the two methods. While the Sequential Pattern Mining model provides sequential patterns for certain activities, the Deep Learning model generates a back-tail and a front-tail of activities with any arbitrary length for to provide a more flexible support tool for project schedulers. © 2020 The Authors. Published by Budapest University of Technology and Economics & Diamond Congress Ltd Peer-review under responsibility of the Scientific Committee of the Creative Construction Conference 2020.