  This paper aims to propose a novel deep learning-integrated framework for
deriving reliable simulation input models through incorporating multi-source
information. The framework sources and extracts multisource data generated from
construction operations, which provides rich information for input modeling.
The framework implements Bayesian deep neural networks to facilitate the
purpose of incorporating richer information in input modeling. A case study on
road paving operation is performed to test the feasibility and applicability of
the proposed framework. Overall, this research enhances input modeling by
deriving detailed input models, thereby, augmenting the decision-making
processes in construction operations. This research also sheds lights on
prompting data-driven simulation through incorporating machine learning
techniques.
