Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks
This work addresses the need for better simulation input modeling in construction operations, though it appears incremental by integrating existing machine learning techniques into a specific domain.
The paper tackled the problem of deriving reliable simulation input models for construction operations by proposing a deep learning-integrated framework that incorporates multi-source data, resulting in enhanced input modeling and improved decision-making processes, as demonstrated in a case study on road paving operations.
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.