Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks

TL;DR

This paper introduces a Bayesian deep learning framework that integrates multisource construction data to improve simulation input models, enhancing decision-making in construction operations.

Contribution

It presents a novel framework combining Bayesian deep neural networks with multisource data for more reliable and detailed construction simulation inputs.

Findings

Successful application to road paving case study

Improved accuracy of simulation input models

Enhanced decision-making capabilities in construction

Abstract

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.