Building Information Modeling (BIM) adoption along with the recent emergence of Internet of Things (IoT) applications provides many unique knowledge and decision-making abilities all through the built environment’s life cycle. The ability to connect online sensors utilized in surroundings in real time has led to the definition of the Digital Twin (DT) of the Building Design. The goal of Digital Twins is to synchronize the physical world with a virtual platform for seamless management and control of the construction process, infrastructure solutions, environmental monitoring, and other life span processes within building design. Most of the researchers focused on either BIM or DT in the construction of building application. In this research work, a novel hybrid model of Digital Twin-Building Information Modeling (DT-BIM) is proposed. This model does the process of identifying the shortage of resources, analyzing requirement, performing decision, dispatching the resources, and updating all the process in the database with the support of Artificial Intelligence (AI). Hence, this hybrid model provides improved results when compared to the implementation of the individual technology to the same application. The study results revealed that these hybrid technologies help in assisting the dispatch systems in the construction projects to a greater extent.
In this article, we investigate stochastic networks optimization using Quadric Lyapunov Algorithm (QLA) with Q-learning perspective. We proposed firstly a model of stochastic queueing networks with power constraints. QLA is then proposed aiming at minimizing an expression containing Lyapunov drift. Based on the analysed similarity between QLA and Q-learning, we show the possibility and feasibility of Q-learning. Simulation of a simple queue network model is carried out, and results using both QLA and Q-learning are compared.
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