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Construction operations scheduling based in hybrid simulation and uncertainty for girder bridges
School research theme: Management of projects
Research supervisors: Dr Laura Florez-Perez and Dr Carlos Galera Zarco
Start date: September 2020
The success of bridge projects holds undeniable significance for global infrastructure strategies. However, the construction industry continues to face prevalent challenges such as inefficiencies, non-performance, and a lack of effective delay factor analysis. These issues can be attributed to inadequate scheduling and insufficient project comprehension prior to the onset of the construction phase. Despite improvements brought by simulation methods, gaps remain, particularly in stochastic scheduling and the inclusion of uncertainty factors. Addressing this gap, this research concentrates on developing an interactive, visually-driven tool that not only predicts the duration of girder bridge projects incorporating construction uncertainties, but also enhances decision-making through detailed, multi-scenario explorations.
The research constructs an innovative three-part framework. The first component involves the extraction, classification, and interpretation of information elements from a generic Building Information Modelling (BIM) model, utilizing Python programming and machine learning algorithms.
Next, the study identifies and classifies uncertainty factors affecting construction operations through comprehensive literature reviews and interviews with industry professionals.
The third component entails the performance of construction hybrid simulations, visualizing the outcomes using BIM 4D animation. This step leverages Discrete Event Simulation (DES) and Agent-Based Simulation (ABS) for an in-depth understanding of project timelines, resource usage, and the potential impact of uncertainty factors on construction operations. A distinctive aspect of the research is the development of an interactive, game-based framework by integrating the Python tool with a state-of-the-art game engine. This integration offers a more interactive platform for scenario analysis, fostering a dynamic environment for decision-making.
In conclusion, the research plan to minimize project delays through accurate prediction of construction operations, create a multitude of scenarios for comprehensive understanding, and improve the scheduling methodology through an interactive tool, thereby enhancing decision-making processes.
