THESIS
2023
1 online resource (xiv, 59 pages) : illustrations (some color)
Abstract
The utilization of precast concrete is rapidly gaining popularity in the construction
industry due to its capacity to enhance building efficiency and quality in comparison to cast-in-situ
concrete. Precast construction differs from cast-in-situ construction as it involves the
manufacturing process of components performed within a factory, followed by the
transportation of these components to the construction site for assembly. The use of repeating
molds and consistent rebar layouts can significantly improve worker efficiency. Therefore,
constructability factors, particularly those related to standardization, should be carefully
considered. However, current research on precast construction primarily focuses on logistics
and sequencing, neglecting the importance of standardization. Conseq...[
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The utilization of precast concrete is rapidly gaining popularity in the construction
industry due to its capacity to enhance building efficiency and quality in comparison to cast-in-situ
concrete. Precast construction differs from cast-in-situ construction as it involves the
manufacturing process of components performed within a factory, followed by the
transportation of these components to the construction site for assembly. The use of repeating
molds and consistent rebar layouts can significantly improve worker efficiency. Therefore,
constructability factors, particularly those related to standardization, should be carefully
considered. However, current research on precast construction primarily focuses on logistics
and sequencing, neglecting the importance of standardization. Consequently, this study
developed a BIM-based framework to examine the relationship between standardization and
construction cost, incorporating the concept of standardization into a constructability score.
This research proposes a framework that incorporates Building Information Modeling
techniques to extract semantic information from architectural plans. Subsequently, a gradient-based
Optimality Criteria method is utilized to optimize the sizing variables of precast
components. Additionally, a hybrid approach called NSGA-II-GD, which combines Non-dominated
Sorting Genetic Algorithm II and Great Deluge Algorithm, is employed to optimize
the rebar layout design of each precast component. While NSGA-II-GD improves
computational speed and provides superior convergence and search space compared to other
GA-based algorithms, it still requires significant operating time. To address this, a graph neural
network (GNN) approach is adopted to predict the rebar layout of components based on the
dataset generated from the NSGA-II-GD approach. The GNN prediction exhibits substantial
time improvement while maintaining comparable results. Results from an illustrative example
demonstrate the existence of an optimal point between construction cost and standardization,
particularly for components subjected to similar stresses.
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