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- W2955527991 abstract "The adoption of precast concrete elements (PCEs) are becoming popular in civil infrastructures. Since quality of connections determines the structure property, design of rebar in PCEs is a mandatory stage in constructions. Due to large number of rebar, complicated shapes of PCEs and complicated rules for arrangement, it is labor-intensive and error-prone for designers to avoid all clashes even using computer software. With the aid of BIM, it is desirable to have an automated and clash-free rebar design. Taking this cue, we introduce a framework with generative adversarial network (GAN) and multi-agent reinforcement learning (MARL) for generating design and automatically avoiding clash of rebar in PCES. We use GAN to generate 2D rebar designs. Then, 2D rebar designs are transformed into digital environments for MARL. In addition, layout of rebar is modelled as path planning of agents in MARL. An illustrative example is presented to test the proposed framework." @default.
- W2955527991 created "2019-07-12" @default.
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- W2955527991 date "2019-06-11" @default.
- W2955527991 modified "2023-09-23" @default.
- W2955527991 title "Automated Clash Free Rebar Design in Precast Concrete Exterior Wall via Generative Adversarial Network and Multi-agent Reinforcement Learning" @default.
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- W2955527991 doi "https://doi.org/10.1007/978-3-030-20454-9_54" @default.
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