Design optimization of obstacle avoidance of intelligent building steel bar by integrating reinforcement learning and BIM technology

• Autorzy: Chai Hong , Guo Junchao

Identyfikatory

DOI

10.24425/ace.2024.148932

• Języki publikacji: EN

Abstrakty

EN

In promoting the construction of prefabricated residential buildings in Yunnan villages and towns, the use of precast concrete elements is unstoppable. Due to the dense arrangement of steel bars at the joints of precast concrete elements, collisions are prone to occur, which can affect the stress of the components and even pose certain safety hazards for the entire construction project. Because the commonly used the steel bar obstacle avoidance method based on building information modeling has low adaptation rate and cannot change the trajectory of the steel bar to avoid collision, a multi-agent reinforcement learning-based model integrating building information modeling is proposed to solve the steel bar collision in reinforced concrete frame. The experimental results show that the probability of obstacle avoidance of the proposed model in three typical beam-column joints is 98.45%, 98.62% and 98.39% respectively, which is 5.16%, 12.81% and 17.50% higher than that of the building information modeling. In the collision-free path design of the same object, the research on the path design of different types of precast concrete elements takes about 3–4 minutes, which is far less than the time spent by experienced structural engineers on collision-free path modeling. The experimental results indicate that the model constructed by the research institute has good performance and has certain reference significance.

Słowa kluczowe

EN

reinforcement learning; BIM information; building information modelling; reinforcement steel bars; precast concrete element; Markov decision process

PL

uczenie się przez wzmacnianie; modelowanie informacji o budynku; pręt stalowy wzmacniający; prefabrykaty betonowe; BIM; proces decyzyjny Markowa

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2024
• Tom: Vol. 70, nr 1
• Strony: 621--634
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Chai Hong

• Yellow River Conservancy Technical Institute, Department of Civil Engineering and Transportation Engineering, 475000 Kaifeng, China

• https://orcid.org/0009-0008-1545-3500

autor

Guo Junchao

• Yellow River Conservancy Technical Institute, Department of Civil Engineering and Transportation Engineering, 475000 Kaifeng, China

• https://orcid.org/0009-0009-9306-6909

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).