Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technology
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2024-03-01
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| Series: | Archives of Civil Engineering |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/130808/ACE_2024_01_36.pdf |
| _version_ | 1797234868161085440 |
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| author | Hong Chai Junchao Guo |
| author_facet | Hong Chai Junchao Guo |
| author_sort | Hong Chai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T16:38:54Z |
| format | Article |
| id | doaj.art-dcd6cfa512884044bcd94f6373043301 |
| institution | Directory Open Access Journal |
| issn | 1230-2945 2300-3103 |
| language | English |
| last_indexed | 2024-04-24T16:38:54Z |
| publishDate | 2024-03-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Civil Engineering |
| spelling | doaj.art-dcd6cfa512884044bcd94f63730433012024-03-29T12:12:25ZengPolish Academy of SciencesArchives of Civil Engineering1230-29452300-31032024-03-01No 1621634https://doi.org/10.24425/ace.2024.148932Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technologyHong Chai0https://orcid.org/0009-0008-1545-3500Junchao Guo1https://orcid.org/0009-0009-9306-6909YellowRiver Conservancy Technical Institute, Department of Civil Engineering and Transportation Engineering, 475000 Kaifeng, ChinaYellowRiver Conservancy Technical Institute, Department of Civil Engineering and Transportation Engineering, 475000 Kaifeng, ChinaIn 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.https://journals.pan.pl/Content/130808/ACE_2024_01_36.pdfreinforcement learningbuilding information modelingreinforcement steel barprecast concrete elementsmarkov decisionbim information |
| spellingShingle | Hong Chai Junchao Guo Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technology Archives of Civil Engineering reinforcement learning building information modeling reinforcement steel bar precast concrete elements markov decision bim information |
| title | Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technology |
| title_full | Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technology |
| title_fullStr | Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technology |
| title_full_unstemmed | Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technology |
| title_short | Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technology |
| title_sort | design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and bim technology |
| topic | reinforcement learning building information modeling reinforcement steel bar precast concrete elements markov decision bim information |
| url | https://journals.pan.pl/Content/130808/ACE_2024_01_36.pdf |
| work_keys_str_mv | AT hongchai designoptimizationofobstacleavoidanceofintelligentbuildingthesteelbarbyintegratingreinforcementlearningandbimtechnology AT junchaoguo designoptimizationofobstacleavoidanceofintelligentbuildingthesteelbarbyintegratingreinforcementlearningandbimtechnology |