سال انتشار:
2020
عنوان انگلیسی مقاله:
Automated clash resolution for reinforcement steel design in concrete frames via Q-learning and Building Information Modeling
ترجمه فارسی عنوان مقاله:
رزولوشن برخورد خودکار برای طراحی فولاد تقویت کننده در قاب های بتنی از طریق یادگیری Q و مدل سازی اطلاعات ساخت
منبع:
Sciencedirect - Elsevier - Automation in Construction, 112 (2020) 103062. doi:10.1016/j.autcon.2019.103062
نویسنده:
Jiepeng Liua,c, Pengkun Liua,2,∗, Liang Fengb,1,∗∗, Wenbo Wub, Dongsheng Lia, Y. Frank Chena
چکیده انگلیسی:
The design of reinforcing steel bars (rebars) is critical to reinforced concrete (RC) structures. Generally, a good
number of rebars are required by a design code, particularly at member connections. As such, rebar clashes (i.e.,
collisions and congestions) would be inevitable. It would be impractical, labor-intensive, and error-prone to
avoid all possible clashes manually or even using standard design software. The building information modeling
(BIM) technology has been utilized by the present architecture, engineering, and construction (ACE) industry for
clash-free rebar designs. However, most existing BIM-based approaches offer the clash resolution strategy for
moving components with an optimization algorithm, and are only applicable to the RC structures with regular
shapes. In particular, the optimized path of rebars cannot be adjusted to avoid the obstacles, thus limiting the
practical applications. Furthermore, most existing studies lack the learning from design code and constructibility
constraints to realize automatic and intelligent arrangement and adjustment of rebars for avoiding the obstacles
encountered in complex RC joints and frame structures. Considering these shortcomings, the authors have recently
proposed an immediate reward-based multi-agent reinforcement learning (MARL) system with BIM, towards
automatic clash-free rebar designs of RC joints without clashes. However, as the immediate reward is
required in the MARL system for guiding the learning of a rebar design, it will not succeed in clash-free rebar
designs of complex RC structures where immediate reward is often unavailable. In this study, this study further
extends the previous work with Q-learning (a model-free reinforcement learning algorithm) for more realistic
path planning considering both immediate and delayed rewards in clash-free rebar designs for real-world RC
structures. In particular, the rebar design problem is treated as a path-planning problem of multi-agent system,
where each rebar is deemed as an intelligence reinforcement learning agent. Next, by employing the Q-learning
as the reinforcement learning engine, the particular form of state, action, and immediate and delayed rewards
for the reinforcement MARL for automatic rebar designs considering more actual constructible constraints and
design codes can be developed. Comprehensive experiments on three typical beam-column joints and a two-story
RC building frame were conducted to evaluate the efficiency of the proposed method. The study results of paths
of rebar designs, success rates, and average time confirm that the proposed framework with MARL and BIM is
effective and efficient.
Keywords: Building Information Modeling | Reinforcement learning | Multi-agent | Q-learning | Rebar design | Clash resolution | Reinforced concrete frame
قیمت: رایگان
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