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Automated clash resolution for reinforcement steel design in concrete frames via Q-learning and Building Information Modeling
رزولوشن برخورد خودکار برای طراحی فولاد تقویت کننده در قاب های بتنی از طریق یادگیری Q و مدل سازی اطلاعات ساخت-2020 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 |
مقاله انگلیسی |
2 |
Knowledge-based system for resolving design clashes in building information models
سیستم دانش بنیان برای حل اختلافات طراحی در ساخت مدلهای اطلاعاتی-2020 Although building information modelling (BIM) has revolutionized building design and construction management,
it is still time-consuming for a BIM project team to coordinate with designers to resolve clashes during the
pre-construction stages. During the construction stage, shop drawings frequently have to be revised because of
cognitive differences between the designers and constructors. These two groups of people view the resolution of
such clashes from their own perspectives because of differences in their inherent knowledge and experience. To
effectively improve the project delivery time, one option is to reduce the number of model revisions during the
construction stage. This could be done by providing a BIM model as a reference. In this model, design clashes can
be resolved from the perspective of the constructor before discussions in design coordination meetings to find
compromises. In this work, an artificial intelligence system for such design clash resolution was developed with
machine learning and heuristic optimizing techniques. In the experiment, we present a real case of a student
residence, in which the mechanical, electrical, and plumbing systems in the basement are used to validate the
effectiveness of the proposed system. The experimental results show the feasibility and effectiveness of the
proposed system. Keywords: Building information modelling | Design clash resolution | Knowledge extraction | Heuristic optimization |
مقاله انگلیسی |