An Intelligent Optimization Method for Large Underground Space Construction Scheme Under Low Carbon Target
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摘要: 大型地下空间的建设是建筑行业转型升级的重要领域。在大型地下空间的施工过程中,如何获取最优化的施工方案,到达绿色环保的目标是亟待解决的问题。以低碳为目标,本研究提出了大型地下空间施工智能优化方法。根据碳排放量的计算方法形成了施工方案优化架构,获取了影响施工能耗的关键因素。基于关键因素的分析,明确了各类构件和施工路径对碳排放的影响机制。在改进的Dijkstra算法的驱动下形成了施工路径的智能优化方法。在此基础上,基于遗传算法优化的BP神经网络形成了碳排放与施工方案的耦合关系。在低碳目标下精准获取最优化的施工方案。以城市副中心三大建筑共享配套设施项目施工现场为例进行案例分析,验证所提出方法的可行性。并通过分析施工碳排放,形成了最佳施工路径。Abstract: The construction of large underground spaces is an important field of transformation and upgrading of the construction industry. In the construction process of large underground spaces, how to obtain the optimal construction scheme and achieve the goal of green environmental protection is an urgent problem to be solved. Aiming at low carbon, the study proposed an intelligent optimization method for large underground space construction. According to the calculation method of carbon emissions, the construction scheme optimization framework was formed, and the key factors affecting the construction energy consumption were obtained. Based on the analysis of key factors, the influence mechanism of various components and construction paths on carbon emissions was clarified. Driven by the improved Dijkstra algorithm, an intelligent optimization method of construction path was formed. On this basis, the BP neural network optimized by genetic algorithm formed the coupling relationship between carbon emission and hoisting scheme. The optimal construction scheme was obtatined accurately under the low-carbon goal. Taking the construction site of the three major building shared facilities project of the city sub-center as an example, the case analysis was carried out to verify the feasibility of the proposed method. By analyzing the construction carbon emissions, the best construction path was formed.
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