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运用基于BIM的建筑多绿色性能目标优化方法最终筛选出的综合最优方案对四项性能目标的权衡较好，且各单项性能最优方案也呈现出较好的均衡性，如图9所示。随后，为验证该方法权衡多绿色性能与设计可能性的探索能力，笔者应用SOM神经网络对全部设计可能性进行聚类，探索了100类设计可能中的50类，说明集成工具平台具有权衡多绿色性能与设计可能性的探索能力（图10）。

（3）建筑绿色性能改善程度比较

分析实验得出的能耗、碳排放、DA、UDI单项性能和综合性能相对最优方案，结果表明：运用基于BIM的建筑多绿色性能目标优化方法所得综合方案相比既有方案在多项性能上均有提升（图11）。

（4）多绿色性能权衡与决策

优化过程所得方案可供设计师在多个绿色性能目标中作出取舍，但大量的方案及目标之间的复杂关系使决策依然不够方便快捷。由于DA超过50%左右时可以认为空间采光良好，所以笔者选取DA值为50%作为边界条件。如图12所示，应用SOM神经网络进行聚类，根据边界条件可选出5组待选方案，其性能目标值如表4所示。通过权衡，最终确定方案E为性能相对最优方案，如图13所示。

7 结语

我国严寒地区建筑由于气候原因，冬季能耗与碳排放量巨大。而大空间建筑由于其体积较大，层高较高而消耗大量能源。因此，探讨严寒地区大空间建筑的节能减排问题具有重大的社会意义。然而在降低能耗与碳排放量的同时，严寒地区大空间建筑的采光性能会因之减弱。为解决这一问题，本文基于BIM平台提出了一种数字化节能设计方法。通过案例研究表明，该方法在严寒地区大空间建筑的综合性能提升方面较为有效，并为BIM平台的的严寒地区大空间建筑综合信息集成一体化打下一定的研究基础。

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