问题描述:
对于纯剪力墙结构,在ETABS中将全部剪力墙指定为同一墙肢标签,则得到的墙肢内力与楼层剪力相等;但如果指定为多个墙肢标签,则各个墙肢内力之和并不等于楼层剪力,为什么?另外,即使指定为同一标签,如果墙肢局部轴方向与全局方向不一致,为什么将墙肢内力由局部轴分解至全局轴后重新合成的内力与楼层剪力也不相等呢?
解答:
首先,该问题出现在反应谱工况的地震分析结果中,而不会出现在底部剪力法的地震分析结果中。所以,出现问题的本质原因是反应谱分析的振型组合!
对于反应谱分析,所有的输出结果都是振型组合值,具体分为两步:
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在各个振型下分别计算相应的物理量(如墙肢内力、楼层剪力等)
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对各个物理量进行CQC或SRSS振型组合后得到峰值响应
在这种情况下得到的不同物理量的值并不存在对应关系,因为这些物理量并非同时获取的,而只是通过数理统计方法进行组合。因此,对反应谱分析的结果进行多墙肢结果的合成以及墙肢内力的分解,事实上都是没有物理意义的,这也导致了与楼层剪力的差异。
在单个振型下的计算结果是满足内力合成与分解的,即多个墙肢的内力和等于楼层剪力、且局部轴方向的内力分解至全局轴后重新合成的内力也与楼层剪力相等。所以,对于底部剪力法的地震分析结果,不会出现本文提到的问题,因为底部剪力法可视为只有一阶振型而无需进行振型组合。
对于多墙肢结果的合成,可以从以下算例中加深理解。
![](data:image/png;base64,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)
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反应谱分析
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底部剪力法
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楼层剪力:直接计算
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墙肢内力:直接计算
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结果:剪力P1+剪力P2=楼层剪力
分析结果如下,qqq为底部剪力法工况,RPx为反应谱工况,与前述结论是一致的。