问题描述:
在SAP2000的各种荷载组合类型中,Range-Add(同号叠加)组合是如何实现的呢?它与线性叠加以及包络组合又有何不同?能否以简单算例加以详细解释?
解答:
Range-Add组合和包络组合都是用于获取不同荷载布置方式下结构响应的包络值。但是,前者考虑的荷载布置方式的数量要比后者多的多。具体来讲,对于同样包含N个荷载的两种组合类型:包络组合只考虑单个荷载依次施加,即只对N种荷载布置方式下的结构响应进行包络;但Range-Add组合考虑1~N个荷载同时施加,即对
M种荷载布置方式下的结构响应进行包络。其中,M的计算公式如下:
![](data:image/png;base64,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)
现在我们以下图所示的双跨连续梁为例,对以上结论加以解释。为了便于演示不同的荷载布置方式,我们将该双跨连续梁平均分为8段,而均布线荷载则可施加在这8段中任意一段或多段内。
此时,如果用户需要考虑均布线荷载最不利的布置方式,可执行以下操作步骤:
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定义8个荷载工况,依次在各个梁段内施加均布线荷载。
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定义Range-Add类型的荷载组合并包含以上8个荷载工况。
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运行分析后查看该荷载组合(最不利布置方式)下的结构响应。
如果对上述8个荷载工况进行线性叠加组合,则等效于在双跨连续梁的全长范围内施加均布线荷载,弯矩图如下所示:
如果对上述8个荷载工况进行包络组合,则程序只对该8个荷载工况下的结构响应进行包络,弯矩图如下所示:
如果对上述8个荷载工况进行Range-Add组合,则参与包络的荷载组合(即荷载布置方式)包括:
![](data:image/png;base64,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)
因此,Range-Add组合将自动对所有可能的255个荷载组合下的结构响应进行包络。事实上,程序内部在进行包络计算时,只需要对各个荷载组合下的计算结果秉承“同号相加”(正值加至 Max,负值加至 Min)的原则即可。最终弯矩图如下所示:
试想,如果不定义Range-Add组合,如何得到与之相同的结果呢?当然,用户可以采用以下操作步骤:
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手动定义全部255个荷载工况,每个工况只包含上述255个荷载组合中某一种荷载布置方式。
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定义包络类型的荷载组合并包含以上所有255个荷载工况。
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运行分析后查看该荷载组合(最不利布置方式)下的结构响应。
显然,该方法的工作量过大,可操作性不强!
Range-Add组合常用于考虑活荷载的最不利布置,尤其是当活荷载起控制作用时,这点尤为重要!除以上连续梁外,Range-Add组合同样适用于考虑楼面活荷载的最不利布置。具体操作时,用户只需在楼板的各个开间内分别布置活荷载并定义相应的荷载工况,然后定义Range-Add类型的荷载组合并包含所有荷载工况即可。