问题描述:
请问,如何在 SAP2000 中输出《吊车梁的绝对最大弯矩》对应的最不利荷载位置?
解答:
移动荷载工况只能输出或显示结构的内力包络图,但无法给出某个截面的某个内力分量的最大值或最小值对应的最不利荷载位置。建议采用多步静力分析间接输出绝对最大弯矩对应的最不利荷载位置,具体操作如下:
第一,定义【车辆活载(Vehicle Live)】荷载模式。注意,车辆荷载定义中的【车辆完全限制在轨道内部】对多步静力分析无效,用户应通过设置起始位置和离散化数据,人为将车辆限制在轨道内。离散化数据用于定义加载点,计算可能的最不利荷载位置。对于多步静力分析,SAP2000 忽略轨道离散化生成的加载点,以“速度 x 时间步”作为加载点的间距,以“速度 x 持续时间”为总的移动距离。
综上,假设两台吊车(全长 8.7m)从左向右沿长度为 10m 的吊车梁移动,移动速度为 1m/s。因此,起始位置为 8.7m,移动距离为 1.3m,持续时间为 1.3s,时间步可取 0.005s,如下所示。
![](data:image/png;base64,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)
第二,根据以上车辆活载自动生成多步静力工况并运行分析。吊车梁的弯矩包络图与《吊车梁的绝对最大弯矩》中移动荷载工况下的弯矩包络图完全相同,如下所示。
第三,在数据表格中查看吊车梁各个测站的弯矩值并以此排序,如下所示。可以看出,绝对最大弯矩发生在 5.375m 和 4.625m 两个截面处,分别对应第 56 和 206 个分析步。
第 56 步对应的第一个车轮的轴心位置为 8.7+(56-1)*0.005*1-0.1=8.875m,即:第二个车轮位于 8.875-3.5=5.375m 处,如以下左图所示。同理,第 206 步对应的第一个车轮的轴心位置为 8.7+(206-1)*0.005*1-0.1=9.625m,即:第三个车轮位于 9.625-3.5-0.75*2=4.625m 处,如以下右图所示。