With the current decaying condition of America’s infrastructure, a need for a more efficient inspection method is evident. The combination of Structural Health Monitoring data and data analysis tools could be a solution. In this study, laboratory data was captured for both a baseline bare-frame and a wooden wall reinforced steel structure. A MATLAB®-based computer program, the in-house SHE™ accomplishes an array of tasks, including signal processing and modal decomposition among others. A SAP2000® model contextualized the experimental data by producing trend behaviors, such as mode order or common mode shapes.ududBoth sequential and cumulative reinforcement detection was performed on the cases of a baseline configuration, a single reinforcing wall case, and a double reinforcing wall case. The coupled translational, twisting, and bending modes were employed by nine damage detection indices. Relative to all others, Flexibility Percentage Difference performed the best, indicating both damage severity and location. While some of the indices proved more effective than others, none were able to practically detect and locate change for an inspector of this structure. ud
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机译:在美国基础设施的当前状况日趋恶化的情况下,显然需要更有效的检查方法。结构健康监测数据和数据分析工具的结合可能是一种解决方案。在这项研究中,捕获了基线裸框和木墙加固钢结构的实验室数据。内部SHE™是基于MATLAB®的计算机程序,可完成一系列任务,包括信号处理和模态分解等。 SAP2000®模型通过产生趋势行为(例如模式顺序或共模形状)来关联实验数据。加强墙案例。九种损伤检测指标采用了耦合的平移,扭曲和弯曲模式。相对于其他所有方面,“柔韧性百分比差异”表现最佳,表明损伤的严重程度和位置。尽管某些索引被证明比其他索引更有效,但没有一个索引能够实际检测和定位此结构的检查员的更改。 ud
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