Structural identification using dynamical parameters (such as the natural vibration frequencies and mode shapes) is an important issue, especially in bridges or high-rise buildings. However, incorrect decisions could happen on the Structural Health Monitoring (SHM) strategy and the Structural System Identification (SSI) analysis that makes the sometimes expensive and time-consuming process useless due to the large uncertainty of the resulting estimations. This paper discusses the role of the SHM strategy and the SSI analysis based on the constrained observability method (COM) and decision trees (DT) in reducing the estimation error. Here, the COM uses subsets of natural frequencies and/or modal-shapes to deal with the nonlinearity of the SSI derived from the operational aspects of the methods, and combines the unknown items including frequencies and mode shapes into an optimization process. Next, a decision-support tool based on decision trees is applied to help engineers to establish the best SHM + SSI strategy yielding the most accurate estimations. The principle and steps of this new method, the combination of constrained observability m,ethod and decision trees, are presented for the first time. After that, a numerical model of a bridge case is used to show how to choose the optimal strategy, when factors such as the structure layout, span length, measurement set, and parameters of the COM are included as decision variables. The importance ranking of these four factors is the layout, measurement set, parameters of the COM, and length through the sensitivity analysis of the COM estimated. Last, a real bridge is used to validate this methodology under the undamaged and damaged scenarios by comparing an Error Index, which shows the optimal SHM + SSI strategy works well no matter the bridge is damaged or not. The presented analysis leads to significant insights that can help the decision-making of the optimal SHM + SSI strategy, avoiding erroneous decisions if this tool is not used beforehand.

使用动力学参数（例如固有振动频率和振型）的结构识别是一个重要的问题，尤其是在桥梁或高层建筑中。但是，结构健康监控（SHM）策略和结构系统识别（SSI）分析可能会做出错误的决定，由于估算结果存在很大的不确定性，有时使昂贵且费时的过程变得毫无用处。本文讨论了基于约束可观察性方法（COM）和决策树（DT）的SHM策略和SSI分析在减少估计误差中的作用。在此，COM使用固有频率和/或模态形状的子集来处理从方法的操作方面得出的SSI的非线性，并将包括频率和模式形状在内的未知项组合到一个优化过程中。接下来，使用基于决策树的决策支持工具来帮助工程师建立最佳SHM + SSI策略，从而产生最准确的估计。首次提出了约束方法的可观测性，方法和决策树的结合原理和步骤。此后，当包括诸如结构布局，跨度长度，测量集和COM参数等因素作为决策变量时，使用桥梁案例的数值模型来说明如何选择最佳策略。通过估计的COM敏感性分析，这四个因素的重要性等级是COM的布局，测量集，COM参数和长度。最后的，通过比较错误指数，可以使用真实的桥梁在未损坏和受损的情况下验证此方法，这表明无论桥梁是否受损，最佳的SHM + SSI策略都能很好地发挥作用。提出的分析得出了重要的见解，可以帮助您做出最佳SHM + SSI策略的决策，从而避免了事先未使用此工具的错误决策。