## 7 Conclusions  
This study presented a review of recent solutions in the field of SHM systems for multi-story buildings and bridges, addressing areas of improvement, limitations and opportunities. The limitations of physics-based and data-driven models in SHM in buildings and bridges were also discussed. This review showed the popularity of SHM systems based on the ML techniques of SVM, NN, and CNN due to the benefits of these algorithms in classification capabilities. The advantages and disadvantages of applying these ML techniques were also discussed in order to provide valuable insight for researchers. In general, a fair comparison between ML models and feature extraction techniques implies a similar definition of damage scenarios (i.e., loss of stiffness, bolt loosening or mass addition) to provide a reasonable comparison. The occurrence of multiple damage locations within the structure is also an underexplored area in damage scenarios. Real-time monitoring with in-service structure systems also needs to be developed. In addition, anomalous sensor data must be taken into account in real-time applications. Real-world structures are more complex than some idealized FE models and must involve a higher degree of uncertainty than those idealized models, so more damage scenarios must be studied to verify damage identification. FE model updating techniques and ML model retraining techniques should be explored for long-term SHM systems in the construction stages of the operational phase. Sensor placement plays a crucial role in the performance of ML-based SHM systems, so more studies should be conducted analyzing this fact. The implications due to constraints imposed by the operational and environmental effects on the model need to be explored in implementation scenarios. For example, the periodicity, in which data should be collected from the monitored system to adjust or update the model due to the degradation of physical properties or operational conditions. Signal processing techniques, such as data fusion, normalization and compression, could improve data quality. In addition, with the development of increasingly powerful devices with higher computing power, signal processing can be performed in the fog computing paradigm. Fog devices can speed up the process of noise filtering, data compression and data fusion in SHM systems to assess the state of the structure after a seismic event. Hybrid approaches that integrate physics-based and data-based models are encouraged to address some of the drawbacks of stand-alone modeling, and this is the approach taken by some of the most recent developments in the SHM field. The need for an SHM system that synergistically integrates the advances reported in the literature in data generation, sensing, processing, model parameter updating and real-time monitoring, including the rapid assessment of post-seismic event response, is envisioned. **Author Contributions:** Conceptualization, A.G.-C. and P.J.E.-A. ; investigation, A.G.-C.; writing--original draft preparation, A.G.-C.; writing--review and editing, P.J.E.-A. ; supervision, P.J.E.-A. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was funded in part by Consejo Nacional de Ciencia y Tecnologia (CONACYT) and by Instituto Politecnico Nacional (IPN) under Grant SIP-20221495. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**