**4. Conclusions**

In this study, present and possible future implementations of a crowdsourcing-based mobile cyber-physical SHM system are presented. Civil infrastructure as physical objects are connected to a cyber-structural model and a response simulation scheme, and the real vibration data obtained from smartphone users are used to calibrate these model parameters. This procedure includes a number of information processing phases such as mobile, server, and administrative components. The mobile platform digitizes structural vibrations via accelerometers and submits it to the server. The server conducts modal identification, returns, and stores the analysis results. The identification results obtained from smartphone sensors are used to update the FEM and increase its accuracy by minimizing the error between the model and the identified modal parameters, which is formerly created with limited information and modeling uncertainties.

Using the updated model as a baseline, structural responses subjected to 151 earthquake records are simulated by time history analyses. The displacement demand distribution obtained from the time history analysis results is evaluated according to the exemplary maximum allowed deflection criteria. Finally, for an earthquake scenario with a wide set of records, one can determine the structural reliability according to the desired performance levels. This information can provide the decision makers with a good foundation for risk assessment, preparedness, and mitigation. Based on the evaluation results of this cyber-physical information flow, the bridge service can be interrupted, structural members can be retrofitted, or the existing structure can be demolished if there is no feasible maintenance scenario. As the volume of invisible operations in computational zone increases, the cyber loops will become more remote and automated.

The framework is demonstrated on an actual pedestrian bridge structure, and the results are presented. The results show that even with limited information, accurate FEMs can be developed with the help of a model updating procedure. Besides, the necessary information is provided by smartphone sensor data and crowdsourcing which solely relies on participatory sensing and pure citizen contribution. Once the physical information is extracted from the sensors, the corresponding data can be combined with a deep mathematical process without any human intervention. Automation, connectivity, scalability, and mobility of the presented platform has a grea<sup>t</sup> potential for future mobile cyber-physical SHM systems. Especially, as the seismic monitoring arrays become dense and abundant (e.g., smartphone seismometers), seismic performance of a structure can be simultaneously evaluated with ubiquitous data according to the reference code regulations and standards.

**Author Contributions:** Conceptualization, E.O. Data curation, E.O.; Formal analysis, E.O.; Investigation, M.Q.F.; Methodology, E.O.; Supervision, M.Q.F.; Visualization, E.O.; Writing—original draft, E.O.; Writing—review & editing, M.Q.F.

**Funding:** This research received no external funding**.**

**Acknowledgments:** The authors would like to acknowledge Demosthenes Long from Public Safety and Daniel Held from Facilities, Columbia University, for their support throughout on-campus pedestrian bridge tests.

**Conflicts of Interest:** The authors declare no conflict of interest.
