## 2 Materials and Methods
### Structural Reliability Estimation  
In the authors' previous studies, SHM-integrated reliability estimation is performed by generating fragility curves of different performance levels taking peak ground acceleration (PGA) as the random variable [46; 47]. This method can result in high computational cost as the number of available seismic ground motions increases. Compatible with the smartphone-based identification procedure presented in this study, it is expected that ground motion demands under a seismic event can be determined by a dense seismic network composed of smartphone seismometers [57]. Besides, as the number of input ground motions increases like in a mobile CPS scenario, accuracy of the fragility curve parameters may run into obstacles due to truncation and round-off errors. Therefore, in this study, the probabilistic structural response is directly obtained from log-normal distribution of the time history analysis results. Damping term in equation of motion is modeled with Rayleigh Damping where the associated matrix is a combination of stiffness and mass proportional damping. Alpha and beta coefficients are determined based on 2% damping ratio at first and third modes. For each ground motion taken from the 1994 Northridge Earthquake, a time history analysis is conducted, and the simulated response is obtained. Because the bridge considered in this study is a high frequency structure compared to the low frequency character of Northridge Earthquake records, it is assumed that the structure undergoes linear behavior and its response can be simulated with linear time history analysis. In this case, secondary performance indicators such as maximum drift or displacement become important as they are decisive in the basic engineering mechanics assumptions. Figure 7: Integration scheme of Matlab and OpenSees software. Therefore, the response from each seismic event is collected in terms of maximum deflection and finally, the distribution demand under the given set of earthquakes is obtained. Looking at the distribution demand as well as the reference code and regulation criteria, it can be predicted whether the structural response will exceed certain performance thresholds. In conclusion, with the proposed reliability estimation framework, the high computational cost of fragility curve development is swapped with a simpler approximation, provided that the ground motion response distribution matches well with log-normal type distribution features. It should be noted that 1994 Northridge Earthquake records perform as an exemplary dataset thanks to the high number of stations and well-distributed strong motion parameters, however, they do not necessarily mirror testbed site conditions presented in this paper. In other words, they are referred for demonstration purposes. In contrast with California, seismicity in the state of New York possesses a lower risk and lacks a comprehensive dataset available to public. However, in the event of ground motion record lack, synthetic ground motions can be generated for a particular site which has designated earthquake spectra [58]. What is more, in a futuristic scenario where there is seismic activity in urban areas, smartphones have shown feasible performance as low-cost seismometers which can be used to detect input ground motion imposed on civil infrastructure [57]. Considering the similar geometry and accordingly similar dynamic behavior of adjacent buildings, out of phase motions are not taken as a primary source of seismic damage. Therefore, forcing function at the boundaries are assumed to be uniform rather than multi-support excitation. It should be noted that changes in boundary conditions can also be monitored with the help of the proposed model updating procedure. Besides, given that the bridge lays on top of campus area with complete occupant access, glass facade integrity can also add additional life safety concerns in case of a seismic event.