## 1 Introduction  
Deriving economical, sustainable, and practical solutions without a compromise in infrastructure safety and integrity is a broad challenge in civil and structural engineering disciplines. The unpredictable nature of hazardous events combined with limited resources lead the current practice to inherit performance-based criteria in structural design and evaluation. Therefore, controlling the extent of structural damage rather than exclusively avoiding it, is the trending principle in up-to-date engineering codes and regulations [1, 2]. Observing the changes in vibration characteristics of structures with the state-of-the-art sensing and processing tools, structural health monitoring (SHM) technologies attract significant attention in research and industry in the last three decades [3, 4, 5, 6]. On the other hand; instrumentation, cabling, operation, and maintenance of SHM systems require labor work, knowhow, and financing; declining the growth rate of SHM use in practice. Especially in the past decade, these drawbacks lead researchersto focus on innovative methods such as noncontact vibration measurement techniques [7; 8; 9], wireless sensor network (WSN) and distributed sensor network (DSN) systems [10; 11; 12; 13; 14; 15; 16], as well as smart [17; 18; 19], mobile [20; 21; 22; 23], and multisensory [24; 25; 26; 27] sensing platforms. Eventually, smartphones are adopted into SHM such that their built-in sensors, operating systems, computation, and wireless communication capabilities can perform as structural vibration measurement devices [28; 29; 30; 31]. The authors' previous works present the first vibration-based SHM system (CS4 SHM) using crowdsourcing power [32] and offer multisensory solutions to citizen-induced errors by considering spatiotemporal [33] and directional [34] uncertainties. Without any prior engineering education and background, citizens as uncontrolled SHM device operators can provide a central server system with ubiquitous vibration data. The acquired data is autonomously processed for modal identification which is an important indicator of structural vibration characteristics. Unlike conventional SHM systems, CS4 SHM points out unorthodox monitoring issues which are concurrently discussed in the upcoming technological boom \"Industry 4.0\", the latter phase of digital revolution [35; 36]. Collecting the distributed crowd sensed information through a central server and conducting modal identification autonomously, civil infrastructures as physical objects are connected with server-side computing in a massive scale forming a CPS [37; 38; 39; 40; 41], or in some cases, an Internet of Things system [42; 43; 44; 45]. This highlights a significant potential to evolve from pure theoretical structural response simulation (FEM) to experiment-aided and calibrated models (model updating) in massive scales. In other words, with the help of autonomous, connected, scalable cyber networks; citizen-engaged sensing; digital (FEM predictions) and physical (field measurements) civil infrastructure representations; monitoring systems can be adopted to the upcoming technological innovations. Aforementioned hybrid models can be used for large-volume analysis to retrieve quick evaluation of structural status. This can be performed by utilizing the modal identification results, calibrating mathematical models, and obtaining the probabilistic failure distribution under a wide range of strong ground motions. Eventually, using identification results as model calibration tools, civil infrastructures' seismic response and structural reliability can be estimated [46; 47] to provide the decision makers with the necessary information. This study presents the last section of a PhD dissertation dedicated to cyber-physical system applications from structural engineering perspective [48]. The paper extends the outcomes of a crowdsourcing-based modal identification platform by modifying FEMs constructed with limited information.