**1. Introduction**

Earthquakes are one of the most destructive natural disasters in history. They are a potential cause of fatalities as well as structure and infrastructure damage in densely inhabited seismic-prone regions. In recent years, earthquakes have wreaked havoc on numerous cities around the world, causing a variety of issues [1–3]. Numerous scholars investigated seismic hazards to human lives and their economic effects [4–7]. Seismic activity is a severe natural force to which civil engineering structures are exposed and poses grave risks to human life [8, 9]. In seismically sensitive regions, constructing structures to resist this force becomes an economic burden. Previous research has examined the detrimental consequences of earthquakes on urban infrastructures [10–13]. This is why earthquake damage assessment is crucial for emergency response, disaster management, and seismic risk mitigation.

Seismic hazard analysis needs the use of region-specific attenuation relations. Since a large number of ground motion prediction equations (GMPEs) can be used to assess a region's seismic hazard, selecting proper GMPEs can significantly impact design and safety evaluation. However, determining an acceptable GMPE has proven to be somewhat challenging [14]. Therefore, establishing strong motion networks is a crucial initiative for seismic risk mitigation. Strong motion networks can provide recorded ground motions to near-real-time seismic damage assessment networks, such as Prompt Assessment of Global Earthquakes for Response [15] and Real-time Earthquake Damage Assessment using City-scale Time History Analysis [16], enabling a robust, accurate assessment. Crucially, the sensor density effect in a network on seismic damage assessment was examined as a detailed case study for Zeytinburnu District, Istanbul, Turkey [17]. They found that the sensor density has a proportional effect on the regional-scale seismic damage assessment accuracy. Today, strong motion networks are operating in Italy [18], Taiwan [19], India [20], Japan [21], Iran [22], Greece [23], Romania [24], the US [25], and so on.

Regarding Turkey, Kandilli Observatory and the Earthquake Research Institute (KOERI) developed Istanbul's Earthquake Rapid Response and Early Warning System (IERREWS) in 2002, which is one of the most advanced strong motion networks in the world. In addition, in July 2008, a 16-station strong motion network was installed in İzmir. Earthquake Research and Implementation Center (ERIC-DAUM) of Dokuz Eylül University (DEU, Izmir), Earthquake Research Department (ERD) of the General Directorate of Disaster Affairs (GDDA, Ankara), Izmir Metropolitan Municipality, and Ministry of Public Works and Settlement collaborated in this network's establishment. The Scientific and Technological Research Council of Turkey (TUBITAK) funded the project to obtain strong motion data for earthquake hazard assessment and to establish a real-time monitoring system in Turkey to address public safety concerns [26].

This work focuses on a case study regarding urban damage assessment in metropolitan Istanbul, Turkey, after a recent moderate earthquake (the *Mw* 5.8 Silivri Earthquake, 2019), as one of the most significant seismic events in the region since two major earthquakes (the Kocaeli and Düzce earthquakes) that struck the region in 1999. The utilization of real-time data obtained from a densely deployed strong motion network and comparison with a GMPE that provides empirical results is one of the most significant merits of this research.

The remaining sections of this chapter are organized as follows: Section 2 represents a literature survey on seismicity in the Marmara Region, earthquake early warning and rapid response system (EEWRRS) in Istanbul, structural health monitoring systems, and next generation attenuation (NGA), and ground motion prediction equations (GMPE) models. Section 3 provides insight near-real-time strong motion network incorporated into the earthquake early warning system in Istanbul. In addition, near-real-time hazard (ground motion distribution) and damage maps are generated using data recorded by the Istanbul Natural Gas Distribution Network Seismic Risk Reduction Project (IGRAS) system. Section 4 introduces a case study regarding a recent offshore earthquake that hit metropolitan Istanbul. In the case study, earthquake hazard maps are created using a proper GMPE, and building damage distribution is estimated. In Section 5, distribution maps were generated based on chosen GMPE following bias correction of phantom stations using data from the strong motion network. Hazard maps using an appropriate GMPE and near-real-time data during the event are compared and discussed in Section 6. Finally, Section 7 concludes the paper by presenting significant findings in two major remarks.
