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Urban Damage Assessment after the Mw 5.8 Silivri Earthquake: The Case of Istanbul City

Written By

Oğuzhan Çetindemir and Abdullah Can Zülfikar

Submitted: 21 December 2022 Reviewed: 03 January 2023 Published: 14 February 2023

DOI: 10.5772/intechopen.109758

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Natural Hazards New Insights Edited by Mohammad Mokhtari

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Natural Hazards - New Insights

Edited by Mohammad Mokhtari

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Abstract

This chapter presents the results of an urban damage assessment after a moderate seismic event, the Mw 5.8 Silivri earthquake, which is the most significant earthquake to have struck the region since two major catastrophic earthquakes, the Mw 7.6 Kocaeli and the Mw 7.1 Düzce earthquakes. First, distribution maps for earthquake parameters and building damages using an appropriate ground motion prediction equation are created for İstanbul. Then, near-real-time hazard and damage distribution maps are generated using the data recorded during the event by the ground motion network established in Istanbul. Comparing the results of the two analyses reveals that the ground motion and damage distributions generated by the selected ground motion prediction equations (GMPEs) are more conservative than those generated by the network, and this is because the actual station data surpass the GMPE’s projections. This research concludes by emphasizing the significance of both GMPEs and densely installed ground motion station networks that capture real-time data during earthquakes and providing motivations for constructing or expanding such systems.

Keywords

  • earthquake damage assessment
  • NGA GMPE
  • near-real-time strong motion network
  • earthquake hazard
  • earthquake risk mitigation

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, 2, 3]. Numerous scholars investigated seismic hazards to human lives and their economic effects [4, 5, 6, 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, 11, 12, 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.

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2. Literature survey

This section mainly focuses on a survey regarding four different aspects. First, high seismicity in Turkey, particularly in the Marmara region, is briefly explained. Secondly, two initiatives functioning seismic risk mitigation measures, earthquake early warning, and structural health monitoring systems in the region, are introduced. Finally, next generation attenuation (NGA) models are discussed.

2.1 Seismicity in Marmara region

Turkey is one of Europe’s most seismically active countries, and it has been struck by multiple catastrophic earthquakes throughout its history. These earthquakes (Mw 6.7 1992 Erzurum, Mw 6.1 1995 Dinar, Mw 6.3 1998 Adana-Ceyhan, Mw 7.6 1999 Kocaeli, Mw 7.2 1999 Düzce, Mw 6.4 2003 Bingöl, Mw 7.2 2011 Van, Mw 7.0 2020 İzmir, and Mw 6.7 2020 Elazığ) resulted in severe economic losses and physical damage, as well as a large number of fatalities. Ambraseys and Finkel [27] documented 32 devastating earthquakes that hit the larger Marmara Sea region between the fourth and nineteenth centuries, affecting Istanbul. Following the devastation caused by the two major earthquakes that struck the Marmara region in 1999, governmental, non-governmental, and academic organizations in Turkey have acknowledged the need for extensive response planning based on rigorous risk analyses of likely seismic hazards in Turkey and Istanbul in particular [5].

Le Pichon, Şengör [28] examined an earthquake scenario and provided a projection for two active fault lines located 20–30 kilometers southwest and south of central Istanbul: (1) the Central Marmara Basin (CMB) and (2) the North Boundary Fault (NBF), respectively. Some researchers found that within the next 30 years, the likelihood of an earthquake of magnitude seven or greater occurring in the Marmara Sea is around 65% Parsons [29, 30]. Considering more than two decades have passed after two severe earthquakes, a possible Istanbul earthquake may be in the near future.

Ansal, Akinci [31] provided significant results on earthquake loss assessment in Istanbul. Based on the 1999 Kocaeli earthquake investigation, they discovered critical parameters for the seismic ground motion simulation. In terms of the loss estimate, the CMB fault section is more critical due to the higher seismic excitation intensity it may cause. Okay, Kaşlılar-Özcan [32] indicated that the CMB segment has not raptured since 1766 and poses an urgent threat to the region. Ergintav, Reilinger [33] determined that the Princes’ Island region of the Marmara Sea is most likely to cause the next Mw > 7 earthquakes along the NAFZ. Recent studies were conducted by Woessner, Laurentiu [34] to construct a reference hazard model for Europe. They reported that the mean peak ground acceleration (PGA) with a 10% probability of exceedance in 50 years provides the highest seismic hazard estimate (PGA ≥ 0.25 g) along the NAFZ, with values up to 0.75 g in the northern Aegean and the Marmara Sea.

2.2 Earthquake early warning and rapid response system (EEWRRS) in Istanbul

Ground motion estimation without earthquake source parameters aims to provide near-real-time (within a few seconds of the event origin time) assessments of earthquake-induced ground motions and associated building damage in Istanbul. It is based on the fact that a dense accelerometric network (approximately 2–3 kilometers between adjacent instruments) is installed in the southern part of the city, where both population density and earthquake hazards are highest. No source parameters (magnitude, rupture length, or mechanism) are required to compute the earthquake-generated ground motion distribution. PGAs and spectral accelerations (SAs) at various periods are interpolated using two-dimensional splines to derive the ground motion values at the center of each 0.01×0.01° geo-cell (1120×830 m grid size). Spectral displacements are used to calculate the seismic demand in the center of each geo-cell. Coherency functions are being used to assess shaking maps created by interpolating discrete ground motion measurements. The ELER hazard module executes the following tasks: (1) using a regional seismotectonic database, determine the most likely site of the earthquake’s origins, (2) the spatial distribution of chosen ground motion parameters at engineering bedrock is estimated using region-specific ground motion prediction models, (3) a bias correction of ground motion estimations using strong motion data where available, (4) estimation of the spatial distribution of chosen site-specific ground motion parameters by the use of a regional geology database and appropriate amplification models, (5) alternatively, ground motion parameters can be estimated site-specifically using Next Generation Attenuation (NGA) models.

Due to the complicated segmentation of the Marmara fault line and its proximity to the city, the Istanbul earthquake rapid response system (IEEWS) designed a robust and straightforward earthquake early warning algorithm based on the exceeding threshold values. To trigger, at least three stations must surpass the threshold level within a 5-second interval. Böse [35] stochastically simulated 280 earthquake scenarios in the Marmara Sea ranging from Mw 4.5 to Mw 7.5 and determined that the average early warning time spans between 8 and 15 seconds, depending on the event’s source location.

The data transmission between the remote stations and the KOERI processing hub is accomplished via fiber optic cable with redundancy provided by a satellite system. The time required to transmit data from remote stations to the KOERI data center is a few milliseconds via fiber optic connections and less than a second via satellites. The continuous online data from these stations is processed at the hub, and subsequent alerts of emerging potentially disastrous ground motions provide real-time warning to vital infrastructures, allowing for the activation of shut-off mechanisms before the damaging waves reach the location.

At the moment, the IEEWS has not issued a public alert. Only the Istanbul Natural Gas Distribution Company (IGDAS) and the Marmaray Tube Tunnel actively utilize the EEW alert to activate automated shut-off systems in these facilities [36]. Both end users manage their own networks, with strong motion stations co-located at high-pressure district gas regulators in the case of IGDAS and spaced throughout the tunnel in the case of Marmaray. Regarding the IGDAS, gas flow is automatically halted at the district regulator level in response to IEEWS alarms and the local site exceeding ground motion parameter threshold values. Local threshold values are determined on a case-by-case basis based on the local building stock.

The Marmaray Tube Tunnel, completed in 2013, is operated by the Turkish State Railways (TCDD). Train operations within a 1.4-kilometer-long tunnel beneath the Bosphorus, which connects the city’s European and Asian sides, can be halted based on a combination of IEEWS alerts and a local threshold exceedance detected by its 26 tunnel sensors. Although IEEWS signals were sent to these vital facilities in recent seismic events such as the Mw 3.8 Yalova on 13 August 2015 and the Mw 4.2 Marmara Sea on 16 November 2015, Mw 4.5 Silivri on 24 September 2019, and the Mw 5.8 on 26 September 2019, no action was taken because the local threshold values were not surpassed.

Along with IEEWS, KOERI has implemented the regional EEW algorithms VS(SC3) and Probabilistic and Evolutionary Early Warning System (PRESTo) as part of the REAKT project. The Virtual Seismologist (VS) algorithm [37] is a network-based Bayesian approach to EEW, and the SED group integrated an operational VS into the evolving Californian EEW prototype system [38] with support from the USGS ShakeAlert project. In 2013, VS was integrated as a set of self-contained modules into the open-source and widely distributed SeisComP3 (SC3) [39, 40] earthquake monitoring software, incorporating an EEW algorithm in the same system that many seismic networks use on a regular basis [41, 42]. This solution is referred to as “VS” (SC3). Further detail can be found in Clinton, Zollo [43]. These applications take advantage of KOERI’s Marmara regional seismic network, which consists of 40 broadband and 30 strong motion seismic stations. PRESTo is currently monitoring 18 of the regional network’s strong motion stations. Scenarios for various seismic events, including the 1999 Mw 7.4 Kocaeli Earthquake, show that a recurrence of this event would provide Istanbul with around 11 seconds of early warning. It is intended to expand the number of stations, including those utilized by PRESTo. Regional EEW algorithms VS (SC3) and PRESTo are not integrated with the existing IEEWS. Therefore, in their current configuration, the VS (SC3) and PRESTo systems would not provide warning in advance of strong motions associated with near-source seismic occurrences such as those in the Marmara Sea. On the other hand, the regional early warning system (EWS) is meant to be used in conjunction with the threshold-based IEEWS to provide warning for remote events that could be catastrophic for tall buildings and long-span bridge structures. On May 24, 2014, the Mw 6.9 Northern Aegean earthquake was intensely felt across Istanbul’s high-rise buildings and was accurately characterized by VS (SC3) within 36 seconds of the origin time. The Mw 6.9 earthquake in Northern Aegean illustrated the importance of merging regional and threshold-based techniques. With the IEEWS and regional EWS algorithms, Istanbul has on-site structural monitoring efforts for historical buildings, high-rise buildings, and suspension bridges. These initiatives are not currently integrated into early warning systems.

2.3 Structural health monitoring systems

Instrumentation of major structures using strong ground motion recorders monitors the vibrations during an earthquake. Comparing these recordings with those made prior to the earthquake can reveal differences in structural response that have relevance for structural damage and a loss in seismic resistance. Therefore, these systems can be integrated into EEW systems to take critical measures for mitigating seismic risk. The structural health of significant structures in the Marmara region, particularly in Istanbul, has begun to be examined due to the region’s high seismicity. Especially after the strong earthquakes in 1999, the public began to demand SHM systems, and the relevant government officials began to meet these demands.

SHM systems are currently monitoring five different long-span cable-supported bridges in the Marmara region. These are the 15 July Martyrs Bridge (the First Bosphorus Bridge), the Fatih Sultan Mehmet Suspension Bridge (the Second Bosphorus Bridge), the Yavuz Sultan Selim Bridge (the Third Bosphorus Bridge), the Osman Gazi Bridge (Izmit Bay Bridge), and the 1915 Çanakkale Bridge (Dardanelles). Regarding their SHM systems, design considerations and further information can be found in [44]. The following are some examples of currently monitored facilities in the Marmara region: Hagia Sophia, The Maltepe Mosque, Sapphire Tower, Kanyon Building, Isbank Tower, Polat Tower, and the Marmaray Tube Tunnel [45].

2.4 Next generation attenuation and ground motion prediction equations models (NGA GMPE)

The initial stage is to mitigate the potential seismic damage by developing earthquake-resistant structures. Additionally, seismic hazard assessments of a region are critical for preventing earthquake-related destruction. Establishing an efficient seismic hazard and a comprehensive seismic risk assessment is crucial for the nation’s sustainable growth.

Seismic hazard analyses demand the application of region-specific attenuation relations. Ground motion prediction equations (GMPEs) are used to determine ground motion parameters required for designing and evaluating vital structures [46]. Since a large number of GMPEs can be used to assess a region’s seismic hazards and risks, selecting proper GMPEs can significantly impact design and safety evaluation [14]. Attenuation relationships allow for the accurate estimation of ground motion parameters (macroseismic intensity, PGA, PGV, and SAs) in terms of magnitude and source-to-site distance, and site features [47].

GMPEs for shallow crustal earthquakes were recently created in the Next Generation Attenuation (NGA). PEER created a number of GMPE models based on the NGA-West1 database (original NGA project). Following that, GMPEs were updated using the NGA-West2 database. The average horizontal component of shallow crustal earthquakes in active tectonic zones is estimated empirically using the PEER NGA-West2 database [48]. While it was designed for tectonically active locations, it is also applicable to other regions. NGA-West2 investigated the regionalization of ground motion properties, including an elastic attenuation, site response, and within-event standard deviation. GMPEs are frequently used to predict ground motions using deterministic and probabilistic seismic hazard analyses. The results of the seismic hazard analysis are used to conduct site-specific seismic analysis and design of facilities, to estimate social and financial losses, and to generate regional seismic hazard maps for use in building standards and financial estimation, among other purposes [47].

The NGA-West2 incorporates regionalization to account for regional changes in far-source distance attenuation and soil response. This is accomplished by using recordings from severe earthquakes in other active tectonic zones and California data [49]. As part of the NGA-West2 project, five GMPEs were developed: Abrahamson, Silva, and Kamai (ASK2014) [48], Boore, Stewart, Seyhan, and Atkinson (BSSA) [50], Campbell and Bozorgnia (CB) [51], Chiou and Youngs (CY2014) [52], and Idriss (I) [53].

The NGA-West2 project developed GMPEs for the purpose of calculating the medians and standard deviations of average horizontal component intensity measures (IMs) for shallow crustal earthquakes in active tectonic zones [50]. The equations were developed using data from a global database containing M 3.0-7.9 events. NGA-West2 GMPEs have a general limit of M8.5 for strike-slip faults, M8.0 for reverse faults, and M7.5 for normal faults, and a rupture distance Rrup or Joyner & Boore distance, Rjb of 0–300 km [53]. The ground motion IMs that comprise the GMPEs’ dependent variables include the horizontal components PGA, PGV, and 5%-damped SAs [50]. These IMs were calculated using the RotD50 parameter [54], which represents the median horizontal single-component ground motion over all non-redundant azimuths. ASK2014 is applicable to magnitudes 3.0–8.5, distances from 0 to 300 km, and spectral periods ranging from 0 to 10 seconds. Regional differences were incorporated into the ASK2014 model based on distances. The ASK2014 model assumes that in active crustal zones, median ground motions at distances smaller than around 80 km are similar worldwide. The model confirmed that median stress dips are similar to those experienced during earthquakes in many active crustal locations such as California, Alaska, Taiwan, Japan, Turkey, Italy, Greece, New Zealand, and Northwest China [48]. BSSA2014 GMPE was developed to be generally applicable for earthquakes with magnitudes of M 3.0 to 8.5 (except for the lack of constraint for M > 7 normal slip events), at distances ranging from 0 to 400 km, at locations with Vs30 values ranging from 150 to 1500 m/s, and for spectral periods ranging from 0.01 to 10 sec [50]. The BSSA2014 model includes regional variation in source, path, and site effects. The CY2014 GMPE model is appropriate for predicting horizontal ground motion amplitudes associated with earthquakes in active tectonic zones that meet the following criteria [49]: (1) 3.5 ≤ M ≤ 8.5 for strike-slip earthquakes, (2) 3.5 ≤ M ≤ 8.0 for reverse and normal faulting earthquakes, (3) ZTOR ≤ 20 km, (4) 0 ≤ RRUP ≤ 300 km and 180 ≤ VS30 ≤ 1500 m/s.

Akcan et al. [55] evaluated the next generation attenuation (NGA) ground motion models that would be used in the region by comparing them to ground motion recordings from the Silivri earthquake in 2019. Among the NGA-West2 models, they found that ASK2014 and CY2014 provide the best fit for local PGA datasets. In conclusion, the high performance of these models proves that they can be used to estimate ground motions in metropolitan Istanbul.

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3. Near-real-time strong motion network in Istanbul

Istanbul has one of the most sophisticated strong motion networks in the world, integrated with Istanbul’s Earthquake Rapid Response and Early Warning System (IERREWS), constructed in 2002 by Kandilli Observatory and the Earthquake Research Institute (KOERI) [56]. In this section, Istanbul’s strong motion network is introduced. Later, the near-real-time hazard maps created by the officials using an earthquake parameter (PGA) recorded from the network are presented.

3.1 Introduction to the network

Apart from structures, a strong earthquake in Istanbul could damage infrastructures like the natural gas distribution network and subsequent damage from gas leaks from damaged pipelines (e.g., building damage, deaths, and injuries from fire and explosions), as well as production failures due to the natural gas cut-off for industrial subscribers. As a result, it is vital to determine the natural gas distribution system’s reliability in the event of an earthquake to calculate the total economic losses and develop appropriate emergency response measures and plans. Following the loss and damage caused by the August 17th, and November 12th, 1999 earthquakes, the local administration, governmental agencies, non-governmental organizations, and scholars recognized the importance of developing elaborate earthquake response plans based on detailed earthquake analyses in Istanbul. This resulted in the Istanbul Metropolitan Municipality commissioning the research as “Updating the Possible Earthquake Losses in Istanbul” [57]. As a result of these investigations, IGDAS and the KOERI conducted a study to design a real-time risk mitigation system for the gas distribution network.

The strong motion network consists of 832 strong motion stations for use in automatic shut-off mechanisms when a ground motion parameter threshold is exceeded. The network’s accelerometer stations were created in collaboration with the Turkish Scientific and Technological Research Center (TUBITAK). KOERI provides firmware for real-time ground motion parameter calculation. As shown in Figure 1, these accelerometers were installed at 832 selected district regulator stations to enable real-time shut-off of gas valves using the parametric data received online from the stations. The network was also built in the city’s south, where population and seismic risk are highest. Figure 2 depicts Istanbul’s night-time population density.

Figure 1.

The location of the strong motion network in Istanbul.

Figure 2.

Night-time population density distribution for metropolitan Istanbul.

The accelerometer stations were configured to compute the following real-time strong motion parameters to initiate the shut-offs: (1) Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV), (2) Arias intensity for ground motions in the x-direction (lax), (3) Cumulative Absolute Velocity integrated with a 5 cm/s2 lower threshold (CAV5) appears to adequately represent the ground motion’s longer-period (lower-frequency) components, (4) As response spectral parameters, Pseudo-Spectral acceleration (PSA), Pseudo-Spectral velocity (PSV), and Spectral displacement (Sd), (5) As initially stated by [58], Spectral Intensity (SI) is defined as the average spectral velocity over the vibration period range [0.1, 2.5] s [59].

The KOERI earthquake rapid response system’s real-time ground motion data and the strong motion network’s accelerometric data are integrated to estimate earthquake hazards (real-time ground motion distribution) and risks (damage distribution). For rapid response purposes, a total of 220 strong motion stations (110 from KOERI and 110 from IGDAS) were first integrated in 2013 [60]. Data integration is carried out using the Earthquake Loss Estimation Routine (ELER)-earthquake shaking map algorithm. The data is merged at the KOERI and IGDAS Scada Centers via two servers. The data is exchanged via a fiber optic virtual private network (VPN).

The Istanbul Natural Gas Distribution Network Seismic Risk Reduction Project (IGRAS) earthquake risk mitigation system comprises three major modules: a scenario earthquake module, a real-time earthquake module, and a map management module. These primary modules are subdivided into the following: (a) an earthquake hazard map module; (b) an IGDAS pipeline infrastructure damage assessment module; and (c) a service box damage assessment module.

The earthquake hazard map module generates grid-based distributions of ground motion parameters such as macroseismic intensity, PGA, PGV, and spectral acceleration for various vibration periods based on the magnitude and location of the earthquake. The module makes use of GMPEs that are appropriate for Istanbul, as well as grid-based local site information such as Vs30 and a fault database. The recorded ground motion parameters from KOERI and IGDAS strong motion networks are used for GMPE bias correction, and integrated final earthquake hazard maps are delivered. The IGDAS pipeline infrastructure damage assessment module associates the earthquake hazard information generated by the earthquake hazard map module with the natural gas infrastructure elements and assesses the damage to the natural gas infrastructure elements analytically. A module for calculating natural gas building damage first connects the earthquake hazard and the building inventory and then calculates the building damages. Then, it correlates the findings of the building damage analysis with the locations of natural gas service boxes and determines the estimated number of damaged ones. Additionally, a GIS-based Web application for the IGRAS system is designed, which allows for online monitoring of scenario-based and real-time earthquake hazard, damage, and loss estimation findings.

3.2 Earthquake hazard maps created by the IGRAS system during the Mw 5.8 Silivri earthquake

During the Mw 5.8 Silivri earthquake, the real-time earthquake ground motion data was obtained by the rapid response and risk mitigation system in İstanbul. Figures 3–5 illustrate PGA hazard maps automatically generated by the network at intervals of about 30 minutes after the earthquake. As the network updates the data collected after the event, the PGA distribution and earthquake information change for each map, especially between the first and the last two maps created. Figures show that the final PGA is between 0.3 and 0.40 g.

Figure 3.

PGA distribution map created at 14:06:50.

Figure 4.

PGA distribution map created at 14:35:40.

Figure 5.

PGA distribution map created at 15:02:14.

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4. Earthquake hazard maps and damage assessment: aftermath of the Mw 5.8 Silivri earthquake

This section will share earthquake hazard maps and results of building damage distribution after the Silivri Earthquake (Mw 5.8). First, a brief introduction about the Silivri Earthquake will be given. Then, seismic hazard maps created using ELER will be presented. Finally, the damage estimations will be depicted based on hazard maps. Details regarding adopted methods for urban damage assessment can be found in the ELER V3.0 Manual [61].

4.1 A brief information on the Mw 5.8 Silivri earthquake

On September 26, 2019, at 13:59 Turkish time (GMT +3), an Mw 5.8 offshore earthquake struck the Marmara Region, Turkey. This earthquake occurred at a depth of 6.99 kilometers from the ground and is located 21.99 kilometers from the nearest settlement, Silivri, in Istanbul. Figure 6 depicts the epicenter of the earthquake. 150 aftershocks with magnitudes ranging from 1.0 to 4.1 were recorded between the mainshock and 07:40 on September 27, 2019 [62]. The Silivri earthquake is significant since it is the region’s largest earthquake since the 1999 Kocaeli (Mw 7.6) and 1999 Düzce (Mw 7.1) earthquakes and their aftershocks. For further information, readers are advised to refer a recent paper by [63].

Figure 6.

The epicenter of the Mw 5.8 Silivri earthquake.

4.2 Earthquake hazard maps during the Silivri earthquake

The earthquake hazard maps are generated by grid-based distributions of ground motion parameters such as macroseismic intensity, PGA, and spectral acceleration for various vibration periods (SA02, SA10) based on the magnitude and location of the earthquake. This is achieved by making use of ground motion prediction equations (GMPEs) that are appropriate for Istanbul, as well as grid-based local site information such as Vs30 and a fault database. Figure 7 illustrates the Vs30 map for metropolitan Istanbul. The choice of proper GMPEs can have a significant impact on design and safety evaluation as mentioned before (see Section 2.4). It is well known that attenuation relationships offer a satisfying estimation of ground motion parameters (PGA and SAs) depending on the magnitude and source-to-site distance.

Figure 7.

Vs30 map for metropolitan Istanbul.

The NGA2014’s relationship with regional factors affecting Turkey in the aftermath of the mid-sized Marmara Sea earthquake was examined by [64]. According to their study, it appears that Chiou and Youngs 2014 (CY2014) provides the best fit to local PGA datasets recorded during the 26 September 2019 Silivri earthquake.

The CY2014 GMPE model is appropriate for predicting horizontal ground motion amplitudes associated with earthquakes in active tectonic zones that meet the following criteria [49]: (1) 3.5 ≤ M ≤ 8.5 for strike-slip earthquakes, (2) 3.5 ≤ M ≤ 8.0 for reverse and normal faulting earthquakes, (3) ZTOR ≤ 20 km, (4) 0 ≤ RRUP ≤ 300 km • 180 ≤ VS30 ≤ 1500 m/s. Therefore, in this work, CY2014 GMPE model is used for generating the earthquake hazard maps.

The Modified Mercalli (MMI) conversion has been achieved through the regression relationships developed by [65]. The distribution maps for earthquake parameters (MMI, PGA, SA02, and SA10) are depicted in Figures 8 and 9. The European coastal part of Istanbul represents the highest intensity, MMI V (Figure 7a). PGA is found 0.1529 g, as seen in Figure 8b. Spectral accelerations descend from the coastline to the midland, as seen in Figure 9.

Figure 8.

Earthquake hazard maps (ground motion distribution) using CY14 GMPE model: (a) MMI, (b) PGA.

Figure 9.

Earthquake hazard maps (ground motion distribution) using CY14 GMPE model: (a) SA02 (b) SA10.

4.3 Building damage assessment

The loss estimation engine for the Level 1 (Loss assessment) module in ELER is based on macroseismic damage estimation tools and aims to analyze both building damage and casualties. In Level 1, the intensity-based empirical vulnerability relationships and casualty vulnerability models based on multiple approaches can be utilized. The intensity grid should be a MATLAB (.mat) file containing a grid matrix and a reference vector created in the Hazard module. The building database file is essentially a Shapefile (.shp) containing the distribution of buildings within each cell. This file may also include the population of each cell for the purpose of computing casualties. In the absence of a population field in the building database, casualty estimates are approximated using the regional population (obtained from the Land Scan population distribution). The vulnerability-ductility as a MATLAB (.mat) file including a table describing the vulnerability, ductility, t parameter, and replacement cost of each building type. ELER scans the building database for each building type specified in the vulnerability-ductility table and, if found, calculates the damage.

For Level 1 analysis, the building inventory and population data consist of grid-based (geocell) building and population distribution. Based on the Risk UE building taxonomy, the building distribution for the Marmara region is used as sample data. In ELER, Turkey’s data is supplied as a model for other regions and nations to develop/incorporate their inventory data.

Damage estimation involves obtaining a cumulative damage probability using a normal distribution for each building type in Level 1. The damage probability distribution is dependent on the vulnerability and ductility factors of each building [66]. They derived the observed damage-based vulnerability approach known as the macroseismic method from the definition supplied by the European Macroseismic Scale [67] utilizing classical probability theory and fuzzy-set theory. As the purpose of a Macroseismic Scale is to acquire a measure of the earthquake’s severity based on the damage sustained by the buildings, the scale itself can be used as a vulnerability model for predicting purposes to provide the likely damage distribution for a given intensity.

As discussed previously, the vulnerability table provides these parameters for each building type. The probability of cumulative damage is discretized to yield the five damage states as D1-slight damage, D2-moderate damage, D3-substantial to heavy damage, D4-very heavy damage, D5-destruction.

Since more significant damage is expected in coastal areas close to the epicenter, only Silivri, Çatalca, Büyükçekmece, Beylikdüzü, Esenyurt, and Avcılar are considered for damage distribution plots. Figure 10 illustrates building damage distribution created employing the MMI results provided from Hazard Module in ELER. Slightly higher distribution levels of damage are seen in Esenyurt, Avcılar, and Beylikdüzü.

Figure 10.

Building damage distribution obtained for the Mw 5.8 Silivri earthquake: (a) D1-slight damage, (b) D2-moderate damage.

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5. Near-real-time hazard and damage distribution maps generated using the data recorded by the strong motion network in Istanbul

The Hazard module estimates spatially distributed intensity and ground motion parameters (PGA, PGV, Sa, and Sd) using region-specific ground motion prediction equations and gridded shear wave velocity data for a given earthquake magnitude and epicenter. In this section, near-real-time earthquake parameters (PGA, SA02, and SA10) and damage (slight and moderate) distribution maps are generated by utilizing data from Istanbul’s strong motion network. The data collected from triggered stations during the Silivri earthquake were used as event data, and distribution maps were plotted based on the GMPE of CY2014 after bias correction of phantom stations by the data utilized from the strong motion network in İstanbul. The source type panel defines the source mechanism associated with the event. For small-magnitude events, the source can be given as a point; for large-magnitude events, the user can specify the source type as a finite fault. The source type was assigned as “point source” since we simulated a moderate earthquake in this case. ELER can use ground motion prediction equations with Vs30 [52, 68, 69, 70] as an input parameter to directly calculate the ground motion values at the surface. This feature was employed because the network recorded the event at the surface level. Custom site condition map is used as in the form of Vs30 grids.

Figures 11–13 show the PGA, SA02, and SA10 distributions, respectively. Of 832 stations, 116 detected ground vibrations during the Silivri earthquake. Some data collected from acceleration stations is seen as a clear difference from the estimation of phantom stations. These abrupt and apparent variations in the distribution maps are shown as circles with a dashed line. As expected, the higher ground accelerations are seen in Silivri and Büyükçekmece’s coastal region. This condition is also the same for SA02. However, Beylikdüzü also has a high SA10 spectral acceleration as other specified coastal counties. PGA is found to be 0.3496 g, as seen in Figure 11.

Figure 11.

PGA distribution maps obtained for the Mw 5.8 Silivri earthquake based on the GMPE of CY2014 after bias correction of phantom stations by the strong motion network in İstanbul.

Figure 12.

SA02 spectral acceleration distribution maps obtained for the Mw 5.8 Silivri earthquake based on the GMPE of CY2014 after bias correction of phantom stations by the strong motion network in İstanbul.

Figure 13.

SA10 spectral acceleration distribution maps obtained for the Mw 5.8 Silivri earthquake based on the GMPE of CY2014 after bias correction of phantom stations by the strong motion network in İstanbul.

The building damage was estimated using the spectral acceleration-displacement-based vulnerability assessment methodology. ELER employs a variety of strategies for achieving this goal in the Level 2 (Loss assessment) module. There are currently two options for the seismic demand spectrum: (1) Euro Code 8 spectrum and (2) IBC 2006 spectrum. In this work, the construction of the 5%-damped elastic response spectra is selected as IBC (International Building Code) 2006. [71] provides a general horizontal elastic acceleration response spectrum. It is defined by (1) spectral accelerations at short period and 1-sec period, respectively, (2) short period and 1-sec period design response spectral accelerations adjusted for the specified site class and damping value (SDS, SD1), (3) corner periods of the constant spectral acceleration region given by T0 = 0.2TS and TS = SD1/SDS, and (4) long-period transition period. It is a regional-dependent parameter, and it is assumed that TL = 5 s herein. In IBC 2006 and NEHRP 2003 Provisions [72], the recommended values for site and damping adjustments are stated. Spectral acceleration values at 0.2 and 1 sec periods are required to generate the IBC-2006 demand spectrum for each geographical unit. The user provides two MATLAB (.mat) files including a grid matrix and a reference vector of 0.2 and 1.0 sec spectral accelerations.

These two spectral acceleration files can be obtained from the previous calculations in Hazard Module or it can be developed by the user in a proper format for Level 2. In this section the spectral accelerations recorded from the strong motion network in İstanbul during the Silivri earthquake were used.

For the estimation of building damage in a Level 2 module, analytical fragility relationships and spectral acceleration-displacement-based vulnerability assessment approaches are applied. In the current case study, Capacity Spectrum Method (CSM-ATC 40) [73] was employed. The CSM is an approximate heuristic method that essentially assumes that a complex non-linear multi-degree-of-freedom system, such as a multi-story building experiencing severe plastic deformations during an earthquake, can be modeled as an equivalent single-degree-of-freedom system with an appropriate level of inelasticity. The simplicity of the procedure is the advantage of the method since no time history analysis is required.

The building and population data for Level 2 analysis are grid-based (geocell) urban building and demographic inventory. For building grouping, the European building taxonomy created as part of the EU-FP5 RISK-UE project [66, 74].

Since more significant damage is expected to occur in coastal areas close to the epicenter, only Silivri, Çatalca, Büyükçekmece, Beylikdüzü, Esenyurt, and Avcılar are considered for damage distribution. Figures 14 and 15 show slight and moderate damage distribution maps of buildings obtained for the Mw 5.8 Silivri earthquake using the strong motion network data, respectively. The higher levels of damage are seen in Silivri, partially in the coastal regions of Büyükçekmece, Beylikdüzü, and Avcılar. In addition, a higher damage distribution is observed in Esenyurt than in other hinterland regions. Because extreme and complete damage are not observed, their maps were not created.

Figure 14.

Slight damage distribution map of buildings obtained for the Mw 5.8 Silivri earthquake.

Figure 15.

Moderate damage distribution map of buildings obtained for the Mw 5.8 Silivri earthquake.

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6. Discussion

The results in Sections 4 and 5 are discussed in terms of PGA, SA02, SA10 (hazard maps), and damage distribution maps (loss assessment) of buildings, with comparisons between attenuation relationships (CY2014) and the distribution maps generated from data recorded by the strong motion network in İstanbul during the Mw 5.8 Silivri earthquake.

  • PGA distribution maps created automatically at Istanbul Natural Gas Distribution Company (IGDAS) Scada Centers by IGDAS strong motion stations require a specific amount of time until the final map is made, providing reliable results of PGA values and earthquake information. This is because earthquake parameters are continuously updated after the event. After corrections are made to earthquake parameters, the map is updated accordingly, and very comparable results are obtained with the map created by using data acquired from the near-real-time ground motion network and the map created at Technical Safety and SCADA Directorate Disaster Management Chiefdom by IGDAS (see Figures 5 and 11).

  • It was determined that the maximum acceleration value of the PGA distribution map drawn with the real station network was roughly 2.3 times higher than the maximum acceleration value obtained in the PGA distribution map drawn with the attenuation relationship utilizing the Level 1 analysis module (see Figures 8b and 11).

  • In Section 5, seismic hazard maps (PGA, SA02, and SA10) were created using the records obtained from the strong motion station network and calculated using an appropriate attenuation relationship (CY2014) after bias correction of phantom stations using the data utilized from the strong motion network in İstanbul. In Figure 13, it is observed that some PGA values recorded by the network are not compatible with the values obtained from the bias correction of phantom stations using CY2014.

  • As mentioned in Section 5, currently, out of the 832 strong motion stations constructed by IGDAS in Istanbul, 116 stations were able to record the offshore event since most of the stations were located in highly populated counties in which seismic risk was considered to be higher. Because most stations were away from the event epicenter, 716 strong motion stations were not triggered.

  • Although the event was felt in many cities in the region (such as Yalova, Kocaeli, Bursa, Kırklareli, and Tekirdag) and its destructive significance was moderate, the significant effects of the earthquake were observed more on the European side of Istanbul, where the epicenter was closer to the region. This can be detected from the PGA distribution maps created using recorded stations right after the earthquake (see Figures 11–13).

  • In Section 4, damage analysis was conducted in Level 1 using the MMI results of GMPE-CY2014 obtained from the Hazard Module in ELER. Section 5 presents the analysis results of Level 2 using SA02 and SA10 recorded from the station network in İstanbul. It is seen that the damage distributions corresponding to the highest damage per cell obtained from Level 2 (Section 5) are approximately 2.4 times more than those obtained from Level 1 (Section 4). The reason for this is that the actual station recordings exceed the CY2014 predictions.

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7. Conclusions

This chapter mainly focuses on urban damage assessment using data acquired from a real-time ground motion network during a recent moderate offshore event, the Silivri earthquake (Mw 5.8), which struck the Istanbul metropolitan area. Earthquake early warning and rapid response systems in İstanbul are introduced as one of the most sophisticated measures to mitigate seismic risk in the world. This highlights the significance of such systems’ beneficial effects in earthquake-prone regions. The major findings of this paper are listed in two aspects as follows:

Urban damage assessment after a moderate earthquake event:

  1. Using data from 116 stations recorded in real-time, seismic hazard (ground motion distribution) maps are created for earthquake parameters (such as PGA, SA02, and SA10). To the authors’ best knowledge, this is the first research conducted for the region using real-time strong motion data collected from densely installed stations to generate a hazard map of a metropolitan area like Istanbul. In addition, it is believed that this is a convenient method to validate the results obtained from attenuation relationships (GMPEs).

  2. Regarding European counties, the highest PGA is monitored in the coastal regions of Silivri and Buyukcekmece, which are the two counties that are nearest to the epicenter (see Figure 11).

  3. In the case that the expected Istanbul earthquake occurs near the Central Marmara Basin (CMB) fault, where the Silivri earthquake occurred, significant damage is also expected in Silivri and along with its coastal line. Higher damage distribution will be seen in some part of the coastal regions of Büyükçekmece, Beylikdüzü, and Avcılar. In addition, Esenyurt may have a greater damage distribution than other hinterland regions mentioned.

  4. Comparing the results of the two analyses reveals that Level 1 damage distributions are more conservative than Level 2 outcomes, and this is because the actual station recordings exceed the CY2014 predictions.

  5. The recorded ground motion parameters from the strong motion networks are used for GMPE bias correction, and integrated final earthquake hazard maps are delivered. In this study, as in previous research stated, CY2014 provides one of the best fits to local PGA datasets recorded during the earthquake, CY2014 was chosen as GMPE for attenuation relationship. However, as stated in the Discussion Section, since the results are more conservative than the actual results, a study with different attenuation relationships as future work will contribute to the literature.

  6. Only the counties nearest to the epicenter are considered for damage distributions map plotting. In addition, extensive and complete damage maps were not plotted because only slight and moderate damage was detected due to the Silivri earthquake as a result of analyses.

  7. Although Level 2 damage distribution results per cell were much higher than Level 1, similar damage distribution patterns were obtained as a result of both analyses (see Figures 10, 14 and 15).

  8. Damage assessments are conducted using the Marmara region’s building inventory provided by ELER as sample data. Utilizing an updated building inventory and Vs30 values will be beneficial for future endeavors.

  9. During the on-site inspection, it was stated that 320 extremely damaged structures were identified. It is believed that there are two reasons behind the difference between analysis results and field inspections. First, the sample inventory data used for the analyses might not be detailed enough to represent the current building stock in the region. Secondly, the ground motion distribution estimated using the GMPE for Level 1 and Level 2 (for phantom stations) is more conservative than actual values. Therefore, it is recommended to use either more compatible GMPEs or sensor density might be considered to be increased, especially on southwest coast of Istanbul.

Beneficial effects of earthquake damage mitigation systems and methods:

  1. Structural health monitoring systems can provide insight into structures’ conditions during and after a seismic event. Comparing the recordings with those made prior to the earthquake can reveal a shift in structural response that have relevance for structural damage and a reduction in seismic resistance. Moreover, these technologies can be linked to early warning and rapid response systems to mitigate seismic risk.

  2. Through an early warning and rapid response system network, besides the physical destructive effects of an earthquake, ensure that the gas systems are shut down in advance to prevent the destructive effects that the big explosions may cause.

  3. GMPEs are prediction equations established based on numerous factors, such as the site’s ground condition, the magnitude of the earthquake, and the type of fault. Consequently, their precision is mostly dependent on these characteristics. Utilizing such equations in seismic hazard analyses necessitates a high level of competence in this discipline. In addition, since the parameters to be used for attenuation relationships calculated with GMPEs will already be inherently present in the recorded acceleration data obtained from the densely established ground motion station network, these experimentally recorded data will yield the most accurate results in terms of ground motion and building damage distributions. However, it is vital to emphasize that the appropriate distribution of the stations provides better resolution for the hazard maps that may be created.

  4. The estimations made with empirical GMPEs will be validated by comparing them with the experimentally recorded data in the field. Thus, the most realistic results in the field during the seismic risk mitigation process will be available to stakeholders for consideration.

In conclusion, this study emphasizes the importance of both GMPEs and densely installed ground motion station networks that collect real-time data during earthquakes and provides motivation for establishing or further developing similar systems.

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Conflict of interest

The authors declare no conflict of interest.

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Written By

Oğuzhan Çetindemir and Abdullah Can Zülfikar

Submitted: 21 December 2022 Reviewed: 03 January 2023 Published: 14 February 2023

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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