A framework for a seismic risk model for Greater Cairo

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Soil Dynamics and Earthquake Engineering 28 (2008) 795–811 www.elsevier.com/locate/soildyn

A framework for a seismic risk model for Greater Cairo A.M. Moharram, A.Y. Elghazouli, J.J. Bommer Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK Received 26 May 2006; received in revised form 20 July 2007; accepted 11 October 2007

Abstract Following the adverse effects caused by the moderate Ms 5.4 event of October 1992, the need to model the risk from earthquakes occurring in or near Cairo was shown to be an essential tool to offset this threat in the future. To provide the necessary elements for a risk model, this paper describes a methodology for developing a ground-shaking model as well as an inventory database for the city. In the first part, a scheme is followed to integrate data on geological structures, seismic sources, seismicity and surface soil conditions to buildup an event-based hazard model. In the second part, a brief review of the history of seismic provisions in Egyptian codes is presented, and a detailed assessment of local maps and information is supplemented by results from street surveys to obtain building stock data and geographical resolutions. On the basis of these studies, the city is divided into a number of census-tracts, or geo-codes, of classified building and soil characteristics, representing a fundamental step towards the development of a full loss model. r 2007 Elsevier Ltd. All rights reserved. Keywords: Seismic risk; Loss modelling; Seismic hazard; Soil classification; Ground shaking; Building inventory

1. Introduction Egypt is located in the north-eastern part of the African continent. The majority of the population lives along the Nile Valley, Nile Delta, and the Gulf of Suez. Greater Cairo is the capital and is comprised of the governorates of Cairo, Giza (urban and rural Giza), and the district of Shubra El-Kheima. Due to economic reasons, there is a high level of migration from rural to urban areas and Greater Cairo attracts most of those migrants. Consequently, the city is now home to about 17 million inhabitants [1], making it one of the most densely populated cities in the world. The historical background, coupled with the rapid growth in urbanisation, has resulted in a variety of building types. The 12 October 1992 (Ms 5.4) event that struck Cairo illustrated the vulnerability of the building stock, especially older structures, due to design, detailing, construction and maintenance issues (e.g. [2–4]). These factors, when combined with the steadily increasing

Corresponding author. Tel.: +44 20 75946021; fax: +44 20 7595934.

E-mail address: [email protected] (A.Y. Elghazouli). 0267-7261/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.soildyn.2007.10.009

population density, clearly emphasise the potential significance of an earthquake occurring in or near Cairo. The development of a risk model for the study region is a multi-tiered process which encompasses, in its general form, establishing a ground-shaking hazard model followed by an estimation of the physical losses to the different categories of the building stock. This paper illustrates the approach adopted for the construction of elastic demand spectra at any site within Greater Cairo. This is followed by implementing a procedure for dividing the city into a number of census tracts of idealised uniform surface geology, and finally evaluating the distribution of buildings within the defined tracts. The above-mentioned process constitutes the basic elements needed for constructing an earthquake loss model for Greater Cairo. Due to the large area of rural Giza, which extends some 300 km into the Western Desert as well as into the eastern bank of the Nile south of Cairo governorate, the study area was confined to the urban portion where the majority of the Giza population is concentrated (see Fig. 1). This work represents the necessary elements for an earthquake loss model, with the aim of providing guidance on improving and accelerating mitigation measures including the retrofit

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Fig. 1. A map of the study area encompassing Cairo governorate, urban Giza, and the district of Shubra El-Kheima. Interior lines represent district boundaries.

of highly vulnerable buildings and enhancement of current seismic design provisions. 2. Ground shaking model 2.1. Sources of seismic hazard Seismic hazard in north Egypt is primarily caused by the interaction amongst the African, Eurasian and Arabian tectonic plates, as well as the Sinai micro-plate which is partially separated from the African plate by rifting along the Gulf of Suez. In addition to activity along these plate margins, mega-shear zones running from southern Turkey to Egypt have been defined by a number of studies (e.g., [5]; Raid et al., 2000). Historical and instrumental seismicity in the region is mainly associated with six tectonic trends, namely the Levant–Aqaba transform system, the northern Red Sea–Gulf of Suez–Cairo–Alexandria trend (Suez trend), the Eastern Mediterranean–Cairo–Fayoum trend [6], Hellenic and Cyprian arcs, Mediterranean coastal dislocation, and the southern Egyptian trends (Fig. 2).

Greater Cairo is remote from any major plate boundaries, with the closest major source being the Suez Rift [9], which forms part of the Suez trend, dominated by normal faults striking parallel to the rift. The Egyptian coastal dislocation trend defined by [10] includes the seismically active region off the Egyptian Mediterranean coast. They attributed the activity of this trend to the continental shelf and the probable deep faults running parallel to the coast. This trend is considered to be the continuation of the subduction activity existing further to the north at the Hellenic and Cyprus arcs. It can be considered to begin from Cyrenaica (Libya) in the west to Alexandria and then north-eastward to Beirut Bay. Events attributed to this trend have a history of affecting Egypt, and mainly Alexandria. The most recent and prominent of these events is the 12 September 1955 earthquake, having a surfacewave magnitude of 6.5 [7]. The source of seismicity of closest proximity to Cairo is the Eastern Mediterranean–Cairo–Fayoum trend, which is sometimes referred to as the Pelusiac trend. Kebeasy [11] stated that this trend extends from the eastern Mediterranean to the east of the Nile Delta and through to Cairo and the Fayoum region. Along the trend, small to moderate earthquakes are observed, the depths of which are confined within the crust and do not define any seismic plane. The zone extends parallel to the Syrian Arc system, and hence Maamoun and Ibrahim [10] attributed this low seismicity to the neo-tectonic activity of the old dislocation zone (Syrian Arc system). Many of the events belonging to this trend occur in the Fayoum area southwest of Cairo such as the well-known Dahshour earthquake of 12 October 1992, a fact that encouraged some researchers to consider the Fayoum area as a separate seismic zone [12]. For site-specific seismic hazard analysis for engineering purposes, the fundamental choice is often presented as being between probabilistic seismic hazard analysis (PSHA) and its deterministic counterpart (DSHA), which is a subject of vigorous—but rarely constructive—debate [13]. For earthquake loss estimation, the choice depends on the objectives of the study rather than the philosophical view of the analyst: if rates or probabilities of losses are required, then an approach that captures the influence of all possible earthquakes is required, which excludes DSHA. However, for a geographically distributed building stock, the use of PSHA-generated ground motions will not yield correct results since it implies that all of the ground-motion scatter is effectively treated as inter-event (i.e., earthquaketo-earthquake) variability and ignores the fact that a significant component of the scatter is due to spatial variability [14]. To obtain robust estimates of loss curves, showing losses against frequency of exceedance, the hazard needs to be modelled through large simulated catalogues of earthquake scenarios, sampling randomly from the interand intra-event components of ground-motion variability. In this preliminary study for Cairo, the objective is not to produce loss curves but rather to explore what could happen in the event of moderately strong earthquakes

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Fig. 2. Epicentral distribution of instrumentally recorded and significant historical events (184BC–1999AD) with surface wave magnitudes greater than 4.0 for an area stretching between 26.7–33.41N and 26.9–35.71E—from the catalogues of [7] and [8].

occurring close to or even within the city. The tectonics and seismicity of Egypt, as well as the history of earthquake damage in Cairo, all point to the main threat coming from nearby, shallow, crustal earthquakes of small-to-moderate magnitude; such events have caused major damage around the world when they have struck dense conurbations of vulnerable building stock (e.g., [15]). The most important examples of such events that have affected Cairo are the Ms 5.8 event of 1847 [16] and the Ms 5.4 Dahshour earthquake of October 1992 [2,3], both originating from the seismically active area to the southwest of Cairo at distances of 54 and 26 km, respectively, from the centre of the city. Larger earthquakes are expected to occur on seismogenic sources to the north and east, but their distance is such that the resulting ground motions in the capital are unlikely to be of importance. The focus of this work is on Greater Cairo, which lies about 200 km from the Mediterranean coast, and 125 km from the Gulf of Suez. Cairo is about 350 km from the entrance to the Gulf from the Red Sea at which most of the considerable activity of the Suez rift, the Red Sea and Levant trends is concentrated. Due to the relatively distant location from these major trends, their contribution to seismic hazard in Cairo can be comparatively insignificant. Another potential source of earthquake shaking are large events in the Hellenic subduction arc, particularly a repeat of the event of 12 October 1856, for which Sieberg [17] reports an intensity in Cairo of VII–VIII. Sieberg’s isoseismal map for this event, reproduced in Theodulidis

[18], presents a rather unusual extended arm that indicates considerably higher intensity around Cairo than elsewhere along the North African coast, including locations closer to the earthquake epicentre. Ambraseys et al. [16] do not report a magnitude for this earthquake, in contrast with the value of 7.5 assigned by Sieberg [17], and report that only 20 houses collapsed in the Egyptian capital, with another 200 dwellings ruined. Ambraseys et al. [16] go on to state that in Alexandria and Cairo the earthquake produced shaking of intensity V and had ‘‘no effect on major engineering works and it was hardly felt south of Helwan.’’ In view of these observations, it was decided to focus the present study only on local, moderate-magnitude events, which are perceived as the principal source of seismic risk in Cairo. Based on the above discussions, the decision was taken to model the hazard from four scenarios based on repetitions of the historical events, using an essentially deterministic approach. The magnitude is that of the 1847 event (i.e. Ms 5.8) and the locations were chosen randomly within the city with one scenario being located at the origin of the 1992 event. Both magnitude and location could be determined through disaggregation of the PSHA, but this would require the selection of both a ground-motion parameter and a return period, and the resulting magnitude-distance scenarios would probably vary from one location to another. Therefore the scenarios were simply located at different points within Greater Cairo, reflecting the fact that in PSHA studies for Egypt (e.g., [9,19]), Cairo

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is located within a broad seismic source zone that includes the city and the location of the historical events mentioned previously, implying that it is assumed that such events could occur anywhere within the source zone and hence within the city. The occurrence of such an earthquake within the city limits of Cairo is possibly a rather conservative scenario, but it enables the model to assess what the impact of such an event—the feasibility of which cannot be discounted on the basis of currently available evidence—would be on the building stock. Among the published seismic hazard studies for Egypt and Cairo are those of Ibrahim and Hattori [20], Sobaih and Khaled [21], Sobaih et al. [22–24] and Riad et al. [19], which are reviewed in detail by Moharram [25]. The study by Riad et al. [19] was widely accepted amongst researchers and was used as a basis for seismic zonation in Egyptian codes. This study involved the definition of 56 source zones of shallow depth seismic activity and six zones of

intermediate depth seismicity. This regional delineation consists of five basic trends: the Greek trend, based on the seismic zone regionalisation of Papazachos [26]—see Figs. 3a,b; the Dead Sea trend, the delineation of which was mainly based on earthquake catalogues for Israel and its vicinity [27]; the Pelusium and Qattara Trends; the Eastern Mediterranean trend and the Aswan area in southern Egypt (Fig. 3c). Five hazard maps were constructed by Riad et al. [19] taking into account all the shallow seismic sources shown in Figs. 3a,c. Additionally, three hazard maps were produced to address the effect of intermediate depth seismicity in Greece on ground shaking in Egypt (Fig. 3b). These maps were proposed for a 10% probability of exceedance in various exposure periods (10, 25, 50, 100 and 250 years) for shallow seismicity and for intermediatedepth seismicity. The most relevant map for the study area (Greater Cairo), which is more influenced by shallow

Fig. 3. Basic components of the PSHA study of Riad et al. [19]: seismic source delineation for shallow and intermediate seismicity in the Greek trend (a and b, respectively, after [26]); seismic zonation for shallow seismicity in the Dead Sea, Pelusium, eastern Mediterranean, and the Aswan trends (c); resulting hazard maps of PGA in gals for an exposure period of 50 years and a probility of exceedance of 10% (d).

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crustal seismicity from intraplate events, is presented in Fig. 3d. The displayed map provides PGA values for an exposure period of 50 years with a 10% probability of being exceeded. 2.2. Classification of near-surface geology The Greater Cairo area extends along both banks of the Nile, and covers the whole flood plain of the river from the foothills of the limestone cliffs of Gebel El-Mokattam in the east to the Pyramids plateau in the west, with an approximate width of 12 km. Commencing from the surface (within the flood plain of the Nile), there is a layer of fill which varies in thickness (0–10 m), followed by natural deposits of medium-to-stiff clay (2–15 m). Below this is an extensive mass of inter-bedded well-graded sands (20–80 m). Finally, some deep boreholes encountered a unit of plastic bluish clay [28]. It should be noted, however, that the near-surface geology is laterally very heterogeneous and can vary greatly even within a very small area. For example, in the desert area outside the flood plain of the Nile, there is no fill or stiff clay layer. Moreover, the sand layer varies significantly and the plastic bluish layer may be inter-bedded within the sand layer. Some deep boreholes do not encounter a plastic clay layer at all and the whole borehole may consist of a deep layer (and in some cases a very thin layer—e.g. Gebel El-Makattam) of sand from the surface until the bedrock is reached. Accurate shear-wave velocity measurements in Egypt are only available for large-scale national projects such as power stations and oil refineries. These are usually located in isolated areas outside the capital hence another parameter must be used. The NEHRP provisions provide soil classifications based on the average measured SPT ¯ blow counts in the upper 30 m of soil (Nð30Þ), as well as other borehole-measured parameters such as the average N-value for cohesionless soil layers (Nch), and the average shear strength for cohesive soil layers (su). The most consistently measured parameter in the borehole logs attained for Greater Cairo was the SPT (N) value and hence it was decided to use it in the soil classification together with the other supporting geotechnical and geological criteria. For this purpose, the results of over 500 boreholes distributed within the study area were collated and utilised. The majority of the boreholes had a lowermost SPT-recorded depth of 30 m or more. However, many were included in the dataset despite having smaller depths, mainly due to the unavailability of other soil information for a large area around the specific location. In order to make use of the essential shallow boreholes encountered in the dataset, the average blow count over the ¯ depth to the lowermost recorded SPT-value (NðdÞ) was ¯ related to Nð30Þ. This can be performed using the statistical approach proposed by Boore [29]. For each borehole log, ¯ values of NðdÞ were calculated at different depths and ¯ ¯ subsequently NðdÞ and Nð30Þ pairs were grouped for different depth increments. A correlation was then ¯ ¯ established to predict Nð30Þ as a function of NðdÞat

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¯ ¯ different depths. Although [NðdÞ, Nð30Þ] plots were found to be accurately represented by linear fitting, a power–law relation gave the best correlation with lowest scatter. Accordingly, for easier graphical representation, linear least-squares fitting was performed on the logarithms of the quantities and the equation used had the following form: ¯ ¯ log Nð30Þ ¼ a log NðdÞ þ b;

(1)

where a and b are the regression coefficients. A plot ¯ showing the fit of the straight lines to log Nð30Þ as a ¯ function of log NðdÞ for different depth increments was hence produced. The fit of the straight lines for the different depths indicated good correlation even for the shallowest depths. To estimate the accuracy of the extrapolation process, the bias calculation procedure adopted by Boore [29] was carried out. This was achieved by comparing the NEHRP [30] classification predicted from the proposed regression equations, applied for different assumed depths, and the actual classification for the 187 boreholes extending to 30 m or more. A change to a softer class was assigned a value of 1, and 1 for a change to a harder class; the error in classification never exceeded one site class. The total error was then calculated by adding the assigned increments and expressed as a percentage of the total number of classifications carried out. The result was plotted as a function of depth in Fig. 4. Also included in the figure are the results of adding the total percentage of erroneous changes to stiffer and softer site classes, or simply the general bias of the extrapolation procedure. The figure indicates a bias towards harder site classes, for all depths. However, the bias decreases with the increase in depth, as expected, and becomes less than 10% for depths greater than about 8 m. The joint plot of the fitted straight lines representing ¯ ¯ Nð30Þ as a function of NðdÞ at a number of different depths revealed a non-trivial correlation between the slope of the regression lines and the depth to the lowermost recorded SPT-value, where the slope increases with increasing depth until it approaches the 1:1 line at a depth of 30 m (see Fig. 5a). A statistical regression was then carried out to estimate the value of the intercept of these lines with the y¯ axis (i.e., at log NðdÞ ¼ 0) by linear fitting as shown in Fig. 5b. This was followed by the derivation of a single ¯ relationship to account for both NðdÞ and the depth in the ¯ derivation of Nð30Þ: ¯ ¯ log Nð30Þ ¼ log NðdÞ

  ¯ logð100Þ  log NðdÞ þ 0:0270ð30  dÞ . logð100Þ

ð2Þ

¯ The predicted values of Nð30Þ using the above equation were compared to the actual values in the dataset encompassing 2330 readings, and a COV of 5.33% was found. This was considered sufficient justification for its use in this study for classifying the soil deposits based on borehole data. For providing a final classification of the soil deposits (Fig. 6), other supporting tools were also considered, such

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Error % (number of holes misclassified)

40 30 20 10

softer

Total (softer and harder)

0 -10

harder

-20 -30 -40

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Depth (m)

¯ ¯ Fig. 4. Error in classification using the regression fit of log Nð30Þ as a function of log NðdÞ, using different lowermost depths to extrapolate, and without accounting for the scatter about the fitted lines. Non-zero values indicate incorrect NEHRP classifications, as a percentage of the total number of classifications carried out, with positive and negative values indicating a predicted class that is softer and harder than the actual NEHRP class, respectively. The solid line represents the total misclassification, whether it is to a softer or a harder than actual class.

d=10

d=15

d=18

d=23

d=29

1000

N(30)

100

10

1

1

10

100 N(d)

1000

X(log(N,30)atlog(N,d) = 0)

X(logN(30)atlogN(d) = 0)

0.6 0.5 0.4 0.3 0.2 X = 0.02703425(30-d) 0.1 0

r2 = 0.98822599 0

5

10

15

20

25

30-d (m)

Fig. 5. A joint plot of the fitted lines for five different depths showing the gradual decrease in the intercept with the y-axis as the depth approaches 30 m (a); and (b) linear fitting to estimate the value of the intercept as a function of d.

as expert opinion of geotechnical practitioners as well as geological and historical data. The generic trend of the borehole analysis results revealed that there are significant differences between soil conditions in different parts of Cairo. This depends on the historical background of the area as well as its proximity to the Nile, those further from the river being on firmer ground. The older parts of the city, which are underlain by a relatively thick layer of fill, are usually shifted towards softer NEHRP classes especially in older parts of Cairo on the eastern bank of the river where the thickness of the layer is greatest. Since SPT tests cannot be measured for rock formations, and since a large part of the city in eastern and southern

Cairo rests on Tertiary formations of limestone, marl and sandstones, it was decided to classify these stiffer sites using other criteria in addition to the available borehole data. Consequently, these zones of stiffer formations were primarily defined and classified as ‘rock’ and ‘soil’; this was carried out using geological maps obtained from local sources [31]. Additionally, guided by the NEHRP provisions for distinguishing between rock and soil sites using borehole data, the sites were classified into classes B or C and mapped with the other boreholes. The rest of the city lies in the flood plain of the Nile. The quantitative procedure using the average SPT blow counts ¯ over the uppermost 30 m of soil Nð30Þ was thus utilised to

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Fig. 6. Classification of soil deposits in the study area according to the NEHRP provisions [30]. Circles from top right to bottom left indicate the locations of scenario events 1, 2, 3 and 4, respectively.

categorise these sites amongst the other NEHRP classes [30]. The site classes for the alluvial Nile plain seem to be dominated by NEHRP classes D and C. Class D is mainly prevailing in areas close to either banks of the Nile and in the Nile islands, while Class C sites appear at the northwestern part of the city. This coincides with the boundary between the alluvial plain and the Hagul formation of loose sand with flint pebbles identified from local sources [31]. 2.3. Ground-motion prediction Different prediction equations derived for various tectonic regimes are available in the literature and used worldwide. Although it may be more convenient to use region-specific empirically derived prediction equations, i.e. relationships derived from strong-motion records belonging to the area under study, this option is not available for a country lacking adequate accelerograms like Egypt.

Hence regional spectral prediction equations were utilised. Since the Fayoum zone, in which the chosen scenarios originate, is characterised by shallow crustal events, empirically derived equations commonly used for active crustal regions were considered such as those derived for Europe by Ambraseys et al. [32] and Berge-Thierry et al. [33] as well as those of Boore et al. [34] and Abrahamson and Silva [35] derived for western United States, although the latter also employs data from other tectonically active shallow crustal regions such as Iran, Taiwan, Italy and Mexico. These equations use a variety of definitions for magnitude, horizontal component, source-to-site distance, as well as other considerations which might be included in some equations but not in others, such as the style-offaulting. A direct approach towards examining the suitability of various relationships in predicting ground motions for the study area is to quantify the accuracy of these candidate

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equations in representing a set of locally obtained records [36]. Typically, such an approach requires a rich database of strong-motion records to cover the different magnitude–distance domains essential to provide an accurate test. These requirements of indigenous strong-motion data still merit a prolonged time-span to compile. A plot of the four candidate equations is shown in Fig. 7 before and after applying the adjustments proposed by Bommer et al. [37]. The figure illustrates the effect of parameter incompatibilities and adjustments on the final ground-motion predictions. More importantly, it reveals the relatively small difference in final predictions after carrying out the suitable adjustments. This could justify the use of any of the equations, but it was finally decided to adopt the equations of Ambraseys et al. [32] since these include data from North Africa. Since normal faulting characterises the seismotectonic province surrounding Greater Cairo (Fayoum zone) from which the scenario events originate, the influence of the style-of-faulting on the amplitude of the ground-motion was taken into account by adjusting the latter equations. Hence, the equations of Bommer et al. [38] were finally used, which are basically a rederivation of the Ambraseys et al. [32] equations with the inclusion of extra coefficients to model the effect of style-of-faulting on ground motions. Finally, response spectra, representing the elastic response of a 5% damped single-degree-of-freedom system located in Greater Cairo, were produced. The spectrum was generated from median values of ground motion since in the HAZUS methodology scatter is included directly [39]. The shape of the spectrum is standardised in order to simplify its application within the capacity spectrum method to assess building performance. The standardised shape is plotted in accordance with the HAZUS standard format [40], which represents spectral acceleration as a function of spectral displacement using pseudo-spectral relationships and comprising three phases: a constant spectral acceleration phase at short

Ambraseys et al. (1996)

periods, a constant spectral velocity phase at longer periods, and a phase of constant spectral displacement at very long periods. The reference spectrum (for NEHRP Class B) is adjusted to consider the amplification of different soil categories using the guidelines in HAZUS [40], which account for soil non-linearity. The soil amplification factors from HAZUS are calibrated to near-surface geological profiles encountered in the United States, and it is acknowledged therefore that they might not be entirely applicable to Greater Cairo. However, this uncertainty could only be removed through site response analyses conducted for several locations in Cairo, for which data on the dynamic soil characteristics are currently not available. Using the HAZUS factors, which account for non-linear soil response, is preferable to employing the soil coefficients of the ground-motion prediction equation to predict the response spectra for each site class.

3. Building stock inventory and classification Creating an inventory database for a large city like Greater Cairo is a major challenge in performing a loss estimation procedure, owing to the effort and time it entails if a detailed and accurate database is desired. In a developing society, where census surveys and accurate evaluations are inadequate, defining the building stock with a satisfactory level of accuracy becomes a very difficult task. Accordingly, inferences based on other supportive sources or on expert opinion and personal communication become extremely important. Moreover, evaluation of national building regulations as well as geographical and historical expansion of the city, provide important complementary information to that obtained from surveys and other sources.

Boore et al. (1997)

Abrahamson & Silva (1997)

1.2 Spectral acceleration (g)

Spectral acceleration (g)

1.2 1 0.8 0.6 0.4 0.2 0 0.01

Berge-Thierry etal.(2002)

0.10

1.00 Period (s)

10.00

1 0.8 0.6 0.4 0.2 0 0.01

0.10

1.00

10.00

Period (s)

Fig. 7. Median 5% damped acceleration response spectra from the four candidate equations for a rock site at an epicentral distance of 5 km, having a magnitude of 5.8. The left figure (a) is obtained by assuming that these equations use the same parameter definitions and hence the independent variables are crudely substituted into the equations without adjustment. The right figure (b) shows the spectra after making adjustments to the different parameters. The vertical grey lines correspond to the key periods defining the HAZUS standardised response spectrum (0.3 and 1 s).

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3.1. National seismic provisions Until the beginning of the last decade, consideration of seismic loading was absent from national codes in Egypt. Buildings were typically designed to resist gravity loads and the only means of lateral load resistance was provided through the consideration of wind loads in some cases. The first official code of practice to consider seismic loading was published by the Ministry of Housing, Utilities and New Communities in 1989—the Reinforced Concrete Code [41]. However, the code overlooked a number of basic seismic considerations, including the influence of soil conditions and dynamic characteristics of buildings. More importantly, for the Greater Cairo area, a crude approach was adopted whereby an arbitrary percentage of building weight was proposed as lateral seismic loading. The loading code issued in 1994 [42] following the 1992 earthquake, provided an approach for determining seismic loads for different types of structures. Although this code represented an improvement in comparison with previous regulations [41], it still adopted significantly simplified assumptions in terms of loading considerations and design procedures. More recently, in 2004 a new loading code [43] was issued, and dealt with most of the shortcomings present in preceding standards, particularly on the loading side. The new code largely follows the same framework adopted in EC8 [44]. It introduces the concept of response spectra and codified force-reduction factors for the design of structures and includes safety verifications relevant to ultimate and serviceability limit states. The ECCS code has also gradually introduced ductility concepts and detailing procedures through its 2001 and 2004 versions [45,46], although these aspects of the code still need considerable improvement and development. It can generally be concluded from the above discussion that the development of national seismic codes is starting to converge to international standards. However, it is also clear that the majority of the current building stock has not been constructed and designed to seismic regulations. Hence with the exception of modern structures constructed in the last 10 or 15 years, it is expected that a high level of seismic vulnerability exists. 3.2. Prevalent building stock From a broad perspective, buildings in Greater Cairo can be classified into engineered and non-engineered structures. The city comprises a large number of buildings designed and constructed in accordance with national codes of practice. Most of these ‘engineered’ buildings were only designed to bear vertical loads, and it has been common practice to ignore lateral loading, with the exception of wind loads in special cases. The vast majority of engineered buildings in Greater Cairo are RC skeletal structures. Flat slab RC is also commonly used in multistorey buildings. A proportion of high- and medium-rise

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flat slab and RC frame structures incorporate concrete cores or shear walls to provide some transverse stiffness to resist wind loads. Moreover, housing commercial and business activities is a common feature in many engineered structures. This usually involves the absence of infill walls and raised clear heights of the ground or first two storeys relative to higher levels, causing irregular stiffness distributions and soft storeys. Unreinforced masonry (URM) also constitutes a significant proportion of both old and modern buildings in Greater Cairo. Mud-brick masonry and stone masonry laid in lime mortar are widely used. Stone masonry buildings are usually found in old parts of Cairo, and are usually in poor condition. Mud-brick masonry buildings are normally dominant within those neighbourhoods of Greater Cairo built on agricultural land. In assessing the distribution of buildings, use was made of official local surveys carried out by the central agency for public mobilisation and statistics [1]. From the latter study, it can be estimated that over 90% of the exposed building stock in Greater Cairo has a very low level of seismic resistance (excluding structures constructed after 1990). Hence it is reasonable to conclude that the overall seismic vulnerability of the building stock is rather high. The main shortcoming in data retrieved from CAPMAS is the absence of height evaluations. The uncertainty associated with available information necessitated the use of satellite imagery and supplementary field surveys, as discussed below, coupled with expert judgement obtained from local sources.

3.3. Geographical and historical expansion In the absence of an extensive study covering the entire range of issues needed to characterise building models and census tracts, it was essential to draw on other indirect sources of information. This has also been driven by the apparent correlation between the historical background and the growth in the number of buildings. This supports the use of maps delineating the urban borders of Cairo at different times to provide the desired spatial definition of the building stock. Towards this objective, a number of maps were retrieved which show the borders of the urban community in the city at different times. A map showing a preliminary breakdown of the building stock according to age is presented in Fig. 8. This does not necessarily imply that all buildings falling into one of these groups entirely belong to the indicated years of construction. This may particularly be the case in older groups where it is expected to find buildings belonging to newer age groups constructed after the demolition of older structures. To reduce this uncertainty and provide missing details, field surveys for chosen areas situated in each of the age groups shown in Fig. 8 were considered highly desirable.

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Fig. 8. Building stock categorisation into four age groups: pre-1950 (I); 1950–1970 (II); 1970–1980 (III); 1980–2000 (IV).

3.4. Field surveys and satellite imagery Despite making use of several sources of data in the previous sections in order to infer a rational classification for the building stock, other supplementary sources of information are still required to provide the level of detail needed to develop a loss estimation procedure that generally follows the well-established HAZUS methodology [40]. In this respect, it was thought that the best way forward was to perform field surveys for representative samples of the building population. At the outset of the survey it was essential to inspect the generic trend of buildings in Cairo using the database compiled in the

previous sections to ensure that the surveyed areas are representative of the prevailing conditions. In this process, a number of observations were made, the most important of which is the high proportion of URM buildings in all age groups. This is not directly evident through rapid surveys of popular areas in Cairo, which are dominated by RC buildings of various heights and structural systems. This can be attributed to the high proportion of informal (or slum) areas which usually comprise a considerable proportion of URM buildings. These areas are not usually accessed easily by vehicles due to their narrow and very crowded streets. Locating these areas is an intricate task since many of them blend into the surrounding affluent

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neighbourhoods. Consequently, to facilitate this task, highresolution satellite images were utilised [47]. The images revealed many randomly located informal areas in both Giza (e.g., Nazlit El-Samman, Ard El-Liwaa, El-Omraniyya, Boulaq El-Dakrour, Embaba) and Cairo (e.g., ElZaytoun, El-Bassatin, Shobra, Boulaq Aboul-Ela). These areas were firstly defined and their proportion within each age group was determined, as indicated in Table 1. The main criteria used to isolate these informal areas were the non-characteristic road networking and lack of uniformity apparent in satellite images. This non-uniformity makes it difficult to separate buildings in a countable manner. Hence, techniques used in other studies, such as that carried out in India by delineating buildings from the shape of roofs [48], is not possible. The streets in these areas are usually narrow, down to less than 1.5 m. In fact, this illustrates the danger imposed in the event of a strong earthquake, where entrance of emergency vehicles and services would be extremely difficult. After defining the informal areas in each age group, a number of different locations were selected for surveys. A minimum of two locations were chosen for each age group (informal and regular) which finally reached a total of 16 areas. Field surveys were carried out afterwards for each of these areas to capture the general features and missing

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details which were difficult to extract using inventory data from official surveys. An evaluation form was prepared and a total of 706 buildings constituting the four age groups classified in the previous section were evaluated. A list of separate building types was prepared which classifies buildings according to the basic structural system, height, and applicable building codes. The use of a method to extrapolate sample characteristics to be representative of the total population of buildings within each age group encompasses an unavoidable degree of uncertainty which cannot be entirely eliminated except by surveying the full population building by building. Since this is clearly prohibitive, a more pragmatic procedure was employed to estimate the distribution of building classes in each age group using the sample distributions obtained from the field surveys and assessments of weighting factors based on the relative proportions of informal and regular areas. A list of 15 building models was prepared as depicted in Table 2. Five basic structural systems were defined and subdivisions by height and design quality were identified. The letters L, M and H (first letter in the second part of the code) are assigned to low, medium and high-rise, respectively. Similarly, the letters G and P (second letter in the second part of the code) were used for good and poor seismic design, respectively. Poor seismic design included

Table 1 Partition of field survey data used in the classification scheme among the different age groups and subgroups I (pre-1950)

Sample size psp Area (km2) pa

II (1950–1970)

III (1970–1980)

IV (1980–2000)

Regular

Informal

Regular

Informal

Regular

Informal

Regular

Informal

111 0.79 25.71 0.64

29 0.21 14.36 0.36

248 0.89 61.26 0.82

30 0.11 13.1 0.18

147 0.77 104.48 0.78

45 0.23 29.99 0.22

76 0.79 157.64 0.91

20 0.21 14.78 0.09

The values of psp and pa signify the proportions of each subgroup (regular and informal) relative to the prevalent age group in terms of number of buildings and area, respectively.

Table 2 Building classification according to the basic structural system, height, and the governing design code Number

Label

Structural type

Number of storeys

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

EC1-LP EC1-MP EC1-HP EC1-LG EC1-MG EC1-HG EC2-HG EC3-L EC3-H EC3-HG EC4-M EC4-H EC4-HG EURM-L EURM-M

Reinforced concrete skeleton with masonry infill, pre-code Reinforced concrete skeleton with masonry infill, pre-code Reinforced concrete skeleton with masonry infill, pre-code Reinforced concrete skeleton with masonry infill, seismically designed Reinforced concrete skeleton with masonry infill, seismically designed Reinforced concrete skeleton with masonry infill, seismically designed Reinforced concrete skeleton with shear walls Reinforced concrete flat slab Reinforced concrete flat slab- 8 or more storeys Reinforced concrete flat slab with shear walls RC mixed system (flat slab+RC skeleton) RC mixed system (flat slab+RC skeleton) RC mixed system (flat slab+RC skeleton+shear walls) Unreinforced masonry—1–4 storeys Unreinforced masonry—5 or more storeys

1–3 4–7 8+ 1–3 4–7 8+ 8+ 1–3 8+ 8+ 4–7 8+ 8+ 1–4 5+

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most building constructed before 1990, as noted in previous sections. Buildings designed and constructed after 1990, or incorporating additional lateral load carrying elements such as cores and structural walls, were considered to exhibit a degree of seismic resistance. Examples of the building types defined in Table 2 are shown in Fig. 9. To assign the general distributions with reasonable accuracy, account should be taken of the relative areas of the building distributions within the regular and informal sectors. Accordingly, the following relationship was adopted to estimate the combined distributions of the building models within each age group: F ðfij ;rÞ I ij ¼ 15 , P ðf ;rÞ F ij

(3)

ðrÞ ðrÞ ðf Þ ðf Þ ðf Þ F ðfij ;rÞ ¼ f ðrÞ ij pai pdi þ f ij pai pdi ,

(4)

j¼1

ðf Þ where f ðrÞ signify the proportions of building ij and f ij model j in age group i in regular and informal areas, respectively; pai(r) and pai(f) are the normalised areas of the regular and informal subdivisions within age group i as shown in Table 1; pdi(r) and pdi(f) are the normalised densities of occupation (dwellings/km2) within regular and informal areas in age group i, respectively; and Iij is the combined proportion of building model j in age group i. The population and building densities in informal areas are typically greater than their regular counterparts, hence a non-weighted addition of the percentage distributions

was considered. The combined building distributions within the four age groups were then determined, and used afterwards to estimate the number distributions of each building model within each geocode. 4. Definition of geocodes In order to provide data suitable for loss estimation procedures, the building stock has to be subdivided into smaller areas with classified building and soil characteristics. The borders of the age groups were firstly simplified by smoothing, in a way that reduces the number of small geocodes by combining them with neighbouring large groups, without resulting in a reduction in the total area of each age group. The soil classification maps prepared in Fig. 6 were then superimposed over a simplified version of Fig. 8 and used to delineate the building stock into a number of geocodes. Some of the very large geocodes were further split into smaller units to provide better estimates of source-to-site distances. A map comprising 191 geocodes (143 in Cairo and 48 in Giza) was accordingly produced as depicted in Fig. 10, using available information including official and supplementary surveys. In order to determine the number of structures belonging to each building model within each geocode, the occupation densities in all age groups were determined largely on the basis of information available from official surveys, in conjunction with data obtained from supplementary surveys and satellite imagery. Whilst this matching process

Fig. 9. Typical building models in Greater Cairo, as illustrated in Table 2. Clockwise from top left: pre-code 5-storey RC skeleton (EC1-MP); pre-code 11storey RC skeleton (EC1-HP); 40-storey RC skeleton with shear walls (EC2-HG); 2-storey URM building with concrete diaphragms (EURM-L); 11storey RC mixed system (flat slab+RC skeleton+shear walls) (EC4-HG); 9-storey RC flat slab (EC3-H).

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Fig. 10. Proposed definition of geocodes. Black solid circles, from top right to bottom left, indicate the locations of Scenario Events 1, 2, 3 and 4, respectively. Elastic response spectra and building model distributions for the four highlighted geocodes are provided in Fig. 11.

involves a degree of approximation and uncertainty, it is believed to provide a reasonably realistic representation of the building occupation densities. After determining the distribution of building numbers within each geocode, the area and distance of the four scenario events from the centroid are established. An example of the overall procedure described herein is illustrated in Fig. 11 for four selected census tracts. Essentially, the seismic demand and building inventory within uniform geological references is applied to the defined geocodes. Elastic response spectra, for the four

geocodes highlighted in Fig. 10, are depicted together with the elastic response spectrum provided by ECCS [46]. The resulting building distribution within each of the four selected geocodes is also included. The figure highlights the significant variation in building distributions between different locations in the city. It also illustrates the wide range of peak spectral accelerations that can result in various geocodes for the scenario events. The next step towards establishing a risk model for the city involves a detailed structural vulnerability assessment of representative building configurations. This is currently

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Geocode 22

Spectral acceleration (g)

0.8

EURM-M

0.7

Scenario 1

EURM-L

0.6

Scenario 2

EC4-HG

Scenario 3

EC4-H

0.5

Scenario 4

EC3-HG

ECCS spectrum

405 18 9 18

EC3-H

0.4 0.3 0.2

9

EC2-HG

18

EC1-HG

23

EC1-MG

81

EC1-HP

257

EC1-MP

0.1 0

344

1007

EC1-LP 0

5

10

15

102

0

20

2000

Spectral displacement (cm)

4000

6000

8000

Number of buildings

Geocode 86

Spectral acceleration (g)

0.8

EURM-M

0.7 0.6

Scenario 2

0.5

Scenario 4

318

EURM-L

Scenario 1

7140

EC3-H

Scenario 3

66

EC2-HG

132

EC1-HG

66

0.3

EC1-MG

66

0.2

EC1-HP

ECCS spectrum

0.4

0

593

EC1-MP

0.1

5656

EC1-LP

0

5

10 15 Spectral displacement (cm)

20

593

0

2000

4000

6000

8000

Number of buildings

Geocode 94 Spectral acceleration (g)

0.8

EURM-M

0.7

Scenario 1

EURM-L

0.6

Scenario 2 Scenario 3

EC2-HG

0.5

Scenario 4

2221 3812 109

EC1-HG

ECCS spectrum

0.4

EC1-MG

0.3

EC1-LG

286 1497 54

EC1-HP

0.2

937

EC1-MP

0.1

6275

EC1-LP

0 0

5

10

15

20

760

0

2000

Spectral displacement (cm)

Geocode 220

Spectral acceleration (g)

0.8

URM-ME

0.7 0.6 0.5

EC3-HG

Scenario 4

EC3-L

ECCS spectrum

146 73 946

EC1-HG

291

EC1-MG

0.3

1761

EC1-LG

0.2

655

EC1-HP

844

EC1-MP

0.1 0

73

EC2-HG

0.4

2503

EC1-LP 0

5

10

15

20

Spectral displacement (cm)

8000

1935

EC4-M

Scenario 3

6000

1106

EURM-L

Scenario 1 Scenario 2

4000

Number of buildings

728

0

2000

4000

6000

8000

Number of buildings

Fig. 11. Elastic response spectra and building model distributions for the four highlighted geocodes in Fig. 10. The spectra are formulated according to the standardised HAZUS format. A plot of the elastic response spectra provided by the ECCS [46] for each of the geocodes is also presented.

underway using a combination of analysis procedures for engineered structures and semi-empirical approaches for non-engineered buildings. Typical models are considered to

represent the overwhelming majority of buildings as depicted from the inventory study. These representative models are designed according to governing code provisions

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guided by readily available structural drawings for masonry and RC buildings in Cairo, in order to reflect the influence of common design and construction practice. On this basis, a suite of capacity and vulnerability functions is obtained for the various building classes. This will be utilised within a capacity spectrum framework to determine damage probability distributions, and to quantify monetary losses inflicted by ground-shaking hazard. 5. Conclusion This paper describes a procedure used for providing the elements necessary for earthquake loss estimation in the city of Greater Cairo. A ground-shaking model and an inventory database are produced making use of information available for the region coupled with supplementary studies including street surveys. The paper also configures a framework for earthquake risk assessment that can be typically implemented in similar areas, characterised by low-to-moderate seismic activity with the limited amount of readily available data. Sources of earthquake hazard are reviewed and, based on the tectonic configuration of the area as well as the history of seismicity, a number of scenarios are chosen. A detailed study is carried out using boreholes compiled from local investigations to produce a geological delineation of different soil deposits, and a comparative assessment of regional ground-motion prediction equations is carried out to select an appropriate relationship. These steps are utilised to estimate site-specific elastic response spectra at any location within the city. Significant spatial variation in the soil amplification characteristics is observed. This underscores the possibility of extending this part of the investigation by expanding the size of the borehole dataset or implementing a more robust microzonation procedure. Modelling ground-shaking hazard is then followed by an extensive investigation to divide the study area into a number of uniform geological references. The adopted process utilises official building surveys available in the literature, knowledge of the history of seismic provisions in Egyptian codes, expert opinion, field surveys performed for representative samples, as well as the soil configuration maps developed in the first part of the study. This approach towards census tract definition appears to be the most pragmatic preference in the light of the inconsistency of compiled official surveys for buildings in the area. Combined with the results from structural vulnerability studies, which are currently underway, the procedures presented in this paper will be extended to provide a geographic quantification of anticipated economic and social losses induced by possible earthquakes. This would offer vital information for future planning activities, mitigation measures and retrofit strategies, as well as guidance towards the feasibility of enhancing seismic provisions in codified regulations. An important by-

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product of the research is to provide data for catastrophe risk insurance-related applications. This is believed to be a useful means for shifting financial liabilities of retrofitting, compensation schemes and reconstruction costs from government finances to insurance and reinsurance institutions. Since there are some potentially liquefiable soils in Greater Cairo, despite the ground water table being rather low, further development of an earthquake loss model for the city should consider this collateral hazard. In general, the impact of liquefaction on building losses in earthquakes affecting urban areas is usually small in comparison to the direct effects of ground shaking [49]. However, for a detailed estimation of earthquake impact in those parts of Cairo where potentially liquefiable soils are encountered, liquefaction-induced ground displacements should be considered, either using the HAZUS approach or other methodologies such as that proposed by Bird et al. [50]. This study is a preliminary exploration of the level of seismic risk in Greater Cairo, and a contribution towards a fully probabilistic assessment of the potential rate of losses obtained using Monte Carlo simulation of earthquakes and their ground-motion fields. In the continued development of the study, attention will need to be given to other potential seismic sources, including large events in the Hellenic and Cyprus arcs, which could, in areas overlain by softer soils, impose appreciable loads on high-rise structures whose natural period of vibration may be excited by the long-period motions from large, distant events. Further work will also examine the robustness of the risk model by tracking the uncertainties associated with the various decisions and factors employed. This can partly be achieved by comparing the predicted risk with damage surveys from previous events. Although such data are scarce for the study region, the damage reports produced in the aftermath of the 1992 earthquake can be used to provide a reasonable assessment. As part of this evaluation, the sensitivity of model predictions will be examined with respect to variations in the main governing parameters, such as those related to ground-motion equations and structural vulnerability procedures. Acknowledgements The support and advice of several experts from Egypt including Mr. R. Annass, Dr. M. Sheta and Dr. H. ElMarsafawy of Ardaman-ACE, and Dr. A. Deif of the Helwan Institute of Astronomy and Geophysics, is gratefully acknowledged. We are also grateful to the anonymous referees and to Dr. Eser Durukal for their insightful reviews and constructive suggestions. References [1] CAPMAS, Central Agency for Public Mobilisation and Statistics. Census survey on the Egyptian population. www.capmas.gov.eg.

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