essential facilities performance study for seismic ... · • extent of induced hazards in terms of...

Essential Facilities Performance Study for Seismic Scenarios in Manhattan

Michael Tantala and George Deodatis

Department of Civil and Environmental Engineering Princeton University

ABSTRACT This paper focuses on the implementation and quantitative results of a detailed critical (essential) facilities analysis, assessing probable damage and facility functionalities for Manhattan from various scenario earthquakes. To this end, a complete building inventory of every building in Manhattan was assembled from a variety of sources and combined with a detailed geotechnical characterization of Manhattan. This building inventory was used to model scenario earthquakes in HAZUS (Hazards US), a standardized earthquake loss estimation methodology and modeling program for these estimations. Essential facilities are those facilities that must necessarily remain in operation after a seismic event for post earthquake recovery operations. These facilities provide required services to victims of an earthquake and are primarily responsible for the rate of recovery in the affected area. Collapsed and burning buildings, spreading fires, homelessness, and social chaos are just a few examples of secondary crises that follow in the wake of an earthquake and magnify the effects of such a disaster. Specifically, this paper will quantify the functionality of essential facilities including hospitals, schools, police stations and fire stations with respect to number of beds, amount of shelter, average travel time for injured to nearest hospital, probable fire ignitions and water demands. The functionality of a structure is directly related to its particular damage state (i.e. slightly damaged facilities will obviously aid recovery operations more than those that are extensively damaged). This paper will also quantify the demands placed on those essential facilities with respect to casualties, injuries and shelter requirements and assess if the facilities functionalities will be capable of accommodating these needs. The methodology for determining injuries and casualties is based on the assumption that there is a strong correlation between building damage (both structural and non-structural) and the number and severity of casualties. Eventually, the aim of this loss estimation project will provide a framework for businesses and agencies to take mitigative action to reduce potential damage and losses, which might be experienced after an earthquake. This study was funded by FEMA Region II and coordinated by the Multidisciplinary Center for Earthquake Engineering Research (MCEER) and the New York State Emergency Management Office (NYSEMO). 1.0 INTRODUCTION The past several decades have witnessed a series of costly and damaging earthquakes. Although earthquake losses in the United States have been predominantly in California, many significant earthquakes have occurred and many more are projected in the areas that have been inactive in the last century (Figure 1-1).

Figure 1-1—Seismicity of the United States: 1899-1990 [1, FEMA, 1995]

New York City’s seismic risk exposure is of increasing concern. The New York City metropolitan area has been classified by the United States Geologic Survey (USGS) to the moderate level for potential earthquakes. In order to be prepared for such natural disasters, it becomes essential to be able to estimate and predict the risks associated with these potential losses. Risk is typically defined by three components: a hazard (the earthquake), the assets involved and the fragility of those assets. For New York City, the probability of a large earthquake is moderate, however it becomes an area of high risk because of its tremendous assets and the fragility of its structures, which have not been seismically designed as most on the West Coast. The present paper documents the findings of an intermediate, second-year study [2, Tantala, 2001], which focuses on seismic risks in the New York City area. The vehicle for performing these loss estimations has been a software tool entitled Hazards US (or HAZUS). The Federal Emergency Management Agency, through the National Institute of Building Science (NIBS) and RMS, Inc., developed this standardized earthquake loss estimation methodology and the computer modeling program HAZUS. It can be used to quantify regional seismic risks and to form the basis for a more coordinated national loss program. HAZUS uses geographic information systems to model the built environment against the backdrop of possible natural disasters. There are three levels of complexity that may be implemented for a HAZUS loss estimation analysis. Level 1 uses default data and creates rapid and crude impressions of damage, but introduces a great deal of uncertainty. Level 2 requires the user to supply modified data (particularly soil and building information) within HAZUS to more accurately depict the study region. Level 3 requires expert supplied techniques to modify the methodology of HAZUS. This is a Level 2 study providing significantly more refined loss estimates by implementing detailed information about the local geology and building inventories.

The objectives of this intermediate study were to:

• Develop a comprehensive building inventory by acquiring detailed building information, performing physical surveys and using expert engineering opinion.

• Perform deterministic and probabilistic HAZUS scenario runs in the New York City area using modified soil information [3, Jacob, 2000] and the new building inventory.

• Perform a detailed HAZUS critical (essential) facilities analysis, assessing damage probabilities and facility functionalities.

• Interpret these results and provide informed insight about the risks involved. This paper will discuss some of the results of the above objectives and discuss in detail the critical facilities analysis and results.

1.1 Why is this Study Important?

Although earthquakes are uncontrollable, the losses they cause can be reduced by building structures that resist earthquake damage, strengthening vulnerable structures, enforcing more strict seismic building codes, matching land use to risk, developing emergency response plans, promoting public awareness of the problem and other means. Although considerable uncertainties exist in building performance, it is generally agreed that the knowledge exists to retrofit or design and construct buildings that are unlikely to collapse in an earthquake [4, U.S. Congress, 1995]. Years of research have yielded a knowledge base that, if applied properly, would result in buildings that are unlikely to collapse in an earthquake and saved lives. This study addresses the first step in this risk reduction: to identify the potential problems. More specifically, this study describes the scale and extent of damage and disruption that may result from potential earthquakes, by providing:

• Quantitative estimates of losses in terms of direct costs for repair and replacement of damaged buildings; direct costs associated with loss of function (like business revenue), casualties, people displaced from residences, quantity of debris and regional economic impacts.

• Functionality losses in terms of loss-of-function and restoration time for buildings, critical facilities such as hospitals, schools, etc.

• Extent of induced hazards in terms of fire-following an earthquake and other occurrences. In assessing the risks involved, this study has made a significant contribution toward improving our understanding of the problem by forecasting potential losses so that strategies may be formed to reduce their impacts. This type of study and its implications are critical in making balanced and informed decisions on managing the seismic risk, at the community, state and national level for Manhattan—a region of low hazard/high risk. The study gives policy makers, practitioners and researchers estimates that will enable them to better understand and grasp what is at risk and to potentially compare these risks to other regions. They may then use this information in developing and implementing cost-effective risk management plans to minimize them. This study is also unique, because it is currently one of the most detailed and site-specific applications of HAZUS or any other earthquake loss estimation. This research has collected information about every building in Manhattan and a large number of site-specific soil data. With this work, it is possible therefore to establish the building inventory information for the island of Manhattan at the level of individual buildings, a unique accomplishment for HAZUS applications. In other words, we were able to establish the required set of parameters for each structure in Manhattan.

1.2 Seismicity of the New York City Area

To assess risk and forecast loss estimates for the New York City Area, it is necessary to discuss and consider the historic seismicity of the region. Earthquakes are not unknown in the New York City metropolitan area and up to a Modified Mercalli Intensity VII (MMI VII) has been observed in historical times (e.g., the 18 December 1737 event, which reportedly caused chimneys to fall in New York City) [5, Coffman,1982]. Seismic hazard in the northeast United States is a subject involving considerable uncertainty [6, Bernreutter, 1984]. Major events in the New York City area include the 18 December 1737 and the 10 August 1884 earthquakes. The 1884 earthquake is the largest and probably best documented event for the New York City area. The earthquake was a strong shock, centered off Rockaway Beach about 17 miles southeast of New York’s City Hall, and felt over 70,000 square miles, from Vermont to Maryland. In New York City, the effects were strong but varied, frightening many but causing very little or no damage. In Manhattan, newspaper reports indicated general alarm and in many portions of lower Manhattan, crockery and bottles rattled but generally did not fall [7, NY Times, 1884].

On the basis of different historical descriptions, it has been estimated that the general intensity of this 1884 event in Manhattan and northwest Brooklyn (not yet a borough) was Modified Mercalli Intensity (MMI) IV and approaching MMI V toward the southeast. Current best estimates of the magnitude based on felt area, Mfa, of the 1737 and the 1884 events are Mfa = 4.5 for the 1737 event (epicenter 41N, 73.75W) and Mfa = 4.9 for the 1884 event (epicenter 40.51N, 73.83W) [8, Scawthorn, 1988]. Additionally as illustrated in Figure 1.2-1, more than 400 earthquakes with magnitude greater than 2.0 have occurred in New York State between 1730 and 1986. This ranks New York as having the third highest earthquake activity level east of the Mississippi River during this period; only South Carolina and Tennessee were more seismically active [9, Isachson, 1991]. Geologists predict that an earthquake of magnitude 5.0 or above on the Richter scale has a 2% probability of occurring in the New York area within the next 50 years [10, Graver, 1995].

Figure 1.2-1—Earthquakes of New England and Adjacent Regions (1638-1995) [11,SEASAME, 1995]

A study conducted in the mid-1980s and another more recent one [12 and 13, Nordenson, 1987 and 2000], characterized the seismicity of New York City as “moderate” and had the following findings:

• Earthquakes with intensity of about Modified Mercalli Intensity (MMI) VII have occurred every 100 years in the New York City area,

• Regional seismicity indicates that earthquakes of MMI VII are likely to occur on average every

100-200 years (i.e., 20 to 40 percent probability of occurrence in 50 years),

• Larger earthquakes with MMI VIII-IX, or magnitude 5.75 to 6.75 (probable upper-bound range) may occur,

• Even larger magnitude and/or higher intensities, at very low levels of probability, cannot be

excluded, and

• New York City seismicity is very similar to that of the Boston area, where local seismic design provisions have been developed and are in effect.

Again, although New York City is a region with low seismic hazard (infrequent damaging earthquakes), it actually has high seismic risk, which result from concentrations of buildings and infrastructure built according to no seismic codes or provisions (however, several taller buildings are designed for strong wind loads, providing resistance to horizontal loads). Considering the area’s historic seismicity, population density, and the condition of the infrastructure and building stock, it is clear that even a moderate earthquake will have considerable consequences in terms of public safety and economic impact.

2.0 METHODOLOGY AND SCENARIO EVENTS The HAZUS methodology involves three basic components: classification of different systems for inventory (in this study, building types and soil information), methods for evaluating the damage and calculating losses, and databases of information on demographics, building information and the regional economy. An earthquake loss estimate can be performed using HAZUS for any location in the nation using only the methodology and default databases, however, more accurate loss estimates can be generated by collecting and incorporating additional (modified) information.

Figure 2.0-1—Earthquake Loss Estimation using HAZUS [14, Buika, 2000]

The HAZUS methodology is outlined in Figure 2.0-1. The first step to perform a loss estimate in HAZUS is to select an area of study, which might be defined by political boundaries (for example, a census tract

or a city). Then select a magnitude and epicenter location (or specify a probabilistic ground motion map) of a scenario earthquake and ground motion attenuation model. This can be based on available knowledge of historic seismicity. Information on local soil conditions can be incorporated to facilitate the mapping of estimated shaking intensities and the probability of permanent ground deformation. Using building capacity and fragility curves, HAZUS estimates damages and loss for the given scenario earthquake. Given appropriately modified input information (e.g., for the building stock), more accurate estimates of loss may be determined. Appropriate demographic information is provided to enable HAZUS to determine casualties and shelter requirements for various earthquake scenarios. The initial stages and year one efforts of this study were implemented and documented in this paper’s predecessors [2 and 15, Nordenson and Tantala, 1999 and 2000]. The first year study involved fact-finding and assessment, with the development of preliminary soil maps and building inventories. The primary objective of the Year One study was to carry out an initial risk characterization for a local impact (Manhattan below 59th Street) and for a regional impact (the NY-NJ-CT tri-state area). With this initial study, the sensitivity of loss estimation was examined with respect to different soil conditions and building inventories and the key variables were ranked. The results of this research indicated that:

• Dramatic differences in total loss estimates between runs done with default values and runs done with improved estimates of soil conditions and building inventories. Differences were more dramatic for smaller magnitude events.

• Total loss estimates in the modified runs can differ significantly from those of the default (in some cases by more than a factor of 10).

• The effect of switching to better estimates of building inventory can be as important as the effect of switching to better estimates of soil conditions.

• Parts of New York City have the unique characteristic of a considerable percentage of tall buildings, that is not reflected in HAZUS’ default values.

• The most sensitive building information required for a refined study are square footage and height (even more than structural type). The least sensitive variable is building age, which has no effect on the loss estimate for East Coast studies.

• It is of paramount importance to establish better estimates for soil conditions and building inventory for the entire New York City Area.

2.1 Sources of Information and Manipulations

This work is a Level 2 study that provides more refined loss estimates by implementing collected,

detailed information on local geology and building inventories to update the default information of HAZUS and eliminate uncertainty in estimates. The collection of soil and building inventory data are without question the most costly part of performing the study. The modified building inventory information was assembled and verified with several sources, which include:

• the New York City, Department of Finance and Operations Research, Mass Appraisal System (MAS) tax assessor’s database,

• the New York City, Department of City Planning, Land Use and Government Agency Planning database [16, City of New York, 1999],

• Sanborn fire insurance maps [17, Sanborn, 1998], • physical survey of several Manhattan census tracts, • aerial photography, • discussion with local engineers and building officials, • limited structural drawings of several buildings, • New York City building code research, • engineering opinion, and • a series of national databases (hospitals, police stations, post offices, schools, etc.) provided

with HAZUS were used for the critical facilities analysis.

Soil type by census tract is another classification used to estimate losses. HAZUS uses the 1997 NEHRP Provisions to classify soil into site classes A, B, C, D or E, as shown in Table 2.1-1. The classification scheme of the NEHRP Provisions is based, in part, on the average shear wave velocity of the upper 30 meters of the local site geology. The modified soil information that was used in this study was provided by a parallel research group at the Lamont-Doherty Earth Observatory of Columbia University. This research group was headed by Dr. Klaus Jacob [24 and 25, Jacob, 1999 and 2000]. For this research, geotechnical data consisting of standard penetration test (SPT) blow counts and standard soil descriptions from construction-related soil borings in New York City were used to assess the effect of near-surface geology on seismic ground-motion site-response (microzonation). In addition, information on depth to bedrock from older borings, which do not contain information on the type of penetrated soils, were also used for microzoning the shaking effects [24, Jacob, 1999]. For detailed information about how the soils were characterized, the reader is referred to another report [25, Jacob, 2000].

Site Site Class Description Shear Wave Velocity (m/sec)Class Minimum Maximum

A HARD ROCKEastern United States sites only

1500

B ROCK 760 1500

C VERY DENSE SOIL AND SOFT ROCKUntrained shear strength us > 2000 psf (us > 100kPa) or N > 50 blows/ft

360 760

D STIFF SOILSStiff soil with undrained shear strength 1000 psf <us < 2000 psf (50 kPa < us < 100 kPa) or 15 < N< 50 blows/ft

180 360

E SOFT SOILSProfile with more than 10 ft (3 m) of soft claydefined as soil with plasticity index PI > 20,moisture content w > 40% and undrained shearstrength us < 1000 psf (50 kPa) (N < 15 blows/ft)

180

F SOILS REQUIRING SITE SPECIFICEVALUATIONS

1. Soils vulnerable to potential failure or collapseunder seismic loading: e.g. liquefiable soils, quick and highly sensitiveclays, collapsible weakly cemented soils.

2. Peats and/or highly organic clays(10 ft (3 m) or thicker layer)

3. Very high plasticity clays:(25 ft (8 m) or thicker layer with plasticity index >75)

4. Very thick soft/medium stiff clays:(120 ft (36 m) or thicker layer)

Table 2.1-1—NEHRP Soil Type Classifications [20, FEMA, 1997]

For a default analysis, HAZUS assumes that all of Manhattan is of stiff soil type ‘D’ (a thick alluvium and soft soil). However, the modified soil information used in this study provided by the Lamont-Doherty Observatory is much more varied as illustrated in Figure 2.1-1.

Soil Classes

defaultOnly one soil type “D”

modified

Hard RockRockDense Soil/Soft RockSoft SoilsSpecial Soils

District Key

District # Neighborhoods

1 Financial District, Tribeca, Battery Park City, Seaport

2 Soho, Greenwich Village, Chinatown, Little Italy, Noho

3 East Village, Lower East Side, Tompkins Square

4 Chelsea, Clinton5 Flatiron, Midtown

6 Gramercy, Murray Hill, Turtle Bay, Tudor City

7Lincoln Square, Upper West Side, Manhattan Valley

8 Upper East Side, Yorkville, Carnegie Hill, lenox Hill

9 Morningside Heights, Hamilton Heights, Manhattanville

10 Central Harlem, Polo Grounds 11 East Harlem12 Washington Heights, Inwood

Figure 2.1-1—Comparison of Default and Modified Soil Map for Manhattan

2.2 Scenario Events—Deterministic and Probabilistic

There are two ways to specify an earthquake event within HAZUS. The first is a deterministic

scenario earthquake where an arbitrary event is defined by a magnitude, epicenter location and depth to fault. Deterministic seismic ground motion demands are calculated for the specified scenario earthquake. For a prescribed event, attenuation relationships are used to calculate ground shaking demand for rock sites (Site Class B), which is then amplified by factors based on local soil conditions when a soil map is supplied by the user. This is why an accurate soil map (like the one in Figure 2.1-1) is so important for reliable results. The six deterministic scenarios considered for this study are summarized in Table 2.2-1. The first three earthquakes vary in magnitude (M = 5.0, 6.0 and 7.0) and have a fixed epicenter at the location of the 1884 historic earthquake (latitude: 40.56° N and longitude: 74° W). The remaining three deterministic earthquakes vary in magnitude and location so that they are compatible with the de-aggregation of the two-percent in fifty years equal hazard computations (i.e., a 2500 year return period). The distances of the magnitude 5.0, 6.0 and 7.0 scenarios were chosen in approximate compliance with the USGS de-aggregation and provided by Dr. Klaus Jacob of the Lamont-Doherty Earth Observatory of Columbia University [18, Jacob, 1999]. The epicenters of the six deterministic scenario earthquakes are displayed in Figure 2.2-1 in relation to Manhattan. It should be noted that scenarios A and D are the same and are repeated for the purposes of comparing results. Scenarios A, B and C have the same epicenter location, but are of increasing magnitude and therefore have a decreasing annual probability of exceedance and average return period. Scenarios D, E and F have different epicenter locations, but are positioned in such a way that they have the same average return period (i.e., the same annual probability of exceedance). The method to calculate probabilities was provided by Dr. Klaus Jacob of the Lamont-Doherty Earth Observatory of Columbia University [2,Tantala, 2001].

Location

Epicenter Latitude/Longitude Annual in 50 years

A 5.0 40.560° N, 74.000° W 2,475 0.042 2.1000B 6.0 same 19,500 0.0102 0.2550C 7.0 same 160,000 0.000006 0.0003

D 5.0 40.560° N, 74.000° W 2,475 0.084 0.0420E 6.0 40.500° N, 73.352° WF 7.0 40.321° N, 73.000° W

Scenario

Average Return Period (Years)

MagnitudeTypeProbabilty of Exceedance (%)

Fixed Location

Variable Location same

same

Det

erm

inis

tic

Table 2.2-1—Deterministic Earthquake Scenarios

Figure 2.2-1—Deterministic Earthquake Scenario Epicenters (Star Symbols)

2.3 Probabilistic Scenarios

The second type of earthquake that can be specified in HAZUS is probabilistic, where the ground

shaking demand is characterized by spectral contour maps developed by the United States Geological Survey (USGS). Essentially, the earthquake is specified by peak ground acceleration (PGA) with a mean recurrence interval (i.e., “the 500 year earthquake”). An example of a USGS PGA contour map for the eastern United States for a 500 year recurrence interval (2% probability of exceedence in 50 years) is shown in Figure 2.3-1. The methodology includes maps for eight probabilistic hazard levels: ranging from ground shaking with a 50% probability of being exceeded in 50 years (100 year return period) to ground shaking with a 2% probability of being exceeded in 50 years (2500 year return period). So there is an option of choosing one of eight hazard return periods between 100 and 2500 years (100, 250, 500, 750, 1000, 1500, 2000, 2500 years). The USGS maps describe ground shaking demand for rock (Site Class B) sites, which the methodology then modifies based on local soil conditions. In contrast to deterministic scenario earthquakes which use a specific seismic event of a specific size and location, probabilistic analyses allow for uncertainties in the locations and rates of earthquake occurrence and levels of ground motion [21, FEMA, 2000].

Ê

60 0 60 120 Miles

-74

-74

-73

-73

-72

-72

40 40

41 41

60 0 60 120 Miles

-74

-74

-73

-73

-72

-72

40 40

41 41

New Jersey

Connecticut

New York

ManhattanManhattan

Figure 2.3-1—USGS Eastern United State Contour Map for PGA with 10% Probability of Exceedance in

50 years (500 year return interval) [22, USGS, 1996] The four probabilistic scenarios chosen for this study (average return periods and probabilities of being exceeded in 50 years) are listed in Table 2.3-16. The fourth probabilistic scenario (J) is an annualized loss estimate. Annualized loss is the expected value of loss in any one year, and is developed by aggregating the losses and their exceedance probabilities over eight hazard return periods between 100 and 2500 years. The annualized loss is discussed in more detail in other papers [2, Tantala, 2001].

Location


G N/A N/A 100 1.000 50.0000H N/A N/A 500 0.200 10.0000I N/A N/A 2,500 0.040 2.0000J Annualized N/A N/A 1 - -

Scenario



Probabilistic

Prob

abili

stic

Table 2.3-1—Probabilistic Earthquake Scenarios

3.0 RISK AND DAMAGE ASSESMENT FOR MANHATTAN

3.1 Regional Description and Exposure Characteristics

Manhattan is approximately 27 square miles and contains 298 census tracts. There are approximately 37,000 buildings in the region with a combined 2.2 billion square feet and a total replacement value (excluding contents) of 347 billion dollars (2001 dollars). Figure 3.1-1 presents the relative distribution of value with respect to the general occupancies. There are over 717 thousand households in the region, which represent the total population of 1.5 million people (1990 Census Bureau data) [23, U.S. Census, 1992]. The distribution of population is shown in Figure 3.1-2 (darker regions indicate a more dense population).

Residential$175.0 B

57%

Commercial$111.3 B

36%

Industrial$5.3 B

2%Other

$16.5 B5%

Residential

Commercial

Industrial

Other

Commercial$125.3 B

36%

Industrial$6.0 B

2%

Residential$197.1 B

57%

Other$18.6 B

5%

Figure 3.1-1—Building Exposure by Occupancy Type with Total Replacement Value

The typical building in Manhattan is:

• six to seven stories high, • built at the turn of the century, • has under 100,000 square feet • is made of either unreinforced masonry or steel, and • is primarily used for either residential multi-family dwelling or commercial professional/technical

services The distribution of the total 2.2 billion square feet varies within Manhattan. Figure 3.1-3 shows this variation by census tract and values for designated neighborhood districts. The Flatiron/Midtown area (District 5 with 18.7%) and the Upper East Side/Lincoln Square area (District 8 with 16.0%) contain the highest concentrations of total square footage and consequently have larger exposure to seismic risk. Beyond square footage, it is also important to know the distributions of the number of buildings (Figure 3.1-4), how old they are (Figure 3.1-5), how tall they are (Figure 3.1-6 ) and what are the combinations of structural type within (Figure 3.1-7). For more detailed information and statistics, readers are direct to other sources [2, Tantala, 2001].

Population Map

Above 15,000

Under 1,000

8,000

4,000

Average Number of People

# Population (Thousands)

% of Total Neighborhoods

1 31 2.1% Financial District, Tribeca, Battery Park City, Seaport

2 94 6.3% Soho, Greenwich Village, Chinatown, Little Italy, Noho

3 156 10.5% East Village, Lower East Side, Tompkins Square

4 89 6.0% Chelsea, Clinton5 37 2.5% Flatiron, Midtown

6 140 9.4% Gramercy, Murray Hill, Turtle Bay, Tudor City

7 208 14.0% Lincoln Square, Upper West Side, Manhattan Valley

8 211 14.2% Upper East Side, Yorkville, Carnegie Hill, lenox Hill

9 107 7.2% Morningside Heights, Hamilton Heights, Manhattanville

10 99 6.6% Central Harlem, Polo Grounds

11 111 7.4% East Harlem12 206 13.8% Washington Heights, Inwood

All 1,487 100%

District Key

Figure 3.1-2—Distribution of Population in Manhattan [2, Tantala, 2001]

Figure 3.1-3—Distribution of Total Square Footage in Manhattan [2, Tantala, 2001]

Square footage distribution

Under 4,000

Above 45,000

16,000

32,000

Distribution ofSquare Footage

District Key

#Total Square

Footage (thousands)

Percentage of Total Neighborhoods

1 219,047 9.9% Financial District, Tribeca, Battery Park City, Seaport

2 128,511 5.8%Soho, Greenwich Village, Chinatown, Little Italy, Noho

3 104,646 4.7% East Village, Lower East Side, Tompkins Square

4 172,803 7.8% Chelsea, Clinton5 414,024 18.7% Flatiron, Midtown

6 286,292 12.9% Gramercy, Murray Hill, Turtle Bay, Tudor City

7 189,741 8.5%Lincoln Square, Upper West Side, Manhattan Valley

8 354,126 16.0% Upper East Side, Yorkville, Carnegie Hill, lenox Hill

9 64,407 2.9% Morningside Heights, Hamilton Heights, Manhattanville

10 63,365 2.9% Central Harlem, Polo Grounds 11 113,794 5.1% East Harlem12 108,748 4.9% Washington Heights, Inwood

All 2,219,504 100%

Number of buildings by Count

Above 400

Under 100

300

200

Average Numberof Buildings

# Number of Buildings Neighborhoods

1 1,674 Financial District, Tribeca, Battery Park City, Seaport

2 4,537 Soho, Greenwich Village, Chinatown, Little Italy, Noho

3 3,731 East Village, Lower East Side, Tompkins Square

4 3,279 Chelsea, Clinton5 3,091 Flatiron, Midtown

6 3,220 Gramercy, Murray Hill, Turtle Bay, Tudor City

7 3,873 Lincoln Square, Upper West Side, Manhattan Valley

8 4,753 Upper East Side, Yorkville, Carnegie Hill, lenox Hill

9 1,881 Morningside Heights, Hamilton Heights, Manhattanville

10 2,700 Central Harlem, Polo Grounds

11 2,069 East Harlem12 2,127 Washington Heights, Inwood

All 36,935

District Key

Figure 3.1-4—Distribution of Number of Structures in Manhattan [2, Tantala, 2001]

Distribution of Age of Buildings

After 1990

Before1885

1960

1915

Average Year of Construction of Buildings

# Average Age Neighborhoods

1 1906 Financial District, Tribeca, Battery Park City, Seaport

2 1918 Soho, Greenwich Village, Chinatown, Little Italy, Noho

3 1914 East Village, Lower East Side, Tompkins Square

4 1916 Chelsea, Clinton5 1920 Flatiron, Midtown

6 1916 Gramercy, Murray Hill, Turtle Bay, Tudor City

7 1926 Lincoln Square, Upper West Side, Manhattan Valley

8 1931 Upper East Side, Yorkville, Carnegie Hill, lenox Hill

9 1926 Morningside Heights, Hamilton Heights, Manhattanville

10 1906 Central Harlem, Polo Grounds

11 1900 East Harlem12 1898 Washington Heights, Inwood

All 1915

District Key

Figure 3.1-5—Distribution of Average Age of Structures in Manhattan [2, Tantala, 2001]

Average number of stories of buildings

Under 2

Above 30

10

20

Average Number of Stories

#Average

Number of Stories

Neighborhoods

1 10.1 Financial District, Tribeca, Battery Park City, Seaport

2 5.2 Soho, Greenwich Village, Chinatown, Little Italy, Noho

3 5.9 East Village, Lower East Side, Tompkins Square

4 5.8 Chelsea, Clinton5 11.2 Flatiron, Midtown

6 8.7 Gramercy, Murray Hill, Turtle Bay, Tudor City

7 7.0 Lincoln Square, Upper West Side, Manhattan Valley

8 7.1 Upper East Side, Yorkville, Carnegie Hill, lenox Hill

9 5.6 Morningside Heights, Hamilton Heights, Manhattanville

10 4.6 Central Harlem, Polo Grounds

11 5.2 East Harlem12 4.5 Washington Heights, Inwood

All 6.7

District Key

Figure 3.1-6—Distribution of Average Number of Stories in Manhattan [2, Tantala, 2001]

The predominant building types by count of general category are: wood, steel, unreinforced masonry (URM) and reinforced concrete. Figure 3.1-7 shows the relative distribution of the number of buildings in each of these four category types between twelve neighborhood districts in Manhattan. This Figure demonstrates that unreinforced masonry is the dominant building type by count, whereas there are few wood buildings.

35 109

1895

880

1000200030004000

Wood Steel URM ConcreteBuilding Type

Num

ber

6 72

2097

5250

1000200030004000

Wood Steel URMConcreteBuilding Type

Num

ber

3 3331178

1600

1000200030004000

Wood Steel URM Concrete

Building Type

Num

ber

1640

2358

2210

1000200030004000


Damage State

Num

ber

14 841627

3440

1000200030004000


Building Type

Num

ber

1739

3665

3480

1000200030004000


Building Type

Num

ber

7 335

4044

1510

1000200030004000


Building Type

Num

ber

21 84

1592

1840

1000200030004000


Building Type

Num

ber

4 572

3129

1680

1000200030004000

Wood Steel URMConcreteBuilding Type

Num

ber

12 125

3258

3360

1000200030004000


Building Type

Num

ber

Financial District,Tribeca, Seaport, Battery Park City

Soho, Greenwich Village,Chinatown, Little Italy,Noho

East Village, Lower East Side, Tompkins Square

Upper East Side, Yorkville, Carnegie Hill, Lenox Hill

Gramercy, Murray Hill, Turtle Bay, Tudor City

East Harlem

Central Harlem, Polo Grounds

Washington Heights,Inwood

Morningside Heights, Hamilton Heights,

Manhattanville

Lincoln Square, Upper West Side, Manhattan Valley

Flatiron,Midtown

Chelsea, Clinton

0 385

2684

2100

1000200030004000

Wood Steel URM ConcreteBuilding Type

Num

ber

2

1334 1605

1500

1000200030004000

Wood Steel URM ConcreteDamage State

Num

ber

Figure 3.1-7—Building Count by Structural Type and Neighborhood [2, Tantala, 2001]

3.2 Earth Science Hazards—PGA and PGV for Scenario Events

Most damage and loss caused by an earthquake is directly or indirectly the result of ground

shaking. Therefore, it is important to evaluate the geographic distribution of ground shaking resulting from the specified scenario earthquake events. The Potential Earth Science Hazard module in HAZUS estimates ground motion using several quantitative parameters, such as Peak Ground Acceleration

(PGA) and Peak Ground Velocity (PGV). PGA and PGV are typically estimated based on the location, size and type of earthquake, and local geology. Again, for a prescribed event, attenuation relationships are used to calculate ground shaking demand for rock sites (Site Class B), which is then amplified by factors based on local soil conditions when a soil map is supplied by the user. The soil profile used for this study is represented in Figure 2.1-1. Lower Manhattan is predominately Soil Type “D” (a thick alluvium or a soft soil). Because Lower Manhattan is closer to the epicenters of the scenario earthquake and because softer soils tend to amplify ground motion, the highest PGA and PGV values would be expected in this vicinity. The remainder of Manhattan has relatively stiffer soils of Type “B” and “C”, which would not amplify earthquake ground much as much as the Type “D” sections in Lower Manhattan. Also, the remainder of Manhattan (upper) is further from the source (epicenter) event than lower Manhattan. The peak acceleration is the maximum acceleration experienced by the particle during the course of the earthquake motion. PGA is a good index of hazard to short buildings, up to about 7 stories. To be a good index means that you will get a strong correlation, if you plot some measure of demand placed on a building (like inter-story displacement or base shear) against PGA for a number of different buildings for a number of different earthquakes. PGA is a simple design parameter because it can be related to a force and then one can design a building to resist a certain horizontal force. The greater the PGA then the greater the chance for damage (particularly for short buildings). PGV, peak ground velocity, is a good index of hazard to taller buildings. Greater PGV generally means more chance for damage in tall buildings. However, it is not clear how to relate velocity to force in order to design a taller building. This study focuses on 10 scenarios, which include 6 deterministic and 4 probabilistic events (Table 3.2-1).

Location


A 5.0 40.560° N, 74.000° W 2,475 0.042 2.1000B 6.0 same 19,500 0.0102 0.2550C 7.0 same 160,000 0.000006 0.0003

D 5.0 40.560° N, 74.000° W 2,475 0.084 0.0420E 6.0 40.500° N, 73.352° WF 7.0 40.321° N, 73.000° W

G N/A N/A 100 1.000 50.0000H N/A N/A 500 0.200 10.0000I N/A N/A 2,500 0.040 2.0000J Annualized N/A N/A 1 - -

Scenario



Fixed Location

Variable Location

Probabilistic

samesame

Det

erm

inis

ticPr

obab

ilist

ic

Table 3.2-1—Deterministic and Probabilistic Earthquake Scenarios The peak ground acceleration (PGA) and peak ground velocity (PGV) estimates for Manhattan are provided in Figures 3.2-1 and 3.2-2 for the scenario events (fixed location, constant probability and the probabilistic events respectively). The average PGA and PGV are also provided for the 12 labeled neighborhood districts. As expected, the largest PGA and PGV values occur generally from the largest magnitude earthquake with the closest epicenter in the regions with the softest soil. Therefore with these scenarios, it is not surprising to see larger PGA and PGV in southern Manhattan near district 1 (the Financial district) and to see the values decrease the further the particular area is from the source event. The results of the other scenarios are available in another report [2, Tantala, 2001].

6.0M5.0M 7.0MFixed Location

PGA, Peak Ground Acceleration

0.05

Above 0.70

0.20

0.40

PGA, % g acceleration

District Key

District # Average PGA

1 0.14472 0.15063 0.15144 0.12645 0.11736 0.12647 0.10888 0.10799 0.112310 0.131811 0.138912 0.1127

All 0.1274

District #Average

PGA1 0.67532 0.60793 0.62974 0.54185 0.53976 0.55727 0.46138 0.48919 0.401110 0.417411 0.441612 0.3535

All 0.5096

District #Average

PGA1 0.37842 0.34943 0.36014 0.29275 0.28586 0.30237 0.23428 0.24389 0.203910 0.233511 0.254012 0.1769

All 0.2763

Figure 3.2-1—Fixed Location Scenarios (Magnitudes 5, 6 and 7), Peak Ground Acceleration

PGV, in/sec


PGV, Peak Ground Velocity

2

Above 25

8

16

District Key

District #Average

PGA1 0.37842 0.34943 0.36014 0.29275 0.28586 0.30237 0.23428 0.24389 0.203910 0.233511 0.254012 0.1769

All 0.2763

District #Average

PGV1 8.582 8.173 8.374 5.735 5.236 5.977 3.888 3.759 3.4910 4.7611 5.2712 3.06

All 5.52

District #Average

PGV1 20.212 19.303 19.694 14.965 14.446 15.827 11.168 10.959 9.9410 12.8011 14.1912 9.11

All 14.38

Figure 3.2-2—Fixed Location Scenarios (Magnitudes 5, 6 and 7), Peak Ground Velocity

3.3 Building Damage

Determining what level of damage buildings experience, is the essential component and heart of

the loss estimation process (which is later used to predict other losses like cost and casualties). The results of damage estimation methods (i.e., damage predictions for model building types for a given level of ground shaking) are used in other modules of the HAZUS methodology to estimate: (1) casualties due to structural damage, including fatalities, (2) monetary losses due to building damage (i.e. cost of repairing or replacing damaged buildings and their contents); (3) monetary losses resulting from building damage and closure (e.g., losses due to business interruption); (4) social impacts (e.g., loss of shelter); and, (5) other economic and social impacts. It is extremely important to keep in mind the relative probabilities of the different scenarios, when comparing the results between each of them (see Table 3.2-1). Specifically, the building damage predictions may be used to study expected damage patterns in a given region for different scenario earthquakes (e.g., to identify the most vulnerable building types, or the areas expected to have the most damaged buildings). HAZUS subdivides building damage into five categories: None, Slight, Moderate, Extensive and Complete. More detailed descriptions of each of these damage states are provided in the HAZUS technical manual [25, FEMA, 2000] for each of the 36 model building types used in this methodology. Figure 3.3-1 illustrates the distribution of these damage states by neighborhood district for the magnitude 7.0 scenario (C). With these figures, it is evident that the most damage occurs for the event that generates the highest PGA and PGV—the Fixed Location, Magnitude 7.0 event (scenario C). This event results in the collapse of 1,667 buildings as shown in Figure 3.3-1. Surprisingly, the most vulnerable district in Manhattan is not the one closest to the epicenter, but rather District 8 (located in the upper East Side), which experienced an average PGA for scenario C of 0.49 g (about half of what the maximum average in District 1 experienced). However, it is reasonable when we consider that District 8 has the most buildings (about 13%) and more specifically the most unreinforced masonry buildings (also 13%). These buildings in District 8 account for 18% of the total square footage in Manhattan, and therefore are the most at risk in a seismic event and will lose accordingly. Table 3.3-2 summarizes the building damage by count for the earthquake scenarios. Scenario C (Fixed Location, Magnitude 7.0) is the worst case, which predicts that 5% of all buildings will have complete damage (the condition which generally produces casualties) and 14% will have extensive damage (the condition which generally produces injuries). In the 1989 Loma Prieta earthquake, for example, 95 percent of the injuries did not involve structural collapse [26, Durkin, 1992].

Event

5.0M 36,120 98% 674 2% 141 < 1% - - - -6.0M 22,587 61% 7,936 21% 5,386 15% 919 2% 107 < 1%7.0M 8,531 23% 8,808 24% 12,600 34% 5,329 14% 1,667 5%

5.0M 36,120 98% 674 2% 141 < 1% - - - -6.0M 33,625 91% 2,535 7% 771 2% 4 < 1% - -7.0M 25,711 70% 7,049 19% 3,752 10% 423 1% - -

100 year 36,724 99% 120 < 1% 91 < 1% - - - -500 year 33,613 91% 2,140 6% 1,003 3% 90 < 1% 89 < 1%

2,500 year 17,052 46% 6,840 19% 4,875 13% 1,014 3% 390 1%Annualized - - - - - - - - - -

Fixed Location

Constant Probability

Probabilistic

CompleteNone Slight Moderate Extensive

Table 3.3-2—Total Number of Buildings in each Damage State for Manhattan Scenario Events

Figure 3.3-1—Distributions of Damage State (None, Slight, Moderate, Extensive and Complete) by Building Count for Fixed Location M7.0 Event to All Buildings [2, Tantala, 2001]

Debris estimates for these scenarios were also assessed and are presented in another report [2, Tantala, 2001].

3.4 Economic Losses

The primary hazard associated with earthquakes is ground shaking, which damages and destroys buildings, bridges and other structures. This damage can cause massive immediate financial losses, casualties, disruptions in essential facilities and services and severe long-term economic and social losses.

The previous section has shown in detail, which structures are most vulnerable (primarily unreinforced masonry) and where the most vulnerable areas are in Manhattan are (Districts 2 and 8, Chinatown and the Upper East Side). Previous has also noted that steel and unreinforced masonry buildings have the highest exposure [2, Tantala, 2001]. Given these building inventory damage estimates, economic loss can be estimated and assessed. The majority of total losses were produced by residential structures (roughly 40-50% of the total loss estimates depending on the scenario). The building related losses are divided in two categories: direct building losses and business interruption losses. The direct building losses are the estimated costs to repair or replace the damage caused to the building and its contents. The business interruption losses are losses associated with inability to operate a business because of the damage sustained during the earthquake. Business interruption losses also include the temporary living expenses for those people displaced from their homes because of the earthquake. Table 3.4-1 summarizes the total loss estimates. As expected, the total loss estimates are larger for more significant events (those with longer average return periods). The greatest loss occurs from event C (a magnitude 7.0 at the 1884 historic site) with $48.1 billion (2001$US). The loss ratios (in the center of Table 3.59) represent the sum of building (or capital) losses divided by the total replacement cost of the entire building inventory in Manhattan (approximately $347 billion dollars). The loss ratio is generally less than 2% for all cases, with the exception of scenario C, which has a significant loss ratio of approximately 7%. Figures 3.4-1 shows the distribution of total loss estimates for scenarios A, B and C (magnitude 5.0, 6.0 and 7.0 events) and the total loss values within each community district. These total loss estimates include economic loss from direct building losses (structural, non-structural, contents) and business interruption losses. Results for other scenarios are available in another source [2, Tantala, 2001].

All values in thousands of 2001$US

StructuralNon-

Structural ContentsInventory

LossRelocation

LossCaptial

Related LossWages Losses

Rental Income Loss

A 5.0 2,475 16,178 336,370 277,003 3,840 0.11% 8,764 4,690 6,162 5,429 658,436 B 6.0 19,500 1,205,841 3,152,458 1,735,681 22,675 1.41% 891,563 597,476 426,282 766,313 8,798,289 C 7.0 160,000 7,169,623 14,637,362 5,141,955 60,186 7.08% 4,921,434 7,368,104 3,870,065 4,943,739 48,112,468

D 5.0 2,475 16,178 336,370 277,003 3,840 0.11% 8,764 4,690 6,162 5,429 658,436 E 6.0 same 95,406 325,512 193,939 2,407 0.14% 59,617 24,022 29,777 44,511 775,191 F 7.0 same 850,993 1,486,296 611,468 7,841 0.76% 622,423 342,133 269,204 548,789 4,739,147

G N/A 100 4,940 5,422 477 1 < 0.01% 3,310 1,109 1,320 2,406 18,985 H N/A 500 177,199 475,025 259,104 3,417 0.21% 119,900 43,405 48,885 103,677 1,230,612 I N/A 2,500 1,657,498 4,266,358 2,207,707 27,001 1.92% 1,203,895 500,143 469,706 1,118,576 11,450,884

J Annualized N/A 1 3,644 9,194 4,277 26 < 0.01% 2,644 1,003 967 2,541 24,296

Magnitude


Income LossesTotal Loss

Loss Ratio (%)

Prob

abili

stic

Probabilistic

Captial Stock Losses

Det

erm

inis

tic

Fixed Location

Variable Location

Scenario Type

Table 3.4-1—Total Loss Estimates (Direct Building and Business Interruption) for All Scenarios

30,000

# 2001$ (Thousands)

%

1 126,100 17%2 87,271 12%3 68,018 9%4 77,292 10%5 138,507 19%6 100,900 14%7 23,336 3%8 56,182 8%9 7,719 1%10 14,319 2%11 32,200 4%12 9,078 1%

All 741,485 100%

# 2001$ (Thousands)

%

1 1,788,845 18%2 1,007,499 10%3 749,408 8%4 1,020,821 10%5 1,896,613 19%6 1,305,355 13%7 427,608 4%8 732,339 7%9 125,148 1%

10 213,304 2%11 466,507 5%12 160,700 2%

All 9,907,983 100%

# 2001$ (Thousands)

%

1 7,099,119 16%2 3,115,885 7%3 2,323,419 5%4 3,924,535 9%5 9,661,923 22%6 6,277,506 15%7 2,274,407 5%8 4,037,941 9%9 667,250 2%

10 904,743 2%11 1,829,316 4%12 937,338 2%

All 43,141,264 100%

Above 400,000

120,000

240,000

Total Loss

2001$ (Thousands)by census tract

District Key

6.0M5.0M 7.0MScenario Earthquakes

Structural+

Contents+

Loss of Use

Figure 3.4-1—Total Loss Estimates (Direct Building and Business Interruption)

for Fixed Location Scenarios (Magnitudes 5, 6 and 7)

3.5 Social Impacts

Building damage has short and long-term implications. In the short term, people are killed and

injured by falling objects. First, let us examine the short-term effects. Most deaths occur in earthquakes when structures collapse. Nearly all of the 63 deaths in the 1989 Loma Prieta earthquake were a result of structural collapse [26, Durkin, 1992]. The second major cause of death in earthquakes is fire. Earthquake-related injuries, in contrast to deaths, often result from nonstructural damage. And unfortunately, in a typical earthquake, many more buildings are damaged than destroyed. It is this damage to buildings and their contents that causes most injuries. In the 1989 Loma Prieta earthquake, for example, 95 percent of the injuries did not involve structural collapse [26, Durkin, 1992]. These injuries are caused by people falling, getting struck by falling or overturned objects. The methodology for determining injuries and casualties is based on the assumption that there is a strong correlation between building damage (both structural and non-structural) and the number and severity of casualties. In smaller earthquakes, non-structural damage will most likely control the casualty estimates. In severe earthquakes where there will be a large number of collapses and partial collapses, there will be a proportionately larger number of fatalities. Data regarding earthquake related injuries are not of the best quality and data are not available across all model building types used in HAZUS. Available data often have insufficient information about the type of structure in which the casualties occurred and how the casualty occurred. As a result, casualty estimates could be off by a factor of as much as 10 in some cases. Nevertheless, injury predictions were made for the two most extreme categories on the HAZUS injury scale shown in Table 3.5-1.

Injury Severity Level Injury Description

Severity 1 Injuries requiring basic medical aid without requiring hospitalization

Severity 2 Injuries requiring a greater degree of medical care and hospitalization, but not expected to progress to a life threatening status

Severity 3 Injuries which pose an immediate life threatening condition if not treated adequately and expeditiously. The majority of these injuries are the result of structural collapse and subsequent entrapment or impairment of the occupants.

Severity 4 Instantaneously killed or mortally injured

Table 3.5-1—Severity 3 and Severity 4 predictions were made using this HAZUS Injury Scale Injury estimates for scenario events (A, B and C) are provided in Figure 3.5-1 and summarized in Table 3.5-2 for all scenarios each occurring at three different time periods. These Figures show the relative distribution of Severity 3 and Severity 4 (also called casualty 3 and 4) in Manhattan and have tabulated potential deaths (Severity 4) for each scenario at three different times of day (2am, 2pm and 5pm). Because of commuting and variations in the population during the day, people are exposed to different structures of varying vulnerability, and injury estimates will consequently vary. These results are tabulated in Table 3.5-2. Although these estimates do seem low, these figures could again be reasonably ten times this (as a conservative estimate), because the methodology estimates SEV 3 (life-threatening) and SEV 4 (instant death) due to the limitations of the model previously discussed. Note also that the methodology always crudely estimates SEV 3 and SEV 4 to be equal and SEV 2 to be roughly ten times SEV 3. It should be remembered that this severities are estimates related to building damage. There will be additional severities (not accounted for in this section) to people exposed to fires following earthquakes. People exposed to fire following earthquakes are further later in this paper.

SEV 2 Require Hospital

SEV 3 Life

Threatening

SEV 4 Instant Death


SEV 3 Life

Threatening

SEV 4 Instant Death


SEV 3 Life

Threatening

SEV 4 Instant Death

A 5.0 2,475 - - - - - - - - -

B 6.0 19,500 110 11 11 210 21 21 130 13 13

C 7.0 160,000 1,650 165 165 2,710 271 271 2,710 271 176

D 5.0 2,475 - - - - - - - - -

E 6.0 same - - - - - - - - -

F 7.0 same 30 3 3 40 4 4 30 3 3

G N/A 100 - - - - - - - - -

H N/A 500 90 9 9 60 1,933 6 50 6 5

I N/A 2,500 560 56 56 510 51 51 360 36 36


2 pm 5 pm2 am

Scenario Type Magnitude

Prob

abili

stic

Probabilistic

Det

erm

inis

tic

Fixed Location

Variable Location

Table 3.5-2— Injury and Casualty Summary for Scenario Earthquake for Multiple Times (2am, 2pm, 5pm) with Severity 2 (requires hospital), Severity 3 (life-threatening) and Severity 4 (instant death) Predictions

Often missing from attempts to measure the effectiveness of earthquakes are very real social losses. Low-income housing, which is often concentrated in older buildings that are less resistant to seismic damage, may be the most severely affected, leading to increases in homelessness and dislocation. This study estimates the probable number of people that are expected to be displaced from their homes due to the earthquake (long term shelter needs) and the number of people that will require accommodations in temporary public shelters (short term shelter needs). The estimates for the scenario earthquake are listed in Table 3.5-3 and can be compared relative to the average functionality at day 1 (the day the earthquake occurs) of the 624 Manhattan schools. Extreme events like Scenarios C and I, which have larger shelter needs and very low school functionality, may not be able to accommodate every person. The average functionality drops dramatically for more significant events (i.e., those with a longer average return period). The distributions of short and long term shelter needs are available in another source [2, Tantala, 2001].

Short Term Long Term % Long Term

A 5.0 2,475 - - - 78%

B 6.0 19,500 15,725 9,612 61% 22%

C 7.0 160,000 114,985 68,562 60% 5%

D 5.0 2,475 - - - 78%

E 6.0 same 93 105 113% 77%

F 7.0 same 8,516 5,542 65% 60%

G N/A 100 - - - 100%

H N/A 500 2,514 1,933 77% 18%

I N/A 2,500 31,413 20,942 67% 5%

Average School Functionality

Shelter

Prob

abili

stic

Probabilistic

Det

erm

inis

tic

Fixed Location

Variable Location

Scenario Type MagnitudeRecurrence

Interval (Years)

Table 3.5-3—Short and Long Term Shelter Needs for Scenario Earthquakes and Average School Functionality for Scenarios

4.0 CRITICAL FACILITIES DAMAGE AND PEFORMANCE IN MANHATTAN After an earthquake strikes, people are at their most vulnerable state. Collapsed and burning buildings, spreading fires, homelessness, and social chaos are just a few examples of secondary crises that follow in the wake of an earthquake and magnify the effects of such a disaster. It is in these critical moments that earthquake recovery response is crucial. The fire department must be able to fight the flames that erupt. Hospitals must be prepared to treat the potential influx of wounded. The police must ensure social order to facilitate post earthquake recovery and to save as many lives as possible. Schools must be open to receive those who have lost their homes and have no place else to turn. These facilities that are so essential to the efficient and effective management of scarce resources in a disaster situation must remain functional in order to mitigate the effects of a naturally occurring event that threatens Manhattan. Essential facilities are those facilities that must necessarily remain in operation after a seismic event for post earthquake recovery operations. The facilities provide required services to victims of an earthquake and are primarily responsible for the rate of recovery in the affected area. By definition then, the structures containing these facilities must remain structurally sound in order to remain fully functional and facilitate provision of services. The functionality of a structure is directly related to its particular damage state (i.e. slightly damaged facilities will obviously aid recovery operations more than those that are extensively damaged). Essential facilities then have a unique relationship to the damage state of an area affected by an earthquake (or any other disaster). Losses to essential facilities will have a magnifying effect on the expected loss in both economic terms and in human lives [27, Schwimmer, 2000]. That is, loss due to an essential facility that is rendered nonfunctional not only includes its own cost, but it should also include the attributed increase in losses for all victims in its particular service area. This is especially true for the types of essential facilities studied in this report. Historically, large magnitude earthquakes have resulted in significant damage and loss of life due to structural failure, however, in many instances, even greater property damage was caused by fire damage related indirectly to the seismic event. The resulting fires from the 1906 San Francisco earthquake destroyed more of the city than the actual earthquake. Seismic damage rendered the water system and fire fighting units inoperable. While the primary cause of life loss is generally due to structural collapse, the second most significant cause of fatalities is fire. The fires resulting from the 1923 Tokyo earthquake contributed to the more than 143,000 deaths [4, U.S. Congress, 1995]. While support services from outside of the affected region can be moved to aid in the recovery efforts, the total loss resulting from an earthquake will be controlled by the reliability of the essential facilities within that region. Furthermore, the expected economic loss in a region that contains a high proportion of interdependent physical and economic infrastructure (e.g. New York City) will be much greater than for a less complex region [28, EERI, 1996]. This principle can be applied to essential facilities in highly complex regions as well. Regions of dense population and a high concentration of valuable infrastructure dependent on specialized essential services, can incur great consequences in the form of economic loss and loss of life. There are many other significant factors involved in the resulting losses that are not discussed, but they are often related to essential facilities, and their ability to improve, maintain, or distribute services.

4.1 Definition of Essential Facility

The terms, “critical” and “essential” facility have several definitions. They may describe facilities that “could seriously affect the public well-being through loss of life, large financial loss, or degradation of the environment if they were to fail” [29, NAS, 1980]. The terms may also be used to describe facilities that may not pose a significant hazard to the public, but must continue to operate in the event of an emergency to provide necessary services [29, NAS, 1980]. The BOCA National Building Code defines essential facilities as structures in “Seismic Hazard Exposure Group III” as required for post-earthquake recovery. This includes buildings that are occupied by (1) fire, rescue, and police stations, (2) surgery or emergency treatment facilities, (3) emergency preparedness centers, (4) post earthquake recovery

vehicle garages, (5) power generating stations and other utilities required as emergency backup facilities, (6) primary communication facilities, and (7) storage facilities for highly toxic materials [30, BOCA, 1996]. For the purposes of this analysis, the definition of “essential facility” will be restricted to the types of facilities that HAZUS (Hazards US) categorizes as such. This will necessarily exclude several types of facilities and include one other. The reasons for this are practical as well as convenient. First, essential facilities will not include those structures that only entail large financial loss or degradation of the environment if they were to fail. This limitation excludes toxic material sites and capital-intensive structures. Power-generating stations will also be excluded as well, since Manhattan facilities do not generate sufficient power to be considered a significant factor. Other facilities that will not be considered, due to limitations in available data, are post earthquake recovery vehicle garages (except fire and police stations) and primary communication facilities. A class of facility that is not explicitly listed in the above definition includes schools. These facilities are essential during both a seismic event to protect the occupants, as well as in post-earthquake recovery as community shelters. The definition used here will focus primarily on those structures that must remain functional after a seismic event in order to provide vital services. Essential facilities will then be defined as those structures or buildings that are required for post-earthquake recovery. This includes buildings that are occupied by fire, rescue, police stations, hospitals and clinics that provide surgery or emergency treatment facilities, and grade school levels ranging from elementary through high school that can provide emergency housing for those displaced by the earthquake.

4.2 Manhattan Essential Facility Inventory

Of the 37,000 structures in Manhattan, 54 are fire-related stations, 23 are police stations, 20 are major medical facilities and 626 are schools. “Fire-related” stations include New York Fire Department (NYFD) engine company stations and independent ladder company stations. “Healthcare-related” facilities include medium and large hospitals only. Smaller hospitals, clinics and special care facilities (like mental institutions, orphanages and partially vacant health facilities) are not considered for this study. Schools include those housing kindergarten through 12th grade levels. Information also exists for universities within the city, but they are also not considered for the purposes of this analysis. Table 4.2.1 summarizes the components of the database of essential facilities.

Facility Number

Fire Station 54Police Station 36Hospital 20School 626

Total 736

Table 4.2-1—Essential Facility Database Summary The structural characteristics of these facilities were determined using the data sources of this research (particularly the MAS database and NYC planning databases). The key information for determining critical facility damage and functionality include: building height and building type. The majority of these structures are typically 40 to 80 years old and constructed of unreinforced masonry (URM) or steel. The average number of height per facility is about 4 stories.

4.2 Medical Facilities Assessment

For hospitals, the number of patients that can be treated is directly related to the functionality of the building’s structural and nonstructural components. Thus, regions with heavily damaged hospitals must either have alternative facilities in other accessible regions or the population will be forced to forego treatment. As a result of transportation difficulties and overwhelmed medical facilities, the affected population will face an increased risk of casualties. Typically, the first 24 hours after an event are the most critical for rescue and recovery to save lives and mitigate serious injuries. Figures 4.2-1 through 4.2-3 show the functionalities of the 20 major medical facilities in Manhattan (which represent about 10,000 beds) with contours that represent the distance to the nearest hospital for those located in each contour range. These figures also show the number of people that will most likely need medical care at a hospital in relation to how far they will likely be to the nearest major medical facility. These people are classified SEV 2 (hospitalization required) and SEV 3 (immediate medical attention). By inspection, most of the area of Manhattan and its neighborhood districts are within 300 meters of a major medical facilities, with the exception of district 7 (Upper West Side) and the upper half of district 12 (Washington and Inwood), which can ranges as larges as 3000 meters or more. One could easily relate distance to the nearest major medical facility to number of trips required to an area of interest from the mean travel time to move that distance. This of course considers Manhattan as an isolated island (i.e. all bridges are unable to be used). However, as the scenario event becomes larger, functionality decreases dramatically (particularly for scenarios C and I) when the number of those requiring hospitalization is greatest. The scenarios considered in these cases are for a 2pm earthquake (the worst case time of occurrence). Table 4.2-1 summarizes the results of the medical facilities. Hospital functionality will most likely be adequate for all scenarios except one. These estimates indicate that there will be an insufficient number of beds (26% functionality) for Scenario C (fixed location, magnitude 7.0).

Medical Facilities

Functionality

Beds Available

Those Requiring

Hospitalization

Ratio of Need to

Available

A 5.0 2,475 96% 9387 - 0%

B 6.0 19,500 63% 6130 224 4%

C 7.0 160,000 26% 2627 2978 113%

D 5.0 2,475 96% 9387 - 0%

E 6.0 same 90% 8797 - 0%

F 7.0 same 73% 7097 41 1%

G N/A 100 99% 9711 - 0%

H N/A 500 51% 5025 66 1%

I N/A 2,500 26% 2627 555 21%

Recurrence Interval (Years)

Prob

abili

stic

Probabilistic

Det

erm

inis

tic

Fixed Location

Variable Location


Table 4.2-1—Summary of Medical Facility Day 1 Functionality and Beds Available and Required

people in need ofhospitalization


District Key

Essential Facilities – Medical (Hospitals)Casualties 2 (Hospitalization Required) + Casualties 3 (Immediate Medical Attention)

distance to nearest major medical facility (meters)

300

Above 4,000

1,200

2,400

medical facilityfunctionality at day 0 (%)

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-100

each dot is5 five people

2pm %1 983 33%2 157 5%3 193 6%4 114 4%5 776 26%6 358 12%7 64 2%8 63 2%9 35 1%10 67 2%11 106 4%12 61 2%

All 2978 100%

Need HospitalDistrict #

Need Hospital

2pm1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -

All -

District #

2pm %1 102 46%2 22 10%3 22 10%4 11 5%5 36 16%6 18 8%7 3 1%8 2 1%9 1 < 1%10 1 1%11 5 2%12 2 1%

All 223 100%

District #

Need Hospital

Average Functionality

96%


63%


26%

bedsavailable

9,387beds

available

6,130beds

available

2,627

Figure 4.2-1— Essential Facilities (Major Hospitals) Day 1 Functionality,

number of people requiring hospitalization and shortest distances between them for Fixed Location Scenarios (Magnitudes 5, 6 and 7)

6.0M5.0M 7.0M

District Key





300

Above 4,000

1,200

2,400


0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-100


2pm %1 16 40%2 5 13%3 2 5%4 3 7%5 5 13%6 2 4%7 0 1%8 0 1%9 0 < 1%10 1 2%11 5 11%12 0 1%

All 40 100%

Beds AvailableDistrict

#

CAS 4 Instant Death2pm

1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -

All -

District #


1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -

All -

District #


96%


90%


73%

bedsavailable

9,387

bedsavailable

8,797beds

available

7,097

Figure 4.2-2— Essential Facilities (Major Hospitals) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them

for Constant Probability Scenarios (Magnitudes 5, 6 and 7)

District Key

500 year event100 year event 2500 year eventProbabilistic





300

Above 4,000

1,200

2,400


0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-100


2pm %1 3 4%2 3 5%3 9 14%4 3 4%5 2 3%6 3 4%7 3 4%8 3 4%9 5 8%10 8 13%11 3 5%12 22 35%

All 66 105%

Need HospitalDistrict

#

2pm %1 16 3%2 28 5%3 47 8%4 21 4%5 15 3%6 21 4%7 29 5%8 30 5%9 72 13%10 64 12%11 84 15%12 128 23%

All 554 100%

CAS 4 Instant DeathDistrict

#


1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -

All -

District #


99%


51%


26%

bedsavailable

9,711beds

available

5,025beds

available

2,627

Figure 4.2-2— Essential Facilities (Major Hospitals) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals)

4.3 School Facilities Assessment

The amount of shelter available is also related to the functionality of the structural and nonstructural components of schools. For schools that can no longer function as a community shelter, other shelters will face an increased burden on their limited resources, and may not even be able to provide services for all requiring assistance. Functionality is derived from the damage state of the particular structure, which is determined, in part by peak ground acceleration (PGA). Figures 4.3-1 through 4.3-2 represent the functionalities of the 624 schools and PGA estimates. Generally, areas of strongest PGA experience the more dramatic decrease of facility functionality. Table 4.3-1 summarizes the results in Figures 4.3-1 through 4.3-3 with the short-term shelter information. Again, due to the limitations of the methodology, these estimates could be a factor of ten larger.

Available Needed

A 5.0 2,475 78% 244,140 - 0%

B 6.0 19,500 22% 68,860 15,725 23%

C 7.0 160,000 5% 15,650 114,985 735%

D 5.0 2,475 78% 244,140 - 0%

E 6.0 same 77% 241,010 93 0%

F 7.0 same 60% 187,800 8,516 5%

G N/A 100 100% 313,000 - 0%

H N/A 500 18% 56,340 2,514 4%

I N/A 2,500 5% 15,650 31,413 201%

Short-Term Shelter % capacity

usedMagnitude

Average Return Period

(Years)

Average School FunctionalityScenario Type

Prob

abili

stic

Probabilistic

Det

erm

inis

tic

Fixed Location

Variable Location

Table 4.3-1—Summary of School Facility Day 1 Functionality and those Requiring Shelter Again, as the scenario event becomes larger, functionality decreases dramatically (particularly for scenarios C and I) when the number of those requiring shelter is greatest. Shelter functionality is most likely adequate for all scenarios except two. These estimates indicate that there maybe be an insufficient shelter (only 5% functionality) for Scenario C (fixed location, magnitude 7.0) and Scenario I (a 2,500 year probabilistic event).


District Key

Essential Facilities – School Functionalitiesand PGA

0.05

Above 0.70

0.20

0.40

ContouredPGA, % g

0-10

School Functionality at Day 0 (%)

10-20

20-30

30-40

40-50

50-60

60-70

70-100


78%


22%


5%

ShelterProvided: 244,140 Needed: 0

ShelterProvided: 68,860Needed: 15,725


Figure 4.3-1—Essential Facilities (Schools) Day 1 Functionality,


6.0M5.0M 7.0M

District Key



0.05

Above 0.70

0.20

0.40

ContouredPGA, % g

0-10


10-20

20-30

30-40

40-50

50-60

60-70

70-100


78%


77%


60%


ShelterProvided: 241,010Needed: 93


Figure 4.3-2—Essential Facilities (Schools) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them


District Key



0.05

Above 0.70

0.20

0.40

ContouredPGA, % g

0-10


10-20

20-30

30-40

40-50

50-60

60-70

70-100


100%


18%


5%




Figure 4.3-3—Essential Facilities (Schools) Day 1 Functionality,

number of people requiring hospitalization and shortest distances between them for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals)

4.4 Police Facilities Assessment

In regions with heavy damage to police stations, impaired police activity could potentially result in looting, and other property damaging crimes that contribute to the overall earthquake impact. Police inactivity would also yield increased social losses in the form of social and economic disruption. Figures 4.4-1 through 4.4-3 show the functionalities of the 36 major emergency rescue facilities in Manhattan (which include police stations) with contours representing the distance to the nearest police station for those located in each contour range. The nearest distance to police facility could be related to average travel time of each trips and number of trips possible per day. These figures also show the number of people that will most likely need rescue. The people that will most likely require rescue and subsequently hospitalization are SEV 3 (immediate medical attention). Again, as the scenario event becomes larger, functionality decreases dramatically (particularly for scenarios C and I). Table 4.4-1 summarizes the results of the emergency rescue facilities. Rescue functionality is most likely adequate for all scenarios except two (scenarios C and I with 4% each).

Emergency Rescue Facilities

Functionality

CAS 3 Require Rescue

A 5.0 2,475 56% -

B 6.0 19,500 19% 21

C 7.0 160,000 4% 271

D 5.0 2,475 56% -

E 6.0 same 57% -

F 7.0 same 58% 4

G N/A 100 100% -

H N/A 500 16% 6

I N/A 2,500 4% 51

Recurrence Interval (Years)

Prob

abili

stic

Probabilistic

Det

erm

inis

tic

Fixed Location

Variable Location


Table 4.4-1—Summary of Police Station Day 1 Functionality and People Requiring Rescue


District Key

Essential Facilities – Police StationsCasualties 3 (Immediate Medical Attention and Rescue Needed)



300

Above 4,000

1,200

2,400


0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-100


2pm %1 89.322 33%2 14.285 5%3 17.551 6%4 10.397 4%5 70.507 26%6 32.512 12%7 5.838 2%8 5.752 2%9 3.219 1%10 6.121 2%11 9.610 4%12 5.589 2%

All 270.70 100%

CAS 3 Need RescueDistrict

#

CAS 3 Need

Rescue2pm

1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -

All -

District #

2pm %1 9.265 46%2 1.981 10%3 2.019 10%4 0.986 5%5 3.252 16%6 1.598 8%7 0.237 1%8 0.150 1%9 0.047 < 1%10 0.131 1%11 0.467 2%12 0.147 1%

All 20.28 100%

District #

CAS 3 Need Rescue


56%


19%


4%

Figure 4.4-1—Essential Facilities (Emergency Response) Day 1 Functionality,


6.0M5.0M 7.0M

District Key



people in needof rescue

distance to nearest major emergency response facility (meters)

300

Above 4,000

1,200

2,400

Emergencyresponsefunctionality at day 0 (%)

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-100

each dotis oneperson

2pm %1 1.47 40%2 0.48 13%3 0.18 5%4 0.27 7%5 0.49 13%6 0.16 4%7 0.04 1%8 0.03 1%9 0.02 < 1%10 0.073 2%11 0.415 11%12 0.025 1%

All 3.64 100%


#

CAS 3 Need

Rescue2pm

1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -

All -

District #

CAS 3 Need

Rescue2pm

1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -

All -

District #


56%


57%


58%

Figure 4.4-2—Essential Facilities (Emergency Response) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them


District Key



people in needof rescue

distance to nearest major emergency response facility (meters)

300

Above 4,000

1,200

2,400

Emergencyresponsefunctionality at day 0 (%)

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-100

each dotis oneperson

2pm %1 0.27 4%2 0.28 5%3 0.86 14%4 0.24 4%5 0.17 3%6 0.24 4%7 0.27 4%8 0.25 4%9 0.47 8%10 0.71 12%11 0.31 5%12 1.96 33%

All 5.99 100%


#2pm %

1 1.42 3%2 2.59 5%3 4.25 8%4 1.95 4%5 1.34 3%6 1.89 4%7 2.60 5%8 2.69 5%9 6.55 13%10 5.82 12%11 7.67 15%12 11.65 23%

All 50.40 100%


#

CAS 3 Need

Rescue2pm

1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -

All -

District #


100%


16%


4%

Figure 4.4-3—Essential Facilities (Emergency Response) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them

for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals)

4.5 Fire Station Facilities Assessment

Fires following earthquakes can cause severe losses. These losses can sometimes outweigh the total losses from the direct damage caused by the earthquake, such as collapse of buildings and disruption of lifelines. Many factors affect the severity of the fires following an earthquake, including: ignition sources, types and density of fuel, weather conditions, functionality of water systems, and the ability of fire fighters to suppress the fires. It should be recognized that a complete fire following earthquake model requires extensive input with respect to the level of readiness of local fire departments and the types and availability (functionality) of water systems [25, FEMA, 2000]. To reduce the input requirements and to account for simplifications in the lifeline module, the fire following earthquake model presented in this report is also simplified. Figure 4.5-1 show the functionalities of 54 major fire stations with contours that represent the probably GPM demand of the fires for scenarios A, B and C. These figures also show relative locations of probably ignitions and the number of people that will most likely be exposed to those fires. These results are summarized in Table 4.5-1. As a general rule, larger scenarios (i.e. larger average return period) indicate more likely fires and a greater chance that the fire stations with limited functionality, will not be able to supply [31, NYFD, 1997] the GPM demands of the fires (in scenario C, demands are more than 14 times of the available GPM supply). Scenario C also has the largest number of property and people exposed ($15.2 billion and 69,000 respectively). These sets of Figures indicate that although the number and relative placement of fire stations seem reasonable, the fragility and capacity of these structures may not be adequate for larger (longer return period) events. More detailed results are available in another report [2, Tantala, 2001].

A 5.0 2,475 10 216 362 50% 10,218 108,000 9%

B 6.0 19,500 111 632 24,620 14% 153,764 30,240 508%

C 7.0 160,000 169 15,200 68,638 7% 213,518 15,120 1412%

D 5.0 2,475 10 216 362 50% 10,218 108,000 9%

E 6.0 same - - - 49% - 105,840 0%

F 7.0 same 14 16 108 47% 9,992 101,520 10%

G N/A 100 - - - 98% - 211,680 0%

H N/A 500 - - - 16% - 34,560 0%

I N/A 2,500 117 6,300 19,045 7% 145,656 15,120 963%Prob

abili

stic

Probabilistic

Det

erm

inis

tic

Fixed Location

Variable Location

$ Exposed (Millions)

# Ignitions

Average Fire Stations

% Functionality



Likely GPM

Supply

% capacity

GPM Demand

People Exposed

Table 4.5-1—Summary of Fire Stations Day 1 Functionality Number of Ignitions, Dollars Exposed, People Exposed and

GPM Supply and Demand Comparison


14%

Ignited Fire

Water Demands to Fight Fires(gallons per minute, GPM)

under1,000

Above 12,000

4,000

8,000

Fire stations

each staris 1 fire ignited


50%


7%

Essential Facilities – Fire Stations and Ignitions

10 ignitions 111 ignitions 169 ignitions

6.0M5.0M 7.0MScenario Earthquakes

Figure 4.5-1—Essential Facilities (Fire Stations) Functionality,

Number of Fire Ignitions, Population Exposed, GPM Demand and Dollar Value Exposed for Fixed Location Scenarios (Magnitudes 5, 6 and 7)

5.0 CONCLUSIONS This paper discussed the implementation and quantitative results of a detailed critical (essential) facilities analysis, assessing probable damage and facility functionalities for Manhattan from various scenario earthquakes. This paper also quantified the functionality of essential facilities including hospitals, schools, police stations and fire stations with respect to number of beds, amount of shelter, average travel time for injured to nearest hospital, probable fire ignitions and water demands. This paper also quantified the demands placed on those essential facilities with respect to casualties, injuries and shelter requirements and assess if the facilities functionalities will be capable of accommodating these needs. Eventually, the aim of this loss estimation project will provide a framework for businesses and agencies to take mitigative action to reduce potential damage and losses, which might be experienced after an earthquake. ACKNOWLEDGEMENTS This research and study would not have been possible without the generous help and assistance provided by the following institutions:

• Federal Emergency Management Agency (FEMA) • Multidisciplinary Center for Earthquake Engineering Research (MCEER) • New York State Emergency Management Office (NYSEMO) • New York City-area Consortium for Earthquake Loss Mitigation (NYCEM) • Lamont-Doherty Earth Observatory, Columbia University (LDEO) • New Jersey State Police Emergency Management Office (NJSP-EMO) • City University of New York (CUNY)

BIOGRAPHIES Michael Tantala is a Ph.D. candidate in the Department of Civil and Environmental Engineering at Princeton University. He received his M.S.E. and M.A. degrees from Princeton in Civil Engineering in 2000 and his B.S.E. degree in Civil Engineering Systems from the University of Pennsylvania in 1998. He conducts applied research in risk assessment and management and structural reliability and has published several papers. His work deals with multiple hazards and addresses many aspects of engineering, insurance and emergency response applications. He has collaborated on several projects for FEMA, the New York City Area Consortium for Earthquake Loss Estimation (NYCEM), the Multidisciplinary Center for Earthquake Engineering Research (MCEER), the New York State Emergency Management Office (NYSEMO) the New Jersey State Police (NJSP), the Technical Chamber of Greece and most recently the Structural Engineers Association of New York (SEAoNY) in assessing damage from the World Trade Center disaster. He has received the American Society of Civil Engineering Shimizu Prize in 1995. George Deodatis, Ph.D. is an Associate Professor in the Civil and Environmental Department at Princeton University. He received his Diploma in Civil Engineering at the National Technical University of Athens in 1982, his MS from Columbia University in 1984 and his PhD from Columbia University in 1987. He worked as a Post-Doctoral Research Scientist at Columbia University from 1987 to 1988. He has published over 50 papers and 2 books. His research interests include probabilistic methods and random processes in civil engineering, engineering mechanics, earthquake engineering and structural dynamics, reliability and safety of structures and mechanical systems. He has received National Science Foundation Young Investigator Award in 1992, the International Association for Structural Safety and Reliability Junior Research Prize in 1997 and the American Society of Civil Engineers Walter Huber Civil Engineering Research Prize in 1998.

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