GOALI Project Description

I Introduction
	Natural hazards in general, and hurricanes in particular, lead to loss of life and tremendous property damage and indirect economic loss for the United States annually (Mileti, 1999).  Hurricanes are severe characterized by intense precipitation, flooding and high winds (at a minimum 18 m/s). Major (or intense) hurricanes (i.e., those of category 3-5 on the Saffir-Simpson hurricane damage potential scale) in the Atlantic Ocean represent one of Nature’s most destructive forces (Gray, 1990), sometimes causing tremendous loss of life and damage to ecology, property, wetlands and coastal estuaries (Gray and Landsea, 1992; Pielke and Landsea, 1998). These intense storms can contain maximum sustained wind speed of greater than 50 m/s with an accompanying storm surge of more than 2.7m (Wilson, 1999). Clearly, the winds and the flooding associated with the hurricanes have a significant impact on structural safety/reliability in the regions that are prone to hurricane (e.g., the East coast of the United States). 
There has been a trend of increasing economic losses from recurring natural hazards and in particular, from hurricanes and other climate-induced hazards. The potential for even greater losses over time is increasing due to development. A recent report from Munich Reinsurnce (“Topics 2000”) shows that insured losses (those protected by insurance) in the United States from natural hazards reached $22 billion in 1999, the second largest loss during the 1990’s to the $26 billion of 1992, the year Hurricane Andrew struck Florida and Louisiana. Furthermore, during the past 50 years, 250 great natural catastrophes in the world claimed the lives of over 1.4 million people and caused economic losses (insured and uninsured) of $960 billion.  The increasing socio-economic costs and the rising potential are placing unprecedented strain on public and private resources. 
During Fall 2001, following a two-day workshop entitled “ Ten Most Wanted : The search for solutions to Reduce Recurring Losses from Natural Hazards”, ASCE and the Institute for Business & Home Safety (IBHS) with support from HUD and USGS prepared a comprehensive report (IBHS, 2001). In this, they identify five main Climate Related Natural Hazards (CRNH) (the sixth being earthquakes) that pose substantial risks to infrastructure safety – hurricanes and severe windstorms, floods, hail, severe winter storms and wildfire. They also recommend that “holistic” solutions are the best ways to meet the challenge faced by communities throughout the nation as they seek to reduce infrastructure vulnerabilities. The U.S. House of Representatives, with substantial member support, is working on bill H.R. 2020 on similar lines, also known as the “Hurricane, Tornado and Related Hazards Research Act”; to promote inter-disciplinary research in understanding and mitigating windstorm related hazard impact in the next 10 years. Two of the seven research and development elements of this bill, specifically mention the need for research in developing “new methodologies for improved loss estimation and risk assessment systems for predicting and evaluating damaging windstorm impacts and for identifying, evaluating, and reliably characterizing windstorm hazards”.
Recognizing this need, the GOALI research proposed here aims to develop an integrated framework to combine the understanding of spatio-temporal variability of hurricane occurrences and physical aspects of infrastructural stability to better estimate/predict infrastructure reliability and consequently, the losses. This will enable optimal planning and management of resources in the short (e.g., disaster preparedness) and long term (e.g., town planning) by public and private sector agencies. 
The proposal is organized as follows: A case for a GOALI approach is followed by objectives and expected significance of this research.  Background information on hurricanes, structural load and failure models and, structural reliability are described next, followed by the proposed approach. Finally, we present “proof of concept” from our ongoing SGER to demonstrate the feasibility of the proposed research.

II Why GOALI?
Considerable research is being focused on understanding hurricane variability (by climate scientists) and also on developing improved structural risk assessment techniques (by structural engineers). There is little communication between these two groups which has lead to the following drawbacks:
(i) Often, structural reliability is estimated in isolation of realistic likelihood estimates of hurricane frequencies and magnitudes. 
(ii) Knowledge of year-to-year variability in occurrence and steering of hurricanes in the Atlantic basin is not incorporated in structural reliability estimation. In particular, this information can be very useful in probabilistic forecast of storm tracks and magnitudes in any given year. 
(iii) The estimation of losses is purely empirical, based on the wind speed and no consideration of structural information. For example, a new structure and a 25 year old structure are assumed to have the same probability of failure for a given wind speed. The life cycle cost of structures is also not considered. These can lead to substantial misrepresentation of losses and consequently sub-optimal decision making.
(iv) The loss data are available with insurance companies and not in the public domain. This makes it very difficult to study the impact of any improved structural reliability model(s). 
Clearly, to address these drawbacks, and in particular to make the research in structural reliability estimation due to hurricanes relevant and useful, an industry partner is crucial. From the industry standpoint a better loss estimation requires a realistic structural reliability model. This mutual convergence of interests and needs naturally lead to the proposed GOALI.
  	This GOALI is in partnership with the company, Risk Engineering (http://www.riskeng.com). Risk Engineering has extensive experience with hurricane track simulation and loss estimation and their clients include several fortune 500 companies. The industry partner will help with testing the impact of the structural reliability model in loss estimation. Furthermore, we will work jointly in evaluating and testing the hurricane track simulation model used by Risk Engineering and also the proposed track model proposed in this research. 

III Objectives and Expected Significance
As described in the introduction, there is a real need for improved loss estimation. This calls for an integration of knowledge from climate science, structural engineering and loss estimation (private sector). This motivates our proposed research, the objectives of which are to:
1. Develop an improved hurricane track model that can generate synthetic tracks, which will preserve the relative frequencies of observed landfalls, wind speeds, precipitation and flooding.  The model should also be able to simulate tracks conditioned upon various climate indicators [e.g., El-Nino Southern Oscillation, North Atlantic Oscillation, etc.] to estimate dynamic (time varying) risk in any given year (i.e., in a predictive mode);
2. Develop methods for combining loads from multiple sources (i.e., wind speed, precipitation and flooding) that are essential to structural reliability estimation;
3. Develop failure states that are physically based and associated with the estimated loads. Thereby, providing the capability to estimate structural failure probability;
4. Develop a module for estimating stationary and time varying structural system reliability, based on material degradation, structural failure probability and life cycle;
5. Develop a loss estimation module that will incorporate the structural system reliability estimates from above.
6.	Develop structural classification scheme based on losses similar to that developed by HAZUS (http://www.fema.gov/hazus) for hurricanes, to complement HAZUS-MH efforts. 
The structural reliability model will be developed for the dominant stock of structures (e.g.,
wood frame , masonry, etc.) in the Southeastern US and Gulf region. The proposed framework will also be tested over this region of the country. The estimated structural reliability and the loss can be visualized using GIS.  
	The industry partner will help with objective 5 and also provide their hurricane track model
for verification and testing (objective 1)

The following outcomes are expected:
1. Synthetic hurricane track simulation model(s) capable of generating tracks and associated precipitation and flooding potential, conditioned upon the large-scale climate state (e.g., El Nino, state of the North Atlantic Ocean, etc.). This will provide probabilistic forecasts of climate information relevant to structural reliability. 
2. Structural failure models for different type of structures (e.g., wood frame and masonry) and consequently, estimation of failure probabilities.
4.  Model for estimating time-varying structural system reliability, and  consequently, life cycle cost and optimal planning.
5.  Methodology to classify structures based on their system reliability and losses. 
6. Loss estimation model that includes estimates of structural system reliability.
All these outcomes will be extremely helpful for their contributions to the knowledge base and intellectual growth in the area of civil infrastructure assessment as well as the reduction of risks induced by hurricanes. This will enable advances in the management of our nation’s civil infrastructure by developmental agencies, state planners, disaster mitigation agencies, and private sector insurers. The outcomes from this study will supplement the HAZUS initiative by FEMA for enhancing the nation’s risk assessment capabilities under natural hazards. The framework developed here will complement the hurricane loss estimation and wind loss estimation module of HAZUS. We propose to work with FEMA and, consequently, the impact will be wide reaching in the public and private sectors. 

IV Background
	Before we describe the proposed methodology of this GOALI research, we provide brief descriptions of hurricane variability, structural loads, failure and reliability estimation aspect.

IV.1.1 Hurricanes and Climate
Hurricanes, with high sustained wind speed (>50m/s and category 3-5 on the Saffir-Simpson damage potential scale; Gray, 1990) and associated storm surges (Wilson, 1999) can cause severe property damage and loss of life estuaries (Gray and Landsea, 1992; Pielke and Landsea, 1998).  Over the years, investigators have identified large-scale climatic factors, such as El Nino phenomenon in the Pacific Ocean, Atlantic Sea Surface Temperatures (SSTs), rainfall over Africa’s Sahel region to name a few, that appear to affect the year-to-year variability in the hurricane activity (Gray, 1984 and Gray, 1990; Goldenberg and Shapiro 1996; Shapiro, 1982; Shapiro, 1989; Shapiro and Goldenberg, 1998; Bove et al., 1998). Preceding winter state of the North Atlantic atmosphere has also been shown to impact the occurrence and steering of hurricanes in the following summer (Elsner and Kocher (2000); Rajagopalan et al., 2003).
	Mechanistically, all these climatic phenomena are believed to regulate tropical storm formation via their effects upon upper tropospheric wind shear (Landsea, 1998). Events that increase shear lead to a weaker hurricane season (i.e. less number of hurricanes) while events that lower wind shear make for an active hurricane season. Therefore, for realistic simulation of hurricane tracks and consequently, the risk estimates, these climate features have to be appropriately included (or conditioned upon) in the track simulation model.     

IV.1.2 Hurricane Track Simulation
Climatologists and agencies such as NOAA’s hurricane center focus mostly on short term forecasting (hours to days) of hurricane tracks once the storm formation is identified. Typically, computationally intense physical models (e.g., Kurihara et al., 1995; Marks, D.G., 1992) initialized with 3-D profile of the storm, are used for this purpose. These models have improved a lot over the years and are very useful in making emergency response decisions (e.g., whether or not to order evacuation etc.).  On the other hand, for seasonal (e.g., resources allocation and planning for emergency response, setting insurance premiums, etc.) and long-term (e.g., infrastructure development) planning, estimates of risks associated with hurricanes are needed. This requires the ability to generate synthetic hurricane tracks consistent with historical statistics and also consistent with the large-scale climate features as mentioned in the previous section. Stochastic models present an attractive alternative. There have been some efforts in seasonal forecasting of hurricane land fall probabilities and extreme winds (Elsner et al., 1998; Elsner and Bossak, 2001; Lehmiller et al., 1997; Hess et al., 1995, to mention a few), but not much work has been done in developing models that generate synthetic tracks. There are some models developed in the private sector – but these models are not public domain. 
Recently, the PI in collaboration with researchers at Columbia University has developed a Markov Chain model and a Track Segment Model (Miller et al., 2003; Kushnir et al., 2002) for hurricane track simulations that reproduce quite well the relative frequency of hurricane occurrences over the entire Atlantic basin. In the Markov model the entire Atlantic basin is divided into grid boxes of 5ox 5o. From the historical hurricane track data, at each grid box, transition probabilities are estimated – i.e. the probability of a storm in the box at the current time step to move into adjacent grid boxes in the next time step. The estimated transition probabilities at each of the grid boxes are used to generate synthetic tracks. The Track Segment Model involves re-sampling segments of historical tracks to generate a synthetic track. Thus, the tracks generated will be a mixture of several historical tracks that are physically consistent. This method is very simple and elegant and has the ability to generate a rich mixture of tracks that are consistent with the historical tracks. Both these models can be conditioned upon on large-scale climate (e.g., El Nino, North Atlantic atmosphere etc.) to realistically capture the steering and occurrence properties. The models performed very well in reproducing the relative frequencies of the historical storms in the Atlantic basin (Miller et al., 2003; Kushnir et al., 2002). Modifications to these models have to be made to incorporate the simulation of wind speeds, precipitation and flooding. Objective 1 of the proposed research intends to develop such a version of the above models.  
IV.2 Structural Loads and Failure
	Load combinations using factored loads are derived from reliability analysis in order to provide consistency of extreme loading situations. Each scenario to which a structure might be exposed during its economic lifetime is considered. In general, the factors for the time-variable loads are selected such that when one variable takes on its maximum lifetime value, others are assumed to be at an arbitrary point-in-time level. The current ASCE 7 Standard (2003) has increased the load factor on wind from 1.3 to 1.6 due to recent studies that have included hurricane winds (Ellingwood and Tekie, 1997; Peterka and Shahid, 1998; Vickery and Twisdale, 1995).
	During a hurricane, coastal regions are subjected to combined wind, storm surge and rainfall. This leads to wind and flood loads that can be expected to be highly correlated due to the common initiating event. Therefore, the assumption that wind load, W, rain load, R, and flood load, Fa, are statistically independent no longer holds, and it is not appropriate to set two of them at arbitrary point-in-time values when the third is at its maximum lifetime value. An excellent first stage of development has been introduced into the Standard based on work of Mehta et al (1998). This includes the consideration of sea storm surge generated by hurricanes, and is based on simulations at 11 East coast sites. The resulting load combinations are applied differentially to coast and near-coast regions.
	In addition to the loads mentioned above, hurricanes can also lead to effects from increased ground water pressure, H, and to fluids, F. As the ASCE 7 Standard states, however, “Statistical data on these loads are limited or nonexistent, and the same procedures used to obtain load factors and load combinations … cannot be applied at the present time. … Further research is needed to develop more accurate load factors.”
	Infrastructure risk and damage prediction due to winds is an active area of research, with implications to emergency planners and insurers that focus on assessing the vulnerability of structures and probabilistic models for hurricane damage prediction (Leicester 1981; Sparks et al., 1994; Holmes, 1996; Huang et al., 2001; Stewart et al., 2003; Cope et al., 2003; Li and Ellingwood, 2003). The structural failure probabilities are estimated by analyzing the combination of component failure modes that contribute to structural failure in the event of hurricanes and subsequently, their probabilities (Cope et al., 2003; Stewart et al., 2003). However, these models do not include a comprehensive representation of the risk due to hurricanes.

IV.3 Building Classification
	HAZUS building types were developed for earthquake damage and there is no such classification for hurricane damage. The classifications can be very helpful for planners in terms of resource allocations while planning for emergency response. However, the new HAZUS-MH aims to classify buildings considering flooding and wind damage. Our initial analysis based on wind response indicates that the earthquake classifications should be recast so that there are five major groups, distinguished primarily by building height and natural period (Siegfried, 2003). After a building is assigned to one classification, additional factors that are important for hurricane response must be accounted for. These are primarily building envelope, building shape, local terrain enhancement, roof type, and connections. Our prior research (Corotis, 1978; Corotis and Sheehan, 1986), has confirmed the importance of geometry characteristics for wood frame buildings (especially roof configuration such as hip or gable), quality of construction detailing and inspection. Thus, a classification scheme based on hurricane losses and the structural type will be a good complement to HAZUS-MH.
 
IV.5 Structural Risk Assessment
Structures have to be designed and maintained, such that the failure probability is associated with an acceptable level of risk that is typically a trade-off between the cost of building and maintaining the structure versus its expected cost of a failure.  The most common way of expressing structural reliability is in terms of a reliability index, b, where a higher b value indicates a lower probability of failure and, consequently, a safer structure. Probabilistic knowledge of loading (e.g., loads due to hurricanes) and resistance (i.e. material strength) is required to estimate b. Reliability methods (Frangopol, 2000) and system component methods (Hendawi and Frangopol 1994) are generally for this purpose. Time varying b (Estes and Frangopol, 2001; Enright and Frangopol, 1998) and consequently, predictions of life-cycle cost and maintenance and repair decisions require future scenarios of loading (i.e. probability density function) and time varying probability of the resistance. Future scenarios of loading are not always easy to obtain, hence a prior assumption is made of the underlying load probability distribution, which is then updated in light of the observations as time goes on, resulting in a Bayesian framework (Enrght and Frangopol, 1999). The Bayesian framework has been applied in the context of system reliability (Enright and Frangopol, 1998), life-cycle prediction and life-cycle cost analysis (Frangopol, 1999). 
	The framework can also include maintenance strategies. For example, Figure 1 shows simulated reliability index profiles of structures under continuous degradation considering different maintenance strategies in Frangopol et al.,(2001). Abrupt reliability degradations simulating the effects of hurricanes will be included into the existing reliability models developed by the Co-PI and co-workers for groups of structures (i.e., buildings and bridges). 

(a)	(b)	
Figure 1: Examples of  Simulation Samples of Reliability Index Profiles: (a) without and (b) with  preventive maintenance
Time varying estimation of structural reliability (b) in the context of hurricane hazards is can have significant impacts in resources planning and management – yet, is relatively unexplored. 

V Proposed Approach and Research Tasks
	The proposed research consists of five major components shown in the flowchart in Figure 2. which are:
1. Improved hurricane track model
2. Load combination
3. Structure failure model
4. Structural reliability model
5. Loss estimation model
6.  Building classification schemes
The tasks associated with these components are described in the following section.

Structural System Reliability Estimation
(Bayesian Framework)

Scenarios Generation
Enhanced Hurricane Track Simulation Model
1. Generate synthetic hurricane tracks with historical likelihood of landfall hurricanes.
2. Simulate winds, precipitation and floods associated with the generated hurricane tracks.
3. Simulate hurricane tracks conditioned upon large-scale climate features (e.g. El Nino). This will be used in the estimation of time-varying risk and in estimating risk in any given year (i.e. in a predictive mode).

Load Combination Natural Hazards 
Methods to combine loads from multiple sources (wind, flooding, precipitation, etc.)

Tasks 3&4

Tasks 6

Loss Estmation
Incorporating Structural Reliability Estimates 

Tasks 5

Structural Failure Model
Structural failure states and their failure probabilities based on combined loads – for a few structures (wood, concrete, etc.,)

Tasks 1&2

Figure 2: Flowchart of the proposed approach

V.1 Task 1: Improved Hurricane Track Model
Our objective is to have a model than can generate realistic synthetic tracks and also the associated precipitation and flooding. 
Model Comparison
The first step will be to test and evaluate the performance of the Markov chain model (Miller et al., 2003) and the Markov-chain based model with the industry partner. As mentioned earlier the model with the industry partner is proprietary information and hence, it is not published. This effort is to provide a benchmark comparison between the two Markov-chain methods and also to provide tools to incorporate climate information in the industry model. For example, it is well known that large-scale climate features such as the state of the North Atlantic Ocean, state of El Nino in the Pacific Ocean etc., can significantly impact the number of hurricane occurrences and also their steering. Therefore, it is intuitive to include such information in the hurricane track models to enable realistic track simulations and consequently, the associated infrastructure risk.  

Enahnced Track Segment Model
Neither of the Markov chain model simulate precipitation/flooding conditions. To obtain realistic loading on the structures we need the precipitation and flooding information in addition to wind speeds. To achieve this we propose to enhance the track segment model. Historical track data (http://www.nhc.noaa.gov) is available for all hurricanes in Atlantic basin since 1900. The data consists of location of the storm, wind speed, pressure at every 6-hour time step. We will also compile precipitation, wind and flooding data at several locations along the east coast of the United States that go with the hurricane data.
The following algorithm is proposed:
i.  Select a random track from the historical tracks, say track J. 
ii. Select a random lifetime from the lifetimes of all the historical tracks. Let us say we chose T as the lifetime.
iii. Select randomly the time steps to travel on track J, say m steps. If m = T we stop.
iv. At the end of m time steps, let us say, we are at location ‘X’. We then look for all the neighboring historical tracks around location J. 
v.  From the neighboring tracks we select one of them that is “closest” to the original track J, with respect to a feature vector that includes the original direction (i.e. angle) of track J, wind speed. Let us say we picked track K. So, at location ‘X’ we jump to track K.
vi.  Repeat iii to v.
vii. If the generated track makes a landfall onto the east coast of the United States – we select the wind speed associated with the track segment that makes the landfall, which becomes the simulated wind speed. We also select the precipitation and flooding data (available from http://www.ncdc.noaa.gov; http://www.usgs.gov) at the stations near the landfall area associated with this segment to generate the precipitation and flooding in that region.
viii.Repeat steps i – vii to simulate an ensemble of hurricane tracks and their associated precipitation and flooding.
ix. The above methodology can be used in conditional simulation. For example, if we are interested in generating the risk estimates for a given year (i.e. dynamic risk) given that there is a forecast of El Nino – then we can repeat steps (i) through (ix) but only using a subset of historical tracks from years that had El Nino occurrences. The same approach can be used for other climate phenomenon such as NAO, Tropical Atlantic sea surface temperatures etc.

Spatial Estimation
Since we are resampling track segments from historical tracks we cannot generate a landfall
over a region that has not seen one in the past. Therefore, the wind speeds, precipitation/flooding information has to be smoothed spatially. We can do this in several ways (i) Assume a box around the region of interest and capture all the simulated information and use this is representative – this works quite well as the precipitation/flood and wind speeds are large-scale phenomenon, especially during hurricanes; (ii) nonparametric smoothing method (Rajagopalan and Lall, 1998; Rajagopalan and Lall, 1999; Loader, 1999) that does local regressions - As the name suggestions, local polynomial is fit to all the values captured in the box draw around the region of interest (i) and the fitted polynomial is used to estimate the wind speed, precipitation etc.  We adapted the first approach in our SGER and preliminary results are presented later in this proposal. 

V.2. Task 2: Load Combination	
	As mentioned earlier, much of the hurricane related structural damage research uses loads only due to winds. While winds are the most important loading during hurricanes, the loads from precipitation and flooding can be substantial. Furthermore, all of these are likely to be highly correlated during a hurricane event. Hence, there is a need to develop procedures/methods to combine loads from all the three components during hurricanes.  In addition, damage due to wind borne debris is of such major importance that it will need to be incorporated, but probably not as a separate load combination. 
	To this end, we will first review the procedures used by Mehta et.al. (1998) and his colleagues in order to develop our own conceptual considerations for the combined effects of wind, storm surge, rainfall and riverine flooding, ground water pressure and fluid forces on structures. We will then develop a comprehensive model related to particular characteristics of the hurricane as well as the site conditions. In particular we will attempt to relate the static and annual characteristics developed by the hurricane modeling in this research to risk-consistent load combination factors.

V.3. Task 3: Structural Failure Models
	Structural failure models tend to be complex since failures can happen due to a large number of events in combination or a small number in isolation. These must be modeled probabilistically, since sub-system failures are not assured even when design loads are exceeded. Failure models involve (i) developing a small number of failure states that are realistic (e.g., no damage, moderate damage, severe damage, complete structure failure); (ii) identifying load levels and combinations that lead to different failure states and (iii) developing failure probability distributions for each state (i.e., fragility curves).
Once this is developed for a given type of structure then for a given hurricane track simulation the steps are as follows: 
(i) Use the load combination method (from the previous task) to estimate the combined load on the structure.
(ii) From the fragility curves obtain the failure probability for each state.
(iii) Use these in the structural reliability model (in the following task) to estimate the overall structural reliability.
(iii) Repeat i-iii for all the simulations. 
This could be done for several simulations for any given year, which will be in a predictive mode; or it could be done for several simulations over time (including aging of the structure), in which case this will yield the structural reliability as a function of duration of exposure.
	In the proposed research we plan to use and build on the work by Li and Ellingwood (2003) for light frame wood structures of one and two stories to wind gusts. This research is a comprehensive analysis of historical trends in wood framing and the relationship to fragility with respect to suction pressures. We will also use the ongoing structural fragility research of K. Gurley and his colleagues (NSF grant CMS-9984663) as well as the gust-pressure curves of ASCE 7-03 2002) to examine more closely the affect of retrofit and upgrading on the reliability for various failure states.  We will also incorporate recent work on damage due to wind borne debris (IBHS, 2003).
	We propose to develop these models for light frame and masonry structures. However, we will select sample structures based on representation in the geographical region of study, utilizing damage data from our industrial partner as well as the new multihazard version of HAZUS.

V.4. Task 4: Bayesian model for structural reliability, risk and life-cycle analysis
	From Tasks 2, 3 and 4, we obtain the time varying failure probability of each state. This is convoluted with the resistance to obtain an estimate of the structural reliability. Thus, the reliability estimate can be obtained for any given year or over a time horizon. The conceptual framework for the application of the Bayesian model to structural reliability and risk analyses is that described in Enright and Frangopol (1999). The model will be improved to accommodate hurricanes. Effect of maintenance (e.g., retrofitting) can also be included (see Figure 1) in the estimation of b . The obtained b  can then be used in the estimation of losses in the next section. Life-cycle costs can also be obtained as part of this framework (Chang and Shinozuka, 1996) which can be useful in maintenance scheduling.

V.5 Task 5: Loss Estimation
	Currently, structural losses due to hurricanes are estimated empirically (statisticallty). The loss data (in $) is obtained from the insurance companies and is related to the maximum sustained wind speed. The resulting statistical relationship is then used to estimate the loss for any given wind speed. These relationships are constructed for large regions (e.g., Florida, Gulf region etc.) and for types of structures (e.g., single family residential, medium rise, etc.). Thus, for each simulation of hurricane track, the maximum wind speed simulated is used to estimate the expected loss using the appropriate empirical relationships constructed from historical data. Repeating this process for the ensemble of simulated tracks will result in a probability distribution function of expected losses.
	 As can be seen the structural reliability information, the age and state of the structure, etc. are not considered in the loss estimation. In this research we propose to address this. The steps are as follows:
(i) For a given ensemble of simulated hurricane tracks, Tasks 2, 3 and 4 will result in the estimate of structural reliability. 
(ii) Step (i) is repeated for the types of structures present in the region with different ages. Thus, obtaining the estimates of reliability for the structures in the portfolio.
(iii) The reliability information from step (ii) in conjunction with the loss estimate of each type of structure in the portfolio will provide the expected loss for the region.
	We will compare these estimates with those from the statistical methods currently in use. We will work close with the industry partner, Risk Engineering (http://www.riskeng.com), who will lead this effort.
V.6 Task 6: Building Classification Scheme
	In this research we will use a building classification system in order to categorize losses. The classification system will be patterned after that developed for the HAZUS earthquake module, which is based on both occupancy class and model building type. The occupancy class is used there primarily for estimates of indirect losses as well as injuries and deaths. Since our research will focus primarily on structural damage, we will work mainly with the model building types.
	Our goal will be to not only have a classification of building types as used within HAZUS, but also to have significant characteristics that allow damage estimation for individual structures. This is something specifically beyond the scope of HAZUS, in which it is made clear that the classification scheme has the purpose of estimating community-wide damage and is not to be used for estimating the performance of individual structures, or the sensitivity of reliability to specific construction details.

VI Proof of Concept
	As mentioned earlier, this proposal is an expansion of the ideas from the SGER (CMS-0335530) grant that we are currently working on. Here we present preliminary results from this grant that demonstrates the feasibility of the integrated framework for structural reliability and loss estimation.
	For a small region (2o latitude x 2o longitude grid box over southern Florida) assuming single story light-frame homes without overhangs and a design life time of 25 years, the following steps were performed to obtain the time-varying structural reliability estimates:
(i) From the historical hurricane data (for the period 1886 – 2002 obtained from http://www.nhc.noaa.gov/) we identified the maximum sustained wind speed in each year. This is essentially, the maximum wind speed of all the hurricanes passing through the grid box.
(ii) A probability density function is fitted to the wind speeds using nonparametric density estimators. These are assumption-free and data-driven methods that can capture any arbitrary density function exhibited by the data. The PI has extensive experience with these methods and has developed stochastic weather generators using these methods (refer to Lall et al., 1996; Rajagopalan et al., 1996; Bowman and Azzalini, 2000 for details on these methods).
(iii) From the historical record we also computed the probability distribution function of the number of hurricane occurrences per year.  
(iv) For each year, the number of hurricane occurrences, say K, was generated from the occurrence probability distribution function obtained from (iii). We then generate K independent sustained winds from the PDFs estimated in (ii) using a smooth-bootstrap technique (Lall et al., 1996). The maximum of the K wind speeds is selected to signify the annual maximum hurricane wind speed. We generated 250 ensembles for each year. This was done for a 25-year period (i.e., the design life of the structure). These simulations did not reflect annual climatic variations, as will be done in the proposed research. 
(v) We converted the hurricane wind speed to 3-second gusts (Simiu et al., 1996), and then used the structural fragility curves developed by Li and Ellingwood (2003) to study one example state of structural failure, that of panel uplift for single story light-frame homes without overhangs, subjected to ASCE 7 Exposure Category B. When panel uplift occurs the damage to the structure is essentially complete. The panels were 4’ X 8’, attached with 8d nails. This construction is typical of houses in the Southeast United States. 
(vi) We conducted an approximate convolution of the fragility curve with the simulated wind speeds, and then computed survival as a function of duration of exposure. This was then converted to failure probability, and we obtained reliability indices by approximate normal fit.
	Figure 3 presents the results of this sample case. A design lifetime of 25 years was selected, over which the cumulative probability of panel uplift increases to about 50%, with an associated decrease in reliability index, β, from about 2 to zero.
	This simple illustration does not reflect repair following partial damage, or the sensitivity to effects such as strengthening and annual weather patterns, but indicates the approach that will be followed. 

Figure 3: Time-varying reliability curve of a single story light-frame homes without overhangs and a design life time of 25 years

VII Work Plan
RESEARCH	Year  1	Year 2	Year 3	
Task 1a:Hurricane Track Model comparisons							
Task 1b: Enhanced Track Segment Model							
Task 2: Load Combination							
Task 3: Structure Failure Models 							
Task 4: Structural Reliability Estimation							
Task 5: Loss Estimation							
Task 6: Building Classification							

	
 VIII GOALI Management Plan
	The project will be coordinated by the PI. The PI will lead the effort on Tasks 1a and 1b, and will work with the industry partner on Task 1a to evaluate and compare their track simulation model. Ross Corotis will be the lead on Task 2, and will share Task 3 with Dan Frangopol, who will be in-charge of Task 4. All the PIs will work together on Task 5, with the industry partner leading the efforts, since they are very familiar with loss estimation and also have all the relevant data. The entire team will work together on Task 6.
	The industry partner is also located in Boulder, hence making it easy for effective collaboration. We will have regular project meetings to coordinate the efforts and during summers we will have students and/or PIs physically spend time in Risk Engineering for a much more fruitful interaction.

IX Educational Component
The proposed research will be fully integrated into the undergraduate and graduate curriculum across the Department of Civil, Environmental, and Architectural Engineering (CEAE) at CU Boulder.  We plan to develop the modules (i.e., the hurricane track simulation module, the Bayesian structural risk/reliability estimation module and GIS interface for visualization) in the proposed framework to be interactive and user friendly. These modules will provide an excellent application of probabilistic concepts to real life problems, that we will incorporate and use in the courses that we teach – which are, Stochastic Methods for Hydro-climatology (CVEN 5833), Probability, Statistics and Decision in Civil Engineering (CVEN 3227) and Structural Reliability (CVEN 5555). 
The subject matter of this research will also contribute to the new interdisciplinary research and education alliance that has been established by CEAE: the Consortium on Advanced Life-Cycle Engineering for Sustainable Civil Environments (COALESCE, http://ceae.colorado.edu/coalesce). This initiative sponsors seminars, workshops and graduate degrees with an interdisciplinary viewpoint on life cycle engineering and sustainability. Finally, the graphical presentation of hurricane risk through GIS and the associated predictions of structural behavior should be an important learning tool for the public at large, and we will work with science centers and other public information sources to make this tool available.  This will include an interactive capability allowing users to specify current and future building scenarios and then see the predicted level of damage to individual buildings as well as communities.

X Results from prior NSF support of the P.I.
SGER: CMS0335530: “A Feasibility Study of Estimation of Structural Systems Reliability under Hurricane Hazard”, (Dan Frangopol and Ross Corotis as Co-PIs). This is an ongoing research since the summer of 2003. This project brings the knowledge of hurricane variability, nonparametric function estimation techniques, structural failure for structural reliability estimation. Preliminary results demonstrating the feasibility of an integrated framework, were presented in section VI above. The proposed research is an expansion of this SGER. We anticipate 1-2 peer-reviewed journal publications.

EAR 9720134: “The Changing Seasons? Detecting and Understanding Climatic Change”, (P.I. U. Lall). This is an ongoing project. The project is aimed at understanding how the seasons evolve in mid-latitudes and the factors that influence them. Six journal papers have been prepared. Further work is ongoing to look at seasonality in the streamflow, precipitation and temperature across the U.S.
	1 Jain, S., U. Lall, and M. E. Mann, 1999: Seasonality and interannual variations of Northern Hemisphere temperature: Equator-to-pole gradient and ocean-land contrast, Journal of Climate, 12(4), 1086 – 1100.
	2. Jain, S., U. Lall, M. E. Mann, and B. Rajagopalan, 2001: Changes in the midwestern U.S. precipitation: Observations and modeled greenhouse warming scenarios, (submitted to Journal of Climate).
	3. Jain, S., and U. Lall, 2000: Magnitude and timing of annual maximum floods: Trends and large-scale climatic associations for the Blacksmith Fork River, Utah. Water Resources Research, 36, 12, 3641-3651.
	4. Jain, S., and U. Lall, 2001: Floods in a changing climate: Does the past represent the future?, Water Resources Research, 37, 12, 3193-3205.
	5. Baldwin, C. and U. Lall, 1998, Seasonality of Streamflow: the Upper Mississippi River, Water Resources Research, (to appear)
	6. Jain, S., U. Lall and B. Rajagopalan, 2003: Low-frequency variability in the west coast U.S. Floods : Diagnostic study of the large-scale climate influences. (in preparation for submission to Water Resources Research).

Results from Prior NSF support of Dan Frangopol
Dr. Dan M. Frangopol has participated in fifteen (eleven as P.I. and four as Co-P.I.) NSF-funded grants in the past sixteen years. The two most recent projects related to the scope of the proposed work are mentioned below. 
CMS-9522166  “Sytem Reliability for Evaluation and Design of Highway Bridges” (Co-PIs, B. Shing and G. Hearn). The main objective of this research was to develop a framework for considering system reliability in evaluation and design of steel and concrete highway bridges. In this regard, both serviceability and ultimate limit states were considered based on the Load and Resistance Factor Design (LRFD) method – which enabled the realizatioin of full potential of system reliability concepts to the advancement of bridge evaluation and design processes.Three doctoral thesis and two chapters resulted from this grant, in addition to the following journal articles:
1. Frangopol, D. M., and Kong, J.S. (2001). “Expected maintenance cost of deteriorating civil infrastructures,” Keynote paper in Life-Cycle Cost Analysis and Design of Civil Infrastructure Systems, ASCE, Reston, 22-47.
	2. Frangopol, D.M. and Estes, A.C. (1999). “Optimum lifetime planning of bridge inspection and repair programs,” Structural Engineering International, IABSE, 9 (3), 219-223.
	3. Frangopol, D.M., Kong, J.S., and Gharaibeh, E.S. (2001). “Reliability-based life-cycle management of    highway bridges,” Journal of Computing in Civil Engineering, ASCE,  15 (1), 27-34.
	4. Estes, A.C., and Frangopol, D.M. (1999). “Repair optimization of highway bridges using system reliability approach,” Journal of Structural Engineering, ASCE, 125 (7), 766-775.
	5. Frangopol, D.M., Lin, K-Y., and Estes, A.C. (1997). “Life-cycle cost design of deteriorating structures,” Journal of Structural Engineering, ASCE, 123(10), 1390-1401. 

CMS-9506435 “Life-Cycle Reliability-Based Evaluation and Design of Structural Systems”. The essential objective of this project was to develop a new method for life-cycle reliability-based multicriteria evaluation and design of structural systems under serviceability, ultimate, and time-dependent deterioration requirements imposed simultaneously. State-of-the-art time-dependent structural reliability methods were used in this research. Successful completion of this project  resulted in the possibility to evaluate and design civil engineering systems, including reliability constraints, by considering life-cycle deterioration effects. Two doctoral theses and two book chapters resulted from this grant, in addition to the following journal articles:
1. Estes, A.C., and Frangopol, D.M. (2001). “Minimum expected cost-oriented optimal maintenance planning for deteriorating structures: Application to concrete bridge decks,” Reliability Engineering & System Safety, 73(3), 281-291.
	2. Estes, A.C., and Frangopol, D.M. (2001). “Using system reliability to evaluate and maintain structural systems,” Computational Structural Engineering, 1(1), 71-80.
	2. Estes, A.C., and Frangopol, D.M. (2001). “Bridge lifetime system reliability under multiple limit states,” Journal of Bridge Engineering, ASCE, 6(6), 523-528. 
	3. Frangopol, D.M., Gharaibeh, E.S., Kong, J.S., and Miyake, M. (2000). “Optimal network-level bridge maintenance planning based on minimum expected cost,” Journal of the Transportation Research Board, Transportation Research Record, 1696(2), National Academy Press, 26-33.
	4. Enright, M.P., and Frangopol, D.M. (2000). “Survey and evaluation of damaged concrete bridges,” Journal of Bridge Engineering, ASCE, 5 (1), 31-38. 

Results from Prior NSF support of Ross Corotis 
Dr. Corotis has participated in several NSF-funded grants in the past decades. The most recent completed project related  to the scope of the proposed work is mentioned below. 
CMS 9596055 “Prediction of Design Wind speeds by Extreme Estimation Procedures”.  The objective of this research work was the estimation of extreme data with return periods one or more orders of magnitude greater than observed record lengths.  The suitability of the ‘peaks over threshold’ method was investigated to provide guidelines for threshold level and optimal parameter estimation.  Analytical expressions for mixed distribution data provide the capability to combine data from different underlying distributions, such as different seasons and climatic conditions in the case of extreme winds.  Simulated data from known distributions verify the approach, and actual wind data have provided calibration. The following journal articles resulted from this grant.
1. “Design Wind Speed Prediction,” A. Dougherty, R. Corotis and A. Segurson, Journal of Structural Engineering, ASCE, Vol. 129(9), 2003.
	2. “Determining Reliable Design Loads for Natural Phenomena,” R. Corotis and A. Dougherty, Natural Hazards Review, ASCE, 2004, in press.
	3. “Extreme Wind Estimation; Theoretical Considerations,” A. Dougherty and R. Corotis, 8th International Conference on Structural Safety and Reliability, November 1997, Kyoto, pp. 1351-1358.
	4. “Extreme Value Theory - Application to Design Wind Prediction,” W. Xu, A. Dougherty and R. Corotis, Third International Conference on Computational Stochastic Mechanics, CSM’98, June 1998, Santorini, Computational Stochastic Mechanics, P. D. Spanos, Editor, Balkema, Rotterdam, 1999, pp. 397-404.
	5.Extreme Value Theory and Mixed Distributions - Applications,” A. Dougherty, R. Corotis and L. Schwartz, Applications of Statistics and Probability ICASP 8, R. Melchers and M. Stewart, Editors, Balkema, Rotterdam, 2000, pp. 27-33.