1.1
Information Technology Applications for Disaster Risk Reduction and Emergency
Response
(Faculty Involved: Fenves, Glaser, Kanafani)
Each year large natural disasters cost the U.S. hundreds of lives, many critical structures,
and billions of dollars in disruption to the economy. In particular, earthquakes present a
substantial risk to the residents and economies of the large urban regions in the Western U.S.,
with probabilities exceeding 60% that a major earthquake will strike northern or southern
California in the next 30-years. Casualty estimates number in the thousands, direct damage
losses are on the order of $100 to $200 billion and indirect losses due to disruptions in the
economic base could be several times greater. Seismic hazard is not confined to California; with
equally significant risks to the central and eastern U.S. from the New Madrid, Boston, and
Charleston earthquake zones.
A recent NRC Report (NRC, 1999) states that improved information on natural disasters is
the key to reducing losses and speeding recovery. Effective decisions by owners, operators and
occupants of buildings is hampered today by the lack of information about the structural safety
of their facilities. We contend that radically new information technologies applied to assessing
structural damage and safety can be used to protect lives and speed the economic recovery of a
city after a large earthquake. The same set of IT scales across a wide range of societal problems.
It will become apparent that these same technologies will be equally effective in response to
tornadoes, hurricanes, fires, and floods. In this section, the IT applications focus on structural
health prognosis of individual buildings and bridges, which present the greatest risk to the public
in an earthquake. With data from densely deployed microsensors in a building we will integrate
distributed sensing network architecture (Culler), building scale service architecture (Katz) ,
adaptive data management (Fraknlin), along with new techniques for structural model updating,
and simulation to provide diagnosis and prognosis of structural safety. We will demonstrate the
effectiveness of this approach by using an experimental testbed and prototype implementation on
the Berkeley campus.
Stand-alone information systems for natural disasters and other emergencies can be hard to
justify from a cost point of view. However, our solution combines sensing, communication,
information processing and evaluation, and visualization tools for other societal-scale
applications as well. Significant examples being adaptive monitoring and control of the
environment and energy usage, as described in Section xxx, and other societal scale applications
such as transportation system management. There is only a marginal cost for integrating
information services for natural disaster risk reduction. Looking into the future, information on
the safety of individual structures can be aggregated for real-time evaluation of inventories of
buildings and lifeline networks, and control of emergency response and recovery services using
regional service networks.
A current example of regional information gathering is the Tri-Net system in Southern
California (Heaton, et al. 1996; NRC, 1999; Trinet, 2001), which is a strong motion instrument
deployment of approximately 750 accelerometers measuring ground acceleration,
communicating by digital telemetry to a central server. The ground motion data collected during
an earthquake is used to develop “shakemaps” showing the distribution of ground motion.
Currently, the shakemap is used to estimate losses (Buicka, et al., 1998; Scrivner, et al. 2000),
although there is no direct measurement or assessment of loss. With the proposed sensor and
distributed service technologies, information gathering can be scaled to much denser coverage of
not only the ground motion but more importantly direct sensing of the effects of the ground
motion on individual structures and aggregates of structures in an urban region.
1.1.1
Structural Health Diagnosis and Prognosis
For the proposed scenario of perhaps thousands of microsensor agents monitoring a large
structure, it is not feasible to merely send back all the recorded signals from the microsensor tier
to a common server (stub to other parts of the proposal). Advances in information technology
contained in this proposal are key to realization of this health prognosis system, for several
reasons.
 A system of thousands of sensors would be hopelessly complex to address from a central
server, require too much power from the wireless nodes, and would overwhelm the radio
bandwidth.Intelligent microsensor and sensor tiers can monitor the evolution of local
damage in real time. We propose to develop an integration of the modeling, data
acquisition, and sensing processes that will allow civil engineers to approach design and
prognostication problems from a new cognitive viewpoint – a move beyond the linear, offline tradition.
 Damage prognosis requires integration of the measuring and modeling process, with
constant updating of the interpretative model and information sensed. We believe that
sensing and modeling are intimately entwined, and the advances in IT proposed herein
make the realization of this paradigm achievable.
We propose a new approach to structural health prognosis, based on evaluation of local
damage, leveraging ubiquitous, cheap, wireless sensor agents. Given that damage begins locally,
we envision a dense-pak of sensor agents placed in swarms around key structural points
throughout a structure, e.g. a dozen autonomous nodes, each carrying a 3-D accelerometer,
distributed around a key beam-column connection. A self-assembling network of sensor agents
will be able to detect small changes in the local system.
By far the most common traditional approach to structural damage prognoses has been global
modal analysis (e.g. McConnell, 1995), although recent full-scale experiments show that modal
analysis is far too insensitive to yield usable information for practical cases (Farrar et al., 2001).
A prime example is the modal analysis work undertaken on the abandoned I-40 bridge across the
Rio Grande river in Albuquerque, NM. It was only after the main longitudinal plate girder was
cut more than 2/3 through that any change was seen in the modal parameters. The first two
modes dropped by a mere 7.6 and 4.4 percent respectively (Farrar and Doebling, 1997), which
would be considered noise in a blind prediction! Global modal analysis is doomed for several
reasons. Structures of interest are complex systems with a great number of degrees of freedom.
Because evolving damage is local, a structure will redistribute internal forces to stiffer members
as particular beams, columns, etc. are weakened. It is only when damage is sufficient to affect
the performance of the entire structure will it be visible through global modal analysis – well
after the safety of the structure is exceeded.
Evaluation of damage in structural terms (diagnosis of cracking, yielding, buckling, etc.) is
not sufficient for making decisions about the safety of a building. A prognosis must be based on
forward simulation of the effects of the damage with the current loading and expected
aftershocks, and requires seamless integration of the measuring and modeling process, with
constant updating of both the model and information sensed. Each building can have an online
model of itself, constantly updated with parameters estimated from the damage detection
network. As a major change in state is detected, the updated model will determine the safety of
the structure in the short term, prioritize the inspection and repair in the longer term, and
reprogram the sensor agents and constitutive model as needed. Information on prognosis may be
condensed into an automatic notification system for occupants. In a simple form it would trigger
an alarm; more sophisticated approaches would provide information on browsers, PDAs, or cell
phones on the safest evacuation routing. This is an important problem for large buildings whose
egress routes may be damaged or hazardous.
Approach to Structural Data Interpretation
Development of analytical tools to capture the evolution of system response in terms of
damage initiation and damage propagation, - understanding the interaction between the structural
system and its components - is essential for performance-based design. The so-called system
identification (SI) approach is a powerful statistical-based tool to quantify and assess system
damage parameters, and has been so applied by many structural researchers (e.g., Beck, 1978;
Safak, 1997; Udwadia, 1985; Werner et. al., 1994; Stewart and Fenves, 1998; Arici and
Mosalam, 2000; Baise and Glaser, 2000; Glaser and Baise, 2000).
System identification requires a model, whether black-box (e.g. a linear filter model) or
white box (a physical model). Identification can be made through the extended Kalman filter
(EKF), (e.g. Lin and Zhang, 1994; Koh and See, 1994;) which has been successfully applied to
the identification of various physical systems. Physical parameters, including elastic moduli
damping coefficients and effects of soil-structure interaction, can be identified.(e.g. Beck and
Katafygiostis, 1998; Smyth et al., 1998; Stewart and Fenves, 1998; Lus et al., 1999; Glaser and
Baise, 2000). Updating of parameterized constitututive models using measured global response
data has been attempted (Hjelmstad, et al. 1995; Fenves and DesRoches, 1997). Integration of
finite element modeling with SI of boundary conditions has been successfully made at
UCBerkeley (Arici and Mosalam, 2000).
The most promising parameterization of an evolving system is a unified methodology based
on Bayesian/State-Space identification and adaptive estimation (Sohn and Law, 1997; Beck and
Katafygiotis, 1998). The Bayesian probabilistic approach has the following advantages: (1)
probabilistic methods have the ability of modeling system disturbances, (2) system identification
problems are usually ill-conditioned which the Bayesian approach can usually regularize, and (3)
the Bayesian approach produces a posterior distribution, instead of a single estimation, hence it
eliminates the risk of incorrect estimation and results in a robust estimation and control method.
Our approach completely extracts all useful information from data, i.e. input and output of a
linear dynamic system, via the sufficient statistics, which are the conditional distribution of
system states with respect to system responses.
With updated models, developed locally, forward simulations can be used to prognosticate
the effects of damage. This is particularly critical when evaluating the safety of a building after a
major earthquake and estimating the probability of collapse in an aftershock. For forward
simulation, parameterized models can be updated and assembled in an object-oriented
framework for simulation (Archer, et al., 1999; McKenna and Fenves, 2000). The models will
be updated locally and assembled over the network in a dynamic process depending on
processing, communication, and power available. Simulations may be centralized or distributed
also. There can be hierarchies of simulation models: reduced parameter sets for rapid estimates,
and more detailed models as processing power becomes available or sensitivity analysis shows
that more refined models will reduce uncertainty in the prognosis.
Milestones
Year 1: Develop microsensor tier and diagnosis applications using distributed service
architectures. Develop model updating procedures and evaluate with small-scale laboratory
tests.
Year 2: Implement sensing and diagnosis/progrognosis on structural specimens of building
frames that will be tested on the Pacific Earthquake Engineering Research Center’s earthquake
simulator (“shaking table”). This will provide a controlled laboratory setting to serve as the firstlevel testbed for the sensors, networking, and algorithms.
Year 3: Field deployment on one of the new buildings planned for construction on the Berkeley
campus during the scope of the project. We anticipate collecting data during construction with
forced-vibration tests to verify the sensors and algorithms. After construction, the new building
will serve as a second testbed for the distributed processing system
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