1.1Information 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.
1.1.1Structural 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, off-line 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 first-level 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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