Motes incorporate communications, processing, sensors, sensor fusion, and power source into a package about a cubic inch in size - essentially a self-organizing and adaptive information utility. Motes monitor physically measurable quantities, such as acceleration, strain, microseismics, temperature, relative humidity, and barometric pressure, throughout buildings, bridges, industrial plants, and other critical structures. Should go before either application area since common to both and shows strong leveraging.

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. Estimates of casualties 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.

We contend that radically new information technologies 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 are described for three applications: (1) structural health prognosis of individual buildings and other structures, (2) real-time evaluation of inventories of buildings and lifeline networks, and (3) adaptive coordination of emergency response and recovery. We will develop a set of tools which allow integration of sensing, information acquisition, evaluation, and modeling that will be basically independent of the detailed algorithm used to prognosticate and evaluate. The test case will be (1) structural health prognosis of a structure, which will involve a particular evaluation module (detailed models of structural behavior). The new science will be in the self-assembling distribution of the sensing and computation at the many layers envisioned in Culler's Experimental Networked Sensor System Architecture. However, by replacing the evaluation module with (2) real-time evaluation of inventories of buildings and lifeline networks, or (3) adaptive coordination of emergency response and recovery modules, the same IT can be used – a "fractal" approach to problems of similar form occurring on several scales.

Structures equipped with dense arrays of self-assembling adaptive intelligent sensor agents (microsensor tier) incorporating new diagnosis/prognosis algorithms, will self-monitor and provide owners and occupants safety and egress information in an emergency. Owners of large manufacturing facilities, transportation networks, and lifeline networks will have accurate and timely information to assess the operational condition of their networks and real-time information to make decisions on restoration after a disaster. Finally, emergency response in most cities is poorly planned, with experience showing it to be chaotic and ineffective. The chaos and ineffectiveness results primarily from of lack, or poor quality, of information on where emergency response services are needed, and how to best deploy personnel, equipment, and material.

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 in structural and transportation systems as described in Section ###. There is only a marginal cost for integrating information services for natural disaster risk reduction.

1.1.1IT Applications for Individual Structures

For the proposed scenario of perhaps thousands of Motes microsensor agents monitoring a large structure, it is not feasible to merely send back all the recorded signals from all the multi-sensored Motesthe microsensor totier 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.(stub to other parts of the proposal)

  • Intelligent localmicrosensor and sensor tiersarrays 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.e, since the nodes function as a local network, able to evaluate data and make decisions (rather than merely collect data).(stub to other parts of the proposal)
  • Damage prognosis requires seamless 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. We therefore propose a new approach to structural health prognosis, based on evaluation of local damage, leveraging ubiquitous, cheap, wireless Motes. 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.

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, Pads, 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 and tidy 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; 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 and damping coefficients, can be identified.(e.g. Beck and Katafygiostis, 1998; Smyth et al., 1998; Lus et al., 1999; Glaser and Baise, 2000). Integration of finite element modeling with SI of boundary conditions has been successfully made at UCBerkeley (Arici and Mosalam, 2000).

Possibly the most promising parameterization of an evolving system is a unified methodology based on Bayesian/State-Space identification and adaptive estimation. 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.

1.1.2IT Applications for Inventories of Buildings and Lifeline Networks

Moving up in scale from individual buildings, owners of multiple buildings (corporate or university campus) are concerned with the effect of a disaster on the operation of their enterprise. In a similar manner, utility networks (electricity, gas, water) and transportation networks (highways, railways, ports and harbors, airports) must synthesize damage information to determine how to restore service. Owners of multiple facilities require assessment of damage, estimate of repairs, prioritization of repair, and acquiring and deploying resources for repair. As practiced today this may take weeks or even months. Availability of real-time information on condition and damage will allow owners to make much more rational and rapid decisions about the safety of their facilities and begin recovery.

1.1.3IT Applications for Regional Emergency Response and Recovery

Emergency response in a major disaster can be chaotic because of lack of detailed information on of the location and severity of damage, needs for human search and rescue, interruptions in communication, transportation interruptions for emergency service personnel and emergency equipment and facilities. The potential for casualties can be reduced substantially by using adaptive networks to prioritize and rout emergency services. Monitored buildings and precincts will be able to communicate relevant information to emergency services about extent and location of damage and needs for search, rescue, and fire response. This information, coupled with Mote array-generated information on the capacity of transportation systems, hospitals, fire fighting equipment, and heavy construction equipment, will allow rapid and effective matching of needs with capabilities.

Information technologies have great promise for speeding recovery of a region after an earthquake and reducing indirect losses. Using current building evaluation procedures, it may take days or weeks after an earthquake to evaluate the safety of building, usually because of lack of information on structural condition and difficulty in making prognoses about the consequences of damage. Buildings which provide self-diagnoses avoid “false” tagging that can significantly delay recovery. Long-term recovery depends on the mobilization of construction services for rebuilding, which has its own set of resource allocation and supply chain management issues that are affected by the spiked demand for construction in a single region and in a short time frame.

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Beck, J.L. (1978). Determining Models of Structures from Earthquake Records. Earthquake Engineering Research Laboratory 78-01. California Institute of Technology.

Beck, J.L., and Katafygiotis, L.S. (1998). Updating Models and Their Uncertainties. I: Baysian Statistical Framework, Journal of Engineering Mechanics, 124(4), p. 455.

Farrar, C.R. and Doebling, S.W., (1997) Lessons Learned from Applications of Vibration-Based Damage Identification Methods to Large Bridge Structures, Proc. of the International Workshop on Structural Health Monitoring, Stanford, CA, Sept 1997, pp. 351-370.

Farrar, C.R., Doebling, S.W., Nix, D.A., (2001) Vibration-Based Structural Damage Identification, Philosophical Transactions of the Royal Society: Mathematical, Physical & Engineering Sciences, 359(1778), pp. 131 - 149

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Smyth, A.W., Masri, S.F., Chassiakos, A.G., and Caughey, T.K. (1999). On-Line Parameter Identification of MDOF Nonlinear Hysteretic Systems, Journal of Engineering Mechanics, 125(2), p. 133.

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