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IntroductionThis paper presents a multi-agent based framework to simulate human and social behaviors during emergency evacuations. Among the many regulatory provisions governing a facility design, one of the key issues identified by facility managers and building inspectors is safe egress. Design of egress for places of public assembly is a formidable problem in facility and safety engineering. There have been numerous incidents reported regarding overcrowding and crushing during emergency situations [1]. In addition to injuries and loss of lives, the accompanying post-disaster psychological suffering, financial loss, and adverse publicity have long-term negative effects on the affected individuals and organizations -the survivors, the victims' families, and the local communities.Among the many factors including overcrowding and evacuation incidents, researchers have come to realize that understanding human and social behaviors in emergencies is crucial to improve crowd safety in places of public assembly [2][3][4][5][6]. In particular, 'nonadaptive crowd behaviors' are recognized to be responsible for the death and injury of most victims in crowd disasters [7]. Nonadaptive crowd behaviors refer to the destructive actions that a crowd may experience in emergency situations, such as stampede, pushing, knocking, and trampling on others. Studying nonadaptive crowd behaviors in emergency situations is difficult since it often requires exposing real people to the actual, possibly dangerous, environment. A good computational tool that takes into consideration the human and social behavior of a crowd could serve as a viable alternative.Commercially available computational tools for the simulation and design of emergency exits exist. However, most of the current computational tools focus on the modeling of spaces and occupancies but rarely take into consideration of human and

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SUMMARYDamage detection techniques have been proposed to exploit changes in modal parameters and to identify the extent and location of damage in large structures. Most of such techniques, however, generally neglect the environmental e ects on modal parameters. Such environmental e ects include changes in loads, boundary conditions, temperature, and humidity. In fact, the changes due to environmental e ects can often mask more subtle structural changes caused by damage. This paper examines a linear adaptive model to discriminate the changes of modal parameters due to temperature changes from those caused by structural damage or other environmental e ects. Data from the Alamosa Canyon Bridge in the state of New Mexico were used to demonstrate the e ectiveness of the adaptive ÿlter for this problem. Results indicate that a linear fourinput (two time and two spatial dimensions) ÿlter to temperature can reproduce the natural variability of the frequencies with respect to time of day. Using this simple model, we attempt to establish a conÿdence interval of the frequencies for a new temperature proÿle in order to discriminate the natural variation due to temperature.

Structural Health Monitoring (SHM) has become an important research problem which has the potential to monitor and ensure the performance and safety of civil structures. Traditional wire-based SHM systems require significant time and cost for cable installation. With the recent advances in wireless communication technology, wireless SHM systems have emerged as a promising alternative solution for rapid, accurate and low-cost structural monitoring. This paper presents a newly designed integrated wireless monitoring system that supports real-time data acquisition from multiple wireless sensing units. The selected wireless transceiver consumes relatively low power and supports long-distance peer-to-peer communication. In addition to hardware, embedded multithreaded software is also designed as an integral component of the proposed wireless monitoring system. A direct result of the multithreaded software paradigm is a wireless sensing unit capable of simultaneous data collection, data interrogation and wireless transmission. A reliable data communication protocol is designed and implemented, enabling robust real-time and near-synchronized data acquisition from multiple wireless sensing units. An integrated prototype system has been fabricated, assembled, and validated in both laboratory tests and a large-scale field test conducted upon the Geumdang Bridge in Icheon, South Korea.

SUMMARYA Bayesian probabilistic approach is presented for the damage detection of multistorey frame structures. In this paper, a Bayesian probabilistic approach is applied to identify multiple damage locations using estimated modal parameters when (1) the measurement data are potentially corrupted with noise, (2) only a small number of degrees of freedom are measured, and (3) a few fundamental modes are estimated. To reduce the potentially intensive computational cost of the proposed method, a branch-and-bound search scheme is proposed and a simpliÿed approach for the modelling of multistorey frame structures is employed. A six-storey shear frame example and two multistorey frame examples, with multiple damage locations, are presented to illustrate the applicability of the proposed approach. ?

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A low-cost wireless sensing unit is designed and fabricated for deployment as the building block of wireless structural health monitoring systems. Finite operational lives of portable power supplies, such as batteries, necessitate optimization of the wireless sensing unit design to attain overall energy efficiency. This is in conflict with the need for wireless radios that have far-reaching communication ranges that require significant amounts of power. As a result, a penalty is incurred by transmitting raw time-history records using scarce system resources such as battery power and bandwidth. Alternatively, a computational core that can accommodate local processing of data is designed and implemented in the wireless sensing unit. The role of the computational core is to perform interrogation tasks of collected raw time-history data and to transmit via the wireless channel the analysis results rather than time-history records. To illustrate the ability of the computational core to execute such embedded engineering analyses, a two-tiered time-series damage detection algorithm is implemented as an example. Using a lumped-mass laboratory structure, local execution of the embedded damage detection method is shown to save energy by avoiding utilization of the wireless channel to transmit raw time-history data.