The Loma Prieta Earthquake: Implications of Structural Damage

The Loma Prieta earthquake provides a wealth of information on the seismic response of a wide variety of structures over a large metropolitan area. Soil amplification at sites distant from the epicenter contributed significantly to the substantial damages developed during the earthquake. Because of the large shaken area, the earthquake provides much useful information for all those interested in earthquake engineering. Structural damages resulting from the earthquake are reviewed herein with emphasis on buildings and bridges. Implications for modern design and retrofit methods are highlighted. Emphasis is placed on the need to carefully consider soil conditions, to treat the structure as a system rather than as an assemblage of independent elements, to explicitly define performance expectations, and to increase efforts to retrofit older seismically hazardous structures

Improving Emergency Response Using Wearable Wireless Sensor Networks and Structural Health Monitoring Systems

Structural collapse and damage which have been occurred during past decades have caused severe failures especially in strategic infrastructures and public buildings. A natural disaster may cause a large number of injuries and deaths expecially in public building which are usually crowded, therefore in order to improve their resilience it is necessary to decrease their probability of failure and improve their emergency response in rescuing the people inside. Since timing is important during an emergency, if the rescue teams search for victims faster, the nummber of survivors can increase. This paper presents a novel practical method to improve the emergency response of rescuers after a disaster using a structural health monitoring system (SHM) and a Wearable Sensor Networks (WSN). The proposed system includes fixed SHM nodes which measure structural related parameters like vibration, humidity, etc. It also includes mobile nodes which are wearable wristbands worn by people to collect data including approximate location of victims and health status. This system provides the rescue teams with data related to the damaged parts of the buildings, number of people who have been trapped inside the building, their location by means of indoor localization, and their vital status. These data which are collected and analyzed in real-time, are being used for building damage level assessment, but also to help rescuers to locate victims faster and save more lives. The fixed and mobile nodes construct a wireless sensor network which stays functional even during power outage by means of rechargeable batteries. Through this system, it is possible to assess the structural health of the building and also improve the emergency response of rescuers by optimizing their time when a disaster occurs

Modelling cascading failures in lifelines using temporal networks

Lifelines are critical infrastructure systems with high interdependency. During a disaster, the interdependency between the lifelines can lead to cascading failures. In the literature, the approaches used to analyze infrastructure interdependencies within the social, political, and economic domains do not properly describe the infrastructures’ emergency management. During an emergency, the response phase is very condensed in time, and the failures that occur are usually amplified through cascading effects in the long-term period. Because of these peculiarities, interdependencies need to be modeled considering the time dimension. The methodology proposed in this paper is based on a modified version of the Input-output Inoperability Model. The lifelines are modeled using graph theory, and perturbations are applied to the elements of the graph, simulating natural or man-made disasters. The cascading effect among the interdependent networks has been simulated using a spatial multilayer approach. The adjancency tensor has been used to for the temporal dimension and its effects. Finally, the numerical results of the simulations with the proposed model are represented by probabilities of failure for each node of the system. As a case study, the methodology has been applied to a nuclear power plant. The model can be adopted to run analysis at different scales, from the regional to the local scales

Seismic Resilience of Electric Power Networks in Urban Areas

Recent natural disasters have raised the question of how communities can recover from extreme events. In the last decade the research has been focusing on analyzing interdependencies between different networks. In this paper the focus is on the distribution power network, developing a method to estimate a realistic grid of a vir-tual city called “Ideal City” freely inspired to the city of Turin in Italy. A software called Matpower devel-oped by the Joint Research Center has been used for the load flow analysis of the power network. Fragility curves, repair costs and downtime are evaluated using FEMA’s database. Finally, a strategy to improve the network resilience is proposed considering the complexity of the environmen

Role of cardiac biomarkers in cognitive impairment and functional decline

This thesis aimed to investigate the link between cardiac biomarkers, cognitive impairment and functional decline in older subjects with a focus on non-invasive markers that are routinely available in clinical practice. The findings of this thesis show that markers of subclinical cardiac dysfunction are linked with accelerated cognitive decline and a higher risk of functional decline. In addition, we provide preliminary findings on the potential role of centrally acting natriuretic peptides in relation to cognitive impairment. Collectively, these findings highlight a complex pathophysiological heart-brain coupling that might extend beyond vascular and hemodynamic factors. Future studies are needed to investigate whether such link warrants mutual screening of the heart and the brain functions, and whether such practice could improve early diagnosis of dementia. Financial support for printing of this thesis was provided by Leiden University Medical Centre, Alzheimer Nederland and Dutch Heart Foundation.LUMC / Geneeskund

Factor Analysis to Evaluate Hospital Resilience

Health care facilities should be able to quickly adapt to catastrophic events such as natural and human-made disasters. One way to reduce the impacts of extreme events is to enhance a hospital's resilience. Resilience is defined as the ability to absorb and recover from hazardous events, containing the effects of disasters when they occur. The goal of this paper is to propose a fast methodology for quantifying disaster resilience of health care facilities. An evaluation of disaster resilience was conducted on empirical data from tertiary hospitals in the San Francisco Bay area. A survey was conducted during a 4-month period using an ad hoc questionnaire, and the collected data were analyzed using factor analysis. A combination of variables was used to describe the characteristics of the hidden factors. Three factors were identified as most representative of hospital disaster resilience: (1)cooperation and training management; (2)resources and equipment capability; and (3)structural and organizational operating procedures. Together they cover 83% of the total variance. The overall level of hospital disaster resilience (R) was calculated by linearly combining the three extracted factors. This methodology provides a relatively simple way to evaluate a hospital's ability to manage extreme events

A first order evaluation of the capacity of a healthcare network under emergency

Immediately after an earthquake a healthcare system within a city, comprising several hospitals, endures an extraordinary demand. This paper proposes a new methodology to estimate whether the hospital network has enough capacity to withstand the emergency caused by an earthquake. The ability of healthcare facilities and to provide a broad spectrum of emergency services immediately after a seismic event is assessed through a metamodel that assumes waiting time as main response parameter to assess the hospital network performance. The First Aid network of San Francisco subjected to a 7.2 Mw magnitude earthquake has been used as case study. The total number of injuries and their distributions among the six major San Francisco’s Emergency Departments have been assessed and compared with their capacity that has been determined using a survey conducted by the medical staff of the hospitals. The numerical results have shown that three of the six considered San Francisco’s hospitals cannot provide emergency services to the estimated injured. Two alternatives have been proposed to improve the performance of the network. The first one redistributes existing resources while the second one considers additional resources by designing a new Emergency Department

Resilience Assessment of City-Scale Transportation Networks Using Monte Carlo Simulation

To improve the resilience of critical infrastructure systems, their intrinsic properties need to be understood and their resilience state needs be identified. In the literature, several methods to evaluate networks’ reliability and resilience can be found. However, the applicability of these methods is usually restricted to small-size net-works. In this paper, the transportation network of a large-scale virtual city is considered as a case study. A random removal of the roads is applied simulating the network’s failure. The network reliability is then calculated using the Destruction Spectrum (D-spectrum) method and a Monte Carlo approach has been developed to generate failure permutations that are necessary for the evaluation of the D-spectrum se. In addition, the Birnbaum Importance Measure (BIM) has been adopted in this study to determine the importance of the net-work’s components. The methodology adopted in this study can be also extended to all network-based systems. The paper also introduces resilience indicators as a soft tool to predict the performance and serviceability of transportation networks

Exploring simulation tools for urban seismic analysis and resilience assessment

Nowadays, the refined models of simulation to evaluate the seismic damage in an urban area are becoming of paramount interest for the scientific community. Regional seismic damage simulation can potentially provide valuable information that can facilitate decision making, enhance planning for disaster mitigation, and reduce human and economic losses. However, the application of refined models is limited because of their high computational cost and needs of highly experienced users. For these reasons, these approaches remain academic experiences. This study proposes a straightforward approach to the problem, at the same time competitive, to simulate the seismic response and to assess the degree of damage at urban scale. At first, the simulation of the standard building is performed using an equivalent single degree of freedom model. Subsequently, the same approach is extended to a number of regular buildings from a virtual city sample for time-history seismic response analysis. The first part of this work is devoted to present the methodology to prepare the one-degree-of-freedom model of the standard building by comparing it with a refined multi degrees of freedom model as a target. Finally, a seismic damage simulation of a virtual city sample is implemented to demonstrate the capacity and advantages of the proposed method at increasing seismic intensities for damage assessment. It is the starting phase for further multi-hazards analyses at the regional scale through agent-based models

Simulating Earthquake Evacuation Using Human Behavior Models

In classical earthquake risk assessment the human behavior is actually not take into account in risk assessment. Agent-based modeling is a simulation technique that has been applied recently in several fields,such as emergency evacuation. The paper is proposing a methodology that includes in agent-based models the human behavior, considering the anxiety effects generated by the crowd and their influence on the evacuation delays. The proposed model is able to take into account the interdependency between the earthquake evacuation process and the corresponding damage of structural and non-structural components which is expressed in term of fragility curves. The software REPAST HPC has been used to implement the model and as case study the earthquake evacuation by a mall located in Oakland has been used. The human behavior model has been calibrated through a survey using a miscellaneous sample from different countries. The model can be used to test future scenarios and help local authorities in situations where the human behavior plays a key role