Development of Dynamic Laboratory Platform for Earthquake Engineering Courses

Small-scale shaking table platforms are usually used in seismic engineering courses to study the structural dynamic behavior of small scale specimens and investigate innovative solutions, such as active and passive control systems. Furthermore, they are also useful to actively involve students in learning programs in higher education. This paper illustrates the development and the teaching effectiveness of a multimodular unidirectional platform to be used by students during dynamic and seismic courses within the Shaking Table Educational Program at the Politecnico di Torino. A unique feature of this platform is that the system was entirely developed by undergraduate students. The project was intended to create a shaking table for earthquake simulation that can measure the structural response using sensors located on a specimen, such as a building, a bridge, or any other type of reduced-scale system. Different types of dynamic tests can be reproduced, such as hybrid simulations and pseudodynamic tests. A survey demonstrates the effectiveness of the laboratory experience during seismic engineering courses to improve student learning capabilities through a teaching activity that involves both theoretical and hands-on experience. Currently, the platform has been extended to accommodate bidirectional shaking table tests with the inclusion of augmented reality tools that allow exploring the response of human behavior during a pedestrian evacuation

Indicator-based method to evaluate community resilience

The capacity of a community to react and resist to an emergency is strictly related to the proper functioning of its own infrastructure systems. A better understanding of critical infrastructure architecture is necessary for defining measures to achieve a better resilience against threats (natural and human threats) in an integrated manner. For this purpose, indicators are perceived as important instruments to measure the resilience of infrastructure systems. Many research activities have been focusing on developing reliable indicators that could be applied at different scales, but research on resilience, which is a multidimensional and transformative concept, is still in the early stages of development. Developing a comprehensive, standardized set of resilience indicators is obviously difficult for such a dynamic, constantly re-shaping and context-dependent concept, Previous studies have highlighted the importance of conceptual frameworks to guide the selection of the indicators, so following the same trend this paper describes the procedure for selecting the proper indicators for community resilience within the PEOPLES framework (Cimellaro et. al 2009). PEOPLES is a holistic framework for defining and measuring disaster resilience of communities at various scales. It is divided into seven dimensions, and each dimension is further divided into several components. An integrated approach is presented that combines both quantitative and qualitative as well as outcome and process indicators, addressing a broad variety of issues such as the security, the geo-politics, the sociology, economy, etc. The methodology classifies the indicators’ location within the seven PEOPLES dimensions and provides a structure for creating a condensed list of indicators. Each indicator is linked to a measure allowing it to be quantified. The measures are expressed by serviceability functions rather than scalar values in order to allow a dynamic measurement of the indicators. Finally, the proposed indicators are weighted and then aggregated into a single serviceability function that describes the functionality of the community in time. The developed methodology has been tested on the critical infrastructures of San Francisco, USA, in order to assess their level of resiliency. Results of the case study show that the methodology introduced to compute the resiliency allows decision makers to derive key-indicators of community resilience that are applicable on a higher level of societal resilience, across different contexts and hazard types (attacks, accidents, etc.). The present work contributes to this growing area of research as it provides a universal tool to quantitatively assess the resilience of communities at multiple scales

A new energetic based ground motion selection and modification algorithm equation

This paper presents a new ground motion modification and selection procedure to be used for performing the response history analysis of structures. The proposed selection and scaling procedure is based on an energetic comparison in a frequency band. The Conditional Mean Spectrum is used as target spectrum while only the records providing a relevant contribution to the hazard at the site are considered. The set of ground motion with the same hysteretic energy demand is obtained matching the acceleration of the target spectrum at the period of interest Tref and selecting only the scaled spectra having a similar Housner intensity in the period range 0.2Tref – 2Tref. A set of records which are spectrum compatible, having a similar hysteretic energy demand are obtained. This last aspect can be reflected in terms of equal damage level expected on the structure, since the damage parameters coming from the response history analyses present a very low dispersion. As a result, the new energetic approach allows selecting a set of ground motion according to the spectrum compatibility criterion, to the frequency content representativeness and to the consistency of the expected structural damage for the given hazard scenario

Resilience of a hospital emergency department under seismic event

The article presents a new simplified model for measuring the resilience of a hospital Emergency Department during a seismic event. The waiting time is used as performance parameter which is first evaluated using a discrete event simulation model of the Emergency Department. Then, a metamodel has been developed from the results of the discrete event simulation model for different emergency codes considering the amplitude of the seismic input and the number of resources available right after the seismic event. Results show that when an earthquake occurs, generating a seismic wave of injured patients going to the Emergency Department, the maximum waiting time is approximately 13h when an emergency plan is not applied. Instead, if the emergency rooms are not functional, due to earthquake damages, the waiting time increases dramatically and the Emergency Department is no more able to provide a proper service to the incoming patients. The proposed Emergency Department model can be used not only to evaluate the performance of existing hospitals during an emergency, but also to design the proper size of a new Emergency Department in a region

Resilience assessment at the state level

This paper presents an analytical approach to evaluate the level of post-disaster adaptation (Bounce-Back) of communities based on their resilience. While resilience is the intrinsic characteristics of a system, adaptation considers external agents in its assessment. The presented work is to some extent a parallelism to the risk assessment concept. Generally, risk is a function of vulnerability, exposure, and hazard, whereas adaptation considers resilience instead of vulnerability in its estimation. This leads to the evaluation of a system’s ability to cope with after-shock consequences and to return back to a functional state rather than the likelihood of a system to experience damage. The paper also proposes a quantitative framework for assessing resilience at the state level based on the Hyogo Framework for Action (HFA), a work done by the UN. HFA has succeeded in assessing the resilience of every state in a quantifiable fashion. HFA estimates the resilience of countries based on a number of indicators that are weighted equally. Those indicators, however, do not contribute equally to the resilience output; therefore, it is necessary to weigh those indicators according to their contribution towards resilience. To do so, we are introducing the Dependence Tree Analysis (DTA), which identifies the strength of relationships between the indicators and the resilience, giving weights to the indicators accordingly. A full case study composed of 37 countries is presented in this paper, where the resilience and the Bounce Back indices of each country are evaluated

Integrated design of Smart Structures

Much of structural control research and applications in civil engineering have been concerned with structures equipped with passive, hybrid, or active control devices in order to enhance structural performance under extraordinary loads. In most cases, the structure and the control system are individually designed and optimized. On the other hand, an exciting consequence of structural control research is that it also opens the door to new possibilities in structural forms and configurations, such as lighter buildings or bridges with longer spans without compromising on structural performance. Moreover, this can only be achieved through integrated design of structures with control elements as an integral part. This paper addresses the integrated design of structures with imbedded control systems and devices. Simultaneous optimization of such controlled structures is considered, showing that new structural forms and configurations can be achieved through integrated design. © 2008 Trans Tech Publications, Switzerlan

Resilience analysis of large scale networks using the D-spectrum method

Infrastructure systems are crucial for the development of communities because they provide essential services to the habitants. Here we focus on the transportation network, which is designed to provide a continuous service to the community. Due to its decisive role in the economy, governments and policy makers have been investing in developing strategies to increase the resilience of this kind of infrastructure against disruptive events. In the literature, several methods to evaluate networks’ reliability and resilience can be found. The applicability of these methods is limited to small networks due to the computational complexities. In this paper, the case of city-scale road transportation networks is tackled. The case study considered in this work is the transportation network of a virtual, city called ‘Ideal City’. First, the road map of the city is transformed into an undirected graph with 15012 nodes and 19614 edges. A non-random gradual removal of the edges has been applied until the network’s failure point is reached. The edge removal process is related to the failure probabilities of the edges when the network is exposed to a certain hazard. In fact, the effect of hazards on the transportation network is not direct. The hazard exposes the building structures on the road sides to a failure risk. These structures if collapsed would cause the adjacent roads to be blocked and thus lose functionality due to the debris falling from the structures. For this purpose, a building infrastructure is modeled and the relationship between the level of damage of building and the amount of debris falling on the adjacent roads is developed. A Monte Carlo approach is used to generate failure permutations of edges considering their failure probabilities. The network reliability is then calculated using the Destruction Spectrum (D-spectrum) approach. In addition, the network’s edges have been ranked from the most to the least important by applying the Birnbaum Importance Measure (BIM). Due to the large size of the network, a number of computational problems have arisen. Therefore, several coding algorithms have been developed to allow evaluating both the reliability and the BIM indexes while avoiding computational errors. The results obtained in this study are used to identify the vulnerable components of the network. The vulnerable components are the ones that should be focused on to improve the overall resilience of the infrastructure. The analysis concept adopted in this study is applicable to all network-based infrastructure systems such as water, gas, transportation, etc. Future work will be oriented towards applying the methodology to other network-based infrastructure systems

A new methodology to model interdependency of Critical Infrastructure Systems during Hurricane Sandy’s event

The paper proposes a methodology to evaluate the resilience of the critical infrastructures networks hit by Hurricane Sandy in October 2012. The region analyzed in the case study is New York metropolitan area which includes New York City and the nearby state of New Jersey. This region was the most affected by the storm and it is one of the most densely populated regions of the United States due to its high concentration of businesses and several critical infrastructures. The identified critical infrastructure systems are highly interconnected, forming a heterogeneous network that is very vulnerable to catastrophic events, such as hurricanes. Due to several existing interdependencies, the systems are subjected to disruptive cascading effects. The disruption of one or more of these systems directly affects people, businesses, the government and leads to additional indirect damages. After a critical comparison of the different methodologies to analyze infrastructure interdependency, the input-output method is selected in order to indentify and rank the different types of dependencies in the network as well as to prioritize the different actions during the restoration process. Previous analyses have shown that power, transportation, and fuel were the most damaged networks in the region generating severe cascading effects due to the interdependencies between them. A series of recommendations to improve the global resilience in the region are provided which will be able to prevent cascading effects and prioritize the recovery effort in the future