Francesco Cavalieri

Probabilistic Assessment of Civil Infrastructure Resilience to Earthquakes

The large losses occurred in the past due to earthquakes, even in highly developed countries, as well as the ensuing prolonged inactivity of the stricken societies, imparted momentum to research into regional seismic impact and community resilience to earthquakes. Need for comprehensive and consistent modeling is apparent, and this work presents a contribution in this direction. The extension of a recently developed civil infrastructure simulation framework to the evaluation of resilience, as well as the introduction of a new infrastructure network-based resilience metric represent the novelties of the article and allow one to explore the effect that sources of uncertainty and key vulnerability factors have on the probability distribution of resilience.

Models for Seismic Vulnerability Analysis of Power Networks: Comparative Assessment

Electric power networks are spatially distributed systems, subject to different magnitude and recurrence of earthquakes, that play a fundamental role in the well-being and safety of communities. Therefore, identification of critical components is of paramount importance in retrofit prioritization. This article presents a comparison of five seismic performance assessment models (M1 to M5) of increasing complexity. The first two models (M1 and M2) approach the problem from a connectivity perspective, whereas the last three (M3 to M5) consider also power flow analysis. To illustrate the utility of the five models, the well-known IEEE-118 test case, assumed to be located in the central United States, is considered. Performances of the five models are compared using both system-level and component-level measures. Spearman rank correlation ρ is computed between results of each model. Highest ρ values, at both system- and component-level, are obtained, as expected, between M1 and M2, and within models M3 to M5. The ρ values between component-level measures are relatively high across all models, indicating that simpler ones (M1 and M2) are appropriate for vulnerability assessment and retrofit prioritization. The complex flow-based models (M3 to M5) are suitable if actual performance of the systems is desired, as it is the case when the power network is considered within a larger set of interconnected infrastructural systems.

Framework for Seismic Hazard Analysis of Spatially Distributed Systems

The analysis of seismic risk to multiple systems of spatially distributed infrastructures presents new challenges in the characterisation of the seismic hazard input. For this purpose a general procedure entitled “Shakefield” is established, which allows for the generation of samples of ground motion fields for both single scenario events, and for stochastically generated sets of events needed for probabilistic seismic risk analysis. For a spatially distributed infrastructure of vulnerable elements, the spatial correlation of the ground motion fields for different measures of the ground motion intensity is incorporated into the simulation procedure. This is extended further to consider spatial cross-correlation between different measures of ground motion intensity. In addition to the characterisation of the seismic hazard from transient ground motion, the simulation procedure is extended to consider secondary geotechnical effects from earthquake shaking. Thus the Shakefield procedure can also characterise the effects site amplification and transient strain, and also provide estimates of permanent ground displacement due to liquefaction, slope displacement and coseismic fault rupture.

Seismic vulnerability analysis of a complex interconnected civil infrastructure

Abstract: This chapter presents a computational framework for probabilistic seismic performance assessment of the network of interdependent infrastructural systems that supports the functions of our society. An increasing number of large-scale failures (effects), initiated by relatively small natural or man-made disturbances (causes), have brought unforeseen disruption to infrastructures in recent years. The disproportion between causes and effects highlights an emergent chaotic behaviour, traceable in the infrastructure complexity and level of interconnectedness. In order to quantify this complexity and emulate the behaviour of the infrastructure as it emerges from that of its components and their interaction, an object-oriented model is developed.

Bayesian Networks and Infrastructure Systems: Computational and Methodological Challenges

This chapter investigates the applicability of Bayesian Network methods to the seismic assessment of large and complex infrastructure systems. While very promising in theory, Bayesian Networks tend to quickly show limitations as soon as the studied systems exceed several dozens of components. Therefore a benchmark study is conducted on small-size virtual systems in order to compare the computational performance of the exact inference of various Bayesian Network formulations, such as the ones based on Minimum Link Sets. It appears that all formulations present some computational bottlenecks, which are either due to the size of Conditional Probability Tables, to the size of clique potentials in the junction-tree algorithm or to the recursive algorithm for the identification of Minimum Link Sets. Moreover, these formulations are limited to connectivity problems, whereas the accurate assessment of infrastructure systems usually requires the use of flow-based performance indicators. To this end, the second part of the chapter introduces a hybrid approach that presents the merit of accessing any type of system performance indicator: it uses simulation-based results and generates the corresponding Bayesian Network by counting the outcomes given the various combinations of events that have been sampled in the simulation. The issue of the system size is also addressed by a thrifty-naive formulation, which limits the number of the components that are involved in the system performance prediction, by applying a cut-off threshold to the correlation coefficients between the components and system states. A higher resolution of this thrifty-naive formulation is also obtained by considering local performance indicators, such as the flow at each sink. This approach is successfully applied to a realistic water supply network of 49 nodes and 71 pipes. Finally the potential of this coupled simulation-Bayesian approach as a decision support system is demonstrated, through probability updating given the observation of local evidences after an event has occurred.

Application to selected transportation and electric networks in Italy

This chapter presents the application of the SYNER-G general methodology to two regional networks in Southern Italy: the road network of Calabria and the medium-high voltage electric power network of Sicily. Modelling and analysis of a road network, already discussed in Chap. 5, is recalled and further expanded here. The corresponding portion of the object-oriented (OO) model as implemented within SYNER-G is discussed. Then, the case study is presented, in terms of the main methodological choices, system properties (topology, vulnerability) and seismic hazard. Results of the (connectivity-only) analysis are then reported. Similarly, further aspects of the power network model implemented within SYNER-G are given before introducing and discussing the Sicily case study. The analysis of the EPN is carried out in terms of power flows.

Fragility Functions of Electric Power Stations

This chapter presents a state-of-the-art on fragility models for the components of Electric Power Networks (EPNs) available in the technical literature. First, the main characteristics of an electric power network and its taxonomy are introduced. Then, the main recent works on fragility functions of electric components are listed, and details are provided for a few selected ones. In the last section, the fragility curves which are most suited for use in the European context are selected, with the indication of parameters and relevant information. The selection has been based both on the data supporting the models and on the adopted systemic approach to the simulation of EPN within the SYNER-G general methodology for infrastructural systems’ vulnerability assessment. The latter adopts a capacitive, detailed flow-based modelling with short-circuits propagation over the network and requires the modelling of the substation internal logic.

Earthquake-altered flooding hazard induced by damage to storm water systems

Abstract Major earthquakes can cause extensive transformations to the land underlying cities, leading to decreased capacity in natural and built drainage systems and, as a consequence, to Increased Flooding Hazard (IFH). This phenomenon causes some areas, which previously were not exposed to flooding, to have the potential to flood, and already flood-prone areas to likely experience increased flood depth during the next rainfall events. This scenario occurred in Christchurch city, New Zealand, after the 2010–2011 Canterbury Earthquake Sequence (CES). The IFH was observed in many urban areas during a series of rainfall events occurred in the years after the CES. This paper proposes a method for analysing and assessing to what extent the earthquake-induced damage to storm water pipelines and the consequent impacts on the connectivity and capacity levels of the pipeline storm water network could contribute to the IFH. A probabilistic analysis, through a Monte Carlo simulation, is suggested for the proposed method so that the uncertainty affecting several key parameters can be accounted for. The proposed probabilistic method for IFH was implemented as an additional module within a recently developed open-source simulation tool, OOFIMS. Results from the added OOFIMS module are presented in terms of maps and cumulative distribution functions of increased flood height and flooded area, impact metrics that can be useful for emergency managers and infrastructure owners. The effectiveness of the proposed method to assess earthquake-altered flooding hazard and the relative OOFIMS-added module are tested using Christchurch as a case-study.

Application to a network of hospitals at regional scale

The seismic performance of a regional Health-Care System (HCS) is investigated. The earthquake effects both on hospitals and on the Road Network (RDN), connecting towns to hospitals, are evaluated and the interaction among them accounted for. Victims move to hospitals until their request for a bed or for a surgical treatment is satisfied, if possible. A novel “dynamic” model for hospitalization is developed and implemented. The road network is modelled in connectivity terms. The vulnerability of hospitals and bridges is expressed by pre-evaluated fragility curves. Seismic hazard is described by a state-of-the-art model. The reliability problem is solved by Monte Carlo simulation. The un-hospitalized victims, the risk that hospitals are unable to provide medical care, the demand of medical care on hospitals, the hospitalization travel time, are among the useful results of the analysis. The methodology is exemplified with reference to a case-study region, with population of 877,000, 20 towns, 5 hospitals and 32 bridges.

Component Fragilities and System Performance of Health Care Facilities

Hospitals belong to the so-called “complex-social” systems since they depend on several components of different nature to function properly and they provide a societal service to citizens. The basic components of a hospital are: the staff, the organization and the facility. They jointly “contribute” to provide medical care to patients. This chapter focus on the seismic assessment of the facility. A hospital has to be capable of providing medical after the occurrence of a major earthquake; hence the facility target performance is set as operational. Such a performance depends on the response of both structural and non-structural elements. Fragility curves for “typical” non-structural elements are provided. A probabilistic-based procedure for the evaluation of the fragility curve of the facility is then derived. Finally, an index adequate to measure the performance of the hospital under emergency condition is proposed.