The Benefit of Informed Risk-Based Management of Civil Infrastructures (original) (raw)
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Quantifying the value of monitoring for post-earthquake emergency management of bridges
Proceedings of 2017 IABSE Symposium, 2017
SHM systems are increasingly installed to increase the knowledge of the structural state and in present practice it is implicitly assumed that the information they bring is beneficial for the structure. This may not always be the case since inappropriate strategies, leading to wrong decisions, may be detrimental for the structural safety or cause economic losses. In this paper a framework based on the concept of Value of Information from pre-posterior Bayesian analysis is applied to forecast the utility associated to the implementation of an SHM system for the case of emergency management of road bridges subjected to seismic risk. An optimization problem is formulated to identify the most efficient decisions regarding traffic restrictions after an earthquake defined as those corresponding to the required level of safety and the minimum associated cost. The choice of a proper structural performance indicator and the methods for the computation of the prior probabilities of damage, of the likelihood functions and of the costs associated with the different possible traffic restrictions are also briefly discussed for the considered application.
Proceedings of the Joint COST TU1402 - COST TU1406 - IABSE WC1 Workshop: The Value of Structural Health Monitoring for the reliable Bridge Management
This paper proposes a framework for quantifying the value of information that can be derived from a structural health monitoring (SHM) system installed on a bridge which may sustain damage in the mainshock of an earthquake and further damage in an aftershock. The pre-posterior Bayesian analysis and the decision tree are the two main tools employed. The evolution of the damage state of the bridge with an SHM system is cast as a time-dependent, stochastic, discrete-state, observable dynamical system. An optimality problem is then formulated how to decide on the adoption of SHM and how to manage traffic and usage of a possibly damaged structure using the information from SHM. The objective function is the expected total cost or risk. The paper then discusses how to quantify bridge damage probability through stochastic seismic hazard and fragility analysis, how to update these probabilities using SHM technologies, and how to quantify bridge failure consequences.
Quantifying the value of SHM for emergency management of bridges at-risk from seismic damage
This paper proposes a framework for quantifying the value of information that can be derived from a structural health monitoring (SHM) system installed on a bridge which may sustain damage in the mainshock of an earthquake and further damage in an aftershock. The pre-posterior Bayesian analysis and the decision tree are the two main tools employed. The evolution of the damage state of the bridge with an SHM system is cast as a time-dependent, stochastic, discrete-state, observable dynamical system. An optimality problem is then formulated how to decide on the adoption of SHM and how to manage traffic and usage of a possibly damaged structure using the information from SHM. The objective function is the expected total cost or risk. The paper then discusses how to quantify bridge damage probability through stochastic seismic hazard and fragility analysis, how to update these probabilities using SHM technologies, and how to quantify bridge failure consequences.
A Bayesian network‐based probabilistic framework for updating aftershock risk of bridges
Earthquake Engineering & Structural Dynamics
The evaluation of a bridge's structural damage state following a seismic event and the decision on whether or not to open it to traffic under the threat of aftershocks (ASs) can significantly benefit from information about the mainshock (MS) earthquake's intensity at the site, the bridge's structural response, and the resulting damage experienced by critical structural components. This paper illustrates a Bayesian network (BN)-based probabilistic framework for updating the AS risk of bridges, allowing integration of such information to reduce the uncertainty in evaluating the risk of bridge failure. Specifically, a BN is developed for describing the probabilistic relationship among various random variables (e.g., earthquakeinduced ground-motion intensity, bridge response parameters, seismic damage, etc.) involved in the seismic damage assessment. This configuration allows users to leverage data observations from seismic stations, structural health monitoring (SHM) sensors and visual inspections (VIs). The framework is applied to a hypothetical bridge in Central Italy exposed to earthquake sequences. The uncertainty reduction in the estimate of the AS damage risk is evaluated by utilising various sources of information. It is shown that the information from accelerometers and VIs can significantly impact bridge damage estimates, thus affecting decision-making under the threat of future ASs. K E Y W O R D S aftershock risk, Bayesian network, joint probabilistic demand model, structural health monitoring, visual inspections This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Capabilities of the Bayesian probabilistic networks approach for earthquake risk management
The present paper considers large-scale risk based decision making in regard to management of earthquake hazards. First an outline is given on existing methodologies on management of earthquake hazards in terms of capabilities and shortcomings. Thereafter a recently developed generic risk assessment framework is introduced which takes basis in a system representation through exposures, direct and indirect consequences as well as vulnerability and robustness. The framework is fully generic in the sense that the characteristics of the system are formulated in terms of risk indicators which may be specified in accordance with the available information concerning a given system. Furthermore, the framework is fully Bayesian such that probabilistic models and consequently also the risk assessments can be updated based on new information of relevance for the decision making. This in turns allows for considering the different decision situations, before, during and after an earthquake takes place, subject to the available information in the different situations. The basic properties of Bayesian Probabilistic Networks (BPN) are shortly introduced. Taking basis in previously developed BPN based risk assessment tools for vulnerability analysis of structures and soil, the presented framework is then illustrated through an example where a risk based decision analysis on possible retrofitting or rebuilding of building structures in a larger part of a city is performed for the two different situations -before and after an earthquake.
The value of visual inspections for emergency management of bridges under seismic hazard
2018
One of the major problems in the aftermath of an earthquake is the management of the emergency inspection operations. Traffic restriction, including limited emergency operations or bridge closure due to safety concerns, may be issued to keep an appropriate level of safety. Visual inspections may be conducted to provide useful information on the damage state of the bridge and support the decision of imposing traffic restriction up to the complete closure of the bridge, or for allowing the immediate use of safe bridges after the event. The cost related to the inspection shall be at least balanced by the uncertainty reduction provided by the inspection data and the benefit is higher when the costs associated with taking a wrong management decision are high, but may be negligible if this is not the case. Practical tools and methods to forecast this benefit before collecting the information exist in classical decision theory, but are seldom applied by engineers. In this paper a framework...
A framework for quantifying and optimizing the value of seismic monitoring of infrastructure
This paper outlines a framework for quantifying and optimizing the value of information from structural health monitoring (SHM) technology deployed on large infrastructure, which may sustain damage in a series of earthquakes (the main and the aftershocks). The evolution of the damage state of the infrastructure without or with SHM is presented as a time-dependent, stochastic, discrete-state, observable and controllable nonlinear dynamical system. The pre-posterior Bayesian analysis and the decision tree are used for quantifying and optimizing the value of SHM information. An optimality problem is then formulated how to decide on the adoption of SHM and how to manage optimally the usage and operations of the possibly damaged infrastructure and its repair schedule using the information from SHM. The objective function to minimize is the expected total cost or risk.
On the application of Bayesian probabilistic networks for earthquake risk management
… conference on structural …, 2005
The present paper considers the application of Bayesian Probabilistic Networks (BPN's) in risk management for portfolios of structures subject to earthquake hazards. The BPN's facilitate that risks are assessed in a generic framework using indicators to relate the generic representation to the specific condition prevailing a given site, soil conditions, structure class, occupancy, etc. Initially a summary of previous work in the area of earthquake risk management is provided. Thereafter the general problem framework for management of earthquake risks is introduced for three different decision situations; before, during and after an earthquake. Following this, a basic introduction on BPN's is provided and it is outlined how the concept of indicators provides an efficient means of representing risks generically and for updating generic models in accordance with site specific information. A generic structural modelling framework is described which facilitates the automatic generation of input files for non-linear structural response analysis using the open source finite element software OpenSees. This framework makes it possible in a straight forward manner to analyse and generate vulnerability curves for several structure classes with a minimum use of man-hours.
Proceedings of 16ECEE – 16th European Conference on Earthquake Engineering. Thessaloniki, Greece, June 18-21, 2018
This paper presents an approach for the rapid seismic loss assessment of infrastructure systems, where all probabilistic variables are modeled through a Bayesian Network (BN). While BN-based approaches have been introduced as promising tools for the risk assessment of systems, they suffer from computational issues (i.e., combinatorial explosion) that prevent their application to large real-world networks that require accurate and complex performance indicators. Therefore, a hybrid BN method is introduced here, where a preliminary Monte Carlo simulation is performed in order to generate a dataset of component damage configurations, which is used to build a simplified BN structure with only a few selected components. The most critical components are selected thanks to an unbiased importance measure computed from a random forest classification. While the proposed approach generates an approximate BN structure that cannot provide exact probability distributions of losses, the application of Bayesian inference in a retro-analysis context (i.e., updating of loss projections given field observations immediately after an earthquake) has a lot of potential as a decision-support system for emergency responders. This method is applied to a road network in France, where evidence such as recorded ground-motions or observed damages is used to update the state of the system. The approximate BN structure has the ability to include complex system performance indicators, such as the additional travel time accounting for traffic flows. A sensitivity analysis on the component selection method and on the number of selected components demonstrates the stability of the posterior distributions, even with very few selected components.