Synergistic combination of systems for structural health monitoring and earthquake early warning for structural health prognosis and diagnosis

Abstract

Earthquake early warning (EEW) systems are currently operating nationwide in Japan and are in beta-testing in California. Such a system detects an earthquake initiation using online signals from a seismic sensor network and broadcasts a warning of the predicted location and magnitude a few seconds to a minute or so before an earthquake hits a site. Such a system can be used synergistically with installed structural health monitoring (SHM) systems to enhance pre-event prognosis and post-event diagnosis of structural health. For pre-event prognosis, the EEW system information can be used to make probabilistic predictions of the anticipated damage to a structure using seismic loss estimation methodologies from performance-based earthquake engineering. These predictions can support decision-making regarding the activation of appropriate mitigation systems, such as stopping traffic from entering a bridge that has a predicted high probability of damage. Since the time between warning and arrival of the strong shaking is very short, probabilistic predictions must be rapidly calculated and the decision making automated for the mitigation actions. For post-event diagnosis, the SHM sensor data can be used in Bayesian updating of the probabilistic damage predictions with the EEW predictions as a prior. Appropriate Bayesian methods for SHM have been published. In this paper, we use pre-trained surrogate models (or emulators) based on machine learning methods to make fast damage and loss predictions that are then used in a cost-benefit decision framework for activation of a mitigation measure. A simple illustrative example of an infrastructure application is presented.

Bayesian Network Methods for Modeling and Reliability Assessment of Infrastructure Systems

“…These studies are hardware system focused, however, rather than in performing probabilistic analysis of the data collected using these systems. Wu and Beck (2012) do use a Bayesian framework and expand their analysis to the monitoring of systems both before and after an earthquake. The pre-event prognosis includes probabilistic predictions of the damage to a structure due to an earthquake, while the post-event diagnosis updates these predictions using Bayesian methods.…”

A low cost multi-sensor system for investigating the structural response of buildings

“…In [Wu and Beck, 2012], an effective SHM system has been designed. It is based on integration of several sensors and hardware components in a modular architecture.…”

“…It is based on integration of several sensors and hardware components in a modular architecture. In this case, efforts have been made to reduce the volume of data to be transmitted, a key aspect for reliability and sustainability of infrastructure, in particular when several constructions are monitored at the same time by a single master node [Wu and Beck, 2012]. In [Rainieri et al, 2008] a landslide case history has been presented which describes an automated monitoring system, using inclinometers to track ongoing creep movements and providing early warning alerts [Myers et al, 2000].…”

ePAD: Earthquake Probability‐Based Automated Decision‐Making Framework for Earthquake Early Warning

“…Other more sophisticated engineering applications include initiating active or semi‐active control systems on civil structures to enhance their earthquake resistance (Maddaloni et al., ), fast structural response analyses to provide information for rapid emergency responses, and automated train/automobiles control systems. Researchers have also proposed potential synergy between EEW and other existing systems, for instance, the use of EEW to provide a prior PDF in probability‐based structural health monitoring (SHM) systems (Wu and Beck, ), or integration of EEW with regional shaking intensity map or damage map for early damage assessment (Hilbring et al., ).…”