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This brief technical report details the code and data structure of the software that has been developed by BRGM for the derivation of shake-maps, to be used in the TURNkey platform. The software has the following features: - generation of shake-maps for PGA and macroseismic intensity; - use of strong-motion measures from seismic stations and felt reports as sources of observation; - integration of site effects through amplification factors; - parametrisation of the computation (choice of map extent, computation grid, GMM and GMICE models, etc.).

This brief technical report details the code and data structure of the algorithm that has been developed by BRGM for the identification of suitable evacuation/rescue routes on a damage road network, to be used as a DSS in the TURNkey platform. The algorithm has the following features: - use as a post-treatment step after the execution of the rapid loss updating tool developed in WP4 (see Report D4.9); - consideration of travel options from the damage area to hospitals, and from rescue centres to the damaged area; - identification of shortest (i.e., fastest travel time) and safest (i.e., lowest probability of disconnection) routes, for a given origin-destination travel; - evaluation of each route via two criteria: expected value of travel time (accounting of probabilities of failure of components along the route) and probability of inaccessibility of the initial route and its back-up options.

This Deliverable Report describes the work carried out in Task 5.4 (“Development of Decision Support System (DSS) for post-event rapid response also accounting for aftershocks”) within TURNkey WP5. The purpose of this task is to integrate the stakeholder performance metrics from Task 5.1 and the predictive models from Task 5.2 (performance of infrastructure systems) with the procedures for Rapid Earthquake Loss Prediction (RELP) (Task 4.5). The updated loss estimates that are output from WP4 constitute the initial state of the affected area (i.e., situational awareness), from which adequate actions may be taken during the crisis period. Accounting for various uncertainties (i.e., the known state of the systems and the variability in the effects of given decisions), the evaluation of various potential actions in a RRE context results in the design of a Decision Support System (DSS) that has the effect of orienting emergency responders towards the most efficient actions, thus reducing the time to emerge from the crisis phase. To this end, this task revolved around two specific focus areas, which are detailed in this report:  Design of a DSS for the post-earthquake accessibility of a road network (Section 3): following the general WP5 methodology, the DSS aims at identifying the best routes to navigate a degraded road network (accounting for uncertainty in the actual state of the road components) depending on the type of user (e.g., emergency responders, healthcare crew, general public, etc.).  Assessment of structural damage that occurs to infrastructure components under mainshock and future aftershock events (Sections 4 and 5): a Bayesian network-based probabilistic framework is developed for updating the risk of damage to bridges during aftershocks, allowing the integration of various types of information to reduce uncertainty in the calculations.

Deliverable from WP3 of TURNkey project

This report details the procedures that have been applied, developed or improved in Task 3.4 for the rapid generation of ground shaking maps (i.e., shake-maps). The report revolves around several actions and technical results, which are organized as follows:  Section 3 summarizes the state-of-the-art of current shake-map algorithms and systems, based on a review by Guérin-Marthe et al. (2021). The USGS ShakeMap v4 approach is compared to a method based on Bayesian updating, which is put forward as one of the technical solutions to be implemented in the TURNkey platform.  Section 4 is based on previous work carried out in Task 3.3.2: in each TB, various GMMs (Ground-Motion Models) are evaluated and ranked, via different scoring metrics. The selected GMMs may then be used for the generation of shake-maps in each TB.  Section 5 details novel research efforts, where the GMM coefficients are updated in order to match the observations. This approach is useful to rapidly update a GMM in a given area for a given earthquake, so that the updated GMM may be reused for subsequent events (e.g., in a seismic sequence) in order to improve the accuracy of ground-shaking estimates. Two parallel and complementary methods are presented, namely a direct calibration of the coefficients (EUC) and a Bayesian updating of the uncertain coefficients in the shake-map (BRGM).  Section 6 discusses site amplification models that are available in each TB: besides local models, Vs30 maps generated at the European level (in the European project SERA) are extracted for each TB. Recommendations are given for each TB on which model should be applied, and whether the SERA model represents a satisfying approximation.  Section 7 explores additional sources of observations that may be used to characterise shake-maps: collection and aggregation of felt reports by EMSC; and extraction of Twitter data and integration as soft evidence in the shake-maps by BRGM. The Bayesian approach for the derivation of shake-maps has been implemented in a Python code, which is briefly described in the companion deliverable report D3.9.

TURNkey Deliverable D 5.2 (Phase 1+ Phase 2) Report on recommendations on Community Resilience and Recovery Models &nbsp