Towards a uniform earthquake risk model for Europe

Seismic risk has been the focus of a number of European projects in recent years, but there has never been a concerted effort amongst the research community to produce a uniform European risk model. The H2020 SERA project has a work package that is dedicated to that objective, with the aim being to produce an exposure model, a set of fragility/vulnerability functions, and socio-economic indicators in order to assess probabilistic seismic risk at a European scale. The partners of the project are working together with the wider seismic risk community through web tools, questionnaires, workshops, and meetings. All of the products of the project will be openly shared with the community on both the OpenQuake platform of the Global Earthquake Model (GEM) and the web platform of the European Facilities for Earthquake Hazard and Risk (EFEHR)

The European Seismic Risk Model 2020 (ESRM20)

This study describes the development of the various components of the European Seismic Risk Model 2020 (ESRM2020) which will be able to generate, using open-source software developed by the GEM Foundation (the Open Quake-engine), a number of Europe-wide risk metrics including average annualised human and economic losses (AAL), probable maximum losses (PML), and risk maps showing the losses for specific return periods or scenario events. The latest developments towards pan-European exposure models for residential and non-residential buildings and fragility/vulnerability models for damage, economic loss and casualty assessment will be presented. For engineered buildings within the exposure model (reinforced concrete, steel), a simulated design is undertaken using the key aspects of seismic design codes in force across Europe over the past 100 years. The designed MDOF building is then transformed to a SDOF model and nonlinear dynamic analyses are run using a large number of ground motion records, after which cloud analysis is used to develop the fragility functions. For non-engineered buildings (unreinforced masonry, confined masonry, adobe), the SDOF models have been directly developed from simplified formulae, experimental tests and previous studies. Collaboration from local experts at various stages of the model development, initiated through workshops, is an important component of the model, as well as the extensive calibration and validation

Tsunami risk communication and management: Contemporary gaps and challenges

Very large tsunamis are associated with low probabilities of occurrence. In many parts of the world, these events have usually occurred in a distant time in the past. As a result, there is low risk perception and a lack of collective memories, making tsunami risk communication both challenging and complex. Furthermore, immense challenges lie ahead as population and risk exposure continue to increase in coastal areas. Through the last decades, tsunamis have caught coastal populations off-guard, providing evidence of lack of preparedness. Recent tsunamis, such as the Indian Ocean Tsunami in 2004, 2011 Tohoku and 2018 Palu, have shaped the way tsunami risk is perceived and acted upon. Based on lessons learned from a selection of past tsunami events, this paper aims to review the existing body of knowledge and the current challenges in tsunami risk communication, and to identify the gaps in the tsunami risk management methodologies. The important lessons provided by the past events call for strengthening community resilience and improvement in risk-informed actions and policy measures. This paper shows that research efforts related to tsunami risk communication remain fragmented. The analysis of tsunami risk together with a thorough understanding of risk communication gaps and challenges is indispensable towards developing and deploying comprehensive disaster risk reduction measures. Moving from a broad and interdisciplinary perspective, the paper suggests that probabilistic hazard and risk assessments could potentially contribute towards better science communication and improved planning and implementation of risk mitigation measures

Tsunami risk communication and management: Contemporary gaps and challenges

Very large tsunamis are associated with low probabilities of occurrence. In many parts of the world, these events have usually occurred in a distant time in the past. As a result, there is low risk perception and a lack of collective memories, making tsunami risk communication both challenging and complex. Furthermore, immense challenges lie ahead as population and risk exposure continue to increase in coastal areas. Through the last decades, tsunamis have caught coastal populations off-guard, providing evidence of lack of preparedness. Recent tsunamis, such as the Indian Ocean Tsunami in 2004, 2011 Tohoku and 2018 Palu, have shaped the way tsunami risk is perceived and acted upon. Based on lessons learned from a selection of past tsunami events, this paper aims to review the existing body of knowledge and the current challenges in tsunami risk communication, and to identify the gaps in the tsunami risk management methodologies. The important lessons provided by the past events call for strengthening community resilience and improvement in risk-informed actions and policy measures. This paper shows that research efforts related to tsunami risk communication remain fragmented. The analysis of tsunami risk together with a thorough understanding of risk communication gaps and challenges is indispensable towards developing and deploying comprehensive disaster risk reduction measures. Moving from a broad and interdisciplinary perspective, the paper suggests that probabilistic hazard and risk assessments could potentially contribute towards better science communication and improved planning and implementation of risk mitigation measures

Tsunami risk communication and management: Contemporary gaps and challenges

Supplementary data: The following is the Supplementary data to this article: Acrobat PDF file (2MB) available at: https://ars.els-cdn.com/content/image/1-s2.0-S2212420921007329-mmc1.pdfCopyright © 2022 The Authors. Very large tsunamis are associated with low probabilities of occurrence. In many parts of the world, these events have usually occurred in a distant time in the past. As a result, there is low risk perception and a lack of collective memories, making tsunami risk communication both challenging and complex. Furthermore, immense challenges lie ahead as population and risk exposure continue to increase in coastal areas. Through the last decades, tsunamis have caught coastal populations off-guard, providing evidence of lack of preparedness. Recent tsunamis, such as the Indian Ocean Tsunami in 2004, 2011 Tohoku and 2018 Palu, have shaped the way tsunami risk is perceived and acted upon. Based on lessons learned from a selection of past tsunami events, this paper aims to review the existing body of knowledge and the current challenges in tsunami risk communication, and to identify the gaps in the tsunami risk management methodologies. The important lessons provided by the past events call for strengthening community resilience and improvement in risk-informed actions and policy measures. This paper shows that research efforts related to tsunami risk communication remain fragmented. The analysis of tsunami risk together with a thorough understanding of risk communication gaps and challenges is indispensable towards developing and deploying comprehensive disaster risk reduction measures. Moving from a broad and interdisciplinary perspective, the paper suggests that probabilistic hazard and risk assessments could potentially contribute towards better science communication and improved planning and implementation of risk mitigation measures.COST (European Cooperation in Science and Technology); Royal Society, UK (grant number CHL\R1\180173); Severo Ochoa Centers of Excellence Program (CEX 2018-000797-S) funded by MCIN/ AEI /10.13039/501100011033; Lloyd's Tercentenary Research Foundation, the Lighthill Risk Network, and the Lloyd's Register Foundation-Data Centric Engineering Programme of the Alan Turing Institute

Mixed model disassembly line balancing problem with fuzzy goals

The collection of used products is the driving force of remanufacturing systems and enterprises can gain significant economic, technical and social benefits from recycling. All products are disassembled up to some level in remanufacturing systems. The best way to disassemble returned products is valid by a well-balanced disassembly line. In this paper, a mixed integer programming (MIP) model is proposed for a mixed model disassembly line balancing (MMDLB) problem with multiple conflicting objectives: (1) minimising the cycle time, (2) minimising the number of disassembly workstations and (3) providing balanced workload per workstation. In most real world MMDLB problems, the targeted goals of decision makers are frequently imprecise or fuzzy because some information may be incomplete and/or unavailable over the planning horizon. This study is the first in the literature to offer the binary fuzzy goal programming (BFGP) and the fuzzy multi-objective programming (FMOP) approaches for the MMDLB problem in order to take into account the vague aspirations of decision makers. An illustrative example based on two industrial products is presented to demonstrate the validity of the proposed models and to compare the performances of the BFGP and the FMOP approaches

artificial bee colony algorithm

This paper presents a fuzzy extension of the disassembly line balancing problem (DLBP) with fuzzy task processing times since uncertainty is the main character of real-world disassembly systems. The processing times of tasks are formulated by triangular fuzzy membership functions. The balance measure function is modified according to fuzzy characteristics of the disassembly line. A hybrid discrete artificial bee colony algorithm is proposed to solve the problem whose performance is studied over a well-known test problem taken from open literature and over a new data set introduced in this study. Furthermore, the influence of the fuzziness on the computational complexity of HDABC is evaluated and the solution quality of the proposed algorithm is compared against discrete and traditional versions of the artificial bee colony algorithm. Computational comparisons demonstrate the superiority of the proposed algorithm. (C) 2014 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved