Testing machine learning models for heuristic building damage assessment applied to the Italian Database of Observed Damage (DaDO)

Assessing or forecasting seismic damage to buildings is an essential issue for earthquake disaster management. In this study, we explore the efficacy of several machine learning models for damage characterization, trained and tested on the database of damage observed after Italian earthquakes (the Database of Observed Damage – DaDO). Six models were considered: regression- and classification-based machine learning models, each using random forest, gradient boosting, and extreme gradient boosting. The structural features considered were divided into two groups: all structural features provided by DaDO or only those considered to be the most reliable and easiest to collect (age, number of storeys, floor area, building height). Macroseismic intensity was also included as an input feature. The seismic damage per building was determined according to the EMS-98 scale observed after seven significant earthquakes occurring in several Italian regions. The results showed that extreme gradient boosting classification is statistically the most efficient method, particularly when considering the basic structural features and grouping the damage according to the traffic-light-based system used; for example, during the post-disaster period (green, yellow, and red), 68 % of buildings were correctly classified. The results obtained by the machine-learning-based heuristic model for damage assessment are of the same order of accuracy (error values were less than 17 %) as those obtained by the traditional RISK-UE method. Finally, the machine learning analysis found that the importance of structural features with respect to damage was conditioned by the level of damage considered.</p

INNOVATIONS in earthquake risk reduction for resilience: RECENT advances and challenges

The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated

A long-term property earthquake insurance: illustration with the housing sector in California

&amp;lt;p&amp;gt;For several seismic-prone countries, current earthquake insurance solutions cover only a small part of the economic loss. Innovative insurance products like parametric insurance are emerging for which the compensation is calculated upon a trigger instead of a claim amount, covering more people but with drawbacks due to probable difference between the insurance compensation and the actual loss. In this paper, new insurance model is proposed, covering earthquake risk for residential houses. Its main characteristics are: (1) the compensation is to rebuild the insured house, instead of paying a financial amount; (2) the model leverages both on long-term financial investment and seismic retrofitting of the insured buildings to make the premium amount affordable; and (3) joint participation of the public authorities and the homebuilder companies in this insurance model are expected since the first ones are the key player in risk prevention plans and the second ones are the beneficiary of this new market (incentivizing repairs/reconstruction and retrofitting works). Results show that in most cases the price (i.e. premium amount and retrofitting costs) for this earthquake insurance model is lower than the premium amount considering the traditional earthquake insurance. For the optimal deductible amount, the decrease can even be three times lower than for classical model, by assuming a contribution from both the public authorities and the homebuilder companies. Such a decrease could raise the rate of California homeowners insured against earthquake risk from 15% up to 50%.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;</jats:p

Modélisation du risque sismique en assurance : étude d’un nouveau modèle pour une meilleure gestion assurantielle

Various scientific reports have highlighted that, both on the history of losses related to natural disasters, and on the average cost expected for the coming years, the seismic risk is the risk for which the uninsured loss is the biggest of all natural disasters. In this context, the subject I chose for my thesis is: " Seismic loss modelling in insurance industry: towards a new model for better claims management". The work is organized in three parts: 1° presentation of the different insurance systems against seismic risk around the world; 2° identification of the main areas to improve risk modelling; 3° proposals for a new insurance model.The insurance systems review focused on the following two countries: France and California. By combining several variables related to the level of risk put into perspective with different economic indicators, we have created a specific maturity scale for seismic insurance. This tool makes it possible to measure the evolution, upwards or downwards, of an insurance market. Applying it to the two countries studied, it appears that none of them is equipped with a sustainable insurance system.In France, the risk of earthquake has been covered by the so-called CAT-NAT scheme. On the basis of all the municipalities declared in natural disaster after an earthquake, a probabilistic model of natural disaster state recognition at the municipality level was developed. By applying it to two representative scenarios of a major earthquake in mainland France, the results show that the state could be put in a difficult position from covering the cost of the disaster under the CAT-NAT scheme.In California, although the risk is significant, only 15% of the population is insured. We conducted a study differentiating the effect of the perception of the seismic risk by the Californians from the price of the insurance. The results show that Californians do not adhere to these insurance plans because of their price, and not by underestimating the risk. This model shows also that the majority of Californians would buy an earthquake insurance if its price was divided by three.According to the scale of maturity previously developed, the evolution of this insurance system requires a better risk modelling. For this reason, we have developed a new method for comparing probabilistic seismic hazards maps with estimated hazard footprints of past earthquakes. Improving stochastic earthquake loss models also requires strengthening the relationship between the damage scale used and associated repair costs. For this reason, a database of the economic consequences of past earthquakes has been created. In addition, an economic model has been developed in order to test existing damage-cost models with historical data previously collected.Finally, the last part of my thesis work focuses on the study of a new insurance model, where the cost of the insurance premiums is allocated to each building. As long as an earthquake does not damage an insured building, premiums are invested to increase the available. When an earthquake occurs and damages an insured building, the insurance company takes care of the repair or reconstruction work. If the accumulated capital is big enough before an earthquake causes damage to a building, it is used to finance seismic retrofitting works. The loss of capital that these generate is then compensated by the resistance gain of the building. This way the cost of the premium paid by the policyholder is reduced and a real commitment to risk prevention is initiated.Plusieurs rapports scientifiques ont mis en évidence que, tant sur l’historique des pertes liées aux catastrophes naturelles, que sur le coût moyen attendu pour les années à venir, le risque sismique est celui pour lequel la perte non-assurée est la plus forte parmi l’ensemble des catastrophes naturelles. Dans ce contexte, le sujet retenu pour ma thèse est : « Modélisation du risque sismique en assurance : étude d’un nouveau modèle pour une meilleure gestion assurantielle ». Les travaux s’organisent en trois parties : 1° présentation des différents systèmes assurantiels contre le risque sismique à travers le monde ; 2° identification des principaux axes d’amélioration de la modélisation du risque ; 3° propositions pour un nouveau modèle assurantiel.La revue des systèmes d’assurance a porté principalement sur les deux pays suivant: la France et la Californie. En combinant plusieurs variables liées au niveau de risque mis en perspective avec différents indicateurs économiques, nous avons créé une échelle de maturité particulière pour l’assurance sismique. Cet outil permet de mesurer l’évolution, à la hausse comme à la baisse, d’un marché d’assurance. En l’appliquant aux deux pays étudiés, il ressort qu’aucun d’eux n’est équipé d’un système assurantiel durable.En France, le risque de tremblement de terre est couvert par le régime CAT-NAT. En étudiant l’ensemble des communes reconnues en état de catastrophe naturelle à la suite d’un séisme, un modèle probabiliste de reconnaissance d’état de catastrophe naturelle a été développé. En l’appliquant à deux scénarios représentatifs d’un séisme majeur en France métropolitaine, les résultats montrent que l’Etat pourrait être mis en difficulté pour payer la charge des sinistres qui lui incombe dans le cadre du régime CAT-NAT.En Californie, malgré que le risque soit important, seulement 15% de la population est assurée. Nous avons mené une étude différenciant l’effet de la perception du risque sismique par les Californiens de celui du prix de l’assurance. Les résultats montrent que les Californiens n’adhèrent pas aux offres d’assurance à cause de leur prix, et non par sous-estimation du risque. Ce modèle démontre aussi que la majorité des Californiens achèteraient une assurance tremblement de terre si son prix était divisé par trois.D’après l’échelle de maturité préalablement développée, l’évolution de ce système assurantiel demande une meilleure modélisation du risque. Pour cela, nous avons élaboré une nouvelle méthode de comparaison entre des cartes d’aléas probabilistes et les modélisations d’empreintes d’aléa. Le perfectionnement des modèles stochastiques de pertes liées à un tremblement de terre nécessite également de renforcer la relation entre l’échelle de dommages utilisée et les coûts de réparation associés. Pour cela, une base de données des conséquences économiques des séismes passés a été constituée. En outre, un modèle économique a été élaboré pour tester les modèles dommages-coût existants avec les données historiques précédemment collectées.Enfin, la dernière partie de mon travail de thèse porte sur l’étude d’un nouveau modèle assurantiel dans lequel le montant des primes est alloué à chaque bâtiment. Tant qu’un séisme n’endommage pas un bâtiment assuré, le montant des primes est investi pour accroître les ressources disponibles. Quand un séisme survient et endommage un bâtiment assuré, la compagnie d’assurance prend en charge les travaux de réparation ou de reconstruction. Si les ressources accumulées sont suffisamment importantes avant qu’un séisme survienne, celles-ci sont utilisées pour financer des travaux de renforcement parasismique. Le coût associé est alors compensée par le gain de résistance du bâtiment. Cela permet ainsi de diminuer la prime payée par l’assuré et créer une boucle vertueuse de prévention des risques

Modélisation du risque sismique en assurance : étude d’un nouveau modèle pour une meilleure gestion assurantielle

Various scientific reports have highlighted that, both on the history of losses related to natural disasters, and on the average cost expected for the coming years, the seismic risk is the risk for which the uninsured loss is the biggest of all natural disasters. In this context, the subject I chose for my thesis is: " Seismic loss modelling in insurance industry: towards a new model for better claims management". The work is organized in three parts: 1° presentation of the different insurance systems against seismic risk around the world; 2° identification of the main areas to improve risk modelling; 3° proposals for a new insurance model.The insurance systems review focused on the following two countries: France and California. By combining several variables related to the level of risk put into perspective with different economic indicators, we have created a specific maturity scale for seismic insurance. This tool makes it possible to measure the evolution, upwards or downwards, of an insurance market. Applying it to the two countries studied, it appears that none of them is equipped with a sustainable insurance system.In France, the risk of earthquake has been covered by the so-called CAT-NAT scheme. On the basis of all the municipalities declared in natural disaster after an earthquake, a probabilistic model of natural disaster state recognition at the municipality level was developed. By applying it to two representative scenarios of a major earthquake in mainland France, the results show that the state could be put in a difficult position from covering the cost of the disaster under the CAT-NAT scheme.In California, although the risk is significant, only 15% of the population is insured. We conducted a study differentiating the effect of the perception of the seismic risk by the Californians from the price of the insurance. The results show that Californians do not adhere to these insurance plans because of their price, and not by underestimating the risk. This model shows also that the majority of Californians would buy an earthquake insurance if its price was divided by three.According to the scale of maturity previously developed, the evolution of this insurance system requires a better risk modelling. For this reason, we have developed a new method for comparing probabilistic seismic hazards maps with estimated hazard footprints of past earthquakes. Improving stochastic earthquake loss models also requires strengthening the relationship between the damage scale used and associated repair costs. For this reason, a database of the economic consequences of past earthquakes has been created. In addition, an economic model has been developed in order to test existing damage-cost models with historical data previously collected.Finally, the last part of my thesis work focuses on the study of a new insurance model, where the cost of the insurance premiums is allocated to each building. As long as an earthquake does not damage an insured building, premiums are invested to increase the available. When an earthquake occurs and damages an insured building, the insurance company takes care of the repair or reconstruction work. If the accumulated capital is big enough before an earthquake causes damage to a building, it is used to finance seismic retrofitting works. The loss of capital that these generate is then compensated by the resistance gain of the building. This way the cost of the premium paid by the policyholder is reduced and a real commitment to risk prevention is initiated.Plusieurs rapports scientifiques ont mis en évidence que, tant sur l’historique des pertes liées aux catastrophes naturelles, que sur le coût moyen attendu pour les années à venir, le risque sismique est celui pour lequel la perte non-assurée est la plus forte parmi l’ensemble des catastrophes naturelles. Dans ce contexte, le sujet retenu pour ma thèse est : « Modélisation du risque sismique en assurance : étude d’un nouveau modèle pour une meilleure gestion assurantielle ». Les travaux s’organisent en trois parties : 1° présentation des différents systèmes assurantiels contre le risque sismique à travers le monde ; 2° identification des principaux axes d’amélioration de la modélisation du risque ; 3° propositions pour un nouveau modèle assurantiel.La revue des systèmes d’assurance a porté principalement sur les deux pays suivant: la France et la Californie. En combinant plusieurs variables liées au niveau de risque mis en perspective avec différents indicateurs économiques, nous avons créé une échelle de maturité particulière pour l’assurance sismique. Cet outil permet de mesurer l’évolution, à la hausse comme à la baisse, d’un marché d’assurance. En l’appliquant aux deux pays étudiés, il ressort qu’aucun d’eux n’est équipé d’un système assurantiel durable.En France, le risque de tremblement de terre est couvert par le régime CAT-NAT. En étudiant l’ensemble des communes reconnues en état de catastrophe naturelle à la suite d’un séisme, un modèle probabiliste de reconnaissance d’état de catastrophe naturelle a été développé. En l’appliquant à deux scénarios représentatifs d’un séisme majeur en France métropolitaine, les résultats montrent que l’Etat pourrait être mis en difficulté pour payer la charge des sinistres qui lui incombe dans le cadre du régime CAT-NAT.En Californie, malgré que le risque soit important, seulement 15% de la population est assurée. Nous avons mené une étude différenciant l’effet de la perception du risque sismique par les Californiens de celui du prix de l’assurance. Les résultats montrent que les Californiens n’adhèrent pas aux offres d’assurance à cause de leur prix, et non par sous-estimation du risque. Ce modèle démontre aussi que la majorité des Californiens achèteraient une assurance tremblement de terre si son prix était divisé par trois.D’après l’échelle de maturité préalablement développée, l’évolution de ce système assurantiel demande une meilleure modélisation du risque. Pour cela, nous avons élaboré une nouvelle méthode de comparaison entre des cartes d’aléas probabilistes et les modélisations d’empreintes d’aléa. Le perfectionnement des modèles stochastiques de pertes liées à un tremblement de terre nécessite également de renforcer la relation entre l’échelle de dommages utilisée et les coûts de réparation associés. Pour cela, une base de données des conséquences économiques des séismes passés a été constituée. En outre, un modèle économique a été élaboré pour tester les modèles dommages-coût existants avec les données historiques précédemment collectées.Enfin, la dernière partie de mon travail de thèse porte sur l’étude d’un nouveau modèle assurantiel dans lequel le montant des primes est alloué à chaque bâtiment. Tant qu’un séisme n’endommage pas un bâtiment assuré, le montant des primes est investi pour accroître les ressources disponibles. Quand un séisme survient et endommage un bâtiment assuré, la compagnie d’assurance prend en charge les travaux de réparation ou de reconstruction. Si les ressources accumulées sont suffisamment importantes avant qu’un séisme survienne, celles-ci sont utilisées pour financer des travaux de renforcement parasismique. Le coût associé est alors compensée par le gain de résistance du bâtiment. Cela permet ainsi de diminuer la prime payée par l’assuré et créer une boucle vertueuse de prévention des risques

Testing machine learning models for heuristic building damage assessment applied to the Italian Database of Observed Damage (DaDO)

Abstract. Assessing or forecasting seismic damage to buildings is an essential issue for earthquake disaster management. In this study, we explore the efficacy of several machine learning models for damage characterization, trained and tested on the database of damage observed after Italian earthquakes (DaDO). Six regression- and classification-based machine learning models were considered: random forest, gradient boosting and extreme gradient boosting. The structural features considered were divided into two groups: all structural features provided by DaDO or only those considered to be the most reliable and easiest to collect (age, number of storeys, floor area, building height). Macroseismic intensity was also included as an input feature. The seismic damage per building was determined according to the EMS-98 scale observed after seven significant earthquakes occurring in several Italian regions. The results showed that extreme gradient boosting classification is statistically the most efficient method, particularly when considering the basic structural features and grouping the damage according to the traffic-light based system used, for example, during the post-disaster period (green, yellow and red). The results obtained by the machine learning-based heuristic model for damage assessment are of the same order of accuracy as those obtained by the traditional Risk-UE method. Finally, the machine learning analysis found that the importance of structural features with respect to damage was conditioned by the level of damage considered. </jats:p

Comparing Probabilistic Seismic Hazard Maps with ShakeMap Footprints for Indonesia

AbstractA number of probabilistic seismic hazard assessment (PSHA) maps have been released for Indonesia over the past few decades. This study proposes a method for testing PSHA maps using U.S. Geological Survey ShakeMap catalog considered as historical seismicity for Indonesia. It consists in counting the number of sites on rock soil for which the independent maximum peak ground acceleration (PGA) of the ShakeMap footprints between May 1968 and May 2018 exceeds the thresholds from the PSHA map studied and in comparing this number with the probability of exceedance given in the PSHA map. Although ShakeMap footprints are not as accurate and complete as continuous recorded ground motion, the spatially distributed ShakeMap covers 7,642,261 grid points, with a resolution of 1  km2, compensating the lack of instrumental data over this period. This data set is large enough for the statistical analysis of independent PGA values on rock sites only. To obtain the subdata set, we develop a new selection process and a new comparison method, considering the uncertainty of ShakeMap estimates. The method is applied to three PSHA maps (Global Seismic Hazard Assessment Program [GSHAP], Global Assessment Report [GAR], and Standar Nasional Indonesia [SNI2017]) for a selection of sites first located in Indonesia and next only in the western part of the country. The results show that SNI2017 provides the best fit with seismicity over the past 50 yr for both sets of rock sites (whole country and western part only). At the opposite, the GAR and GSHAP seismic hazard maps only fit the seismicity observed for the set of rock sites in western Indonesia. This result indicates that this method can only conclude on the spatial scale of the analysis and cannot be extrapolated to any other spatial resolution.</jats:p

An analysis of the California earthquake insurance market since its early stages

&amp;lt;p&amp;gt;Despite California being a highly seismic prone region, around 85% of people are not covered against this risk. This situation results from more than 100 years of evolution since the first earthquake insurance cover after the 1906 San Francisco earthquake. To understand this evolution, two analyses have been performed: the first one at the market level and the second one at the insured people level.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;At the market level, as many variables as the premium amount, the risk monitoring, the funding sources of prevention plans, the insurance company&amp;amp;#8217;s solvency and the attractiveness of earthquake insurance solutions, have been investigated. By cross-analysing data collected and analysing the evolution with time, three different phases have been identified in the earthquake insurance market history.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;At insured people level, a database is built from 18 different data sources about earthquake insurance, gathering data since 1921. Next, a new model is developed to assess the rate of homeowners insured against this risk, according to their risk awareness and the average annual insurance premium amount.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;These two analyses are finally used to investigate in which extent the California earthquake insurance market could reach again 40% of people insured, like in 1993 and 1996. Even if results show that a widespread belief that a devastating earthquake is imminent could bring such a situation, only a new earthquake insurance model will allow to achieve this goal in a sustainable way. In that respect, the efficiency of two current initiatives to bring more people to get an earthquake insurance: &amp;quot;Earthquake Brace and Bolt&amp;quot; and &amp;quot;JumpStart Recovery&amp;quot;, is assessed at the light of the analyses performed previously in this paper.&amp;lt;/p&amp;gt; </jats:p