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The present work proposes a simulation-based Bayesian method for parameter estimation and fragility model selection for mutually exclusive and collectively exhaustive (MECE) damage states. This method uses an adaptive Markov chain Monte Carlo simulation (MCMC) based on likelihood estimation using point-wise intensity values. It identifies the simplest model that fits the data best, among the set of viable fragility models considered. The proposed methodology is demonstrated for empirical fragility assessments for two different tsunami events and different classes of buildings with varying numbers of observed damage and flow depth data pairs. As case studies, observed pairs of data for flow depth and the corresponding damage level from the South Pacific tsunami on 29 September 2009 and the Sulawesi–Palu tsunami on 28 September 2018 are used. Damage data related to a total of five different building classes are analysed. It is shown that the proposed methodology is stable and efficient for data sets with a very low number of damage versus intensity data pairs and cases in which observed data are missing for some of the damage levels.

SUMMARY The reliable forecasting of seismic sequences following a main shock has practical implications because effective post-event response is crucial in earthquake-stricken regions, aftershocks can progressively cause increased damage and compound economic losses. In the South Iceland Seismic Zone (SISZ), one of two large transform zones in Iceland where earthquake hazard is the highest, an intense seismic sequence took place during 17–24 June 2000, starting with a ${M}_{\rm{w}}$ 6.4 main shock on 17 June 2000, followed by another ${M}_{\rm{w}}$ 6.5 main shock four days later and on a different fault. Both earthquakes caused considerable damage and incurred heavy economic losses. They were immediately followed by intense aftershock activity on the causative faults and triggered earthquakes as far as 80 km away along the transform zone. To investigate the feasibility of forecasting the progression of such complex sequences, we calibrated a spatio-temporal epidemic-type aftershock sequence (ETAS) clustering model to the June 2000 seismic sequence in the framework of Bayesian statistics. Short-term seismicity forecasts were carried out for various forecasting intervals and compared with the observations, the first generated a few hours after the first main shock and followed by daily forecasts. The reliability of the early forecasts was seen to depend on the initial model parameters. By using an adaptive parameter inference approach where the posteriors from each preceding forecasting interval served as informative priors for the next, the fast convergence of the parametric values was ensured. As a result, the 16–84 percentile range of the forecasted number of events captured the actual number of observed events in all daily forecasts, and the model exhibited a strong spatial forecasting ability, even only a few hours after the main shock, and over all subsequent daily forecasts. We present the spatio-temporal ETAS parameters for the June 2000 sequence as ideal candidates of prior estimates for future operational earthquake forecasting of other Icelandic aftershock sequences. Past seismic sequences need to be analysed retrospectively to confirm the stability of the parameters of this study, effectively enable the application of the Bayesian ETAS model as an operational earthquake forecasting system for aftershocks in Iceland.

Forecasting the spatio-temporal occurrence of events is at the core of Operational Earthquake Forecasting, which is of great interest for risk management, particularly during ongoing seismic sequences. Epidemic type aftershock sequence (ETAS) models are powerful tools to estimate the occurrence of events during earthquake sequences. In this context, a robust seismicity forecasting framework based on Bayesian-inference has been adapted to the Patras and Aegio region in western Greece (one of the most seismically active parts of Mediterranean), and an incremental adaptive algorithm is introduced to train the priors for ETAS model parameters. The seismicity forecasting is capable of accounting for uncertainty in the model parameters as well as variations in the sequence of events that may happen during the forecasting interval. Six seismic sequences between 1995 and 2018 were selected with main shock moment magnitudesMw≥6.0. The ETAS model was adapted for each seismic sequence. The number of forecasted events withMw≥4.5 and their spatial distribution was retrospectively compared with the as-recorded earthquake catalogue, confirming a good agreement between the forecasts and observations. The results show that the adapted model can be used immediately after a severe main shock to statistically predict potentially damaging earthquakes during the ongoing seismic sequence. The seismicity forecasts were translated to short-term daily exceedance rates for different thresholds of peak ground acceleration. The results reveal that the seismic hazard increased by up to 33 times in the case of the damaging 1995Mw6.5 earthquake in the city of Aegio. However, the results confirmed that in all six studied sequences, the increased seismic hazard decayed rapidly during the 2 d after the main shock, and remained relatively high in the following days (roughly ten times the long-term time-independent hazard).

Special issue EPOS a Research Infrastructure in solid Earth: open science and innovation .-- 21 pages, 8 figures, Tsunamis constitute a significant hazard for European coastal populations, and the impact of tsunami events worldwide can extend well beyond the coastal regions directly affected. Understanding the complex mechanisms of tsunami generation, propagation, and inundation, as well as managing the tsunami risk, requires multidisciplinary research and infrastructures that cross national boundaries. Recent decades have seen both great advances in tsunami science and consolidation of the European tsunami research community. A recurring theme has been the need for a sustainable platform for coordinated tsunami community activities and a hub for tsunami services. Following about three years of preparation, in July 2021, the European tsunami community attained the status of Candidate Thematic Core Service (cTCS) within the European Plate Observing System (EPOS) Research Infrastructure. Within a transition period of three years, the Tsunami candidate TCS is anticipated to develop into a fully operational EPOS TCS. We here outline the path taken to reach this point, and the envisaged form of the future EPOS TCS Tsunami. Our cTCS is planned to be organised within four thematic pillars: (1) Support to Tsunami Service Providers, (2) Tsunami Data, (3) Numerical Models, and (4) Hazard and Risk Products. We outline how identified needs in tsunami science and tsunami risk mitigation will be addressed within this structure and how participation within EPOS will become an integration point for community development, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive kernel Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017-2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively.

Introduction: Nowadays, comparing the effectiveness of treatment methods to reduce the psychological problems of patients with diabetes is considered by various psychologists. The aim of this study was to compare the effectiveness of compassion therapy and cognitive-behavioral therapy in emotional schemas and resilience in patients with diabetes Type 2 Materials and Methods: The research method is applied in terms of purpose, in terms of implementation of an all-experimental experiment with pre-test, post-test and follow-up design. The statistical population of this study included all diabetic patients referred to Pasteur Hospital in Bam in the second half of 2018 who met the inclusion criteria with a total of 152 people. Sampling method was available. From the statistical population, 45 people were selected and randomly divided into three groups (15 people in cognitive-behavioral therapy group, 15 people in compassion therapy and 15 people in control group). Subjects answered Connor and Davidson Resilience Questionnaires and Leahy Emotional Schemas before and after training. Data were analyzed using covariance .... multivariate method and Tukey's follow-up test. Results: Findings showed that compassion therapy and cognitive-behavioral therapy improve emotional schemas and increase resilience. The effect of compassion therapy was greater than cognitive-behavioral therapy and maintained its effect over time. Conclusion: The results showed that compassion therapy had a greater effect on improving emotional schemas and increasing resilience in patients with type 2 diabetes. Keywords: Cognitive-behavioral therapy, compassion therapy, emotional schemas, resilience

The present work proposes a simulation-based Bayesian method for parameter estimation and fragility model selection for mutually exclusive and collectively exhaustive (MECE) damage states. This method uses an adaptive Markov chain Monte Carlo simulation (MCMC) based on likelihood estimation using point-wise intensity values. It identifies the simplest model that fits the data best, among the set of viable fragility models considered. The proposed methodology is demonstrated for empirical fragility assessments for two different tsunami events and different classes of buildings with varying numbers of observed damage and flow depth data pairs. As case studies, observed pairs of data for flow depth and the corresponding damage level from the South Pacific tsunami on 29 September 2009 and the Sulawesi–Palu tsunami on 28 September 2018 are used. Damage data related to a total of five different building classes are analysed. It is shown that the proposed methodology is stable and efficient for data sets with a very low number of damage versus intensity data pairs and cases in which observed data are missing for some of the damage levels.

In performance-based earthquake engineering, the suitability of an intensity measure (IM) is expressed through its efficiency and sufficiency. An efficient IM leads to a small record-to-record variability in the estimation of demand given seismic intensity. A sufficient IM is one that renders the estimation of demand for all intensity levels independent of all other ground motion parameters. Given that establishing sufficiency is not a trivial task, the relative sufficiency measure (RSM) has been proposed previously based on information theory concepts. RSM can be employed for quantifying the relative sufficiency of an IM with respect to another IM by the amount of extra information that it relays on average about the ground motion for the estimation of a demand parameter of interest. RSM has been so far conditioned on a linear logarithmic regression probability model, better known as the Cloud Analysis (CA), which relies on unscaled ground-motion records. This work lays out the methodology for estimating both the efficiency and RSM in terms of the damage measure directly (instead of demand parameter) and by employing alternative nonlinear dynamic analysis procedures (NDAPs), such as, a modified version of CA that considers the collapse-cases explicitly and the incremental dynamic analysis. It is demonstrated that the RSM can be sensitive to the NDAP employed, while it does not demonstrate significant sensitivity to the limit state of interest. An alternative measure of efficiency (known in literature as proficiency), directly measured as the dispersion in the fragility curve, shows more sensitivity to the limit state.

Recent earthquakes have exposed the vulnerability of existing buildings; this is demonstrated by damage incurred after moderate-to-high magnitude earthquakes. This stresses the need to exploit available data from different sources to develop reliable seismic risk components. As far as it regards empirical fragility assessment, accurate estimation of ground-shaking at the location of buildings of interest is as crucial as the accurate evaluation of observed damage for these buildings. This implies that explicit consideration of the uncertainties in the prediction of ground shaking leads to more robust empirical fragility curves. In such context, the simulation-based methods can be employed to provide fragility estimates that integrate over the space of plausible ground-shaking fields. These ground-shaking fields are generated according to the joint probability distribution of ground-shaking at the location of the buildings of interest considering the spatial correlation structure in the ground motion prediction residuals and updated based on the registered ground shaking data and observed damage. As an alternative to the embedded coefficients in the ground motion prediction equations accounting for subsoil categories, stratigraphic coefficients can be applied directly to the ground motion fields at the engineering bedrock level. Empirical fragility curves obtained using the observed damage in the aftermath of Amatrice Earthquake for residential masonry buildings show that explicit consideration of the uncertainty in the prediction of ground-shaking significantly affects the results.

Quantitative safety checking is an essential part of performance-based design and retrofit of new and existing construction. The intensity-based demand and capacity factor design (DCFD) is a practical closed-form safety-checking format that lends itself quite well to visual interpretation. Adopting the critical demand to capacity ratio as a global damage measure directly, skipping the engineering demand parameter, helps in identifying the onset of the prescribed performance levels. For each intensity level, the contribution to the error in the DCFD format in logarithmic domain is visualized as the distance between the hazard curve and its tangent at median intensity at the onset of the performance level weighted by the probability density of the intensity-based capacity. The latter reaches its maximum value at the median intensity at the onset of the performance level, where the error in hazard is zero, and decays with a rate that depends on the logarithmic standard deviation of fragility. The proposed intensity-based DCFD provides accurate safety-checking estimates that are always on the safe side for concave mono-curvature hazard curves in the logarithmic scale.

The injection of fluids into the subsoil, which takes place during the exploitation of geothermal resources, may affect the quality of the air and the water in the subsoil, and it may be the source of induced earthquakes. A decision support system based on multi-hazard risk (seismic and environmental) analysis can help control the exploitation and keep risk in tolerable levels. In this work, we describe a traffic light system of this type applied in the case of the Basel geothermal site. This system relies on a Bayesian network to compute the probability of the red-amber-green lights for a given distribution of the rate of injection of fluids. The network has a single parent node for the injection rate. Here, we used data on European Plate Observing System Thematic Core Service for Anthropogenic Hazards for the injection rate, the air quality, and the water quality to characterize the links between the parent node and the nodes for air and water quality. The link between the injection rate and the node for the ground motion intensity measure was characterized based on an event-based probabilistic seismic hazard analysis using stochastic earthquake catalogs generated by a physics-based model. Subsequently, arbitrary thresholds of the peak ground velocity, steam production rate, and proppant injection rate, were selected for the amber and red lights to characterize the links between the corresponding nodes and their descendant node, i.e., the traffic light. The probabilities of the traffic lights were computed, and the probability of the red light was disaggregated to identify its driver in different scenarios.

Methodology:A fragility model expresses the probability of exceeding certain damage levels for a given level of intensity for a specific class of buildings or infrastructure. An empirical tsunami fragility curve for a given damage level is derived based on observed pairs of data for the tsunami intensity measure and the corresponding damage level. Tsunami inundation depth and/or flow velocity are usually adopted as scalar intensity measures (they can also be employed together as a vector-valued intensity measure). Physical damage levels are usually defined in a hierarchical manner, implying discrete, mutually exclusive, and collectively exhaustive (MECE) damage states. This means that the fragility curves for consecutive hierarchical damage levels must not intersect. It is clear that by fitting empirical fragility curves to each single damage level, this condition is not automatically satisfied. To overcome this problem, ordered (“parallel”) fragility models or partially ordered models have been adopted in the literature to derive fragility curves for MECE damage states. Empirical tsunami fragility curves are usually constructed using generalized linear regression models by adopting probit, logit, or the complementary loglog link functions. As far as model comparison and selection are concerned, established statistical approaches have been used in recent literature to identify the optimal link function among those mentioned above. Moreover, for estimating the uncertainty in the resulting empirical fragility curves, bootstrap resampling has been commonly used.The present work proposes a simulation-based Bayesian method for inference and model class selection to perform ensemble modelling of the tsunami fragility curves for MECE damage states and the related uncertainties for a given class of buildings. The method uses adaptive Markov Chain Monte Carlo Simulation (MCMC), based on likelihood estimation using point-wise intensity values, to estimate the fragility model parameters and the uncertainties. Among the set of viable fragility models considered, Bayesian model class selection is used to identify the simplest model that fits the data best (i.e., is a parsimonious model). The proposed method provides consistent parameter estimation and confidence intervals for MECE the damage states and identifies the best fragility model class among the pool of viable models, based on a single set of simulation realizations. The whole procedure is provided as open-source software on the site of the European Tsunami Risk Service (https://eurotsunamirisk.org/software/) and is also available as a standalone docker application.Application:As the case-study application, the central South Pacific region-wide tsunami on September 29, 2009 is used. The tsunami was triggered by an unprecedented earthquake doublet (Mw 8.1 and Mw 8.0). The tsunami seriously impacted numerous locations in the central South Pacific. Herein, the damage data related to 120 brick masonry residential buildings associated with the reconnaissance survey sites of American Samoa and Samoa islands were utilized as a proof of concept. A six-tier damage scale is considered, and tsunami inundation depth has been used as the intensity measure. Keywords: probabilistic tsunami risk analysis, tsunami fragility, Bayesian inference, model class selection

Methodology:Induced earthquakes have peculiar characteristics such as, relatively shallow depths, small magnitude, correlation with field operations, non-GR recurrence law, and eventually non-homogenous Poisson recurrence time. Indeed, induced seismicity tends to cluster in limited volumes near the wells where field operations (e.g., fluids injection, extraction, fracking, etc.) are performed. A novel and fully-probabilistic simulation-based procedure is presented for providing temporal and volumetric predictions of induced events’ occurrence in a prescribed forecasting time interval (in the order of hours or days). The procedure aims at exploiting the information provided by the ongoing sequence in quasi-real time (even in the presence of very limited registered data) to adaptively update the seismicity forecasts based on the incoming information as it becomes available. The clustering of seismic events in volume (3D seismicity) and time is modelled based on an Epidemic Type Aftershock Sequence (ETAS) model. The proposed 3D ETAS model encompasses a decoupled depth-area volumetric probabilistic kernel that incorporates kernel density functions for areal extent as well as the focal depth. The ETAS parameters are going to be re-calibrated in order to take into account non-GR long-term temporal boundary conditions in case of induced seismicity. Moreover, exact spatial integrals will be used to consider the 3D boundary conditions. The proposed procedure considers the uncertainties in the earthquake occurrence model parameters in a Bayesian updating framework. Pairing up the Bayesian inference and the suitable efficient simulation schemes (using Markov Chain Monte Carlo Simulation) provides the possibility of performing the forecasting procedure with minimum (or no) need of human interference.Application:The procedure is demonstrated through retrospective forecasting of induced seismicity recorded at the Geysers geothermal field in northern California in the time period of 2011-2015. Injection of cold water and heavier liquids in the hot reservoir caused induced earthquakes with moment magnitudes in the range of [0.0, 4.0] and depth ranging up to 5 km. The proposed procedure is examined for both Bayesian updating of the proposed 3D ETAS model parameters and forecasting of the number of events of interest expected to occur in various time intervals before and after a number of main events within the seismic sequence. The seismicity is predicted within a confidence interval from the mean estimate. Adding a kernel density for the focal depth and moving towards the 3D seismicity forecasting leads to the forecasted number of events that better match the events that actually took place in the forecasting interval, as compared to the 2D ETAS model. Therefore, it is concluded that the proposed 3D ETAS model is quite effective in case of induced seismicity.

Probabilistic tsunami hazard and risk analysis (PTHA/PTRA) is an emerging scientific discipline within the tsuanmi community and allows potentially to incorporate the diverse sources of uncertainty into disaster prevention, preparedness, and mitigation activities. While there are a number of successful applications of this paradigm, it is still an emerging field with a number of unresolved research questions. In a collaborative effort members of the COST Action AGITHAR assessed the existing research gaps for PTHA/PTRA and identified almost 50 different topics worth of further research. An ad hoc expert judgement was conducted to weight these open questions with respect to their expected impact on the quality of the PTHA/PTRA results and their difficulty to be answered. The results of this collaborative effort will be reported highlighting the most challenging and most severe research gaps.The presentation is based on the following publication:J. Behrens, F. Løvholt, F. Jalayer, et al. (2021): Probabilistic Tsunami Hazard and Risk Analysis – A Review of Research Gaps, Frontiers in Earth Science, 9:114, DOI:10.3389/feart.2021.628772.

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.pdf Copyright © 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.

Probabilistic seismic hazard analysis (PSHA) encompasses quantitative estimation of seismic hazard at a site by considering all plausible earthquake scenarios. The outcome of a PSHA is often reported as the mean rate of exceeding a specific ground motion intensity measure at a given site. This study attempts to perform PSHA for the western area of the city Naples (southern Italy) by employing the most advanced methods and new databases; namely, DISS3.2 (Database of Individual Seismogenic Sources) and CPTI15 (Parametric Catalogue of Italian Earthquakes). Seismogenic models include individual seismogenic structures/faults liable to generating major earthquakes with magnitude greater than 5.5, and background areal source model to evaluate the effect of earthquakes with magnitude less than 5.5. The PSHA is built up based on the long-term earthquake recurrence on seismogenic tectonic faults and the spatial distribution of historical earthquakes. Site amplification is considered based on seismic microzonation maps derived for the western area of Naples. The microzonation maps delineate expected levels of ground motion amplification based on reliable geological and geotechnical subsoil models. Hazard maps are derived for a number of return periods for ground-shaking in terms of peak ground acceleration and 5%-damped pseudo-spectral acceleration at a range of periods that are representative of the existing construction within the area. Detailed comparisons of the PSHA results with Italian national hazard maps and the code-based design spectra emphasize the importance of performing site-specific PSHA with explicit consideration of site effects.

On Sunday November 12, 2017, at 18:18:16 UTC, (21:48:16 local time), a strong earthquake with Mw7.3 occurred in western Iran in the border region between Iran and Iraq in vicinity of the Sarpol-e Zahab town. Unfortunately, this catastrophic seismic event caused 572 causalities, thousands of injured and vast amounts of damage to the buildings, houses and infrastructures in the epicentral area. The mainshock of this seismic sequence was felt in the entire western and central provinces of Iran and surrounding areas. The main event was preceded by a foreshock with magnitude 4.5 about 43 minutes before the mainshock that warned the local residence to leave their home and possibly reduced the number of human casualties. More than 2500 aftershocks with magnitude greater than 2.5 have been reported up to January 2019 with the largest registered aftershock of Mw6.4. A novel and fully-probabilistic procedure is adopted for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval (in the order of hours or days). The procedure aims at exploiting the information provided by the ongoing seismic sequence in quasi-real time. The versatility of the Bayesian inference is exploited to adaptively update the forecasts based on the incoming information as it becomes available. The aftershock clustering in space and time is modelled based on an Epidemic Type Aftershock Sequence (ETAS). One of the main novelties of the proposed procedure is that it considers the uncertainties in the aftershock occurrence model and its model parameters. This is done by moving within a framework of robust reliability assessment which enables the treatment of uncertainties in an integrated manner. Pairing up the Bayesian robust reliability framework and the suitable simulation schemes (Markov Chain Monte Carlo Simulation) provides the possibility of performing the whole forecasting procedure with minimum (or no) need of human interference. The fully simulation-based procedure is examined for both Bayesian model updating of ETAS spatio-temporal model and robust operational forecasting of the number of events of interest expected to happen in various time intervals after main events within the sequence. The seismicity is predicted within a confidence interval from the mean estimate.

The uncertainty in the concrete compressive strength is one of the most challenging issues in the seismic assessment of an existing RC building. This works characterizes the compressive concrete strength based on both in-situ destructive and non- destructive tests. A large set of data including (destructive) core tests and SONREB (rebound number S and ultrasonic velocity V) non-destructive test results, measured on the same structural elements of different existing RC buildings, has been collected. Then, a regression model is employed in order to derive predictive expressions for calculating the compressive concrete strength Rc based on the ultrasonic velocity V and on the rebound number S measurements. The work shows also that linear logarithmic regression (V-Rc) can be used instead of the multilinear logarithmic regression (V-S-Rc) without significant loss of accuracy.

Italian code provides the option for non-linear time-history analysis for the multi-degree-of-freedom (MDOF) structures (NTC). A non-linear time-history analysis procedure known as “Cloud Analysis” employs the struc- tural response to a suite of un-scaled ground motion records. Cloud Analysis results can be directly implemented in es- tablished procedures for performance-based safety-checking of existing buildings. It can be shown that the Cloud Anal- ysis lends itself quite well also to NTC-based safety-checking with non-linear dynamic analysis. A closed-form factor can quantify the ratio between performance-based and NTC-based safety-checking global demand to capacity ratios for the structure. The procedure is demonstrated for a nonlinear model with distributed plasticity of a frame belonging to an existing RC school structure modelled considering the flexural-shear-axial interactions.

The Italian code requires spectrum compatibility with mean spectrum for a suite of accelerograms selected for time-history analysis. Although these requirements define minimum acceptability criteria, it is likely that code-based non-linear dynamic analysis is going to be done based on limited number of records. Performance-based safety-checking provides formal basis for addressing the record-to-record variability and the epistemic uncertainties due to limited number of records and in the estimation of the seismic hazard curve. “Cloud Analysis” is a non-linear time-history analysis procedure that employs the structural response to un-scaled ground motion records and can be directly implemented in performance-based safety-checking. This paper interprets the code-based provisions in a performance-based key and applies further restrictions to spectrum-compatible record selection aiming to implement Cloud Analysis. It is shown that, by multiplying a closed-form coefficient, code-based safety ratio could be transformed into simplified performance-based safety ratio. It is shown that, as a proof of concept, if the partial safety factors in the code are set to unity, this coefficient is going to be on average slightly larger than unity. The paper provides the basis for propagating the epistemic uncertainties due to limited sample size and in the seismic hazard curve to the performance-based safety ratio both in a rigorous and simplified manner. If epistemic uncertainties are considered, the average code-based safety checking could end up being unconservative with respect to performance-based procedures when the number of records is small. However, it is shown that performance-based safety checking is possible with no extra structural analyses.

Methodology:The implementation of short-term forecasts for emergency response management in the immediate aftermath of a seismic event, and in the presence of an ongoing seismic sequence, requires two basic components: scientific advisories expressed in terms of risk assessment, and protocols that establish how the scientific results can be translated into decisions/actions for risk mitigation. The operational earthquake forecasting framework is geared towards providing scientific advisories in the form of time-dependent probabilities expressing seismicity, hazard and risk that can be practically translated into decisions. Considering the triggered sequence of aftershocks in the process of post-event decision-making and prioritization of emergency operations still seems to need and to deserve much more attention. To this end, the adopted novel and fully-probabilistic procedure succeeds in providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval (in the order of hours or days). The procedure aims at exploiting the information provided by the ongoing seismic sequence in quasi-real time considering the time needed for registering and transmitting the data. The versatility of the Bayesian inference is exploited to adaptively update the forecasts based on the incoming information as it becomes available. The aftershock clustering in space and time is modelled based on an Epidemic Type Aftershock Sequence (ETAS). One of the main novelties of the proposed procedure is that it considers the uncertainties in the aftershock occurrence model and its model parameters. This is done by moving within a framework of robust reliability assessment which enables the treatment of uncertainties in an integrated manner. Pairing up the Bayesian robust reliability framework and the suitable simulation schemes (Markov Chain Monte Carlo Simulation) provides the possibility of performing the whole forecasting procedure with minimum (or no) need of human interference.Application:This procedure is demonstrated through a retrospective application to early forecasting of seismicity associated with the 2017 Sarpol-e Zahab seismic sequence activities. On Sunday November 12, 2017, at 18:18:16 UTC, (21:48:16 local time), a strong earthquake with Mw7.3 occurred in western Iran in the border region between Iran and Iraq in vicinity of the Sarpol-e Zahab town. Unfortunately, this catastrophic seismic event caused 572 causalities, thousands of injured and vast amounts of damage to the buildings, houses and infrastructures in the epicentral area. The mainshock of this seismic sequence was felt in the entire western and central provinces of Iran and surrounding areas. The main event was preceded by a foreshock with magnitude 4.5 about 43 minutes before the mainshock that warned the local residence to leave their home and possibly reduced the number of human casualties. More than 2500 aftershocks with magnitude greater than 2.5 have been reported up to January 2019 with the largest registered aftershock of Mw6.4. The fully simulation-based procedure is examined for both Bayesian model updating of ETAS spatio-temporal model and robust operational forecasting of the number of events of interest expected to happen in various time intervals after main events within the sequence. The seismicity is predicted within a confidence interval from the mean estimate.

Modelling uncertainty can significantly affect the structural seismic reliability assessment. However, the limit state excursion due to this type of uncertainty may not be described by a Poisson process as it lacks renewal properties with the occurrence of each earthquake event. Furthermore, considering uncertainties related to ground motion representation by employing recorded ground motions together with modelling uncertainties is not a trivial task. Robust fragility assessment, proposed previously by the authors, employs the structural response to recorded ground motion as data in order to update prescribed seismic fragility models. Robust fragility can be extremely efficient for considering also the structural modelling uncertainties by creating a dataset of one-to-one assignments of structural model realizations and as-recorded ground motions. This can reduce the computational effort by more than 1 order of magnitude. However, it should be kept in mind that the fragility concept itself is based on the underlying assumption of Poisson-type renewal. Using the concept of updated robust reliability, considering both the uncertainty in ground motion representation based on as-recorded ground motion and non ergodic modelling uncertainties, the error introduced through structural reliability assessment by using the robust fragility is quantified. It is shown through specific application to an existing RC frame that this error is quite small when the product of the time interval and the standard deviation of failure rate is small and is on the conservative side.

Flood risk maps for the built environment can be obtained by integrating geo-spatial information on hazard, vulnerability and exposure. They provide precious support for strategic urban planning and decision-making. These maps, generated in a probabilistic framework, can consider various sources of uncertainty in the flood risk assessment such as the occurrence of extreme flooding events, future land use and land cover, characteristics of the buildings, and exposure to flooding. This paper investigates how these maps can be used in complicated urban context such as developing countries, where engineers are forced to work with scarce or little data. Specifically, a detailed investigation on the city of Addis Ababa, Ethiopia, has been conducted. Although the city of Addis Ababa is undergoing extensive formal housing development, it is most likely that the informal settlements will continue to constitute a significant portion of urban housing landscape in the years to come. Recent research findings and field work from a large project (FP7-CLUVA) are employed in order to provide a quantified basis for decision-making between alternative adaptation strategies for informal buildings in Addis Ababa. Risk maps, obtained by up-scaling more accurate risk assessment results at neighborhood level, are adopted for risk zoning of the urban residential texture within the city. This provides risk-based criteria for both identifying suitable flood adaptation strategies and prioritizing between viable risk mitigation measures.

Delineation of flood risk hotspots can be considered as one of the first steps in an integrated methodology for urban flood risk management and mitigation. This paper presents a step-by-step methodology in a GIS-based framework for identifying flooding risk hotspots for residential buildings. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. The novelty of this paper consists in the fact that the flood-prone areas (the TWI thresholds) are identified through a maximum likelihood method (MLE) based both on inundation profiles calculated for a specific return period (TR), and on information about the extent of historical flooding in the area of interest. Furthermore, Bayesian parameter updating is employed in order to estimate the TWI threshold by employing the historical extent as prior information and the inundation map for calculating the likelihood function. For different statistics of the TWI threshold, the map of potentially flood-prone areas is overlaid with the map of residential urban morphology units in order to delineate the residential flooding risk urban hotspots. Overlaying the delineated urban hotspots with geo-spatial census datasets, the number of people affected by flooding is estimated. These kind of screening procedures are particularly useful for locations where there is a lack of detailed data or where it is difficult to perform accurate flood risk assessment. In fact, an application of the proposed procedure is demonstrated for the identification of urban flooding risk hotspots in the city of Ouagadougou, capital of Burkina Faso, a city for which the observed spatial extent of a major flood event in 2009 and a calculated inundation map for a return period of 300 years are both available.

It is desirable that nonlinear dynamic analyses for structural fragility assessment are performed using unscaled ground motions. The widespread use of a simple dynamic analysis procedure known as Cloud Analysis, which uses unscaled records and linear regression, has been impeded by its alleged inaccuracies. This paper investigates fragility assessment based on Cloud Analysis by adopting, as the performance variable, a scalar demand to capacity ratio that is equal to unity at the onset of limit state. It is shown that the Cloud Analysis, performed based on a careful choice of records, leads to reasonable and efficient fragility estimates. There are 2 main rules to keep in mind for record selection: to make sure that a good portion of the records leads to a demand to capacity ratio greater than unity and that the dispersion in records' seismic intensity is considerable. An inevitable consequence of implementing these rules is that one often needs to deal with the so‐called collapse cases. To formally consider the collapse cases, a 5‐parameter fragility model is proposed that mixes the simple regression in the logarithmic scale with logistic regression. The joint distribution of fragility parameters can be obtained by adopting a Markov Chain Monte Carlo simulation scheme leading directly to the fragility and its confidence intervals. The resulting fragility curves compare reasonably with those obtained from the Incremental Dynamic Analysis and Multiple Stripe Analysis with (variable) conditional spectrum–compatible suites of records at different intensity levels for 3 older reinforced concrete frames with shear‐, shear‐flexure‐, and flexure‐dominant behavior.

Evaluation of the uncertainty related to concrete compressive strength is one of the important aspects in the seismic assessment of an existing RC building. This works aims to characterize the compressive concrete strength based on both in-situ destructive and non-destructive test measurements. A large database of the (destructive) core tests and SONREB (hammer rebound and ultrasonic pulse measurements) non-destructive test results for the same structural elements has been gathered from different existing RC buildings mainly located in Campania region, Italy and constructed in the last century between the decades 30’ and 90’. A regression model is employed in order to derive predictive expressions for calculating the compressive concrete strength based on the ultrasonic test measurements. The paper further investigates the quantification in terms of relative weights of the concrete strength based on ultrasonic test results for seismic performance assessment of existing RC buildings. The calculated relative weights for ultrasonic tests are compared with the NTC 2018 code recommended relative weights of 1 and 1/3 for destructive and non-destructive tests, relatively.

The first days elapsed after the occurrence of an earthquake and its triggered aftershocks are crucial in terms of emergency decision-making. To this end, the adopted novel and fully-probabilistic procedure succeeds in providing spatio-temporal predictions of aftershock oc-currence in a prescribed forecasting time interval (in the order of hours or days). The proce-dure aims at exploiting the information provided by the ongoing seismic sequence in quasi-real time. The versatility of the Bayesian inference is exploited to adaptively update the fore-casts based on the incoming information as it becomes available. The aftershock clustering in space and time is modelled based on an Epidemic Type Aftershock Sequence (ETAS) model. One of the main novelties of the proposed procedure is that it considers the uncertainties in the aftershock occurrence model and its model parameters. This is done by pairing up the Bayesian robust reliability framework and the suitable simulation schemes (Markov Chain Monte Carlo Simulation) provides the possibility of performing the whole forecasting proce-dure with minimum (or no) need of human interference. This procedure is demonstrated through a retrospective spatio-temporal early forecasting of seismicity associated with the 2016 Amatrice-Norcia seismic sequence in central Italy. Seismicity forecasts are issued with various time intervals in the first few days after the main events within the sequence.

Incremental dynamic analysis (IDA) is the most frequently used non-linear dynamic analysis procedure for seismic fragility assessment. Nevertheless, its implementation involves compli-cations such as heavy computational burden and potential bias in the results due to excessive scaling of the ground motion records. Cloud Analysis is an alternative nonlinear dynamic analysis procedure based on the structural response to as-recorded ground motions. Cloud Analysis does not suffer from the above-mentioned complications for IDA; however, it may lead to results that reveal too much sensitivity to the choice of the ground motion records. Anovel hybrid nonlinear dynamic analysis procedure referred to as “Cloud to IDA” exploits the predictive capacity provided by Cloud Analysis to perform Incremental Dynamic Analysis (IDA) in a very efficient manner and with very little amount of scaling --without any loss of accuracy with respect to IDA. The procedure adopts as a systemic damage measure the criti-cal demand to capacity ratio (DCR). This facilitates to a great extent identification of the in-tensity values at the onset of the limit state (where DCR is equal to unity by definition) and thereby the implementation of the IDA procedure. Cloud to IDA procedure is applied to the transverse perimeter frame of an older seven-storey reinforced concrete building in Van Nuys, US. This frame is modeled in OpenSees with fiber sections considering flexural-shear-axial interactions and bar slip due to fixed-end rotations. The proposed Cloud to IDA leads to results that are identical to IDA, when the same set of records are used. All of this is possible with a number of analyses that is sensibly lower with respect to IDA.

The original N2 method for reasonably regular buildings oscillating predominantly in a single mode envisioned a two-stage analysis where the first stage involved the non-linear static analysis of the MDOF system using alternative monotonically increasing lateral load patterns and the second stage involved the non-linear time-history analysis of the equivalent SDOF system. Today, the N2 method constitutes the backbone of Eurocode 8 and the Italian building Code (NTC2018) as far as it regards the seismic design and assessment of buildings. In the codebased approach, the equivalent SDOF system is defined as an elastic-perfectly-plastic model and the structural analysis is performed through the inelastic design spectrum. On the other hand, non-linear dynamic time-history analysis of the MDOF system subjected to un-scaled ground motion records, better known as the Cloud Analysis, is noteworthy due to its simplicity, relatively small number of records employed and its preservation of the original frequency content. A modified version of the Cloud Analysis can effectively consider the cases of structural collapse and/or numerical non-convergence. This modified version of the Cloud analysis proves ideal for implementation in the context of the N2 method and the code-based provisions since it can be applied as a dynamic analysis procedure directly to the code-based equivalent elasticperfectly- plastic SDOF model. This method can be applied for seismic design and assessment both for the serviceability and the ultimate limit states. The proposed non-linear dynamic analysis procedure is applied to a frame belonging to a pre-seismic code school building in Avellino (Campania), located in southern Italy. The frame is modelled by considering the interaction between the shear, axial force and flexure and the rigid-end rotation due to bar slip.

Assessment of the long-term risk profile considering the triggered seismic sequence is ren-dered complicated by the short-term clustering of aftershock events following the main event and the increased potential for damage accumulation. The assessment of long-term seismic risk profile for the main seismic events is based on the renewal of the structure to a pre-scribed state after each seismic event. In the case of a triggered aftershock sequence, it is very unlikely that the structure is repaired back to its intact condition during early phases of an ongoing sequence. Therefore, considering the (magnifying) effect of cumulative damage due to the triggered aftershock sequence in long-term seismic risk profile is not a trivial task. One viable strategy is to evaluate the increase in the (time-invariant) limit state excursion proba-bility due to the combined effect of short-term aftershock clustering and the resulting cumula-tive damage for a prescribed post-event short-term time interval. As expected, in case the structure is assumed to be renewed to a given hypothetic damage state (such as main-shock damaged) after each event occurs, the formulation for limit state probability simplifies to a closed-form Poissonian formulation. The methodology is applied to an existing 3-story RC moment-resisting frame in central Italy. The Poissonian closed-form solution, with the hy-pothesis of renewal back to the main-shock damaged configuration, is shown to successfully capture the increase in the limit state probability due to the effect of the triggered sequence.

Significant structural and non-structural damage suffered by residential buildings due to re-cent earthquakes world-wide provide a sad testimony to the vulnerability of the existing build-ing stock. This evidence stresses the need to develop accurate seismic vulnerability assessment tools in the service of strategic planning and applicable at a territorial scale. The empirical fragility curves have been used quite often in order to characterize the vulnerability of prescribed buildings classes as opposed to individual buildings. In this approach, the fra-gility curve represents both the building-to-building variability within the class and the uncer-tainty in the building class response to future earthquakes. Development of empirical fragility curves for classes of buildings depends to a large extent on the pairs of seismic ground shak-ing intensity and observed damage data gathered from spatially distributed buildings. The seismic intensity experienced at the site of each building is usually estimated by employing maps of ground shaking for the earthquake of interest (a.k.a., shakeMaps). The accuracy in the estimation of the ground shaking intensity is arguably as important as that of the evalua-tion of the incurred damage. The former involves explicit characterization of the uncertainties in the ground-shaking intensity estimation. This can be achieved through application of ground motion prediction equations and the corresponding spatial correlation structure in the residuals (of the predictive equation), resulting in generation of random fields of ground shaking. Each field represents a plausible distribution of ground motion intensity for a given earthquake scenario. Then, the random ground shaking field can be updated based on availa-ble observations of ground shaking intensity from nearby stations. The generated random fields can be further modified to consider the local stratigraphic and topographic amplifica-tion. For a given damage pattern observed for a prescribed building class, the generation of random ground motion fields leads to a set of plausible empirical fragility curves (corre-sponding to exceedance of different damage states). The fragility curves and their 16th, 50thand 84th percentiles, obtained based on the generated random ground shaking fields and ob-served damage in the aftermath of Amatrice 2016 Earthquake (based on Copernicus EMS damage maps) show reasonable agreement with the empirical fragility curves available in the literature for similar buildings classes.

Reliable seismic hazard analyses are crucial to mitigate seismic risk. When dealing with induced seismicity the standard Probabilistic Seismic Hazard Analysis (PSHA) has to be modified because of the peculiar characteristics of the induced events. In particular, the relative shallow depths, small magnitude, a correlation with field operations, and eventually non-Poisson recurrence time. In addition to the well-known problem of estimating the maximum expected magnitude, it is important to take into account how the industrial field operations affect the temporal and spatial distribution of the earthquakes. In fact, during specific stages of the project the seismicity may be hard to be modelled as a Poisson process—as usually done in the standard PSHA—and can cluster near the well or migrate toward hazardous known or—even worse—not known faults. Here we present a technique in which we modify the standard PSHA to compute time-dependent seismic hazard. The technique allows using non-Poisson models (BPT, Weibull, gamma and ETAS) whose parameters are fitted using the seismicity record during distinct stages of the field operations. As a test case, the procedure has been implemented by using data recorded at St. Gallen deep geothermal field, Switzerland, during fluid injection. The results suggest that seismic hazard analyses, using appropriate recurrence model, ground motion predictive equations, and maximum magnitude allow the expected ground-motion to be reliably predicted in the study area. The predictions can support site managers to decide how to proceed with the project avoiding adverse consequences.

Summary Calculating the limit state (LS) exceedance probability for a structure considering the main seismic event and the triggered aftershocks (AS) is complicated both by the time-dependent rate of aftershock occurrence and also by the cumulative damage caused by the sequence of events. Taking advantage of a methodology developed previously by the authors for post-mainshock (MS) risk assessment, the LS probability due to a sequence of mainshock and the triggered aftershocks is calculated for a given aftershock forecasting time window. The proposed formulation takes into account both the time-dependent rate of aftershock occurrence and also the damage accumulation due to the triggered aftershocks. It is demonstrated that an existing reinforced concrete moment-resisting frame with infills subjected to the main event and the triggered sequence exceeds the near-collapse LS. On the other hand, the structure does not reach the onset of near-collapse LS when the effect of triggered aftershocks is not considered. It is shown, based on simplifying assumptions, that the derived formulation yields asymptotically to the same Poisson-type functional form used when the cumulative damage is not being considered. This leads to a range of approximate solutions by substituting the fragilities calculated for intact, MS-damaged, and MS-plus-one-AS-damaged structures in the asymptotic simplified formulation. The latter two approximate solutions provide good agreement with the derived formulation. Even when the fragility of intact structure is employed, the approximate solution (considering only the time-dependent rate of aftershock occurrence) leads to higher risk estimates compared with those obtained based on only the mainshock. Copyright © 2016 John Wiley & Sons, Ltd.

In an attempt to conduct a back analysis of the damage caused to a 19th century masonry structure due to the October 2009 flash flood/debris flow event in Scaletta Zanclea, a flood discharge hydrograph is reconstructed in the ungauged conditions. The hydrograph for the solid discharge is then estimated by scaling up the liquid volume to the estimated debris volume. The debris flow diffusion is simulated by solving the differential equations for a single-phase two-dimensional (2D) flow that employs triangular mesh elements, also taking into account the channelling of the flow through the buildings. The damage to the building is modelled using 2D finite shell elements and is based on the maximum hydraulic action caused by the debris flow. The boundary conditions provided by the openings, floor slabs, orthogonal wall panels and the foundations are also modelled. The reconstruction of the event and the damage to the case-study building confirm the location of the damage induced by the event.

A rational evaluation of the seismic risk of urban areas cannot neglect the variability of the ground motion due to site amplification and the induced effects, such as soil liquefaction and slope stability. In this framework, the microzonation maps certainly represent the most adequate tool to account for this element in the seismic risk and for planning mitigation strategies. This study shows the preliminary results of a multidisciplinary research, which deals with the seismic microzonation of the Western area of Napoli. The selected case study is a challenging choice, as the seismic hazard is affected by both tectonic and volcanic seismicity, which in historical time differently affected the urban setting. The latter, in turn, results highly heterogeneous, as consisting of a mixing of both masonry and concrete structures. The adopted approach followed the recommendations of both the National and International Guidelines, with reference to the multi-levels methods. The collection, homogenization and synthesis of a significant number of existing data permitted the development of reliable geological and geotechnical subsoil models, leading to Grade I and II seismic microzonation maps, characterized by different expected amplification of ground motion and instability due to the liquefaction. Such maps represent a key for a quantitative assessment of seismic performance of buildings and infrastructures, in view of the expected urban requalification of the area.

Demand and Capacity Factor Design (DCFD) is a probability-based safety-checking format for performance-based seismic design and assessment of structures. Inspired from the original DCFD formulation for seismic excitation, this work proposes a similar performance-based safety-checking format for flooding, adopting the flood height as the intensity measure. The proposed DCFD formulation implements the fragility/hazard parameters for flooding. The structural fragility is evaluated by adopting an efficient and simulation-based method yielding the so-called “robust” fragility curve and an associated plus/minus one-standard deviation interval. The structural performance is measured by the (critical) demand to capacity ratio for the weakest element of the weakest wall within the structure, subjected to a combination of hydro-static, hydro-dynamic and accidental debris impact loads. Analogous to the incremental dynamic analysis method proposed for seismic demand assessment, an incremental flood height analysis is used to monitor the structural performance as a function of increasing water height. For each structural modelling configuration, generated based on the characterization of uncertainties in loading and material mechanical properties, the incremental flood height analysis is employed in order to calculate the critical water height corresponding to a demand to capacity ratio of unity. The application of the proposed methodology is demonstrated for both flood fragility/risk assessment and comparative screening of various viable flood mitigation strategies for a non-engineered building made of cement bricks in Dar Es Salaam, Africa.

Incremental dynamic analysis (IDA) is a procedure in which a structure is subjected to a suite of ground motion records, scaled to multiple levels of intensity and leading to corresponding curves of response versus intensity. However, implementation of IDA usually involves a significant computational effort. In this work, a simple and efficient solution for IDA analysis with only few points, based on the structural response to un-scaled records (a.k.a the “cloud”), has been implemented. The transverse frame of a shear-critical seven-storey older RC building in Van Nuys, CA, which is modeled in Opensees with fiber-section considering the flexural-shear-axial interactions and the bar slip, is employed. It is demonstrated that the simplified IDA, obtained based on a significantly lower computational effort with respect to the full IDA, provides reliable results in terms of the statistics of structural response (e.g., mean and mean plus/minus one standard deviation) versus intensity and structural fragility.

Incremental dynamic analysis (IDA) leads to curves expressed in terms of structural response versus intensity, commonly known as the IDA curves. It is known that implementation of IDA usually involves significant computational effort and most often significant scaling of the original records to various intensity levels. Employing as the performance variable the critical demand to capacity ratio (DCR) throughout the structure, which is equal to unity at the onset of the limit state, facilitates the identification of the intensity values at the onset of a desired limit state and hence the implementation of the IDA procedure. Employing the structural response to un-scaled records and the corresponding regression-based response predictions (a.k.a., the “Cloud Analysis”) helps in identifying the range of intensity values corresponding to demand to capacity ratio values in the vicinity of unity. The Cloud to IDA procedure for structural fragility assessment is proposed on the premise of exploiting the Cloud Analysis results to obtain the IDA curves both with minimum number of analyses and minimum amount of scaling. The transverse frame of a shear-critical 7-story older RC building in Van Nuys, CA, which is modeled in Opensees with fiber-section considering the flexural-shear-axial interactions and the bar slip, is employed as a case study. It is demonstrated, by comparing the results to IDA and other state of the art non-linear dynamic procedures based on no scaling or spectral-shape-compatible scaling, that the Cloud to IDA procedure leads to reliable results in terms of structural fragility and risk for the prescribed limit state.

A probabilistic method is proposed for conducting groundwater-related hazard assessments that are useful for the risk management of deep excavations in saturated soils in urban areas. The design of deep excavations, construction procedures and execution methods aimed at realizing underground infrastructure are characterized by major uncertainties related to soil properties, construction imperfections and hydrogeological site conditions. During the construction stages, geometrical imperfections and ineffective technologies to control wall seepage can strongly affect the level of risk to the adjacent built environment, leading to severe damages. The aim of this study is to provide an assessment of the leakage probability in reinforced concrete (RC) retaining walls. We model the main geo-structural factors that influence trench stability and the key geometric and technological parameters that affect construction imperfections as relevant random variables. Moreover, through Bayesian updating, useful recommendations are provided regarding the effectiveness and influence of the monitoring phase on reducing the failure probability.

The majority of bridge infrastructures in Italy were built in the 1960s and ‘70s without any specific seismic provision being made. As a consequence, it is expected that these bridges would be highly vulnerable if subjected to a significant seismic event. Given this background, it is natural that the rapid and accurate assessment of economic losses incurred to the bridge infrastructure as a result of such an event could play a crucial role in emergency management in the immediate aftermath of an earthquake. Focusing on the infrastructure system of highway bridges in the Campania region in Italy, this paper demonstrates how both state-of-the-art methodologies in portfolio loss assessment and the available data can be used to assess the probability distribution of the repair costs incurred due to the 1980 Irpinia earthquake. Formulating a probabilistic loss assessment for a portfolio of bridges as a standard Monte Carlo simulation problem helps to resolve the spatial risk integral efficiently. One of the specific features of this case study is the use of statistical methods for updating models of: (a) ground motion predictions, (b) vulnerability/fragility and (c) exposure/costs, based on the available data. It has been observed that alternative hypotheses concerning the ground motion correlation structure can significantly affect the distribution of direct economic losses. Furthermore, updating the ground motion prediction based on available recordings may significantly reduce the dispersion in the estimate of the direct economic losses.

In this paper, we develop and apply a multi-dimensional vulnerability assessment framework for understanding the impacts of climate change-induced hazards in Sub-Saharan African cities. The research was carried out within the European/African FP7 project CLimate change and Urban Vulnerability in Africa, which investigated climate change-induced risks, assessed vulnerability and proposed policy initiatives in five African cities. Dar es Salaam (Tanzania) was used as a main case with a particular focus on urban flooding. The multi-dimensional assessment covered the physical, institutional, attitudinal and asset factors influencing urban vulnerability. Multiple methods were applied to cover the full range of vulnerabilities and to identify potential response strategies, including: model-based forecasts, spatial analyses, document studies, interviews and stakeholder workshops. We demonstrate the potential of the approach to assessing several dimensions of vulnerability and illustrate the complexity of urban vulnerability at different scales: households (e.g., lacking assets); communities (e.g., situated in low-lying areas, lacking urban services and green areas); and entire cities (e.g., facing encroachment on green and flood-prone land). Scenario modeling suggests that vulnerability will continue to increase strongly due to the expected loss of agricultural land at the urban fringes and loss of green space within the city. However, weak institutional commitment and capacity limit the potential for strategic coordination and action. To better adapt to urban flooding and thereby reduce vulnerability and build resilience, we suggest working across dimensions and scales, integrating climate change issues in city-level plans and strategies and enabling local actions to initiate a ‘learning-by-doing’ process of adaptation.

In the immediate aftermath of a strong earthquake and in the presence of an ongoing aftershock sequence, scientific advisories in terms of seismicity forecasts play quite a crucial role in emergency decision-making and risk mitigation. Epidemic Type Aftershock Sequence (ETAS) models are frequently used for forecasting the spatio-temporal evolution of seismicity in the short-term. We propose robust forecasting of seismicity based on ETAS model, by exploiting the link between Bayesian inference and Markov Chain Monte Carlo Simulation. The methodology considers the uncertainty not only in the model parameters, conditioned on the available catalogue of events occurred before the forecasting interval, but also the uncertainty in the sequence of events that are going to happen during the forecasting interval. We demonstrate the methodology by retrospective early forecasting of seismicity associated with the 2016 Amatrice seismic sequence activities in central Italy. We provide robust spatio-temporal short-term seismicity forecasts with various time intervals in the first few days elapsed after each of the three main events within the sequence, which can predict the seismicity within plus/minus two standard deviations from the mean estimate within the few hours elapsed after the main event.

A wide area around the town of Naples is mantled by shallow unsaturated volcanoclastic soils that are highly susceptible to fast rainfall-induced flow-like landslides. Some casualties and huge damage recorded in the last twenty years testify the serious threat posed by such events. Due to the impact of these phenomena, the local research community is strongly committed in studies whose results have allowed to understand some key aspects of the triggering and propagation mechanisms. However, the way to run for risk mitigation is still long: given the density of population and of infrastructure, the setting up of reliable early warning systems would be a fundamental tool to this aim. Based on a rich data-base about the features of the rainfall-induced landslides in unsaturated volcanoclastic soils occurred on January, 10th, 1997, in a small area located in the Sorrento peninsula, and the history of precipitations occurred in the same area in the last fifty years, the paper examines the relation between rainstorms and landslides, showing the fundamental role of the recent precipitation history.

The frequency and severity of natural catastrophes have increased significantly over the last few years, with countries around the world having to face huge economic and human losses. Italy in particular is very seismic-prone, being located in the precise area of convergence between the African and Eurasian lithospheric plates. In addition, most Italian cities are densely populated and have many old and historical buildings, making the country even more exposed and vulnerable in terms of potential losses. Recently, new regulations gave householders the chance to buy insurance against earthquakes. Unfortunately, the widespread risk perception in Italy is very low among the population. In addition, insurance premiums can be extremely high given the low-probability-high-risk of cash flow and insolvency problems potentially incurred by an insurance company if there is a high magnitude earthquake. The aim of the methodology proposed in this paper is to give insurers an engineering instrument with which to quantify expected losses in the case of an earthquake. This will enable insurance companies to model innovative and more affordable financial products by optimizing the quantification of premiums. Seismic events with a magnitude greater than 4 from 217 a.C. to 2012 have been selected from the historical catalogue of the National Institute of Geophysics and Volcanology, and statistical simulations of earthquake scenarios are performed for each of them. In particular, the peak ground acceleration is simulated, based on the ground motion prediction equation of Bindi et al. (Bull Earthq Eng 7(3):591–608, 2009). Actual exposure is assumed for the Italian building stock, which is modelled according to the database of the National Institute of Statistics. Finally, in order to compute the total losses for the entire national building stock, the annual expected losses are quantified according to the procedure demonstrated in Asprone et al. (Struct Saf 44:70–79, 2013).

Quantifying the impact of modelling uncertainty on the seismic performance assessment is a crucial issue for existing buildings, considering the partial information available related to material properties, construction details and the uncertainty in the capacity models. The effect of structural modelling uncertainties on the seismic performance of existing buildings can be -under certain circumstances- comparable to that of uncertainty in ground motion representation. In this work, a modified version of Cloud analysis considering the (eventual) cases of global dynamic instability and adopting the critical demand to capacity ratio as the damage measure/decision variable, based on coupling the simple regression in the logarithmic space of structural response versus seismic intensity for a suite of registered records with logistic regression, has been implemented to consider the record-to-record variability, structural modelling uncertainties and the uncertainties in the parameters of the adopted fragility model. For each of the registered records within the suite of ground motion records, a different realization of the structural model has been generated through a standard Monte Carlo Simulation procedure. A Bayesian version of the Cloud method is employed, in which the uncertainty in the structural fragility model parameters is considered. This leads to a robust fragility estimate-reflecting both record-to-record variability and structural modeling uncertainties-- and a desired confidence interval defined around it -reflecting the uncertainty in the fragility model parameters. The longitudinal frame of an existing building in Van Nuys, CA, modeled in OpenSees considering the flexural-shear-axial interaction, has been employed in order to demonstrate this procedure. The critical demand to capacity ratio adopted as the damage measure/decision variable, corresponding to the component or mechanism that leads the structure closest to the onset of limit state (e.g., near collapse), is adopted as the structural response parameter. This structural response parameter can encompass both ductile and fragile failure mechanisms. Moreover, it can register a possible shift in the governing failure mechanism with increasing intensity. The selection of the suite of ground motion records has been based on a set of criteria that ensure the statistical significance of the linear regression in predicting the structural response as a function of the intensity measure.

Recent devastating earthquakes around the world have shown the vulnerability and deficiencies of existing reinforced concrete (RC) frame structures. In particular, recent research on seismic risk analysis have highlighted that nonductile concrete frame structures are much more susceptible to collapse than modern code-conforming frames. Therefore, for this type of structures, it is necessary to accurately model materials and members to capture the flexure, shear and flexure-shear failure modes in members and the potential collapse of the structure. In this paper, alternative retrofit methods are evaluated for these older frame buildings using a nonlinear structural performance assessment methodology. As a case study, the longitudinal frame of an existing building is modeled, including the effect of flexural-shear-axial load interaction and the bar slip deformational component in order to be able to capture column shear and axial failures. A probability-based framework is implemented in order to assess the structural performance and safety at each chosen performance level. This study shows that it is crucial to choose the most effective retrofit strategy based on the assessed performance of the bare frame.