Cloud Analysis is based on simple regression in the logarithmic space of structural response versus seismic intensity for a set of registered records. A Bayesian take on the Cloud Analysis, presented herein, manages to take into account both record-to-record variability and other sources of uncertainty related to structural modelling. First, the structural response to a suite of ground motions, applied to different realizations of the structural model generated through a standard Monte Carlo, is obtained. The resulting suite of structural response is going to be used as “data” in order to update the joint probability distribution function for the two regression parameters and the conditional logarithmic standard deviation. In the next stage, large-sample MC simulation based on the updated joint probability distribution is used to generate a set of plausible fragility curves. The robust fragility is estimated as the average of the generated fragility curves. The dispersion in the robust fragility is estimated as the variance of the plausible fragility curves generated. The plus/minus one standard deviation confidence interval for the robust fragility depends on the size of the sample of “data” employed. Application of the Bayesian Cloud procedure for an existing RC frame designed only for gravity-loading demonstrates the effect of structural modelling uncertainties, such as the uncertainties in component capacities and those related to construction details. Moreover, a comparison of the resulting robust fragility curves with fragility curves obtained based on the Incremental Dynamic Analysis shows a significant dependence on both the structural performance measure adopted and the selection of the records

The urban informal settlements are particularly vulnerable to flooding events, due to both their generally poor quality of construction and high population density. An integrated approach to the analysis of flooding risk of informal settlements should take into account, and propagate, the many sources of uncertainty affecting the problem, ranging from the characterization of rainfall curve and flooding hazard to the characterization of the vulnerability of the portfolio of buildings. This paper proposes a probabilistic and modular approach for calculating the flooding risk in terms of the mean annual frequency of exceeding a specific limit state for each building within the informal settlement and the expected number of people affected (if the area is not evacuated). The flooding risk in this approach is calculated by the convolution of flooding hazard and flooding fragility for a specified limit state for each structure within the portfolio of buildings. This is achieved by employing the flooding height as an intermediate variable bridging over the fragility and hazard calculations. The focus of this paper is on an ultimate limit state where the life of slum dwellers is endangered by flooding. The fragility is calculated by using a logic tree procedure where several possible combinations of building features/construction details, and their eventual outcome in terms of the necessity to perform structural analysis or the application of nominal threshold flood heights, are taken into account. The logic tree branch probabilities are characterized based on both the orthophoto recognition and the sample in situ building survey. The application of the methodology is presented for Suna, a sub-ward of Dar es Salaam City (Tanzania) in the Msimbazi River basin having a high concentration of informal settlement

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Tsunamis are unpredictable and infrequent but potentially large impact natural disasters. To prepare, mitigate and prevent losses from tsunamis, probabilistic hazard and risk analysis methods have been developed and have proved useful. However, large gaps and uncertainties still exist and many steps in the assessment methods lack information, theoretical foundation, or commonly accepted methods. Moreover, applied methods have very different levels of maturity, from already advanced probabilistic tsunami hazard analysis for earthquake sources, to less mature probabilistic risk analysis. In this review we give an overview of the current state of probabilistic tsunami hazard and risk analysis. Identifying research gaps, we offer suggestions for future research directions. An extensive literature list allows for branching into diverse aspects of this scientific approach.

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The Central Italy earthquake sequence initiated on 24 August 2016 with a moment magnitude M6.1 event, followed by two earthquakes (M5.9 and M6.5) on 26 and 30 October, caused significant damage and loss of life in the town of Amatrice and other nearby villages and hamlets. The significance of this sequence led to a major international reconnaissance effort to thoroughly examine the effects of this disaster. Specifically, this paper presents evidences of strong local site effects (i.e., amplification of seismic waves because of stratigraphic and topographic effects that leads to damage concentration in certain areas). It also examines the damage patterns observed along the entire sequence of events in association with the spatial distribution of ground motion intensity with emphasis on the clearly distinct performance of reinforced concrete and masonry structures under multiple excitations. The paper concludes with a critical assessment of past retrofit measures efficiency and a series of lessons learned as per the behavior of structures to a sequence of strong earthquake events.

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The Kathmandu Valley is within a seismically active region with only few recorded strong-motion data. Geophysical information in the Valley is also sparse. In addition, the absence of an open database which compiles in situ geophysical tests, borehole records, and geotechnical laboratory data is affecting the advancement of knowledge in the region. This article presents SAFER/GEO-591 database, named after the Engineering and Physical Science Research Council (EPSRC)-funded project Seismic Safety and Resilience of Schools in Nepal (SAFER). SAFER/GEO-591 contains data from groundwater wells and boreholes originally commissioned for research and commercial purposes. This work describes (1) the quality assessment and harmonization process conducted on the dataset, (2) the variation of shear-wave velocity ( VS) measurements and geotechnical parameters with depth and elevation in the Valley, (3) the current understanding of the Valley sediment/bedrock topography, and finally (4) new geological cross sections. A companion article presents an updated VS30 map across the Valley based on the contributions of this article. The database can be downloaded from the University of Bristol repository via DOI: https://doi.org/10.5523/bris.3gjcvx51lnpuv269xsa1yrb0rw