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This paper presents an investigation of the collapse of a 325-year-old multi-tiered heritage temple during the 2015 Gorkha earthquake in Kathmandu, Nepal. The research comprises a reconnaissance survey followed by a geotechnical investigation and numerical back-analysis carried out to understand the potential causes of the collapse. The assessment of the structural configuration of the temple indicated seismic vulnerability in the design due to the presence of discontinuous columns over the height of the temple and age-weakened bonding in the masonry walls. The geotechnical investigation revealed the presence of competent soil strata at the location, assisting the survey which indicated no differential or excessive settlement in the foundation. A series of cyclic triaxial tests were conducted on samples recovered during the geotechnical investigation to determine dynamic behaviour of the soil. Further, dynamic analysis of the plinth of the temple under the recorded acceleration–time history indicated a maximum drift percentage of 1.4% and residual relative displacement of 32 mm suggesting the potential reason behind the collapse. The output of this research will support seismic rehabilitation of ancient structures within World Heritage sites across Nepal and effective action plans to safeguard them against future earthquake hazard.

Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L’Aquila’s earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L’Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts’ rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L’Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years, the reconstruction is still ongoing and that criticism of the recovery reported in the literature is also found in the tweets. Based on our analysis, the critical day to collect most of the data is the anniversary’s exact day. Tweets from citizens and/or news agencies, which are more likely to express the reality experienced, are therefore more useful in understanding recovery than tweets from government officials and/or governmental institutions. From the total 4349 tweets, we can state that 2488 (57%) were correctly classified by the pre-trained SA machine learning algorithm developed by MonkeyLearn, while 1861 (43%) were misclassified. It means an overall accuracy (ACC) of 57% and a misclassification rate of 43% by the algorithm. We argue that our results have the potential to serve as a benchmark that can be used to compare other post-disaster recovery processes using the same Twitter-based SA on their anniversaries.

Disaster events cause detrimental impacts for communities across the globe, ranging from large numbers of fatalities and injuries, to the loss of homes and devastating financial impacts. Emergency professionals are facedwith the challenge of providing sustainable solutions to mitigate these consequences and require tools to aid the assessment of potential impacts. Current modelling tools have either focused on modelling either the microscale (e.g. individual confined spaces such as buildings or stadiums) or the macroscale (e.g. city scale). The aim of thisresearch is to create microscale agent-based modelling (ABM) tools, incorporating a realistic representation of human behaviours, which will help management professionals assess and improve their contingency plans for emergency scenarios. The focus has been on creating a microscale agent-based model of a pedestrian pavement and crossroads, to include overtaking and giving way, alongside the inclusion of varied population characteristics. This research has found that by improving pedestrian interactions (e.g. overtaking and giving way interactions) on pavements and at crossroads more robust travel time estimates can be achieved. To produce more realistic behaviour traits, microscale models should consider: (1) varied walking speed (2) population density, (3) patience level and (4) an exit split percentage for crossroads. Comparisons to 1.34 m/s (3mph) models without additional variables show the travel times may be misrepresentative by up to 78% in pavements and 305% in crossroads for some population types. This has the potential to cause cascading effects such as a significant increase in fatalities or injuries as communities cannot reach safety in the anticipated time.

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 38 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by this data. We conclude that modern reconnaissance missions can not rely on a single data source and that different data sources should complement each other, validate collected data, or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important source of data.

Disasters affect millions of people annually, causing large numbers of fatalities, detrimental economic impact and the displacement of communities. Policy-makers, researchers and industry professionals are regularly faced with these consequences and therefore require tools to assess the potential impacts and provide sustainable solutions, often with only very limited information. This paper focuses on the themes of “disaster management”, “natural hazards” and “simulation”, aiming to identify current research trends using bibliometric analysis. This analysis technique combines quantitative and statistical methods to identify these trends, assess quality and measure development. The study has concluded that natural hazards (73%) are more predominant in research than man-made hazards (14%). Of the man-made hazards covered, terrorism is the most prevalent (83%). The most frequent disaster types are climate related, and in this study hydrological (20%), geophysical (20%), meteorological (15%) and climatological (5%) were the most frequently researched. Asia experiences the highest number of disaster events as a continent but in this study was only included in 11% of papers, with North America being the most recurrent (59%). There were some surprising omissions, such as Africa, which did not feature in a single paper. Despite the inclusion of key words “simulation” and “agent based” in the searches, the study did not demonstrate there is a large volume of research being carried out using numerical modelling techniques. Finally, research is appearing to take a reactive rather than proactive approach to disaster management planning, but the merit of this approach is questionable.

COVID-19 has claimed more than 2.7 × 106 lives and resulted in over 124 × 106 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled.

Molecular dynamics or MD simulation is gradually maturing into a tool for constructing in vivo models of living cells in atomistic details. The feasibility of such models is bolstered by integrating the simulations with data from microscopic, tomographic and spectroscopic experiments on exascale supercomputers, facilitated by the use of deep learning technologies. Over time, MD simulation has evolved from tens of thousands of atoms to over 100 million atoms comprising an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium. In this chapter, we present a step-by-step outline for preparing, executing and analyzing such large-scale MD simulations of biological systems that are essential to life processes. All scripts are provided via GitHub.

The 2020 Zagreb earthquake occurred on Sunday 22 March 2020. This earthquake was the first that happened during the lockdown imposed by governments to stop spreading the COVID-19. This fact makes the event interesting as a multi-hazard phenomenon. The lockdown made it not possible to deploy an earthquake reconnaissance mission. Therefore, it was necessary to undertake a remote mission supported by the monitoring and analysing social media (SM) platforms, such as Twitter and Instagram. This paper presents details and analysis of this monitoring and how it may help understand the impacts of an earthquake. In our work, we first identified the hashtags related to the event. Through the LastQuake app, we obtained the intensity reports from affected people and comments and pictures useful for damage assessment. The team obtained 59,246 tweets posted between the 20th March and the 30th April 2020 and 31,911 comments from LastQuake app users written on the day of the earthquake. Images from posts and comments were used for remote assessment of damage in buildings. Sentiment analysis (SA) was applied to tweets and comments related to the event to assess emergency management during the relief phase after the earthquake. Our work shows that only a limited number of pictures collected through social media were suitable for damage assessment of individual buildings. However, they were still useful as a proxy estimation of damages in some areas of Zagreb and surroundings. We also found SA supported by machine learning a valuable method to assess and identify critical aspects of the emergency and early recovery post-disaster phases. Applying SA we identified the most affected areas, the damages in the non-structural elements in hospitals, the support of collaborative networks for the evacuation of patients and the role of Ministers in the early recovery.

Molecular dynamics or MD simulation is gradually maturing into a tool for constructing in vivo models of living cells in atomistic details. The feasibility of such models is bolstered by integrating the simulations with data from microscopic, tomographic and spectroscopic experiments on exascale supercomputers, facilitated by the use of deep learning technologies. Over time, MD simulation has evolved from tens of thousands of atoms to over 100 million atoms comprising an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium. In this chapter, we present a step-by-step outline for preparing, executing and analyzing such large-scale MD simulations of biological systems that are essential to life processes. All scripts are provided via GitHub.

Author(s): Arrington, John; Brown, Ben; Beher, Steve; Cromaz, Mario; Dart, Eli; Edwards, Robert; Heyes, Graham; Jones, Clinton; Lauret, Jerome; Liddick, Sean; Mantica, Paul; Melo, Andrew; Monga, Inder; Morris, Brent; Pinkenburg, Chris; Porter, Jeff; Rai, Gulshan; Rockwell, Thomas; Rotman, Lauren; Simon, Richard; Stromsness, Rune; Wefel, Paul; Wiedlea, Andrew; Wilkinson, Sean; Winkler, Linda; Zurawski, Jason

The 2015 Gorkha Earthquake was a humanitarian disaster but also a cultural catastrophe that damaged and destroyed historic monuments across Nepal, including those within the Kathmandu Valley UNESCO World Heritage Property. In the rush to rebuild, traditionally constructed foundations are being removed and replaced with modern materials without assessments of whether these contributed to the collapse of a monument. Generally undertaken without scientific recording, these interventions have led to the irreversible destruction of earlier subsurface phases of cultural activity and the potential loss of evidence for successful traditional seismic adaptations and risk reduction strategies, with no research into whether modern materials, such as concrete and steel, would offer enhanced resilience. In response to this context, multidisciplinary post-disaster investigations were undertaken between 2015 and 2018, including archaeological excavation, geophysical survey, geoarchaeological analysis, linked to architectural and engineering studies, to begin to evaluate and assess the damage to, and seismic adaptations of, historic structures within Nepal’s Kathmandu Valley. Where possible, we draw on archaeoseismological approaches for the identification and classification of Earthquake Archaeological Effects (EAEs) at selected monuments damaged by the 2015 Gorkha Earthquake. Lessons learned from evidence of potential weaknesses, as well as historic ‘risk-sensitive tactics’ of hazard reduction within monuments, are now being incorporated into reconstruction and rehabilitation initiatives alongside the development of methods for the protection of heritage in the face of future earthquakes.

Synchrotron light sources are routinely used to perform imaging experiments. In this paper, we review the relevant computational stages, identify bottlenecks, and highlight future opportunities to streamline data acquisition for experimental microscopy workflows. We demonstrate our preliminary exploration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.

This paper presents the observations during the UK Earthquake Engineering Field Investigation Team (EEFIT)'s post-earthquake reconnaissance missions to the September 20, 2009 Padang (Mw7.6), March 11, 2011 Tōhoku (Mw9.0) and April 16, 2016 Muisne (Mw7.8) earthquakes. The performance of buildings and geotechnical structures within the affected regions were investigated to gain insights on their design and construction deficiencies. Findings on these damages observed are compared along with the characteristics of the earthquake and nature of building codes in these countries. They include building damages attributed to a combination of structural resonance, deficiencies in reinforcement detailing, vulnerability to soft story collapse, ground settlement, soil liquefaction, and landslides. It was demonstrated that buildings which were severely damaged had natural building frequencies coinciding with the dominant frequencies of the ground shaking. The locations of damage of several such buildings showed insufficient confining reinforcements and lapping of stirrup links. Soft story collapses were also observed in the three earthquakes, although many were attributed to old building codes that were less effective. In areas affected by the Muisne earthquake, soft story collapses were mainly found at mid height of the building rather than at the ground floor as observed in the Padang and Tōhoku earthquakes, likely due to extension of building long after the bottom floors were completed. Such extension of building can either lead to local reduction in capacity due to weaker concrete-rebar bonding, possibly insufficient lapping of reinforcement, as well as increased axial loads. In the aspect of geotechnical failure, foundations of buildings found on piles performed reasonably well, except for areas affected by soil liquefaction. Landslides occurred following these earthquakes led to large concentration of casualties and property losses, motivating the EEFIT teams to invest efforts in hazard mapping and ground-truthing exercises using satellite images at Padang and Muisne earthquakes respectively. Such geospatial tools applied in these three earthquakes were reviewed and demonstrated to be capable of identifying landslide sites and producing reliable landslide hazard map.

Disasters affect millions of people annually, causing large social impacts, including high numbers of fatalities, the displacement of communities, and detrimental economic impacts. Emergency professionals recurrently tackle these impacts and therefore need assessment methods to understand potential consequences and deliver sustainable resolutions, regularly with incomplete information. Current models simulating human behaviours and movement exist but are bespoke in nature and non-transferable (solving one problem only), meaning it is not possible to keep software “current” or future proofed. The aim of this research is to create an agent-based modelling (ABM) framework tool, incorporating robust models of human behaviour, to help management professionals develop and test their contingency plans for emergency scenarios. The focus has been on creating a macroscale evacuation ABM for a case study area, to assess whether the inclusion of varied population characteristics and group behaviours affect evacuation time. This research has found that by enhancing the representation of human behaviour more accurate predictions of evacuation time can be produced. To produce more robust human behaviour, models must include: (1) population characteristics (such as age, sex, and mobility), (2) grouping of agents, and (3) a walking speed ratio. Without the inclusion of adequate population characteristics, e.g. using agents with only a 1.34 m/s (3mph) walking speed, the evacuation model produced misleading evacuation times, potentially increasing by 109% for some population types. This may result in knock-on effects, such as significant increases in fatalities, or injuries as populations cannot leave their homes to safety in the expected time.

The Sendai Framework acknowledges the importance of having safe, resilient school infrastructure for continued access to education, and for their role in post-disaster recovery; however, in many countries this ideal is still only aspirational. This was evidenced in the 2015 Gorkha earthquake, highlighting major vulnerabilities within Nepal's school infrastructure, creating huge demand for school reconstruction in earthquake-affected areas, and school upgrade efforts in the rest of Nepal. This paper presents findings of a study into school reconstruction projects undertaken following the 2015 earthquake. The data presented was collected within two phases of fieldwork in Nepal, through 20 stakeholder interviews, both at a high-level, exploring broader involvement across multiple projects, and for individual case-specific schools. This research identified six key challenges affecting school reconstruction; however, these differ in impact and are perceived differently by different stakeholders, affecting how they are considered within projects. Among the challenges, accessibility and transportation was reported as the greatest challenge representing a relative challenge of 0.75 on a scale of zero to one. Our research also highlights good practices, including providing training, or transporting materials outside of the monsoon season, that when implemented reduce timescales and budgets, and improve the quality of the reconstruction. We also found that there is little transfer of good practice between projects. Therefore, we argue that by highlighting better practice, our findings have the potential to help Build Back Better efforts in line with Sendai Framework targets, by improving the delivery of school reconstruction programmes in Nepal, or for other countries with similar contexts.

This erratum is published as author name was published as V. Novellis and needs to be correctly read as Viviana Novelli.

Traditionally, the ATLAS experiment at Large Hadron Collider (LHC) has utilized distributed resources as provided by the Worldwide LHC Computing Grid (WLCG) to support data distribution, data analysis and simulations. For example, the ATLAS experiment uses a geographically distributed grid of approximately 200,000 cores continuously (250 000 cores at peak), (over 1,000 million core-hours per year) to process, simulate, and analyze its data (todays total data volume of ATLAS is more than 300 PB). After the early success in discovering a new particle consistent with the long-awaited Higgs boson, ATLAS is continuing the precision measurements necessary for further discoveries. Planned high-luminosity LHC upgrade and related ATLAS detector upgrades, that are necessary for physics searches beyond Standard Model, pose serious challenge for ATLAS computing. Data volumes are expected to increase at higher energy and luminosity, causing the storage and computing needs to grow at a much higher pace than the flat budget technology evolution (see Fig. 1). The need for simulation and analysis will overwhelm the expected capacity of WLCG computing facilities unless the range and precision of physics studies will be curtailed.

After the 2004 Boxing Day tsunami, much of the world's effort to defend against tsunami concentrated on tsunami warning and evacuation. The 2011 Great East Japan Earthquake and Tsunami led to direct and indirect losses as well as the deaths of many vulnerable members of Japan's coastal communities. This event has resulted in Japan rethinking and revising its design codes for sea defence structures. The new guidance emerging from this process is a valuable resource for other countries re-evaluating their own current mitigation strategies and this paper presents details of this process. The paper starts with the history of sea defence design standards in Japan and explains the process of revision of design guidelines since 2011. Examples of sea defences that failed and have since been rebuilt, observed during the two Earthquake Engineering Field Investigation Team (EEFIT) missions of 2011 and 2013, are also presented. The paper concludes with a discussion of international approaches and their application to nuclear power stations in Japan and the UK.

Cultural heritage structural documentation is of great importance in terms of historical preservation, tourism, educational and spiritual values. Cultural heritage across the world, and in Nepal in particular, is at risk from various natural hazards (e.g. earthquakes, flooding, rainfall etc), poor maintenance and preservation, and even human destruction. This paper evaluates the feasibility of low-cost photogrammetric modelling cultural heritage sites, and explores the practicality of using photogrammetry in Nepal. The full pipeline of 3D modelling for heritage documentation and conservation, including visualisation, reconstruction, and structure analysis, is proposed. In addition, crowdsourcing is discussed as a method of data collection of growing prominence.

Due to their influence on design guidelines, there is great interest in quantifying the possible impacts of climate change on extreme rainfall return levels. These studies can be affected by existing distortions in available rainfall historical series. The effect of the temporal aggregation (or time resolution), ta, of rainfall observations on the estimation of trends in annual maxima over the last 100 years is examined here. We have used long-term historical rainfall observations with various temporal aggregations, due to the progress of recording systems through time, at 10 representative meteorological stations located in an inland region of Central Italy. Series of annual maximum rainfall depths, Hd, for given durations, d, have been then derived. It is well known that Hd values derived from rainfall data characterized by every ta may involve underestimation errors, that for ta> ≈10 min can become important. Considering that all selected stations were installed in the first half of the twentieth century, each Hd series can be assumed as inhomogeneous since it contains values obtained by rainfall data with ta values ranging from fine (e.g. 1 min) to coarse (e.g. 24 h),thus with different levels of underestimation. By using a recently developed mathematical relation between average underestimation error and the ratio ta/d, we then correct each Hd value has been corrected through two different approaches, obtaining quasi-homogeneous series. Successively, commonly used climatic trend tests, including least-squares linear trend analysis, Mann-Kendall, Spearman's rank correlation, and Sen's method, have been applied to the inhomogeneous and quasi-homogeneous Hd series. The results show that the underestimation of Hd values with coarse ta plays a significant role in the analysis of the effects of climatic change on extreme rainfalls. Specifically, the correction of the Hd values can change the sign of the trend from positive to negative, mainly for series characterized by high probability to include Hd values with ta/d = 1.

Short-duration, high-intensity rainfall causes significant disruption to transport operations, and climate change is projected to increase the frequency and intensity of these events. Disruption costs of flooding are currently calculated using crude approaches. To support improved business cases for adapting urban infrastructure to climate change, this paper presents an integrated framework that couples simulations of flooding and transport to calculate the impacts of disruption. A function, constructed from a range of observational and experimental data sources, is used to relate flood depth to vehicle speed, which is more realistic than the typical approach of categorizing a road as either blocked or free flowing. The framework is demonstrated on Newcastle upon Tyne in the United Kingdom and shows that by the 2080s disruption across the city from a 1-in-50-year event could increase by 66%. A criticality index is developed and is shown to provide an effective metric to prioritize intervention options in the road network. In this case, just two adaptation interventions can reduce travel delays across the city by 32%.

For a few decades the local rainfall measurements are generally obtained by tipping bucket sensors, that allow to record each tipping time corresponding to a well-known rain depth. However, a considerable part of rainfall data to be used in the hydrological practice is available in aggregated form within constant time intervals. This can produce undesirable effects, like the underestimation of the annual maximum rainfall depth, Hd, associated with a given duration, d, that is the basic quantity in the development of rainfall depth-duration-frequency relationships. The errors in the evaluation of Hd from data characterized by a coarse temporal aggregation, ta, and a procedure to reduce the non-homogeneity of the Hd series are here investigated. Our results show that for ta = 1 min the underestimation is practically negligible, whereas for larger temporal aggregations with d = ta the error in a single Hd can reach values up to 50% and in a series of Hd in the average up to 17%. Relationships between the non-dimensional ratio ta/d and the average underestimation of Hd, derived through continuous rainfall data observed in many stations of Central Italy, are presented to overcome this issue. These equations allow to improve the Hd estimates and the associated depth-duration-frequency curves at least in areas with similar climatic conditions. The effect of the correction of the Hd series on the rainfall depth-duration-frequency curves is quantified. Our results indicate that the improvements obtained by the proposed procedure are of the order of 10%.

Currently, there is a great deal of interest in assessing the resilience of infrastructure systems. Much of this interest stems from the realisation that these systems are not only critical to civil defence but also, given the correct set of circumstances, can fail catastrophically. Three case studies are presented that show how network theory, which has been successfully applied to other fields, can also be used to help understand potential vulnerabilities in infrastructure systems. Through these case studies it is shown that traditional network theory can be extended to analyse infrastructures that are large, spatially distributed systems, or that carry flows of resources or are interconnected with other infrastructure systems. These methods demonstrate how this approach can help infrastructure designers, owners and operators to make rapid assessments of vulnerabilities in their systems and to identify components that are more important to the functioning of the these networks. Furthermore, this approach provides a basis for identifying and prioritising appropriate measures to improve the reliability of infrastructure at the systems scale.

This paper applies graph theory metrics to network flow models, with the aim of assessing the possibility of using these metrics to identify vulnerable areas within infrastructure systems. To achieve this, a reduced complexity flow model that can be used to simulate flows in infrastructure networks is developed. The reason for developing this model is not to make the analysis easier, but to reduce the physical problem to its most basic level and therefore produce the most general flow model (i.e., applicable to the widest range of infrastructure networks). An initial assessment of the applicability of graph theory metrics to infrastructure networks is made by comparing the distribution of flows, calculated using this model, to the shortest average path length in three of the most recognized classes of network—scale-free networks, small-world networks, and random graph models—and it is demonstrated that for all three classes of network there is a strong correlation. This suggests that at least part...

Historical electrical disturbances highlight the impact of extreme weather on power system resilience. Even though the occurrence of such events is rare, the severity of their potential impact calls for (i) developing suitable resilience assessment techniques to capture their impacts and (ii) assessing relevant strategies to mitigate them. This paper aims to provide fundamentals insights on the modelling and quantification of power systems resilience. Specifically, a fragility model of individual components and then of the whole transmission system is built for mapping the real-time impact of severe weather, with focus on wind events, on their failure probabilities. A probabilistic multi-temporal and multi-regional resilience assessment methodology, based on optimal power flow and sequential Monte Carlo simulation, is then introduced, allowing the assessment of the spatiotemporal impact of a windstorm moving across a transmission network. Different risk-based resilience enhancement (or “adaptation”) measures are evaluated, which are driven by the resilience achievement worth (RAW) index of the individual transmission components. The methodology is demonstrated using a test version of the Great Britain’s system. As key outputs, the results demonstrate how, by using a mix of infrastructure and operational indices, it is possible to effectively quantify system resilience to extreme weather, identify and prioritize critical network sections, whose criticality depends on the weather intensity, and assess the technical benefits of different adaptation measures to enhance resilience.

The 25th April 2015 M7.6 Gorkha earthquake caused significant damage to buildings and infrastructure in both Kathmandu and surrounding areas as well as triggering numerous, large landslides. This resulted in the loss of approximately 8600 lives. In order to learn how the impact of such events can be reduced on communities both in Nepal and elsewhere, the Earthquake Engineering Field Investigation Team (EEFIT) reconnaissance mission was undertaken, aiming to look at damage patterns within the country. Passive, microtremor recordings in severely damaged areas of the Kathmandu Valley, as well as at the main seismic recording station in Kathmandu (USGS station KATNP) are used to determined preliminary shear wave velocity (Vs) profiles for each site. These profiles are converted into spectral acceleration using the input motion of the Gorkha earthquake. The results are limited, but show clear site amplification within the Siddhitol Region. The resulting ground motions exceed the design levels from the Nepalese Building Codes, indicating the need for site-specific hazard analysis and for revision of the building code to address the effect of site amplification.

A sequence of two strike-slip earthquakes occurred on April 14 and 16, 2016 in the intraplate region of Kyushu Island, Japan, apart from subduction zones, and caused significant damage and disruption to the Kumamoto region. The analyses of regional seismic catalog and available strong motion recordings reveal striking characteristics of the events, such as migrating seismicity, earthquake surface rupture, and major foreshock-mainshock earthquake sequences. To gain valuable lessons from the events, a UK Earthquake Engineering Field Investigation Team (EEFIT) was dispatched to Kumamoto, and earthquake damage surveys were conducted to relate observed earthquake characteristics to building and infrastructure damage caused by the earthquakes. The lessons learnt from the reconnaissance mission have important implications on current seismic design practice regarding the required seismic resistance of structures under multiple shocks and the seismic design of infrastructure subject to large ground deformation. The observations also highlight the consequences of cascading geological hazards on community resilience. To share the gathered damage data widely, geo-tagged photos are organized using Google Earth and the kmz file is made publicly available.

Concrete can be affected by freeze/thaw damage from the point of placing to being fully cured. The lack of available test data in the early life performance of concrete, with polypropylene fibre additions was a key factor justifying this research. This work examines the effects of freeze/thaw cycles starting at 5 days of curing where the concrete has reached about half of the design strength. The test methods used to evaluate durability were weight loss, final compressive strength and relative pulse velocity. A freeze/thaw test was carried to ASTM 666 B for 300 cycles and enhanced freeze/thaw protection was observed by the use of Type 1 polypropylene fibres in concrete when compared to plain and air entrained concrete. Fibres had the ability to entrain air and this is believed to be part of the reason for the demonstrated improvement in freeze/thaw durability.

On 30 September 2009 at 05:16 pm local time (10:16 am UTC), a major earthquake registering a magnitude M w 7.6 occurred off the coast of Padang in West Sumatra. This paper presents the observations from a reconnaissance mission that investigated the performance of structures, infrastructure and geotechnical structures within the affected region. As well as presenting the detailed observations made on the mission, insights as to why structures failed and deficiencies in the construction practices of the region are also presented. Considering the high seismic risk of this area and that seismic gaps suggest another earthquake with a magnitude greater than 8.7 may occur, these observations are particularly significant. One of the vital features of this earthquake was a number of very large and devastating landslides that occurred in the district of Pariaman. These landslides were not only extensive, but had an extremely high lethality. Another key feature of the earthquake was the surprisingly high number of government buildings that collapsed. The paper concludes by presenting a number of recommendations for both the rebuilding of failed structures as well as for hazard assessments of existing structures and geotechnical features.

The UK Earthquake Engineering Field Investigation Team (Eefit) was established as an independent society in 1982. Between 1984 and 2011, it carried out field missions to 29 earthquake zones, with reports on all of them freely available online. Over a hundred UK-based engineers have participated, split almost equally between industry and academia. There have been a number of significant benefits, including training through observations of the practical effects of ground shaking, fostering strong links between practising and academic participants, establishing international links and gathering data for research into a wide range of earthquake engineering topics. The paper outlines Eefit’s history and development, sets out its achievements and presents details of a recent 5-year government grant that will extend its effectiveness still further.

In the last 50 years, there have been many incidences of failure of gravity quay walls. These failures are often associated with significant deformation of liquefiable soil deposits. Gravity quay wall failures have stimulated great progress in the development of deformation-based design methods for geotechnical structures. In this paper, the effective-stress analysis method has been used in conjunction with a generalised elasto-plasticity constitutive model implemented into a finite element procedure. Various monotonic and cyclic triaxial paths are simulated in order to demonstrate the capabilities of the constitutive model. The FEM is validated by back analysis of a typical Port Island PC1 caisson type quay wall, which was damaged during the 1995 Hyogoken-Nanbu earthquake. The numerical results are compared with the observed data obtained consisting of seaward displacement, settlement and tilting. In addition, both the influence of permeability, on the generation of pore water pressure and the influence of the relative density of the backfill and foundation layers, on the residual deformation of gravity quay walls are investigated.

Extreme weather may have a significant influence on the resilience of transmission systems. However, modelling the impact of weather is very challenging due to its stochastic and unpredicted nature and behaviour. To cope with these challenges, this paper presents a Sequential Monte Carlo based time-series model for evaluating the effect of weather on power system components, with focus on the wind effect on transmission lines and towers, and in turn on the entire transmission power infrastructure. The concept of fragility curves is used, which express the failure probabilities of the components as a continuous function of the wind speed. The mapping of the wind profile on these fragility curves provides the weather-affected operational state of the transmission lines and towers at every simulation time step. The model is illustrated using a simplified 29-bus model of the transmission network of Great Britain (GB). The simulation results highlight and quantify how the GB test network becomes less resilient for extreme wind events, and the effectiveness of mitigation strategies such as network reinforcement or redundancy.