Flood hazards can affect road networks by closing roads, extending travel distances and lengthening travel times. This paper innovatively integrates flood hazard, road network topology and vehicle vulnerability via a severity factor to assess the accessibility of exposed links and the performance of the whole network. The overall network functionality loss under different flood return periods is assessed by evaluating the functionality of each network link for cars and SUVs. The most vulnerable links are identified and used to assess the performance of the entire network using topology-based metrics such as average shortest paths and isochrones. The case study of Bristol (UK) is investigated. It is demonstrated that network status is a function of vehicle typology, with SUVs exhibiting naturally stronger resistance to flood than cars. This finding can support preparedness strategies of road networks in the face of future flood events.

Hydraulic actions resulting in scour are a major cause of bridge failures worldwide. The development of scour is affected by various factors including structural geometry, river morphology and weather conditions. Detection of scour is often carried out using visual inspection-based methods. Visual inspection requires the expertise of divers which incurs high levels of risk, especially during times of high-water flow. Therefore, sensing technologies are becoming increasingly popular with the aim to supplement or replace higher risk visual inspection approaches. Specification of Structural Health Monitoring (SHM) systems is an important activity for bridge engineers and asset managers. This activity can be assisted by SHM rating frameworks, the use of which gives evidence to support specification of monitoring systems for individual bridges. In this paper, three previously published scour rating frameworks are compared using a bridge case study database.

Digitalisation and the Internet of Things (IoT) help city councils improve services, increase productivity and reduce costs. City-scale monitoring of traffic and pollution enables the development of insights into low-air quality areas and the introduction of improvements. IoT provides a platform for the intelligent interconnection of everyday objects and has become an integral part of a citizen's life. Anyone can monitor from their fitness to the air quality of their immediate environment using everyday technologies. With caveats around privacy and accuracy, such data could even complement those collected by authorities at city-scale, for validating or improving policies. The authors explore the hierarchies of urban sensing from citizen-to city-scale, how sensing at different levels may be interlinked, and the challenges of managing the urban IoT. The authors provide examples from the UK, map the data generation processes across levels of urban hierarchies and discuss the role of emerging sociotechnical urban sensing infrastructures, that is, independent, open, and transparent capabilities that facilitate stakeholder engagement and collection and curation of grassroots data. The authors discuss how such capabilities can become a conduit for the alignment of community- and city-level action via an example of tracking the use of shared electric bicycles in Bristol, UK.

Visual inspection remains an essential tool for assessing structural damage. Damage detection is a challenging task for those specifying, designing, and deploying SHM systems. Often only traditional visual inspection processes are available to determine the type and extent of structural damage. For bridge structures in the UK, a regime of general (every two years) and principal (every six years) inspections is often followed. Such visual inspections are time-consuming and costly in terms of both labour and financial resources. Therefore, the possibility of completing more of the bridge visual inspection process offsite has many potential benefits for bridge owners and managers during the service life of the asset. Recent research conducted at the University of Bristol in collaboration with industrial partners has examined how to make the best use of metrics derived from visual inspection data when assessing bridge condition and planning maintenance activities. Recent research into which aspects of the current visual inspection regime in the UK could potentially be moved offsite has also been carried out. This paper summarises these research efforts and discusses how AI may be used as part of future enhancements to visual inspection data capture and analysis.

Urban resilience to natural hazards could make our cities less vulnerable to adverse weather events. However, the implementation of resilience actions is currently not effective, as mechanisms to facilitate collaboration among involved stakeholders are missing. This paper for the first time explores causal mapping as a method to disassemble major issues of urban resilience into a more manageable understanding, and thus identify key objectives, barriers and opportunities in thinking “resilient cities”. In this study, a cognitive-mapping-based workshop was held to elicit information from stakeholders in the remit of urban resilience to flooding. The statements and connections identified during the workshop led a consolidated map, analysed using the StrategyFinder software. This analysis highlighted barriers related to data availability, silo-based approaches and lack of funding; it also evidenced shared goals, such as the need to protect the built environment and minimise impact from flooding. Overall, causal mapping resulted a powerful analytical tool for improving understanding of the complex dynamics of urban resilience, identifying key variables and relationships, as well as eliciting information from stakeholders. Furthermore, this approach facilitated systems thinking, communication and collaboration. This enhanced understanding is fundamental for advancing strategies for future planning, contributing to urban sustainability and liveability.

On-site sanitation systems (OSS), such as pit latrines, are an important source of methane (CH₄), with emissions increasing when they are wet, and this occurs when anaerobic conditions dominate. This paper presents the development of a model, which uses seasonal changes in groundwater to account for the fluctuating inundation of pit latrines, and, therefore, the associated CH₄ emissions from varying degrees of anerobic conditions are examined. Given that observed timeseries of groundwater table depth at high enough spatial and temporal resolutions are often difficult to obtain in low- and middle-income countries (LMICs), inverse distance weighted (IDW) interpolation is used to generate values for a whole region, which is then used, alongside average pit latrine depth, to determine areas of pit latrine inundation. Outcomes are further informed with open-source contextual data, covering population, urban/rural split, and sanitation facility data, before using methodologies from the Intergovernmental Panel on Climate Change (IPCC) to generate CH₄ emissions data. As a case study, we use data from Senegal to illustrate how this model works. Results show total CH₄ emissions for the month of January to be ~1.69 kt CH₄. We have also discussed the potential use of satellite remote sensing data in regions where access to historical groundwater data is limited. Understanding when the pit conditions are most likely to change could lead to incentives for better management strategies, as well as a reduction in CH₄ production.

Housing in informal settlements often lacks construction techniques that adopt criteria of resilience to natural hazards. Smartphones are rapidly diffusing in economically developing countries. The aim of this study was to assess the current use of smartphones by the masons of the informal settlements of Iringa, Tanzania, and to identify pathways for improving their construction practices. Data were collected through a mixed method approach that includes in-depth interviews, surveys, and a focus group with masons. The results reveal that only a few masons received formal training, most of them have never interacted with a qualified engineer nor consulted trained professionals when needed. Most masons own a smartphone and they extensively use it to gather technical information from the web, transfer money through mobile payments, share images of construction details, and promote their work on social media. The broad use of smartphones shows potential for enhancing construction quality. This article presents a unique analysis of the use of smartphones in the construction of informal settlements in Tanzania, which could be extended to other countries. Based on the findings, new strategies are proposed to engage with local stakeholders and foster the exchange of technical knowledge for safer settlements via smartphones.

Scour is a significant cause of bridge failure, and resulting bridge closures are likely to generate significant disruption to infrastructure networks. The management of scour-susceptible bridges is a significant challenge for improving transport resilience, but tends to be heuristic and qualitative. Such assessments often suffer from insufficient knowledge of key factors and require assumptions, which may increase their estimation and relative uncertainty. Analysis of publicly available technical documents reveals that various definitions of “risk” are adopted, as well as multiple approaches are applied. This paper has three objectives: (i) to illustrate the concept of risk in bridge scour management; (ii) to propose a simple scoring system to analyse existing risk-based approaches to manage bridge scour; and (iii) to analyse and compare such approaches on the basis of the obtained scores. A sample of nine documents containing bridge scour risk assessment practices or approaches was analysed using the developed rating system.

The visual inspection of bridges is a major undertaking for asset owners and operators. In the UK, visual inspections require inspectors to visit bridges on site and often at night and under unfavourable weather conditions. Therefore, it would be beneficial to move some of the visual inspection process off site. This paper studies whether the defect classification aspects of the inspection process could be conducted remotely using photographs. This study examines the defect ratings assigned by ten survey participants who were tasked with examining photographs from visual inspections of ten UK bridges. The survey results were compared with the results from the general inspections previously carried out for the bridges in question. From this data set, the differences in the ratings given and the extent to which defects are missed were examined. The results show that a higher number of defects were identified for a given bridge by the remote inspectors. Statistical analysis shows that aggregated defects rated by off-site inspectors tend to be more severe and of a higher priority rating compared with those from the on-site inspectors. The results also indicate that there is closer agreement between on-site and off-site inspectors for defects of a higher severity rating.

With the increase of the frequency and severity of flooding events, coupled with population growth, the risks posed to people from flooding is ever more apparent. This paper proposes a methodology to examine the risks posed to vehicles' occupants and pedestrians simultaneously in an urban context. Through considering stability functions of a range of vehicle types and pedestrian, a risk assessment profile for a vehicle occupant was derived. Using a historical 1-in-20-year rainfall flood event that took place in the city of Exeter (UK) in 2014, and a synthetic 1 in 100-year rainfall flood event, the potential risks posed to vehicle occupants were analysed. The results showed that for these events the potential risks posed to people travelling by car and caught in flood waters were likely to be more severe if they were to remain within their vehicles than if they were to exit said vehicles. Analysis of the changes in risk over time further revealed that if a vehicle was to become immobilised in flood water, they would only have a short timeframe (~10 min) before the level of risk increases. This is a critical finding, highlighting that remaining inside an immobilised vehicle during flood event and waiting for assistance may increase the level the risk the individual is exposed to, with the results showing the significance of such studies in reducing the risk of flooding to people.

The conservation of cultural heritage artefacts often requires particular conditions and a bespoke setup. We investigate the use of computational fluid dynamics (CFD) modelling to obtain high-resolution data of the present setup to conserve the SS Great Britain: the first iron-hulled screw-propelled ocean-going ship, preserved in its original dry dock in Bristol (UK). The conservation efforts focus on the dry air curtain to maintain the hull at low relative humidity to prevent corrosion, hence avoiding further degradation. The proposed computational model captures the key features, enabling the study of the air curtain efficacy, retaining an overall uncomplicated setup which is seen to be robust in a sensitivity analysis. The analysis of the transport of dry air focuses on the near-wall region, hence the interface between fluid and solid surface, in which exchange of species occurs, such as humidity and temperature. The near-wall fluid dynamics is described by the wall shear stress, divergence of wall shear stress, surface shear lines and wall shear stress critical points. The results provide a complete description of the flow near the hull surface and identify regions where the air curtain maintains adequate protection. The investigation highlights the use of air curtains on solid surfaces together with the analysis techniques necessary to describe the fluid dynamics. The modelling assumptions proposed are shown to be effective in investigating the air curtain. The solution to the computational model is compared to sensor data, showing good agreement on the ship's hull.

The 2030 Sustainable Development Goals (SDGs) offer a blueprint for global peace and prosperity, while conserving natural ecosystems and resources for the planet. However, factors such as climate-induced weather extremes and other High-Impact Low-Probability (HILP) events on their own can devastate lives and livelihoods. When a pandemic affects us, as COVID-19 has, any concurrent hazards interacting with it highlight additional challenges to disaster and emergency management worldwide. Such amplified effects contribute to greater societal and environmental risks, with cross-cutting impacts and exposing inequities. Hence, understanding how a pandemic affects the management of concurrent hazards and HILP is vital in disaster risk reduction practice. This study reviews the contemporary literature and utilizes data from the Emergency Events Database (EM-DAT) to unpack how multiple extreme events have interacted with the coronavirus pandemic and affected the progress in achieving the SDGs. This study is especially urgent, given the multidimensional societal impacts of the COVID-19 pandemic amidst climate change. Results indicate that mainstreaming risk management into development planning can mitigate the adverse effects of disasters. Successes in addressing compound risks have helped us understand the value of new technologies, such as the use of drones and robots to limit human exposure. Enhancing data collection efforts to enable inclusive sentinel systems can improve surveillance and effective response to future risk challenges. Stay-at-home policies put in place during the pandemic for virus containment have highlighted the need to holistically consider the built environment and socio-economic exigencies when addressing the pandemic’s physical and mental health impacts, and could also aid in the context of increasing climate-induced extreme events. As we have seen, such policies, services, and technologies, along with good nutrition, can significantly help safeguard health and well-being in pandemic times, especially when simultaneously faced with ubiquitous climate-induced extreme events. In the final decade of SDG actions, these measures may help in efforts to “Leave No One Behind”, enhance human–environment relations, and propel society to embrace sustainable policies and lifestyles that facilitate building back better in a post-pandemic world. Concerted actions that directly target the compounding effects of different interacting hazards should be a critical priority of the Sendai Framework by 2030.

Accurate spatial distribution information of rainfall is essential to rainfall-induced hazard predictions and statistical interpolation methods may serve as a useful tool to produce a detailed distribution from coarse data sources. Although numerous comparison studies about different interpolation methods have been conducted on irregular rain-gauge networks, there is a need to perform such work on the increasingly available gridded rainfall data. Carried out in the Emilia-Romagna region (Italy) from 2008 to 2018, this study aims to examine accurate and appropriate interpolation methods to produce finer rainfall surface maps based on the 0.25° × 0.25° ERA5 gridded precipitation datasets. Five interpolation techniques, namely Thiessen polygons, Inverse Distance Weighting (IDW), Thin Plate Spline (TPS), Ordinary Kriging (OK) and ordinary Co-Kriging (CoK), have been selected and compared at different time scales (annual, monthly and annual maximum daily precipitation). To assess the accuracy, the leave-one-out-cross-validation test was used by using the indexes of Bias, correlation coefficient, the Nash-Sutcliffe Efficiency, Root Mean Square Error and the Kling-Gupta Efficiency (KGE). Additionally, visual inspections are employed to evaluate the plausibility of the interpolated maps. Results show: (a) All five interpolation methods have certain capabilities to improve spatial resolution, but they fail on accuracy at the daily scale. The OK generally outperforms the other four methods, while TPS shows better performance through the visual inspection at the monthly scale; (b) Unlike in the case of the interpolation using conventional point-based rain-gauge data, the multivariate method CoK is inferior to the univariate ones and the p-power of the IDW also differs and (c) Winter and spring results are better than those of summer and autumn. This study has provided useful guidance on choosing suitable rainfall interpolation methods for gridded datasets, which can be expanded in other regions and data sources to explore the generality of the conclusions.

Visual inspection remains key for assessing the condition of bridges and hence assisting with planning and maintenance activities. There have been many efforts to improve or supplement visual inspection processes using new sensing technologies and data capture methods to usher in an era of 'smart bridges' or 'smart infrastructure'. One method to improve data capture is a 'remote inspection' where inspectors use digital photographs of a bridge to identify and grade structural defects to the standard of a 'General inspection' (GI). In this paper, survey data is presented to help formulate a preliminary assessment of the potential for engineers to implement this possible evolution of the visual inspection process. A potential Schema for remote visual inspections is developed and presented as a conceptual web application. The focus on the development of the Schema includes the need for ease of use by inspectors and integration of collected digital data into bridge management systems. The suggested platform is seen as a transitional method to aid in the long-term implementation of further automation of the inspection process. The system architecture is provided along with possible technologies that may support or enhance it, as well as a discussion of the potential barriers to implementation.

This article presents a methodology aimed at enhancing infrastructure resilience to extreme weather events while ensuring provision of critical services at community level. It describes critical infrastructure systems and services as features of a resilient community, considering that the accessibility to critical services is conditioned to the availability of critical infrastructure systems in different ways. The methodology is written as a mixed-integer linear programming model. The selection of appropriate planning and operational measures for resilience enhancements is made with the objective of ensuring provision of critical services under severe weather conditions subject to technical operating constraints. Furthermore, the methodology is demonstrated in a realistic setting in Stockport, UK, with an impact assessment of disruptions to power and transportation infrastructure systems caused by flooding on the provision of distinct critical services.

Many communities around the world are facing increasing flood-induced damage to bridges due to climate change and rising urbanisation. It is thus crucial to understand how different bridge types suffer from flooding and how this may affect surrounding networks. Despite the large body of literature for seismic and hurricane taxonomies, few classifications exist for bridges at flood risk. In this work, existing global bridge classifications were reviewed in order to derive a bridge–flood taxonomy. The review revealed that existing studies mainly classify bridges according to the superstructure material, whereas subclasses consider superstructure and substructure components. A taxonomy of 20 attributes for riverine roadway bridges susceptible to flood hazards is proposed in this paper. Its applicability for three bridge datasets in the UK was verified. The results showed that the considered datasets have data for 13 attributes, which can be used to derive regional bridge classes. In general, the taxonomy is functional for standardising different bridge datasets and applying/developing damage models for given bridge portfolios of flood-prone countries. Future works could apply the taxonomy to additional bridge datasets within a network for risk assessments; the proposed taxonomy could also be extended to allow integration with functionality and restoration models.