Climate change poses escalating risks to bridge infrastructure, with short-term hazards–such as flash floods, scour, snowfall, wildfires and windstorms–interacting with long-term stressors like corrosion and thermal effects to compromise safety and functionality. The paper synthesises interdisciplinary research on these challenges, and highlights actionable adaptation strategies to enhance resilience at both asset and network levels. Two critical yet often overlooked dimensions in resilience-based bridge management are emphasised: the unique challenges of adapting heritage bridges, and the integration of human-centered approaches. These dimensions, supported by emerging digital technologies such as digital twins, IoT-enabled monitoring and AI-driven predictive tools, contribute to both the resilience and social sustainability of bridge infrastructure. By integrating technical, cultural and social considerations, the paper provides a foundational perspective for rethinking current design, preservation and maintenance practices, and for advancing infrastructure that is not only resilient to physical stressors but also socially sustainable amid accelerating climate challenges.

Here, we analyse the concept of plasticity and its application in diverse research fields such as physics, neuroscience, and biology. Historically, plasticity broadly refers to a system's capacity to undergo lasting changes in response to external inputs. This concept has been separated from the concept of elasticity, where changes are considered temporary and reversible. Both concepts were originally developed within physics and engineering, where plastic change happens when a material crosses a yield point. We propose a ‘minimal model’ to unify the concepts of elasticity, resilience, and plasticity across disciplines by mathematically formalising the transition between elastic and plastic changes. The model defines plasticity as the system's ability to reconfigure its internal parameters when it crosses a yield point, changing how it responds to new inputs. The framework we propose provides a common conceptual tool to facilitate communication across disciplines ranging from engineering to history and art. It can be applied to explain crucial differences between generally applied but still vague concepts, such as resilience and adaptation in different disciplines. Therefore, the model provides a basis for interdisciplinary applications and further exploration of plasticity across disciplines.

The emergence of Industry 5.0, following the widespread adoption of Industry 4.0, marks a pivotal shift in digitalization and industrial operations. This article explores the implications of Industry 5.0 principles and enabling technologies within the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C) industry, with a particular focus on the conservation of built cultural heritage environments. The results obtained from a systematic literature review and an online survey are summarized and discussed. Results reveal that artificial intelligence and digital twins are the most frequently studied enabling technologies in this context, while sustainability emerges as the dominant principle in the discourse surrounding this novel paradigm. Conversely, the principles of resilience and human-centrism remain underexplored, highlighting the need for further research to achieve a holistic implementation of Industry 5.0 in conservation practices. Furthermore, although awareness of Industry 5.0’s potential is growing, its adoption in heritage conservation remains limited due to knowledge gaps, inadequate training, and resource constraints. This underscores the need for comprehensive strategies to integrate Industry 5.0 principles and technologies into the conservation of built cultural heritage. Insights presented are intended to guide conservation practitioners seeking best practices, inform policymakers promoting technological adoption, and inspire researchers to address existing gaps and drive further innovation.

Traffic congestion has been one of the most important issues in urban areas, which results in pollution, fuel cost, loss of time (work hours), stress and anxiety. It is possible to increase the traffic network efficiency through solutions such as Intelligent Transport Systems (ITS) by adapting the existing network to ongoing operational conditions, especially in bottle neck conditions. In this study to minimize travel time losses, speed limits are optimized to adapt the traffic network to its operational conditions in real-time. To do so, an intelligent agent is developed to estimate the traffic in part of the M50 motorway in Dublin and is given the capability to learn and change the operational scenarios of the motorway that allow it to perform online management of its speeds. Results, tested in SUMO, indicate that the intelligent agent can reduce the travel time at peak congestion by a maximum of 60% in average travel times for a period of 10 min, and it has an overall significant benefit to alleviate congestion in the M50 section of interest during peak morning and afternoon times.

Laser powder bed fusion (LPBF), a prominent metal-based additive manufacturing (AM) technique, enables the production of complex, neat-net-shape components with minimal material waste and reduced lead times. However, achieving high final product quality is challenging due to numerous process variables and intricate, nonlinear interactions introduced by LPBF’s thermal-mechanical mechanisms. This study employs a copula-based analysis using multi-physics numerical simulations to probabilistically map relationships among process and part quality variables. Dependence and tail-dependence analyses are performed to provide deeper insights into variable interactions, enabling the identification of preferred operational windows with a balanced trade-off between product quality and productivity. The developed methodology advances the understanding of uncertainty propagation in LPBF, contributing toward improved process optimization, repeatability, and reliability.

The evolving energy landscape in Europe is showing concrete signals that hydrogen will play a central role in the energy transition scenario. In this light, a report of the European Hydrogen Backbone pinpoints no less than forty existing projects focused on the commissioning of several kilometers of hydrogen pipelines in the following years. Hence, ensuring a safe operability of these systems represents a topic worthy of investigation and marked by significant challenges, especially given the unique properties that make hydrogen a potentially hazardous substance. Established techniques may prove helpful in supporting the development of dedicated prevention and mitigation strategies for hydrogen systems. Among these, Risk-Based Inspection (RBI) could represent an effective tool to design inspection programs aimed at the detection of hydrogen-induced damages, especially for components working in pressurized environments, including pipeline materials. However, the lack of operational experience associated with emerging technologies may lead to the adoption of over-conservative safety measures, which could impact the economic attractiveness of these systems. Therefore, this study proposes an evolution of conventional RBI planning by implementing concepts of safety economics and optimization modelling, thus building a novel approach named “Cost-Informed Risk-Based Inspection” (CIRBI). The proposed methodology is therefore applied to a case study of inspection techniques potentially suitable for pipeline materials (i.e., API X-series pipeline steels), showcasing its potential as a self-standing approach for inspection planning while also demonstrating the insight that it may provide to ensure a safe operability of hydrogen pipelines.

The adoption of a novel industry paradigm is an untamed problem that requires strong social consensus and involves a high degree of technological uncertainty. To solve this problem a multi-actor engagement and agreement are needed. In this article, the methodology and the findings obtained after conducting a stakeholder analysis to understand how different actors could work together towards the adoption of Industry 5.0 principles and enabling technologies are presented. The analysis has been framed within a case study dealing with the conservation of historical bridges in the city of Oslo, Norway. The education institutions of the city were assumed as the problem owners. This research indicates that the Ministry of Transport and the Ministry of Climate and Environment, along with their subordinate agencies (Statens Vegvesen and Riksantikvaren, respectively) together with Oslo Kommune and its Cultural Heritage Office, possess the critical financial and regulatory resources necessary for adopting this paradigm. Their leadership and capacity to mobilise resources are pivotal in incentivising other stakeholders. Such resources should be driven towards a suitable business model, the adoption of human-centric digital twins as enabling technology, the establishment of interdisciplinary collaborations between the identified stakeholders, and the up-skilling/re-skilling of the industry workforce.

Corrosion is a deterioration phenomenon of buried long-distance pipelines involving complex dynamic processes. The complexity poses challenges to addressing the safety concerns caused by corrosion. In recent years, the concept of resilience has been introduced into the assessment of engineering systems. However, there is a limited effort in quantitatively assessing the resilience of a pipeline's response to corrosion. This work aims to develop a novel framework to quantify the resilience of pipelines against corrosion while considering the resilience evolution induced by future corrosion growth, dynamic in-line inspection (ILI) plans, and distinct repair strategies (re-coating, composite material reinforcements, and pipe replacement). Pipeline Service Resilience (PSR) is modeled as a function of absorption, adaptability, and restoration capabilities based on the time-dependent burst pressure metric. Dynamic Monte Carlo Simulation technique is employed to model the potential resilience evolution scenarios to predict the PSR. The proposed framework is demonstrated on an in-service pipeline. The case results show that the PSR value ranges from 0.8943 to 1 due to the uncertainty of the resilience evolution process. Noteworthy impacts on PSR include repair time, ILI intervals, anti-corrosion ability, decision-making time, corrosion depth growth rate, and corrosion length growth rate (in decreasing order of sensitivity). The proposed methodology can potentially emerge as a significant tool for evaluating pipeline resilience under corrosion.

Understanding and enhancing the resilience of transport networks against climate-induced extreme events, such as wildfires, is critical to minimizing disruptions and their societal impacts. In this context, resilience is essential for effectively coping with these hazards, as road disruptions can hinder evacuation efforts, reduce accessibility, and lead to significant economic losses. Despite scientific progress, existing resilience assessment frameworks have limitations, including scenario-specific results and limited consideration of the underlying resilience concepts. To address these limitations, this paper introduces a resilience framework based on dynamic thresholds and characteristic curves to evaluate system recovery capacity. The framework incorporates a temporal dimension, allowing for the analysis of recovery time and recovery rate, which depend on the resources available for recovery activities. The characteristic curves illustrate system resilience by capturing key information on the preparedness, response, and recovery capacities inherent in each network. Consequently, the framework offers a more comprehensive view of system behavior during the recovery stage, as demonstrated through its application to a Portuguese case study. The insights gained can assist stakeholders in determining the feasibility of strengthening system resilience through enhanced response and recovery efforts, as well as in identifying when it is critical to reinforce resilience at earlier stages through adaptation measures.

This paper introduces a computation-free method for evaluating and prioritizing simulation-based stress tests for resilience assessment of transport systems. It enables infrastructure managers to efficiently screen and rank stress tests, optimizing the selection process to maximize insights into system resilience while minimizing computational demands. Stress tests have been proven to be a practical tool for understanding and mitigating the impact of disruptive events, yet conducting all possible tests using simulations, particularly for complex systems including plausible scenarios to account for climate change and other stressors, is computationally impractical, thus discouraging their use in practice. To address this, the paper suggests a methodology to estimate the impact of stress tests on risks at no computation cost and rank them accordingly to be selected for more detailed assessment. It uses the results of an initial risk assessment and, through a novel implementation of importance sampling and Bootstrapping resampling, selects subsets of the initial results to mimic specific stress test conditions, estimating their impact on risks. The methodology was validated through application to a Swiss road network facing flooding, demonstrating its practical effectiveness in identifying stress tests with significant potential impact on risks, hence having higher priority for more detailed assessment. In the presented case study, the proposed method enabled instant screening of 80 stress test scenarios, saving approximately 56 weeks of computation.

This paper addresses the growing need to shift wildfire management strategies from suppression to greater preparedness and adaptation in response to increasingly frequent and intense wildfire events. Traditional approaches prioritize suppression actions, but this study emphasizes the combined role of adaptation measures and suppression efforts in enhancing resilience to wildfires. While suppression tackles immediate threats, adaptation aims to reduce long-term vulnerabilities and enhance resilience to future wildfire risks. The European Union has made significant efforts to promote fire-resistant territories, but gaps persist in adaptation knowledge and preparedness. To address this, the study demonstrates the effectiveness of a resilient-preparedness framework to analyze the systemic impact of adaptation measures. Subsequently, the framework is extended to identify the most cost-effective combination of measures to enhance system resilience. The methodology employs a genetic multi-objective algorithm to identify the most effective set of adaptation measures across various wildfire intensities and dimensions of resilience, including physical, operational, and social aspects. By integrating grey, green, and soft adaptation measures, the methodology contributes to understanding how to enhance the wildfire resilience of road networks. Overall, it serves as a decision-support tool to guide initiatives under the EU Green Deal and improve wildfire management strategies.

The Scholarship of Teaching and Learning (SoTL) pertains to scholarly endeavors centered on the pedagogical aspects of teaching and learning, and its principal objective is the enhancement of students’ educational experiences. This systematic literature review addresses the following questions: (1) In what capacity do educators within the field of civil and structural engineering (CaSE) engage with SoTL?, and (2) What are the benefits of implementing a SoTL for CaSE educators? The scope of the review encompasses SoTL studies specifically developed by CaSE educators and implemented within CaSE teaching and learning environments. Findings are synthesized and disseminated via a bibliometric analysis and a narrative synthesis. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. It was found that CaSE educators participate in SoTL endeavors through diverse approaches; however, such involvement remains more of an exception than a common practice. The insufficiency of existing benefits and incentives, if any, serves as a barrier hindering broader engagement and participation in SoTL activities.

This paper presents the main findings of the JRC report “Impact of climate change on the corrosion of the European reinforced concrete building stock” [1]. It evaluates the climate change-induced carbonation in reinforced concrete buildings in the EU Member States up to year 2100 and the time for corrosion onset and the repair costs under moderate and extreme CO2 emissions scenarios. The results indicate that, without climate change, natural aging of buildings would not lead to corrosion by 2100, as the carbonation depth would remain smaller than the concrete cover depth. However, if more severe climate change scenarios are considered, corresponding to the case when the emissions targets are not met, specifically the Paris Agreement's goal of limiting global warming to well below 2°C and pursuing efforts to limit it to 1.5°C, the potential economic costs and welfare losses in some EU countries could be substantial. Climate change-induced carbonation is expected to affect the 20th-century building stock, but not the recently constructed buildings meeting modern European standards for concrete cover durability. Adaptation measures for the building stock are proposed.

Manufacturing imperfections are an inherent aspect of the production process, affecting the reliability and performance of all engineered components, including actuators. This work investigates the effects of manufacturing uncertainty in coil assembly. Given the manufacturing tolerances of geometrical and material properties of wires and coils, the coil’s electrical response is studied from a probabilistic perspective. While many researchers acknowledge these uncertainties, they often do not quantify their effects on the system’s response. We propose the use of copulas to model nonlinear relationships and quantify the probabilistic dependence between design variables (i.e., wire and coil’s properties) and their effects on electrical responses. Additionally, tolerances of the design variables are determined through unstructured expert elicitation. The results show the impact of the thickness of the wire’s insulation in the performance of the coil. Furthermore, we establish a design space that is studied probabilistically, allowing for probabilistic design optimization.

Due to the inherent uncertainties in manufacturing properties and intrinsic variability of materials, the assumption of homogeneous input variables is generally not justified. As a result, stochastic forward problems have emerged as a tool to incorporate these uncertainties into numerical simulations, improving model prediction capability in structural analyses. Although most of the existing methods focus on the consideration of stochastic loading, the recently developed statFEM employs a Bayesian paradigm to incorporate data and propagate uncertainties from random physical properties in finite element models. This tool, however, is not developed for cases when Gaussian assumptions are inadequate. The present work provides a copula-based approach embedded into the statFEM methodology to propagate uncertainty from arbitrarily distributed physical properties. The random variables are defined in terms of a Gaussian Copula Process, where samples are drawn from a latent variable governed by a Gaussian Process and then brought respectively to copula and marginal spaces, producing random variables with the desired distribution while retaining the usually desired smooth Gaussian dependence in the spatial domain. The quality of the approximated results is then assessed in a simplified 1D Poisson problem by comparing with Monte Carlo sampling results for different random diffusion coefficients, demonstrating that the method is capable of providing good responses for non-Gaussian physical parameters.

Recent advancements in intelligent transportation systems and data analytics within transportation systems present a significant opportunity to enhance operational efficiency. In this context, the pivotal role of intelligent agents in achieving real-time optimisation for traffic management is highlighted. Such agents can predict and decide autonomously and can be trained to understand the underlying complexities of the traffic in real-time. In this paper, an innovative framework to perform real-time traffic optimal management decisions is proposed. Its rationale uses a fusion of data observations and simulation to enable an autonomous agent capable of accurate adaptive traffic management. A Case Study of application is developed using the M50 motorway in Dublin, where the speed limits are applied as adaptive parameters for optimal traffic management. Results show that the intelligent agent can autonomously predict travel times and decide in real-time the optimal speed limits to impose on a motorway when signs of congestion are found. The agent can reduce the mean travel time of a time interval by up to 55 % and the mean waiting time by up to 69 % in a situation of congestion. The average travel times of the studied M50 junction have significantly improved, showing the potential of autonomous agents in enhancing real-time optimal traffic management.

Industry 4.0 has led to digitalization and an increase in industrial activity. However, it has recently been recognized as inadequate for achieving European goals by 2030. Therefore, a novel Industry 5.0 paradigm has emerged in response to the unexpected negative effects caused by its predecessor. Industry 5.0 is mainly based on three foundational ideas: i) human-centrism, ii) resilience, and iii) sustainability. Human-centric solutions and human-machine-interaction; bio-inspired technologies and smart materials; real time-based digital twins and simulation; cyber safe data transmission, storage, and analysis; artificial intelligence; and energy efficiency and trustworthy autonomy have been recognized as the enabling technologies of this transformative vision. This paper outlines the protocol adopted to conduct a systematic literature review with the aim of exploring how the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C) industry can adapt and be better prepared to embrace novel Industry 5.0 principles and enabling technologies, ultimately resulting in enhanced conservation practices for the built cultural heritage environment.

Around the world, an increasing amount of bridge infrastructure is ageing. The resources involved in the reassessment of existing assets often exceed available resources and many bridges lack a minimum structural assessment. Therefore, there is a need for comprehensive and quantitative approaches to assess all the assets in the bridge network to reduce the risk of collapsing, damage to infrastructure, and economic losses. This paper proposes a methodology to quantify the structural criticality of bridges at a network level. To accomplish this, long-run site-specific simulations are conducted using Bayesian Networks and bivariate copulas, utilizing recorded traffic data obtained from permanent counting stations. To enhance the dataset, information from Weigh-in-Motion systems from different regions was integrated through a matching process. Subsequently, the structural response resulting from the simulated traffic is assessed, and the extreme values of the traffic load effects are obtained for selected return periods. Site-specific bridge criticality as a performance indicator for traffic load effects is derived by comparing the extreme load effects with the design load effects. The outcomes are mapped to facilitate visualization employing an open-source geographic information system application. To illustrate the application of the methodology, a total of 576 bridges within a national highway network are investigated, and a comparison with a popular simplified method is shown. The methodology herein presented can be used to assist in assessing the condition of a bridge network and prioritizing maintenance and repair activities by identifying potential bridges subjected to major load stress.

This paper explores the use of simulation-based training for mathematical learning in undergraduate and graduate mathematics, science, and engineering courses. Simulation-based training offers the advantages of active learning and inquiry-based learning techniques. Furthermore, it provides extensive flexibility, ranging from user-level usage of simulations to the modification or creation of new possibilities by the student, thus engaging different cognitive levels to achieve the learning objectives. This is particularly interesting in groups consisting of students from diverse backgrounds and levels, due to factors such as their international origin or varying prior education, especially in interdisciplinary Master’s degree programmes. Additionally, in online or blended environments (which have become widespread during the last years), simulation-based learning has the advantage of granting students a certain degree of autonomy, which can, to some extent, compensate for the absence of the instructor’s physical presence.

Travel-time prediction is a critical component of Intelligent Transportation Systems (ITS), offering vital information for tasks such as accident detection, congestion management, and traffic flow optimisation. Accurate predictions are highly dependent on the selection of relevant features. In this study, a two-stage methodology is proposed which consists of two layers of Optimisation Algorithm and one Data-Driven method (OA2DD) to enhance the accuracy and efficiency of travel-time prediction. The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. The proposed OA2DD method was applied to a case study on the M50 motorway in Dublin. Results show that OA2DD improves the convergence curve and reduces the number of selected features by up to 50 %, leading to a 56 % reduction in computational costs. Furthermore, using the selected features from OA2DD, reduced the prediction error by up to 29 % compared to the full feature set and other feature selection methods, demonstrating the method's effectiveness and robustness.