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.

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.

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 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.

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.

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.

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.

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.

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.

Despite its recent adoption, Industry 5.0 has attracted significant attention from researchers across various fields. However, the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C) industry, particularly in the context of built cultural heritage conservation, has lagged in this regard. This study aims to gain a deeper understanding of conservation professionals’ perceptions regarding the adoption of Industry 5.0 principles and enabling technologies, as well as the perceived barriers and the skills needed to address them. A survey questionnaire was designed, tested, and implemented to collect relevant data. Analysis of the collected data reveals that, although there is a clear recognition of the significance of Industry 5.0 principles and enabling technologies, their application in built cultural heritage conservation remains limited. Future initiatives should prioritise bridging knowledge gaps, enhancing training programmes, and securing necessary resources to overcome these existing barriers.