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.

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.

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.

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 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.

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.

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.

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.

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.

Digital twins are expected to facilitate the digital transformation of the architecture, construction, engineering, management, operation, and conservation industry. One of their main applications in the field of bridge engineering is the possibility of real-time damage detection. To achieve this, advanced anomaly detection algorithms need to be validated to alert bridge operators on the risks related to the detected anomalies. A synthetic data generation framework has been proposed to generate benchmark databases for testing and validation as a cheaper and faster alternative to the collection of real monitoring data. In this paper we present the S101 Bridge, which has been selected as first case study for the generation of the proposed synthetic database. A finite element model of the bridge was developed and calibrated based on the reported natural frequencies and mode shapes of the actual structure available in the literature. The calibration process is described in detail along with the sensitivity analysis of the considered parameters. The results indicate a strong agreement between the first four natural frequencies and mode shapes of the experimental and numerical bridge model. It is concluded that the calibrated model is suitable for the generation of bride digital twins what-if scenarios, which will be of great significance for both practitioners and the research community.

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.

This paper introduces a novel extension of the multi-system optimisation method, known as the 3C concept, tailored for optimising budget allocation for bridge interventions at the network level. This extended methodology accounts for the interdependencies among bridges due to their spatial proximity within the network. It incorporates direct and user costs, bridge performance indicators, and a bridge deterioration model. A real-world case study involving a portfolio of 555 bridges demonstrates the practicality of the methodology, efficiently determining the optimal intervention sequence. Over an 18-year analysis period, the proposed methodology achieved a 23% reduction in total costs by combining repairs for bridges with high to severe damage and maintenance for the others. This represents a significant improvement compared to the traditional approach, used by bridge management agencies, which relies exclusively on maintenance. The optimised procedure outperforms human intuition in managing complex bridge networks, particularly over extended periods. This methodology can assist transportation agencies in implementing and exploring various scenarios by adjusting the time between consecutive interventions and budget constraints, supporting comprehensive analysis and informed decision-making.

Climate change is causing an increase in the frequency and intensity of wildfires, demonstrating that our capacity to respond to them is insufficient. Therefore, it is necessary to reconsider wildfire management policies, practices, and decision-support tools, extending beyond emergency measures. This study presents the extension of a GIS-based methodology for fire analysis, providing decision-making support for the implementation of new fire-related policies for road transportation infrastructure. It represents a novel contribution that facilitates the transition towards proactive wildfire policies. The framework is demonstrated to support informed decision-making, addressing both reactive actions, i.e., emergency response, and the evaluation of proactive adaptation measures at a system level. The results suggest that landscape management policies can play an important role in improving the resilience of road networks to wildfires.

Factors such as human activity and climate change are contributing to an increase in the frequency and intensity of wildfires. This problem has challenged society's knowledge, response capacity, and resilience, revealing its inadequacy to cope with the new wildfire regime characterized by extreme wildfire events (EWE). Policies on wildfire management mainly focus on suppression and managing emergencies, which may be insufficient to reduce EWE's incidence and cope with its impact. Consequently, there is a lack of tools to support decision-making in wildfire management in other important aspects, such as prevention and protection. This study examines global wildfire policies specifically in the Iberian Peninsula (Portugal and Spain), including cross-border policies. A GIS-based tool to evaluate different normal and extreme wildfire management policies is applied to a cross-border case study, paying attention to the impact on critical land-based transport systems. A relevant outcome of the tool application is that suppression must be complemented with other wildfire management strategies in the analyzed area. The gained insights can help stakeholders to improve decision-making in wildfire management to successfully address EWE.

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.

Regardless of the current challenges and limitations of Digital Twins, there seems to be only one school of thought and a consensus toward their future adoption for design, construction, management, operation, and decommissioning of bridges. This novel paradigm surged within the Industry 4.0 context and was fostered by evolving technologies such as artificial intelligence, internet of things, big data, among others. Digital twins have now been recognized as an enabling technology towards the development and adoption of Industry 5.0 principles. Such principles are human-centrism, resilience, and sustainability. In this paper we explore the increasingly important role that humans will have within existing bridge digital twin frameworks and how the human-centrism principle could be better implemented within such context. Moreover, we also discuss how a successful implementation of human centrism could uphold and maximize the resilience and sustainability of existing bridges, especially those with cultural heritage value.

This book focuses on how to apply optimization algorithms to specific optimization problems. It does so using python, scipy and pymoo. It contains the preparations for the practical sessions in CME4501 Engineering Systems Optimization.

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.

In the last decade, extreme wildfire events (EWE) have left severe economic, environmental, and social impacts worldwide. Notable instances in 2023 occurred in Canada, Greece, and Maui in the U.S. Canada and Greece set records for the worst fire season, with the largest burned area in both regions. Meanwhile, Maui witnessed the deadliest wildfire in U.S. history, claiming over 100 lives. The societal consequences have reached unprecedented levels, highlighting the insufficient capacity to respond to this new fire regime, even with intensified suppression efforts (Bowman et al. 2017). The evolution of the new wildfire regime stems from complex interactions of biological, meteorological, physical, and social factors. The convergence of weather patterns and fuel conditions are the factors that most influence wildfire behavior. Weather patterns influence fuel conditions such as moisture and the formation of the physical conditions necessary for wildfire spread, such as unstable atmospheres, pyro convection, or the new interaction between drought, high air temperatures, low relative humidity, and wind variations. Besides, fuel conditions such as continuity, quantity, flammability, and vegetation management directly influence wildfire behavior (Rogers et al. 2020). Consequently, climate change and human-related factors compound the challenges of EWE. Human-related factors encompass social aspects such as rural depopulation, cultural elements such as neglect and arson, and political dimensions, namely, land management, afforestation, and forest restoration. Current actions to reverse climate change may not be sufficient (IPCC, 2022). In this sense, specific measures addressing human-related factors, particularly in land management, can enhance wildfire resilience against these phenomena' increasing occurrence and intensity.
Human intervention has modified the natural environment, increasing susceptibility to wildfires. For instance, the severity of recent fires in Maui (2023) is linked to the proliferation of invasive grasses covering significant portions of the islands. In Portugal, extensive and highly flammable eucalyptus plantations have reduced the country's resilience to wildfires (Weston, 2023). In addition, Portugal has extensive areas of undermanaged forests and shrublands that facilitate the occurrence of frequent, huge, and uncontrolled wildfires (Fernandes et al. 2016). This evidence the importance of effective landscape management as a key strategy for reducing landscape flammability and fuel continuity. Preparedness and adaptation activities become imperative for promoting wildfire resilience in the medium and long term, potentially mitigating the consequences of the new wildfire regimen (Loepfe, Martinez-Vilalta, and Piñol 2012).
Therefore, one of the main challenges for wildland fire scientists and managers is to promote more resilient landscapes and consequently, there is an eminent need for tools to support decision-making in this domain. Various frameworks exist for modelling fuel connectivity and assessing the spatial influence on fire spread, e.g., (Loehman, Keane, and Holsinger 2020; Sá et al. 2022; Aparício et al. 2022). However, these models are intrinsically attached to propagation models that primarily aim to predict wildfire occurrence, specifically fire ignition points. This connection introduces high uncertainty, especially considering that a significant portion of forest fires, particularly in the European Union, result from arson. In Portugal, for instance, 98% of fires are attributed to arson. Existing models fail to capture this high level of uncertainty adequately. Moreover, current methods are increasingly specialized, focusing on specific scenarios. Nevertheless, their limited ability to extrapolate and apply to diverse situations or conditions raises concerns about the conclusiveness of decision-making based on the analysis of a restricted number of fire events (Arango et al, 2023).
To address these issues, this study proposes the use of a Geographic Information System (GIS)-based methodology for fire analysis, serving as a more effective tool for landscape fuel management. This tool evaluates exposure by considering various fuels, encompassing both built and natural environments. Unlike other models, this tool does not require the definition of the wildfire conditions and the location of the fire ignition, thereby eliminating associated uncertainties. Instead, the tool focuses on the system's ability to cope with such events, incorporates different intensities of wildfires including EWE, and conducts analyses at the system level. It has previously demonstrated its effectiveness in assessing various adaptation measures, capturing the influence of different fuels (sources or barriers) in exposure assessment. This study shows the tool's efficacy in landscape management by applying different fuel treatment strategies to reduce exposure to wildfires. For this, the exposure level of a case study in the Leiria region of Portugal is compared to the conditions that led to the devastating fire in 2017 and future conditions. Future scenarios involve two cases: one without implementing fuel treatment strategies and another using treatment strategies. This approach provides stakeholders with pertinent information to support necessary changes in forest management and the development of fire-resilient landscapes. The results suggest that the tool can significantly contribute to achieving certain goals outlined in the European Green Deal.