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.

@en

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.

@en

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.

@en

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.

@en

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.

@en

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.@en

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.@en

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.@en

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.

@en

The authors regret the acknowledgments in the published article are incomplete. The complete acknowledgments are as follows: “This research was supported by the Mexican National Council for Science and Technology (CONACYT) under project number 2019-000021-01EXTF-00564 CVU 784544. The authors would like to thank Dr. Andrés Antonio Torres Acosta, Research Professor at the Department of Sustainable and Civil Technologies, School of Engineering and Sciences, Tecnológico de Monterrey for his contributions to this research.” The authors would like to apologise for any inconvenience caused. ____________________________.

@en

Concrete is clearly one of the most predominant-used material in both residential and non-residential structures across Europe (Peled and Fishman, 2021). A reinforced concrete (RC) structure is expected to satisfy criteria for serviceability, structural integrity, and stability over its designed operational lifespan, without significant loss of utility or excessive unforeseen maintenance (for general requirements see also EN 1990). Comprehending the degradation mechanisms that impact these structures is essential for accurately estimating their service life and formulating cost-effective maintenance strategies. The main mechanisms responsible for concrete degradation include corrosion caused by carbonation and the presence of chloride ions, freeze-thaw cycles, sulphate attack and erosion due to high-velocity water flow, ice, or wind-blown sand.@en

Large-scale electrification of heavy-duty road freight faces challenges including scarcity of charging infrastructure and high battery costs. Dynamic charging could help overcome these challenges by enabling trucks to charge while driving. Important additional benefits for carriers related to lower required sizes and longer lifetimes of batteries could justify the required investments. The study investigates the optimal configuration of network sections to be electrified so that the balance between costs and benefits turns out positive. A case study for a highway network spanning 4 countries in Europe suggests that dynamic charging can lead to a significant reduction in overall transport system costs, up to very large network sizes. The study supports the decision-making of policymakers and road authorities by providing new insights into the costs and benefits of dynamic charging networks, and simultaneously considering the perspectives of investors and users.

@en

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.@en

The rise of Industry 4.0 has led to a rapid increase in digitalization and industrial operations. However, it has recently been deemed insufficient in fulfilling European objectives for 2030. In response, and to counteract the unintended negative consequences triggered by Industry 4.0, Industry 5.0 has been introduced. The purpose of this article is to shed light on how the architecture, engineering, construction, management, operation, and conservation industry can adapt and better prepare to embrace novel Industry 5.0 principles and enabling technologies, ultimately resulting in enhanced conservation practices for the built cultural heritage environment. To achieve this, a systematic literature review was conducted following the PRISMA methodology. The principal results of this article highlight the work of different conservation professionals and our views on the potential of Industry 5.0 for enhancing conservation practices. Major conclusions indicate that artificial intelligence and digital twins are the two most studied technologies in the field. Sustainability is broadly discussed throughout the analyzed literature, whereas resilience and human centrism require further research and implementation efforts to achieve a holistic Industry 5.0 adoption. The significant scientific novelty of this work lies in the comprehensive scope of the review in terms of principles and enabling technologies, with a particular emphasis on heritage buildings. Thus, it is valuable for conservation practitioners seeking best practices, for policymakers as it suggests ways to encourage the adoption of novel technologies and principles in conservation, and for researchers as it highlights gaps and stimulates further paths of research and innovation.@en

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.

@en

In addressing the impact of climate change on the corrosion of RC buildings, it is crucial to distinguish between two adaptation strategies. The first refers to measures integrated during the design phase for new structures. The second involves strategies implemented during the service phase to enhance the resilience of existing structures. This differentiation ensures that both new and existing buildings are equipped to withstand the challenges posed by a changing climate. This section focuses on the first while the strategies implemented during the service life of the existing buildings are discussed in the following section.@en

Climate change is leading to a rise in the occurrence and intensity of wildfires, exacerbated by the growing encroachment of communities into the natural environment, posing challenges to our global capacity to respond to wildfires. During wildfire events, road transport infrastructure becomes crucial for the evacuation of people and accessibility to an emergency by first responders. Nevertheless, resilience management of transportation infrastructure affected by wildfires is poorly considered, despite its relevant role and high exposure to wildfires. Therefore, this study proposes a new methodology to estimate the priority level for wildfire preparation by combining exposure and criticality of road transportation infrastructure to wildfire hazards with consideration of different wildfire categories. The analysis is conducted at the system level considering interdependencies and redundancies among infrastructure components and using a geographic information system (GIS) to automate the modelling process and visualization of results. The proposed methodology is applied to a case study in the Leiria region of Portugal, demonstrating its utility in prioritizing economic resources and decision-making for areas requiring preparation. This approach can serve as a resilience-based tool for decision-making, supporting the implementation of effective adaptation strategies to enhance wildfire resilience.@en

The use of sensitivity analysis is essential in model development for the purposes of calibration, verification, factor prioritization, and mechanism reduction. While most contributions to sensitivity methods focus on the average model response, this paper proposes a new sensitivity method focusing on the extreme response and structural limit states, which combines an extreme-oriented sensitivity method with polynomial chaos expansion. This enables engineers to perform sensitivity analysis near given limit states and visualize the relevance of input factors to different design criteria and corresponding thresholds. The polynomial chaos expansion is used to approximate the model output and alleviate the computational cost in sensitivity analysis, which features sparsity and adaptivity to enhance efficiency. The accuracy and efficiency of the method are verified in a truss structure, which is then illustrated on a dynamic train–track–bridge system. The role of the input factors in response variability is clarified, which differs in terms of the design criteria chosen for sensitivity analysis. The method incorporates multi-scenarios and can thus be useful to support decision-making in design and management of engineering structures.

@en

The Digital Twin (DT) concept, as understood nowadays, appeared in the early 2000s as an attempt to create virtual replicas of physical assets, such as bridges, that can be used to examine, monitor and manage their performance. Up to this day, it has been successfully applied in the fields of aeronautics, manufacturing, medicine, and more recently, in the architecture, engineering, and construction industry. The DT of a bridge requires the creation of a virtual replica of the real-life asset, along with the connection and feedback of information channel between the two of them. This connection is currently achieved through the generation of real-time data by the placement of sensors in the real bridge and the application of structural health monitoring techniques to analyze such data. This connection could result in a complex, time-consuming, and expensive process which would hinder the creation of DT prototypes for development purposes in the bridge engineering field. This paper aims at exploring the currently available synthetic data generation methodologies and tools, which could be used as a faster and a more economically feasible alternative to real monitoring, for the creation and development of DT prototypes of bridges, for both industry and research-oriented purposes. A synthetic data generation framework is proposed that can produce FAIR benchmark databases that are based on Findability, Accessibility, Interoperability, and Reuse, which could be used in the prototyping of bridge DTs. Finally, tentative future improvements in this topic are discussed.@en

Transition zones such as level crossing and bridge approaches are critical links in railway networks due to higher degradation rates and maintenance needs. In this context, parametric optimization has been applied to improve the design in transition zones; however, it requires a more computationally efficient tool to support repetitive function evaluations, since the involved vehicle–track dynamic simulations are becoming more expensive to evaluate. For this purpose, a surrogate-based simulation methodology is proposed to search for an optimal combination of parameters relevant to the geometry and elasticity of track structures. Specifically, the presented methodology integrates finite element (FE)-based modeling with surrogate-assisted optimization: (1) the FE model is developed to characterize the dynamic behavior of a level crossing under a moving vehicle; (2) the optimization problem is formulated upon this mechanical model by extending the expensive FE simulations to an adaptive surrogate modeling scheme. This integration facilitates efficient exploration of the track design space (thereby reducing the computational cost), and a reasonable balance can be achieved between solution quality and computational effort. The methodology is applied to a Dutch railway case. Results show that compared to a reference design, the optimized design significantly improves performance indicators relevant to wheel–rail contact forces and energy dissipation in the ballast layer. The solution brings great potential in achieving a more desirable vehicle–track interaction and improving the connecting performance between level crossings and transitions. The methodology is applicable to other railway structures and may also contribute to improvements in current track design practices.

@en