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.

Bridge infrastructure has great economic, social, and cultural value. Nevertheless, many of the infrastructural assets are in poor conservation condition as has been recently evidenced by the collapse of several bridges worldwide. The objective of this systematic review is to collect and synthesize state-of-the-art knowledge and information about how bridge information modeling, finite element modeling, and bridge health monitoring are combined and used in the creation of digital twins (DT) of bridges, and how these models could generate damage scenarios to be used by anomaly detection algorithms for damage detection on bridges, especially in bridges with cultural heritage value. A total of 76 relevant studies from 2017 up to 2022 have been taken into account in this review. The synthesis results show a consensus toward the future adoption of DT for bridge design, management, and operation among the scientific community and bridge practitioners. The main gaps identified are related to the lack of software interoperability, the required improvement of the performance of anomaly-detection algorithms, and the approach definition to be adopted for the integration of DT at the macro scale. Other potential developments are related to the implementation of Industry 5.0 concepts and ideas within DT frameworks.

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.

Changes in a construction project schedule can impact the project's planned duration, resulting in penalties. A manual trial-and-error probabilistic approach is usually conducted to find an appropriate set of corrective measures to mitigate delays of the overall project. However, this approach does not capture the actual goal-oriented behavior of project managers who react to the actual scenarios causing delays, leading to a fundamental modeling error. Moreover, it does not employ control and automation concepts when finding the optimal mitigation strategy. To remove this modeling error and to automate the mitigation process, the Mitigation Controller (MitC) software is developed. The MitC searches for the most cost-effective set of mitigation measures considering risk events and durations uncertainties of activities. Moreover, the MitC captures activity correlations and enables contractual penalty/reward schemes in the simulation. As a result, it returns the most effective mitigation strategy that minimizes the mitigation cost and penalty and maximizes the reward potential. The Mitigation Controller introduced here constitutes an open-source code written in Matlab

Assessing flood risks on road infrastructures is critical for the definition of mitigation strategies and adaptation processes. Some efforts have been made to conduct a regional flood risk assessment to support the decision-making process of exposed areas. However, these approaches focus on the physical damage of civil infrastructures without considering indirect impacts resulting from social aspects or traffic delays due to the functionality loss of transportation infrastructures. Moreover, existing methodologies do not include a proper assessment of the uncertainties involved in the risk quantification. This work aims to provide a consistent quantitative flood risk estimation and influence factor modelling for road infrastructures. To this end, a Flood Risk Factor (FRF) is computed as a function of hazard, vulnerability, and infrastructure importance factors. A Bayesian Network (BN) is constructed for considering the interdependencies among the selected input factors, as well as accounting for the uncertainties involved in the modelling process. The proposed approach allows weighting the relevant factors differently to compute the FRF and improves the understanding of the causal relations between them. The suggested method is applied to a case study located in the region of Santarem Portugal, allowing the identification of the sub-basins where the road network has the highest risks and illustrating the potential of Bayesian inference techniques for updating the model when new information becomes available.

In traffic networks, some elements are more prone to suffer or to create disruptive situations, and the identification of these elements becomes a challenge due to the large number of possible threats. The following paper presents two new methodologies to identify and rank vulnerable and critical links of traffic networks. These methodologies use the Fisher Information Matrix, and the analysis of eigenvalues and eigenvectors, to systematically rank the links of a network. The identification is done by using traffic variables, such as the demand, the travel time, and the network's flow. For the ranking of the links, disruptions are considered in all the possible locations of the network, and the effects are systematically evaluated. In addition, the evaluation of traffic resilience is included in the process to validate the results. Finally, both methodologies are tested in a real network to infer on the validity of the results.

Probabilistic Monte Carlo simulations are often used to determine a project's completion time given a required probability level. During project execution, schedule changes negatively affect the probability of meeting the project's completion time. A manual trial and error approach is then conducted to find a set of mitigation measures to again arrive at the required probability level. These are then implemented as scheduled activities. The mitigation controller (MitC) proposed in this paper automates the search for finding the most cost-effective set of mitigation measures using multiobjective linear optimization so that the probability of timely completion remains at the required level. It considers different types of uncertainties and risk events in the probabilistic simulation. Moreover, it removes the fundamental modeling error that exists in the traditional probabilistic approach by incorporating human control and adaptive behavior in the simulation. Its usefulness is demonstrated using an illustrative example derived from a recent Dutch construction project in which delay is not permitted. It is shown that the MitC is capable of identifying the most effective mitigation strategies allowing for substantial cost savings.

The present work reviews the implementation of adaptive metamodeling for reliability analysis with emphasis in four main types of metamodels: response surfaces, polynomial chaos expansions, support vector machines, and Kriging models. The discussion presented is motivated by the identified spread and little interaction between metamodeling techniques in reliability, which makes it challenging for practitioners to decide which one to consider in a context of implementation. The conceptual problem of reliability analysis and the theoretical description of the four models is presented, and complemented by a comparative discussion of applications with identification of new areas of interest. The different considerations that influence the efficiency of adaptive metamodeling are reviewed, with extension to applicability discussions for the four models researched. Despite all adaptive techniques contributing to achieve significant gains in the amount of effort required for reliability analysis, and with minimal trade-off in accuracy, they should not be expected to perform equally in regard to the dependence on the reliability problem being addressed. Cross application of methodologies, bridging the gap between methodology and application, and ensembles are some of new areas of research interest identified. One of the major critical considerations for adaptive metamodeling, and that has been target of limited research, is the need for comprehensive techniques that allow a blind selection of the most adequate model with relation to the problem in–hand. To conclude, the extensive and comprehensive discussion presented aims to be a first step for the unification of the field of adaptive metamodeling in reliability; so that future implementations do not exclusively follow individual lines of research that progressively become more narrow in scope, but also seek transversal developments in the field of adaptive metamodeling for reliability analysis.

When analysing the performance of bike-sharing scheme (BSS) stations, it is common to find stations that are located in specific points that capture the interest of users, whereas nearby stations are clearly underused. This uneven behaviour is not totally understood. This paper discusses the potential factors influencing station attractiveness, supported by the related literature on cyclists’ and pedestrians’ preferences and the characteristics of the stations themselves. The existing literature addresses these topics independently, while this work unites them by proposing a non data-extensive methodology that allows the attractiveness of BSS stations to be assessed. Attractiveness in this context is understood as the set of physical, environmental and service-related features of a bike station that make it more appealing for BSS users than nearby stations. Special attention is paid to differentiating objective features, based on facts, from subjective features, those influenced by personal perceptions. This classification becomes important in this context because subjective aspects can change from one geographical location to another, making the findings related to these aspects difficult to apply to other regions. Moreover, the assessment of the stations’ levels of safety and security is included. Thus, the proposed measure of attractiveness of BSS stations provides a balanced overview of several features. The consideration of station attractiveness when designing BSS layouts will help to refine the design of new layouts and will assist in conducting an appropriate diagnostic evaluation of the existing ones. This tool will allow urban and transportation planners to reduce re-balancing costs and to maximise user satisfaction at a low cost, which have a direct impact on improving the urban sustainability. The proposed method is applied to the Dublin bike sharing scheme, Dublinbikes, with good performance results.