Collision avoidance in maritime navigation, particularly between autonomous and conventional vessels, involves iterative and dynamic processes. Traditional path planning models often neglect the behaviours of surrounding vessels, while path predictive models tend to ignore ship interaction features essential in collision scenarios. This study proposes a decision-making framework for collision avoidance, particularly focused on the interaction between autonomous and conventional vessels. The framework integrates a human-preference-aware navigational trajectory prediction model into the collision avoidance process, enhancing the decision-making capabilities in dynamic and interactive environments. We first model human-controlled ship navigational preferences using a Long Short-Term Memory (LSTM) autoencoder combined with K-means clustering, by extracting key preferences from ship pairs identified through AIS data. These preferences, which reflect strategic trajectory adjustments in response to collision risks, are then incorporated into trajectory prediction using a Multi-Task Learning Sequence-to-Sequence (seq2seq) attention LSTM model. The predicted trajectories provide a basis for the decision-making framework, including a local path planner and a trajectory tracking controller, designed to dynamically adjust the predicted reference path for collision-free navigation and ensure its accurate tracking. The framework was validated using AIS data from the port of Rotterdam, identifying four distinct navigational preferences by combining an LSTM-autoencoder and clustering techniques and demonstrating improved prediction accuracy compared to other existing models. Simulation tests demonstrate that the framework utilises the predicted trajectories to inform decision-making, ensuring accurate path tracking while dynamically addressing collision risks for autonomous ships. By providing preference-aware and adaptive reference trajectories, the framework reduces the likelihood of MASS trajectory misinterpretation by conventional ships, thereby supporting proactive collision avoidance in mixed waterborne transport environments.

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This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important for MASS navigation, offering a framework for situational awareness, including contextual information fusion and decision-making evidence. This research enriches the KM model with collision avoidance rules from the International Regulations for Preventing Collisions at Sea (COLREGs), building upon our previous work on MASS's efficient and COLREGs-compliant navigation in encounter scenarios. The model provides navigational context, covers COLREGs rules and environmental factors, and recommends MASS actions for various scenarios as suggested by COLREGs. Moreover, an adapted DWA, tailored to maritime navigation, accounts for specific constraints and safety measures for MASS, utilising KM-derived situational awareness as constraints in its cost function for path planning. A significant innovation introduced here is a tiered safety distance model featuring proactive, defensive, and collision buffers to ensure rule-compliant and effective collision avoidance. This scheme enables MASS to take timely collision avoidance actions at both proactive and defensive distances, in line with COLREGs recommendations. The effectiveness of the KM-DWA algorithm is validated by comparing it with the basic DWA algorithm in single- and multi-vessel encounter scenarios. The experiment outcomes illustrate the integrated approach's superiority in terms of COLREGs compliance and collision avoidance rate, emphasising its ability to support COLREGs-compliant decision-making and enhance situational awareness in autonomous maritime operations.

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The scientific evaluation of a dam's economic life is necessary to determine its management strategy rationally. However, most studies have mainly focused on analyzing the current economic characteristics of reservoir dams without considering their long-term development trend. And they have failed to incorporate the potential loss caused by dam breaches as one of the operating costs for dams. Consequently, this results in the inability to scientifically calculate the economic life of reservoir dam operations. Therefore, this study analyzed the factors influencing the economic life of reservoir dams in term of safety, costs, benefits and society, and established an economic life evaluation indicator system for reservoir dams. Based on the analyses of costs and benefits of reservoir dams, an economic life evaluation model for reservoir dams was constructed by comprehensively considering the impact of potential dam breaches and the social impact of reservoir dams on the production and lives of local residents. Moreover, based on economic theory, the economics of various types of management measures for reservoir dams and their impact on the economic life of dams were analyzed. The proposed model was applied to the Luhun Reservoir in Henan Province, China, to quantify the comprehensive costs and benefits of its operation for each year after 1990 s, analyze the trend of its “cost-benefit” relationship, and calculate its expected economic life. The results showed that when considering its own risks and social impacts, the expected economic life of the Luhun Reservoir was 74 years. The Luhun Reservoir is expected to operate up to its non-economic life span till 2039. Finally, suggestions for expanding the economic life of dams were proposed. Potential dam breach losses can significantly reduce the economic life of reservoir dams. And risk management measures for reservoir dams should be particularly strengthened during daily operations and management.

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Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.

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Background: Human errors are widely acknowledged as the primary cause of structural failures in the construction industry. Research has found that such errors arise from the situation created by human factors and organizational factors embedded in the task context. However, these contextual factors have not been adequately addressed in the construction industry. Therefore, this study aims to identify the critical Human and Organizational Factors (HOFs) that influence structural safety in frequently performed tasks in structural design and construction. Methods: Through a comprehensive literature review, a framework consisting of potential critical factors called the HOPE framework, is presented. To identify the most critical HOFs that contribute to human error occurrences, a questionnaire survey to experts in the Dutch construction industry was conducted. Finally, the resulting framework was compared with three actual structural failures for validation. Results: This study shows that the HOFs should be extended with project-related factors (P) and working environment-related factors (E) due to the fact that these task contextual conditions play a significant role in shaping professionals' on-the-job performance. Furthermore, a survey identified 14 HOFs as critical in contributing to an error-prone situation in the structural design and construction tasks. Conclusion: The presented HOPE framework and the identified critical HOFs for structural safety can assist engineers with better hazard identification and quality assurance in practice.

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In recent years, dam failures have occurred frequently because of extreme weather, posing a significant threat to downstream residents. The establishment of emergency shelters is crucial for reducing casualties. The selection of suitable shelters depends on key information such as the number and distribution of affected people, and the effective capacity and accessibility of the shelters. However, previous studies on siting shelters did not fully consider population distribution differences at a finer scale. This limitation hinders the accuracy of estimating the number of affected people. In addition, most studies ignored the impact of extreme rainfall on the effective capacity and accessibility of shelters, leading to a low applicability of the shelter selection results. Therefore, in this study, land-use and land-cover change (LUCC) and nighttime lighting data were used to simulate population distribution and determine the number and distribution of affected people. Qualified candidate shelters were obtained based on screening criteria, and their effective capacity and accessibility information under different weather conditions were quantified. Considering factors such as population transfer efficiency, construction cost and shelter capacity constraints, a multi-objective siting model was established and solved using the non-dominated sorting genetic algorithm II (NSGA- II) to obtain the final siting scheme. The method was applied to the Dafangying Reservoir, and the results showed the following: (1) The overall mean relative error (MRE) of the population in the 35 downstream streets was 11.16 %, with good fitting accuracy. The simulation results truly reflect the population distribution. (2) Normal weather screening generated 352 qualified candidate shelters, whereas extreme rainfall weather screening generated 266 candidate shelters. (3) Based on the population distribution and weather factors, four scenarios were set up, with 63, 106, 73, and 131 shelters selected. These two factors have a significant impact on the selection of shelters and the allocation of evacuees, and should be considered in the event of a dam-failure floods.

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Existing models for correlating global mortality rates with underlying country-specific factors overlook the variations in the effects of these factors on mortality across different countries. These may arise from social, cultural, and political complexities which are usually not measurable and are therefore referred to as unobserved heterogeneity in the statistical literature. Unobserved heterogeneity leads to biased parameter estimates in the models, erroneous inferences about the effects of factors contributing to mortalities, and ultimately inefficient policies. In this paper, latent class modelling is proposed for capturing such unobserved heterogeneity on the cases of traffic mortality and COVID-19 mortality. The ‘pyramid’ model of safety management is used as a common framework for model formulation. The proposed latent class model is an extension of the Negative Binomial (NB) model used in risk epidemiology. The model is tested with data from 105 countries, retrieved from international databases, including socioeconomic, infrastructure, exposure, transport, and COVID-19 variables. The results suggest that there exist two (different) latent country classes in both causes of mortality. The probability of a country belonging to a certain latent class is a much more efficient metric of country membership than previous deterministic groupings (e.g. income or geographic). Variables such as the elderly population, the GDP per capita or the level of motorization, have different effects in different country classes; these effects are not identifiable by conventional statistical modelling. The impact of ignoring unobserved heterogeneity in country mortality modelling is shown by comparing the results with those of conventional NB models.

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The development of advanced ship positioning and intelligent sensing technologies has transformed navigation at sea, moving beyond reliance on captains’ experience and standard routes. The trajectories traversed by ships at sea contain valuable data that can be mined to map maritime transportation networks and inform intelligent navigation systems. Ship trajectory data at scale enables discovery of the underlying network of maritime routes, providing key insights for applications like intelligent navigation, abnormal behavior detection, trajectory prediction, and maritime traffic pattern analysis. This study reviews the development of research on maritime route networks (MRNs) derived from ship trajectory data. It summarizes the technical process to construct a MRN, contrasting approaches for identifying waypoints, extracting routes, and representing the overall maritime traffic network structure. Finally, this study explores potential applications of MRNs and anticipates promising future research directions in this domain.

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This study focuses on measuring the influence of critical Human and Organizational Factors (HOFs) on human error occurrence in structural design and construction tasks within the context of the Dutch construction industry. The primary research question addressed in this paper concerns the extent of HOFs’ contribution to human error occurrence. To answer this question, the Classical Model for Structured Expert Judgement (SEJ) is employed, enabling experts to provide their judgments on task Human Error Probability (HEP) influenced by different HOFs, which are subsequently aggregated mathematically. SEJ is chosen as a suitable approach due to the limited availability of applicable data in the construction sector. As a result, the impacts of HOFs are quantified as multipliers, representing the ratio between the observed or evaluated task HEP and its baseline value. These multipliers are then compared with corresponding multipliers from existing Human Reliability Analysis methods and studies. The findings reveal that fitness-for-duty, organizational characteristics and fragmentation exhibit the most pronounced negative effects, whereas complexity, attitude and fitness-for-duty demonstrate the most significant positive impacts on task performance. These results offer valuable insights that can be applied to enhance structural safety assurance practices.

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A reduction in phishing threats is of increasing importance to organizations. One part of this effort is to provide training to employees, so that they are able to identify and avoid phishing emails. Yet further, simulated phishing emails are used to test whether employees will both identify and report a suspicious email. We worked with a partner bank to examine a repository of many thousands of reported emails from a behavioural perspective. We divide reported emails into categories and examine reporting trends over time relative to training and phishing simulation campaigns. Among our findings, the level of reporting of benign emails is comparable to the number of malicious emails reported, and we see indications that training and simulations amplify the reporting of benign emails. Our analysis uncovers reporting patterns for unique reporters per email campaign as a promising indicator for the security-related culture around phishing prevention. Evidence from our analysis informs recommendations, such as providing reporting infrastructure for reporting not only malicious emails, but also benign but suspicious work-related emails, in a manner that minimises the disruption for users erring on the side of caution when assessing emails.

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This paper introduces a novel stochastic inverse method that utilizes perturbation theory and advanced intelligence techniques to solve the multi-parameter identification problem of concrete dams using displacement field monitoring data. The proposed method considers the uncertainties associated with the dam displacement monitoring data, which are comprised of two distinct sources: the first is related to stochastic mechanical properties of the dam, and the second is due to observation errors. The displacements at different measuring points generated by dam mechanical properties exhibit spatial correlation, while the observation errors at different points can be considered statistically random. In this context, the inversion formulas are derived for unknown stochastic parameters of the dam by combining perturbation equations and Taylor expansion methods. An improved meta-heuristic optimization method is employed to identify the mean of stochastic parameters, while mathematical and statistical methods are used to determine the variance of stochastic parameters. The feasibility of the proposed method is verified through numerical examples of a typical dam section under different conditions. Additionally, the paper discusses and demonstrates the applicability of this method in a practical dam project. Results indicate that this method can effectively capture the uncertainty of dam's mechanical properties and separates them from observation errors.

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Maritime Autonomous Surface Ships (MASS) have gained much attention as a safer and more efficient mode of transportation and a potential solution to reduce the workload of seafarers. Despite the highly sophisticated autonomous systems that enable MASS to make independent decisions, the presence of humans on board or in the loop of safety management highlights the need for effective human-machine interaction. However, a potentially systematic review of critical aspects of human-MASS interaction has not yet been conducted. In this paper, we aim to fill this gap by reviewing the literature related to human-MASS interaction from four crucial perspectives: the state of the art of human-MASS interaction, situational awareness for MASS, collision avoidance methods for MASS within a mixed waterborne transport system (MWTS), and human trust in MASS. Our review reveals that human-MASS interaction for safety and efficiency mainly focuses on four key aspects: (i) human factors, (ii) available technologies supporting the autonomy of MASS, (iii) system analysis and design for human-MASS interaction, and (iv) potential requirements regarding regulations. Moreover, we provide a detailed discussion of the three fundamental factors that influence human-MASS interaction, including situational awareness, decision-making for MASS in a mixed waterborne transport system, and human trust in the autonomous system of MASS. Finally, based on our analysis, we propose an integrated framework of human-MASS interaction in which these human factors are taken into account. We anticipate that these factors and their interaction will receive more attention to improve the safety and efficiency of MASS.

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Although signal countdown timers (SCTs) are likely to enhance efficiency at signalized intersections, there is little research on how they affect road users’ behavior. The present study explores factors associated with driver behavior through two approaches to examine how SCTs influence drivers’ actions toward pedestrians violating red lights. In the first approach, through an on-road questionnaire survey, the self-reported behavior of 369 drivers when crossing an intersection enabled with SCTs was analyzed. In the second approach, the drivers’ behavior was studied through naturalistic driving studies at two signalized intersections equipped with SCTs in Babol, Iran. Analyzing vehicle–pedestrian conflicts indicated that the presence of SCTs had a significant influence on driving behavior. Also, the ending seconds of green lights, as critical times of the SCTs, led to changes in driving behavior. Increasing the vehicle speed, changing lanes, and concurrent increases of speed and changing lanes were the common driver actions affected by critical times of the SCTs. Finally, the effect of critical times on drivers’ actions during conflicts was modeled by using the binary and multinomial logistic methods. The results show that SCTs are an external factor that can lead to risky driver behavior, such as errors and violations that might increase the potential for pedestrian accidents.@en

The safety of maritime autonomous surface ships (MASS) in mixed waterborne transport system (MWTS) depends on effective situational awareness (SA) distribution among MASS, manned ships, and various stakeholders, such as Vessel Traffic Service (VTS), Remote Control Center (RCC) and Fairway Shipping Agency. This paper focuses on the research question: How can situational awareness be effectively distributed among these entities in mixed waterborne transport? The research objective is to develop a distributed situational awareness framework that unifies SA among these stakeholders, ensuring safe navigation and compatibility with users of different roles. To achieve this objective, the proposed framework incorporates three key concepts: individual SA, authority-based SA, and distributed SA. Individual SA, previously introduced in our study, is responsible for each ship's SA, while authority-based SA accounts for the SA of human operators supervising the waterborne transport system, such as VTS operators and fairway agency personnel. Distributed SA generates guiding messages for ships based on the situational awareness from both individual SA and authority-based SA, thereby enabling regulation-based and traffic control-based recommendations for waterborne transport (e.g., ship speed and course adjustments). The research methodology employs ontology-based modelling to implement the framework, constructing a domain knowledge network. A case study is conducted as an essential part of the research methodology, presenting how the framework perform the situational awareness from different aspects and inconsistency detection among manned ships, MASS, VTS operators, and so on. Semantic Web Rule Language (SWRL) is utilized to detect inconsistencies and generate guidance messages for ships. Through these cases, we demonstrate how the proposed Ontology-based framework can reconcile inconsistencies between individual and authority-based SA, leading to a safer and more effective waterborne transport.@en

The safety and reliability of autonomous ships are critical for the successful realization of an autonomous maritime ecosystem. Research and collaboration between governments, industry, and academia are vital in achieving this goal. This paper conducts a bibliometric review of the research on the risk, safety, and reliability of autonomous ships aiming to provide researchers and maritime stakeholders with a structured overview of the topics, development trends, and collaboration networks in this research field. 417 papers published between 2011 and 2022 were identified covering 940 authors, 31 countries, and 227 journals. Three main themes were determined in this research domain: “safety engineering and risk assessment for decision making”, “navigation safety and collision avoidance”, and “cybersecurity risk analysis”. Meanwhile, it was identified that research on cybersecurity in autonomous shipping is moving to overlap with safety, which requires future co-analysis methods. Additionally, the analysis of the most cited 30 papers suggests that further research is needed in the topics of unmanned machinery operation risks, online risk tools, system-theoretic safety analysis, human factor, and the determination of suitable risk acceptance criteria for safety assessment of autonomous ships. Furthermore, the analysis revealed that the development of unambiguous COLREGs regulation is crucial for the development of safe collision avoidance algorithms for MASS. It was identified that the publication by Fan et al., (2020) is a key publication in this research field, while the journals of Ocean Engineering, Reliability Engineering & System Safety, and Safety Science are the key journals publishing on autonomous ship safety and reliability.@en

Recent research in transport safety focuses on the processing of large amounts of available data by means of intelligent systems, in order to decrease the number of accidents for transportation users. Several Machine Learning (ML) and Artificial Intelligence (AI) applications have been developed to address safety problems and improve efficiency of transportation systems. However exchange of knowledge between transport modes has been limited. This paper reviews the ML and AI methods used in different transport modes (road, rail, maritime and aviation) to address safety problems, in order to identify good practices and experiences that can be transferable between transport modes. The methods examined include statistical and econometric methods, algorithmic approaches, classification and clustering methods, artificial neural networks (ANN) as well as optimization and dimension reduction techniques. Our research reveals the increasing interest of transportation researchers and practitioners in AI applications for crash prediction, incident/failure detection, pattern identification, driver/operator or route assistance, as well as optimization problems. The most popular and efficient methods used in all transport modes are ANN, SVM, Hidden Markov Models and Bayesian models. The type of the analytical technique is mainly driven by the purpose of the safety analysis performed. Finally, a wider variety of AI and ML methodologies is observed in road transport mode, which also appears to concentrate a higher, and constantly increasing, number of studies compared to the other modes.@en

Both intentional attacks and accidental technical failures can lead to abnormal behaviour in components of industrial control systems. In our previous work, we developed a framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two classes, including knowledge elicitation to construct the directed acyclic graph of BN models. In this paper, we add a systematic method for knowledge elicitation to construct the Conditional Probability Tables (CPTs) of BN models, thereby completing a holistic framework to distinguish between attacks and technical failures. In order to elicit reliable probabilities from experts, we need to reduce the workload of experts in probability elicitation by reducing the number of conditional probabilities to elicit and facilitating individual probability entry. We utilise DeMorgan models to reduce the number of conditional probabilities to elicit as they are suitable for modelling opposing influences i.e., combinations of influences that promote and inhibit the child event. To facilitate individual probability entry, we use probability scales with numerical and verbal anchors. We demonstrate the proposed approach using an example from the water management domain.@en

Cascade reservoirs are essential in water resource utilization, flood control, and disaster mitigation. They have been built in several rivers worldwide. The rank classification for cascade reservoirs has significant practical implications for resource distribution decisions and risk management strategies and is also essential for ensuring the security of the basin. Internationally, reservoirs are typically classified based on project scale and dam failure consequences, whereas the risk transmission and superposition effect renders the classification method of reservoirs not directly applicable to cascade reservoirs. Therefore, to address this issue, this study proposed a rank classification method for cascade reservoirs. First, based on the scale, benefits, and risk consequences of dam failure, an index system for the rank classification of cascade reservoirs was established. Second, by constructing social risk criteria for cascade reservoirs and establishing a link among “project rank-reliability index-annual failure probability,” the allowable dam failure losses for different project ranks were determined. Thereafter, considering risk transmission and superposition, the rank classification standard and index quantification method for cascade reservoirs were proposed. Finally, five cascade reservoirs were selected for a feasibility study. The proposed method provides a supplement to the current rank classification standard of water conservancy projects in China and can also serve as a reference for risk assessment and classification management of cascade reservoirs in other countries.@en

A broad review of the existing literature concerning Human and Organizational Factors (HOFs) and human errors influencing structural safety is presented in this study. Publications on this research topic were collected from the Scopus database. Two research focal points of this topic, namely modelling and evaluating the human error effects on structural reliability, and identifying causal factors for structural defects and failures, have been recognized and discussed with an in-depth literature review. The review of studies with a model focus summarizes the models and methods that have been developed to evaluate structural reliability considering human error effects. Besides, the review of publications on the factor subject outlines the most acknowledged HOFs that influence structural safety. Moreover, an additional spotlight was given to the studies from the offshore industry for the advanced development in HOFs and contributing the first complete Human Reliability Analysis (HRA) method for structural reliability analysis. In conclusion, this study provides a holistic overview of the knowledge developed in existing research on the topic of HOFs and human error influencing structural safety. Furthermore, current developments and challenges are reflected, and future research directions are explored for academics entering and working in this field. Additionally, the insights into HOFs generated from this review can assist engineers with better hazard identification and quality assurance in practice.

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Inland shipping is a key modality for freight transport between the seaport of Rotterdam and the industrial areas in Germany and Switzerland. The recent droughts of 2018, 2019 and 2022 have clearly demonstrated how discharge related supply chain disruptions cause substantial economic damages in the hinterland. The IPCC predicts that climate change will increase the variability in water cycles globally, making future extremes more frequent and more severe. In-depth insight into the response of inland shipping to discharge extremes is crucial to better anticipate and potentially mitigate this climate risk. Existing literature takes (a small number of) representative vessels and estimates corridor scale climate risks through extrapolation. Recent droughts have shown that this approach may give unrealistic results. Newspaper articles and reports from the sector suggest that the fleet composition and vessel deployment change during high and low discharge extremes, and cascading effects are likely to occur. So far, however, no objective data on this phenomenon has been reported in literature. This paper analyses ten years of IVS and discharge data, for the period between 2010 and 2020, revealing in detail for the first time how discharge levels and vessel deployment are related. This improved insight into shipping response is crucial for any corridor to accurately estimate the climate risk of discharge extremes. While this paper focuses on the Rhine corridor, the proposed method is applicable to other corridors as well.@en