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.

The advancement of smart shipping and autonomous navigation relies on Automatic Identification System (AIS) data, which provides essential ship trajectory information. However, raw AIS data lacks semantic context, making behavior annotation crucial for understanding navigation tasks and processes. Existing research faces two challenges: (1) a lack of clarity on which semantics should be abstracted for effective behavior annotation, and (2) insufficient consideration of spatial interactions between ship maneuvering and the navigational environment, particularly topological interactions. These issues complicate data extraction and hinder machine learning-based applications such as explainable trajectory prediction. This paper proposes a comprehensive framework for semantic annotation and indexing of ship behavior. The framework deconstructs ship behavior into a unified data structure using a relational database, where three types of behavior semantics are defined, including atomic, topological, and traffic behavior. Atomic behaviors (e.g., move and stop) are extracted to annotate raw trajectories, while topological behaviors, describing interactions between trajectories and the environment, are modelled using an improved Dimensionally Extended 9-Intersection Model (DE-9IM). The combination of these semantics enables the annotation of higher-level traffic behavior. The model is further evaluated via behavior annotation statistics, demonstrating its effectiveness in annotation and indexing high-level ship behavior.

While environmental life cycle assessment is an established method for predicting environmental impacts over the lifetime of a structure or building, and is supported by ISO-norms, it overlooks social impacts such as structural safety. The more comprehensive life cycle sustainability
assessment, which also includes economic and societal sustainability, is not as mature. There is especially a lack of quantitative indicators for the societal impacts of a structure, which form part of social life cycle assessment.

This paper investigates the use of an existing societal indicator, the Life Quality Index, which has not been used in social life cycle assessment before. It has, however, been used previously in structural engineering applications to establish societally acceptable and economically optimal failure probabilities of structures. In this paper, this use is compared to the most recent guidelines on social life cycle assessment by the United Nations Environmental Programme.

This paper proposes that the current use of the life quality index can be part of the social impact assessment phase of social life cycle assessment. It then forms part of a social mechanism within an impact pathway approach, one of the two approaches towards social impact assessment proposed by the guidelines. This is demonstrated using an example based on the design of a simple structure, following the four phases of a life cycle assessment. The demonstrated approach is able to combine societal and economic considerations, making it a promising candidate for future applications in life cycle sustainability assessment of structures.

Security risks, such as sabotage and cyberattacks, are an increasing threat to business and government processes. They originate from malicious human action, of which often exact historical information is lacking. Thus, the judgment and assessment of security professionals is the primary input for security risk management, a subjective probabilistic approach. In this study, we explore the information sources professionals, in both the physical and cybersecurity domain, use for this purpose, improving understanding of their daily praxis. Sources of security risk information are collected, their quality and trustworthiness is assessed, and their use is analyzed. Quality is assessed by experienced security practitioners applying the NATO system for intelligence evaluation, with source intention as additional criterion. Actual use is analyzed among security professionals. The results consist of a comparative ranking of both assessed quality and daily use of sources. Experts are ranked first for perceived quality and are also most relied upon in daily praxis, and individual/personal experience comes second. The additional criterion of source intention explained the lower level of use of information from science. This study provides the basis for enhancing security risk management by a more conscious selection of sources.

The increasing amount of activities at sea, including the development of offshore wind parks, result in a more confined space for shipping, requiring the assessment of risk changes regarding nautical safety and the design of potential mitigation measures. The main contribution of this paper is the transparent evaluation of allision probabilities, based on an event-based approach. This enables a structural consideration of conditional probabilities, and supports uniting quantitative and qualitative analyses. The event-based approach allows evaluating the outcomes from various perspectives: scales, conditions, behaviour and dependencies. The analysis outcomes are represented in a concept called “event table”, from which these perspectives can be extracted. Consequently, from this single data structure, insights can be gained ranging from spatial variations of the risk (highly detailed or global patterns), to detailed distinction between the most important influencing factors (varying from vessel type to environmental condition). It is furthermore possible to switch between wind-park specific risks and assessment of operational and strategic risk-mitigating measures for the entire area. The core feature of incorporating multiple perspectives not only allows various views on the safety risks, providing a better understanding of the most important contributing factors, as well as effectiveness of intervention measures. Our analysis shows the added value of additional distance between shipping lanes and wind parks in the spatial design, and we demonstrate how our multi-perspective approach supports the strategic and operational decisions around the availability and deployment of emergency response vessels.

Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role in enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded in bibliometric analysis in this field. To explore the research evolution and knowledge frontier in the field of maritime safety for autonomous shipping, a bibliometric analysis was conducted using 719 publications from the Web of Science database, covering the period from 2000 up to May 2024. This study utilized VOSviewer, alongside traditional literature analysis methods, to construct a knowledge network map and perform cluster analysis, thereby identifying research hotspots, evolution trends, and emerging knowledge frontiers. The findings reveal a robust cooperative network among journals, researchers, research institutions, and countries or regions, underscoring the interdisciplinary nature of this research domain. Through the review, we found that maritime safety machine learning methods are evolving toward a systematic and comprehensive direction, and the integration with AI and human interaction may be the next bellwether. Future research will concentrate on three main areas: evolving safety objectives towards proactive management and autonomous coordination, developing advanced safety technologies, such as bio-inspired sensors, quantum machine learning, and self-healing systems, and enhancing decision-making with machine learning algorithms such as generative adversarial networks (GANs), hierarchical reinforcement learning (HRL), and federated learning. By visualizing collaborative networks, analyzing evolutionary trends, and identifying research hotspots, this study lays a groundwork for pioneering advancements and sets a visionary angle for the future of safety in autonomous shipping. Moreover, it also facilitates partnerships between industry and academia, making for concerted efforts in the domain of USVs.

In recent years, the relationship between academia and the fossil fuel industry has become a focal point of intense debate. This concern arises from the fear that corporate funding might skew research activities. A significant development in this area is the adoption of policies by a Dutch university, and discussions in several others, prohibiting research funded by the fossil fuel industry. These policies aim to safeguard academic freedom and integrity. Despite this, there has been little discussion on the myriad challenges, implications, and possible unintended consequences, particularly in the realm of safety-and-security research. As such, this manuscript delves into the complex transition towards a fossil-fuel-free society, examining it through the lenses of safety science and sociotechnical systems. It emphasizes the vital importance of collective responsibility in ensuring systemic safety and security as we navigate towards achieving the sustainable development goals. This journey requires a delicate balance between the objectives of safety and sustainability, along with a deep understanding of the security implications of decreasing our dependence on the fossil fuel industry. The strategy of distancing academic research from fossil fuel industries, commonly seen as a positive step, also demands a nuanced consideration of its broader impacts, including the setting of precedents for addressing other existential and systemic risks. Instead, we argue for the establishment of robust governance structures rooted in restorative justice principles. Such frameworks can facilitate productive dialogue with underrepresented groups, motivate the fossil fuel industry towards sustainable practices, and safeguard the integrity of scholarly research. This approach not only addresses immediate concerns related to fossil fuels but also lays the groundwork for a more inclusive and equitable model of climate risk research, essential for tackling the multifaceted challenges of our era.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.