Effectively identifying the distribution pattern and influencing factors of ship collision risk is crucial for ensuring navigation safety, operation, and management efficiency in port waters. First, this paper used the Ising model theory to extract ship data from the automatic identification system (AIS) and investigate the mutual influence mechanism among ships. Second, by applying the Ising model, a calculation model was developed to determine ship collision risk values in port waters. This model takes into account various influential factors, including the number of ship track crossing frequency, density distribution, velocity dispersion, and spacing. By integrating these factors, the model enables a quantitative analysis of the ship collision risk situation in port waters. Finally, to demonstrate the effectiveness of the proposed Ising model, a case study was conducted using Qingdao Port as an example. Through this case study, the paper analyzes the patterns of ship collision risks in the port area. The findings reveal that the Ising model effectively identifies areas with higher collision risk, enhancing the identification of ship navigation risks and contributing to overall navigation safety management in port waters. The results indicate that the proposed collision risk Ising model can quantitatively assess the distribution of collision risks for ships in restricted water areas such as ports. These findings contribute to the identification of ship navigation risks in port waters.
Deep foundation construction is characterized by significant unpredictability and growing uncertainty, which creates dynamic risks. Given that deep foundation construction is vulnerable to extreme weather conditions and intense human activity, the assessment of its hazards is of utmost relevance for construction safety management. To do so, this research used a powerful interpretive structural model (ISM) as a foundational framework to analyze the safety risk factors in deep foundation pits; the study also revealed the dynamic properties of the system by utilizing the special benefits of system dynamics (SD), which enabled a deeper comprehension of the system’s complex behavior over time. To aid in successful risk assessment, a novel risk causal feedback diagram model that is adept at navigating the system’s complexity and temporal fluctuations was established. The suggested model combines the advantages of SD and ISM to produce a solid and trustworthy framework for thoroughly evaluating hazards in deep foundation projects. Its all-encompassing methodology provides a more thorough and precise investigation, giving building experts useful information to strengthen safety measures. This study conducts a case simulation experiment on a resettlement housing project in Tong’an District, Xiamen City, to show the model’s applicability. Additionally, this research suggests matching risk control strategies and develops plans to maximize risk management and raise the deep foundation pit’s level of safety.
Construction investment and geological risk of a railway project are significantly influenced by the alignment design. Thus, for railways in earthquake-prone regions, the seismic risks should be addressed at the alignment decision-making stage. However, this is a challenging problem that should balance cost and risk appropriately. Especially in mountainous regions, besides direct ground shaking, earthquake-induced landslides greatly threaten railways’ construction and operation. Unfortunately, no existing studies in this field have accounted for that factor. In this paper, a novel potential earthquake-induced landslide risk model is proposed for mountain railway alignment optimization. In this model, a probabilistic seismic hazard analysis, critical acceleration computation, and landslide displacement estimation are first integrated. Together with the consideration of railway structures’ damage states, damage ratios, and restoration functions, the direct and indirect monetary losses caused by landslides to railways with specified alignments are evaluated. Then, the aforementioned analyses are incorporated into a previous cost-risk model and solved with a particle swarm optimization (PSO) algorithm. Finally, the model’s effectiveness is tested in a complex railway example. It is found that the studied region is landslide prone, and railway structures, especially bridges, are vulnerable to landslides. Also, a biobjective analysis reveals the alignments can be more sensitive to risks than to costs. Lastly, according to the detailed engineering outputs, the computer-generated alignment is 11.8% less expensive and 27.2% safer than the best manually designed solution.
Piping erosion is a crucial trigger for dam breaches. However, the effects of inherent spatial variability on seepage properties have not been considered adequately, which could lead to a significant underestimation of the risk of piping-erosion-induced dam failure. Additionally, the complex formation mechanism of erosion pipe formation poses challenges in determining the seepage path. This study proposes a probabilistic evaluation framework which combines a hydraulic–mechanical coupling method with random finite-element analysis. Failure indicators, namely hydraulic gradient and kinetic energy, are utilized within this framework. Based on the proposed framework, the spatial variability of soil properties can be considered effectively, and three cases of dams were analyzed. The results show that the proposed framework can provide a macroscopic visualization of the erosion pipe process. In addition, this framework reveals piping erosion occurrence in approximately 40% of hydraulic samples, whereas deterministic analyses fail to detect any instances of piping erosion. This suggests that deterministic analysis considerably underestimates the risk of piping erosion in practice. The effects of the depth of antiseepage measurements on the formation process of piping erosion are discussed. The results indicate that a medium-depth cut-off wall can meet the impervious requirements and reduce the construction cost in engineering practice.
Based on the stochastic process multiple threshold crossings analysis formula, the impact of structural response random process crossing-threshold duration on structural fatigue reliability is studied, which was not involved in fatigue reliability analysis before. This paper considers the type of structural stress stochastic processes with Wiener characteristics in engineering and establishes a novel method considering the random response crossing-threshold duration for fatigue reliability evaluation. The fatigue cumulative damage considering random response crossing-threshold duration is derived analytically based on the assumption that cumulative damage is linear to crossing-threshold duration. This study makes the fatigue reliability analysis closer to the reality.
Substations are vital components of electricity supply, representing a weak point in a power network due to their vulnerability to flood events. Pole-mounted substations can effectively mitigate inundation failures by elevating electrical equipment. However, the supporting structures of such substations often are not designed to withstand flood flows, and thus are prone to structural failure. This paper proposes a generalized framework to quantify the structural failure probabilities of pole-mounted substations and to assess their structural resilience to flooding. The generalized framework was applied to a case-study location in Malaysia, where serious flood events are common and pole-mounted substations abound. The study first identifies and quantifies the flood effects on the poles, including pure hydrodynamic forces, the impact of floating debris, debris damming effects, and scouring. The quantified flood effects then are compared with the structural capacity of a typical pole-mounted substation structure and its foundation, to derive a capacity threshold curve for structural failure. The failure probability is illustrated via fragility curves for different flood depths and risk curves for different flood and wind return periods, to assess further the substation’s structural resilience. The aforementioned curves are based on a stochastic distribution of flood depths and velocities represented by a normalized Weibull function. This approach can be adapted easily to depict flood conditions for any given location. Overall, the results of this paper can help stakeholders, including those designing and managing substation structures, to quantify, assess, and further enhance the flood resilience of power-supply networks.
The unique natural geographic environment at high altitudes, characterized by low partial pressure, significantly reduces blood oxygen levels and affects human systems. These impacts directly influence drivers’ psychophysiological states, increasing their mental workload and compromising driving safety. Currently, two-lane highways represent the primary type of plateau highways, comprising over 90% of the overall mileage. Therefore, it is essential to examine the minimum radius of the horizontal curve applied to two-lane highways in plateau areas, considering the effects of low atmospheric pressure and hypoxia conditions of plateau areas on drivers. The segments from Nyingchi to the Mountain Shegyla on G318 in the Tibetan plateau region were selected for the field experiment. The model for the simulated experiment was established using the UC/Win-Road software to increase the adequate sample size. The consistency of the field and simulated experiments was validated using the paired sample t-test. Sample entropy is adopted to process the collected multisource data from field and simulation experiments, including the velocity of the vehicles and the heart rate and electroencephalogram of the drivers. Further, principal component analysis was used to evaluate a sample entropy index (SEI), which comprehensively represented the psychophysiological state of drivers. Subsequently, the correlation of SEI with the radius is anatomized, validating that the lateral acceleration ah was the index of most significant influence on SEI from the perspective of driving dynamics. Values of the minimum radius of the horizontal curve for two-lane highways in plateau areas were proposed to amend the current Chinese design specifications for highway alignments. Overall, this research could be essential in alignment design in plateau areas and conspicuously deepens our theoretical and practical understanding of driving safety under low-atmospheric pressure and hypoxia environments.
In this paper, a bimodal normal distribution model of ship traffic flow is developed to more accurately model the probability of ships colliding with buoys in a two-way navigable channel and reduce the estimation error caused by the normal distribution model. To the best of our knowledge, this is the first introduced bimodal normal distribution into ship traffic flow in a two-way navigable channel. An opposition-based learning multi-verse optimizer (OLMVO) is proposed to minimize the fitting error of the probability density function and obtain the optimal parameters in the bimodal model using a data-driven method from the automatic identification system (AIS). The obtained results show that the probability of ship-buoy collision increases with the increase of B, σ1, and Bb, and decreases with the increase of μ1. Two application examples in Xiamen Port and Quanzhou Port are illustrated to validate the effectiveness of the proposed modeling method. This study enriches the meticulous research on the probability of ships colliding with buoys in two-way navigable channels. The proposed method can inform decisions and recommendations for the safety management of the buoy competent department, the maritime department, and the buoy management.
The probability model of ship-buoy collision can help put forward ways to reduce the potential of collision incidents, such as widening the channel and adjusting the two-way navigable channel. This study aims to assist in establishing a rational, comprehensive, and scientific navigational aid system, which allows to reduce the number of accidents in ship navigation, decrease the chances of navigational aids being struck, and reduce the costs associated with navigational aid management. It aims to provide high-quality navigation services for vessels entering and exiting ports. This study has already been applied in practical projects such as the “Research on the Displacement Model of Floating Light Buoys Based on Telemetry Big Data and Its Application Demonstration in Xiamen Port.”
Geometrical simplifications, the choice of boundary conditions, modeling of connections and the determination of material property values are the steps that constitute the process of idealizing a finite element model. Therefore, there are many unknown and uncertain variables that affect how the numerical model results differ from the measured experimental data. Updating mass and stiffness by experimental modal data has been extensively studied. But identifying damping characteristics represents a next level of difficulty due to their not well-established sources. Generalized proportional damping may solve this issue by using information on damping ratio measurements, because an arbitrary variation of damping ratios can be modeled accurately by using this approach. The sensitivity method is one among several methodologies to quickly perform a model identification. Here, a prototype metallic framed structure has its mode frequencies and damping ratios measured and used to update a finite element model. Results of a test campaign of the experimental results along with the finite element model update steps are presented and show little computational effort to obtain good agreement for numerical and experimental results. A cloud of sample plots for measured parameters and the identified ones and covariance confirm the good agreement especially for the damping parameters.
To explore the entire runout process of a soil–rock mixture (SRM) slope triggered by an earthquake, large deformation finite element (LDFE) analyses combing two-phase random media are conducted. The feasibility and reliability of the LDFE method used in the current study to simulate the landslide process are checked by comparing the shape after the landslide and the runout distance with the results of the previous study; then, a two-phase random medium is incorporated into the finite element model to mimic the SRM slope. Monte Carlo simulation is performed to calculate the runout distance of the SRM slope considering the random distribution of rock, and the effect of rock volume fraction and horizontal peak acceleration is revealed by conducting probabilistic analyses. The findings of this research might facilitate the risk assessment of SRM landslides.
During the process of firefighting on the vehicle deck of a roll on/roll off cargo and passenger (ROPAX) vessel, firewater accumulation and cargo shift can lead to the unexpected and rapid worsening of the situation. This paper provides insight into the impact of firewater accumulation and assesses the risk of a resulting cargo shift. A novel approach is proposed for ensuring that vessel stability remains within safe margins of operation once firewater accumulation has occurred. First, the critical listing angle at which vehicles on deck have a high risk of shifting is identified for a particular vessel. Second, a new iterative method is designed to calculate the weight and moment of the accumulated firewater causing the vessel to reach its critical angle. Third, the vessel’s stability under the combined influence of firewater accumulation and total cargo shift is calculated. Finally, the method is validated through consideration of historical firewater accumulation and cargo shift incidents on ROPAX vessels. The computational results derived show that, during firefighting operations, action should be temporarily ceased if the vessel lists to its critical angle. Failing to do so leads to the increased likelihood of the vessel capsizing and becoming a total loss.
An integrated ship domain at close quarters is proposed based on the mutual interaction and maneuvering capabilities of the meeting ships. Firstly, the significance and conditions of mutual interaction and maneuvering capabilities of the ships in the process of avoiding collision are clarified. Secondly, by combining the main particulars and maneuvering characteristics of the encounter ships, an identical ship domain is formed. To determine the radius of the ship domain, the universal advance and tactical diameters of the ships are derived from a great deal of collected turning circle maneuver data from around the world. Following this approach, the integrated ship domain defines a sufficient safe area when ships are meeting at close quarters. Finally, a simulation study is carried out and further illustrates that the integrated ship domain can address the inconsistent assessment problem of collision risk between the encounter ships with different principal specifics, and the proposed domain is simple and applicable to almost all ships.
Community resilience is the fundamental capacity of a community to cope with a crisis or disruption and mitigate the adverse effects of a disaster. Identifying and quantifying community resilience before a disaster occurs is increasingly becoming a prerequisite for managers to make informed decisions and take action. Assessing community resilience for multiple disasters is more complex and dynamic than for a single disaster. This paper proposes a multidisaster community resilience assessment method that comprises three indexes: the topology and seismic performance–based physical resilience index (TSP-PRI), the medical care–based health resilience index (MC-HRI), and the capital and population–based socioeconomic resilience index (CP-SRI). These indexes are computed using accurate and objective data on building information, road information, and government statistical yearbooks. GIS offer rich geospatial analysis functions for network infrastructure systems that involve geographic references. A plug-in has been developed in ArcGIS Pro to link these data to geospatial modeling, which can automatically calculate the TSP-PRI, MC-HRI, and CP-SRI. This decision-making tool can be used to systematically and visually examine the disaster characteristics and topological attributes of communities before and after the occurrence of disasters. Furthermore, the k-means clustering algorithm was applied to classify the types and characteristics of these three indexes to prioritize investments for different communities. A case study of community waterlogging and earthquakes in Nanjing, China, is presented to show the feasibility and effectiveness of the proposed approach.
Assessing community resilience prior to a disaster is crucial for informed decision-making and effective action by managers. This study presents a thorough assessment method that includes three indexes for assessing the resilience of various communities in multidisaster scenarios. To facilitate the assessment process, a plug-in has been developed in ArcGIS Pro, enabling automated computation of these indexes using reliable and objective data. A case study was conducted in 11 districts of Nanjing, China, examining the flooding and earthquake disasters. The districts were then grouped into clusters with similar resilience characteristics, utilizing the k-means clustering algorithm. This facilitated the prioritization of investments in different communities. The proposed method offers a comprehensive and quantitative framework that helps managers to measure and compare community resilience across districts and disasters. Furthermore, the method has the potential to be generalized and applied to other communities or countries, providing a valuable framework for resilience assessment in diverse settings.
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