ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

The paper reviews the distance functions that have been or could be used to perform stochastic model updating via approximate Bayesian computation, namely: (1) Euclidean distance; (2) Bhattacharyya distance; (3) Bray–Curtis distance; and (4) 1-Wasserstein distance functions. For each of these distance functions, details on their mathematical formalism and implementation are provided along with an evaluation of their respective advantages and disadvantages illustrated though a numerical study. Subsequently, to provide a basis for comparison in the stochastic model updating performance by the respective distance functions, two case studies in the form of model validation problems are presented. The first problem is based on the 2014 NASA-LaRC Multidisciplinary Uncertainty Quantification Challenge. The second problem is based on the 2008 thermal problem presented by Sandia National Laboratories. Both problems provide a relatively complex and realistic setting to assess the strengths and robustness of each distance function in performing approximate Bayesian computation under polymorphic uncertainty and limited data for a one-dimensional output space. The comparison will be done based on the following: (1) the precision of the inference results on the inferred parameters; and (2) the precision of the corresponding stochastic model output against a set of validation data.

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 $a_h$ 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.

Abstract

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 $B$, ${\sigma }_1$, and $B_b$, and decreases with the increase of ${\mu }_1$. 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.

Practical Applications

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.

Abstract

Traditional facade design practices are mostly based on the designers’ intuition or a single performance criterion such as U- and g-values, which only describe a single facade property. However, facade optimal design in an early stage has a significant influence on the final overall building performance in regard to energy consumption, economical cost, ${\mathrm{CO}}_2$ emission, etc. The main challenge is represented by several sources of uncertainty during the life-cycle. In this paper, the goal of finding a sustainable facade design solution is addressed through the reliability framework of sustainable and resilient engineering (SRE), which is used to predict probabilistic performances, whereas the design alternatives are ranked through the multiattribute utility theory. The adopted metric for ranking these alternatives is the generalized expected utility (GEU), which incorporates the expected utility as a particular case. It is recognized that, under limited and incomplete information, decision-makers are risk-averse, which may provide a suboptimal design. Within the GEU, the optimal design under uncertainty is modeled through superquantiles, which allow evaluating the optimal design with respect to frequent events (i.e., service limit states) and extreme events (i.e., ultimate limit state). It is shown that SRE can include traditional design approaches as particular cases. The main features of the proposed framework are demonstrated through an application to a facade design of a hypothetical commercial office in Singapore with three types of facade options. With the output of the simulations through the EnergyPlus software, the alternatives are successfully ranked using SRE and compared with traditional design practices. The case study shows that the proposed methodology has the potential to overcome the limitation of the current design process and to guide the procurement processes in early facade design.

Practical Applications

Under the growing climate crisis, the environmental impact’s relief requirements have motivated the design of sustainable buildings. Traditional facade design practices are mostly based on designers’ intuition, personal experience, and provisions of national codes and standards. This approach may be neither efficient nor flexible and can only satisfy the minimum requirements of the projects and national design standards. Actually, it is quite likely to fail in meeting requirements of different involved stakeholders, especially with the reference to sustainable design. In this study, a novel multicriteria early-stage designs of building facades, grounded on a framework of sustainable and resilient engineering, is presented. The comprehensive consideration of the uncertainties within the decision-making process provides interpretability and explainability to the design choices, e.g., the moisture effect on the uncertainty of the thermal conductivity of opaque facade materials. A benchmark case study shows that: (1) sustainable design may sometimes be hardly incorporated by the traditional design approaches; (2) the current practices may give rise to unconservative designs; and (3) the proposed approach is a viable solution for sustainable facade designs.

Soil has spatial variability, which means that soil properties at different locations are different but correlated. To represent the spatial variability of soil surrounding a pile, the random field method (RFM) is usually adopted to discretize a random field into a number of random variables. Then, the first-order reliability analysis method (FORM) is modified and employed to perform reliability analysis, and the load-transfer method (LTM) is adopted to compute the bearing capacity of the pile. To reduce the computation cost of the reliability analysis and random field simulation, a FORM-LTM-variance reduction method (VRM) method is proposed to conduct reliability analysis for single pile in spatially variable soil, in which VRM is adopted to transfer a random field into a random variable over a characteristic length. By comparing the reliability indices using FORM-LTM-RFM and FORM-LTM-VRM, analytical formulas of the characteristic lengths under different pile lengths, coefficients of variation (COVs), and autocorrelation distances (ACDs) are computed. Benefitting from the computation accuracy and efficiency of the FORM-LTM-VRM with analytical formulas of characteristic length, resistance factors in LRFD for the reliability-based design of single pile in spatially variable soil can be easily computed for different safety levels. The accuracy and efficiency of the FORM-LTM-VRM with analytical formulas of characteristic length are demonstrated by a case study of a vertically loaded pile.

In engineering analysis, propagating parametric probability boxes (p-boxes) remains a challenge because a computationally expensive nested solution scheme is involved. To tackle this challenge, this paper proposes a novel optimization-integration method to propagate parametric probability boxes (p-boxes), mainly focusing on estimating the lower and upper bounds of structural response expectation for linear and moderately nonlinear problems. A model-based optimization scheme, named Bayesian global optimization, is first introduced to explore the space of distribution parameters. Subsequently, an efficient numerical integration method, named unscented transform, is employed to estimate the response expectation with a given set of distribution parameters. Compared with existing optimization-integration methods, the proposed method has three advantages. First, the response expectation bounds are successively estimated, allowing for the reuse of samples generated from the lower-bound estimation in the upper-bound estimation. Second, the approximation error introduced by the numerical integration method is considered. Third, computational efficiency in both the optimization and integration processes is improved. Four applications are investigated to validate the effectiveness of the proposed method, showing its ability to balance computational efficiency and accuracy when evaluating response expectation bounds.

Reinforcement corrosion is an important part of durability research of concrete structures. The process of reinforcement corrosion is highly random due to various factors, and the effect of spatial distribution of reinforcement corrosion is obvious in practical structures. This paper took corroded reinforced concrete beams as the research object, summarized the numerical characteristics of reinforcement corrosion in the test, and then established cross section corrosion extent random field models. Finally, this paper carried out a time-varying reliability analysis of the bending and shear capacity of the corroded reinforced concrete beams considering the time-varying effect of corrosion. The results showed that the failure probability of the flexural and shear bearing capacity of corroded RC beams is close to zero at the early stage of corrosion, the reliability index of the flexural bearing capacity is lower than the recommended value of 3.2 after 30 years, and the failure probability increases rapidly after 40 years. The shear capacity is lower than the reliability index 3.7 after 20 years, and the failure probability is greater than 0.3844 after 25 years.

Bayesian networks (BNs) facilitate the establishment and communication of complex and large probabilistic models that are best characterized through local dependences and hierarchical structures. In addition, they enable Bayesian updating of the model with new observations. This has motivated the application of BNs to the reliability assessment of large infrastructure networks. In order to make use of fast inference algorithms, previous research has mostly focused on discrete BNs. The size of the infrastructure networks that can be handled in such an approach is limited due to computational issues, and continuous random variables must be discretized. As an alternative, we propose the use of Gibbs sampling for approximate inference in such BNs. Because standard Gibbs sampling is inefficient in determining small failure probabilities, which are common in reliability problems, we introduce subset simulation, an advanced sampling technique, to BN inference. We also show how the samples from subset simulation can be used to estimate component importance measures. The approach is demonstrated by application to two road networks subjected to earthquakes.

Electric and gas investor-owned utilities operate in a regulated environment and are scrutinized by media and stakeholders for key strategic and operational decisions. Some decisions entail significant risk, requiring special attention to risk tolerances and attitudes including risk aversion. Utilities typically institute enterprise risk management programs to efficiently and effectively manage safety, reliability, and financial risks for their customers, employees, and communities in a changing climate with intensifying risks, such as wildfire. Consequences from such events could include human life and property losses, health effects, environmental damage, service loss, and other indirect financial and economic impacts. A spectrum of risk quantification and management methods are available for assessing these hazards. Varying risk tolerances and attitudes of stakeholders creates situations that are central to decision-making where the safety, service delivery reliability, rate affordability, and the financial wellbeing of entities come together and interact in a complex manner. This paper sets context, defines key terms, and develops an innovative approach for methodically reflecting risk tolerance and attitude with a focus on risk aversion in informing risk management decisions by offering flexibility to account for preferences by stakeholders in a structured manner. The concept of risk-aversion amplification factors is proposed to reflect attitudes and preferences of decision makers in typical economic models used in benefit-cost analysis. Such amplification factors can be calibrated by applicable markets, such as insurance and catastrophe bond markets and used to estimate certainty-equivalent valuations of risks, costs, and benefits. The proposed methods are illustrated in the context of wildfire risk management for communities and utilities using two examples from these domains for enhancing resilience.

To predict power outages for upcoming storm events and assess the resilience of power systems, accurate models of power outage probabilities are necessary. Although physics-based fragility models have been developed to forecast structural failure probabilities under strong winds, such methods may capture limited failure modes and could struggle to predict more-random outages that are not directly related to strong winds. Data-driven machine learning methods have emerged for power outage predictions considering multiple failure modes, although they may struggle to predict cases with limited data, such as extreme events. To integrate the two different approaches, a novel model is proposed to probabilistically combine physics-based and machine-learning predictions. Fragility analyses of various transmission structure types and tree species subject to wind loads were conducted for the prediction of wind-induced structural damages. An extreme gradient boosting (XGboost) machine learning model trained on infrastructure, weather, topographic, and vegetation characteristics is used for the prediction of nonstructural outage probabilities. The framework is demonstrated through a case study of the overhead transmission system in the New England region of the US over 44 storm events from 2015 to 2020. The results show an improved agreement between predicted and observed outages, as well as correlations between predicted probability and observed outage rate, highlighting the model’s effectiveness for estimations of storm impacts and grid vulnerability.

Abstract

Exploring the causes and evolution of ship collisions plays an important role in accident prevention and risk control. This paper proposes a hybrid analyzing framework based on man–machine–environment–management (MMEM) and a Bayesian network (BN), and applied the analyzing framework to causal analysis of ship collisions in Zhejiang coastal waters based on 107 cases of ship collision accidents. The MMEM frame is utilized to guide influencing factors (IFs) and results of IF identification and causal chain analysis are used as basis for structuring the BN model, using 1 and 0 to indicate whether an IF occurs or not, respectively, in regard to an accident. This marking method was used to deal with each of 107 accident cases in order to form set of sample data, of which 97 cases were used as training cases, 10 cases were used as testing cases. The model was trained by using the set of sample data and the expectation maximization (EM) algorithm to obtain the conditional probability tables (CPTs). A 20-node BN model with the ability to predict the probability of occurrence of ship collision was established. The BN model was verified through sensitivity analysis–based validation, $k$-folds cross-validation, and testing cases–based validation. The validation results show the correctness and usability of the model. Through backward reasoning–based analysis, maximum likelihood cause chain analysis, and sensitivity analysis, it was found that human error (H) is the main IF resulting in ship collisions; the causal chain that maximizes the likelihood of an accident occurring is H1 (Improper lookout) $\to$ H4 (Underestimation of collision) $\to$ H7 (Failure to take effective collision avoidance measures) $\to$ H (Human error) $\to$ C (Ship collision). H1, H7, and H9 (Improper emergency handling) have relatively high sensitivity and greater impact on collision accidents. The results show that the proposed MMEM-BN–based analyzing framework is applicable. The analyzing framework and results of causal analysis will provide theoretical and practical supports for exploring origin of accidents, revealing evolutionary mechanism of accidents and for taking targeted risk control measures.

Practical Applications

Ship collision is one of the most serious marine accidents. When it occurs, it is likely to lead to serious casualties and economic losses. This study analyzed the causes and evolution of ship collision accidents to provide reference for accident prevention. The paper proposes a man–machine–environment–management and Bayesian network (MMEM-BN)–based analyzing framework (work flow) for causal analysis of ship collisions. Using the analyzing framework, influencing factors of ship collisions in Zhejiang coastal waters were identified under the MMEM framework and a BN model of ship collisions was established. Analysis of the results showed that human factors are the key causes resulting in collision accidents, and improper emergency handling or Failure to take effective collision avoidance measures have a greater impact on collision accidents. The proposed MMEM-BN–based analyzing framework was verified to be applicable. The proposed analyzing framework and causal analysis of ship collisions in Zhejiang coastal waters will provide theoretical and practical support for exploring the origin of accidents, determining the evolutionary mechanism of accidents, and taking targeted risk control measures.

Inland ship collisions in traffic separation scheme (TSS) water usually cause serious consequences. Research on ship collision risk early warning and collision avoidance assistant decision making can reduce the possibility of collision accidents caused by humans and reduce collision accidents and provide a basis for ships’ autonomous collision avoidance decisions. A digital traffic environment model for the typical inland TSS waters is constructed as the basis of navigation warning and decision making. A collision risk warning framework, which takes the intention of collision avoidance for the target ship into account, is established based on the dead reckoning and domain of the ship under TSS waterway constraints. In addition, a ship nonlinear maneuvering process deduction-based dynamic adaptive collision avoidance decision method is proposed under the premise of satisfying the Regulation of the People’s Republic of China on Inland River Collision Avoidance and good seamanship. The simulation experiments proved that the research could realize dynamic collision risk warnings under different encounter situations and provide effective assistant collision avoidance decisions in typical inland TSS waters.

In active learning Kriging (AK) reliability-based analysis, a surrogate model is trained in a stepwise manner and used to evaluate the reliability of the desired system by reducing the computational cost of analysis. While extensive studies were conducted on advancing the AK reliability methods by developing new learning functions, limited work studied the effect of AK configuration on the accuracy, efficiency, and consistency of the AK reliability analysis. AK configuration is defined herein as a unique set of Kriging correlation, Kriging regression, learning function, and AK reliability method for the AK procedure. This paper presents six metric systems to evaluate the performance of AK reliability analysis based on AK configurations including the comprehensive metric system (CMS), the weighted metric system (WMS) with local optimized weights or average optimized weights (LOW or AOW), and modified desirability function, and two original desirability functions used for multiple response optimization. The ranking optimizes four scaled indexes as measures of accuracy, efficiency, and consistency of the reliability analysis. The metrics are developed and applied to four diverse examples, where a total of 14,400 AK reliability analyses were considered. The results show the validity of the metric systems to rank AK configurations based on their performance.

Resilience analysis of civil structures and infrastructure systems is a powerful approach to quantifying an object’s ability to prepare for, recover from, and adapt to disruptive events. The resilience is typically measured probabilistically by the integration of the time-variant performance function, which is by nature a stochastic process as it is affected by many uncertain factors such as hazard occurrences and posthazard recoveries. Resilience evaluation could be challenging in many cases with imprecise probability information on the time-variant performance function. In this paper, a novel method for the assessment of imprecise resilience is presented, which deals with resilience problems with nonprobabilistic performance function. The proposed method, producing lower and upper bounds for imprecise resilience, has benefited from that for imprecise reliability as documented in the literature, motivated by the similarity between reliability and resilience. Two types of stochastic processes, namely log-Gamma and lognormal processes, are employed to model the performance function, with which the explicit form of resilience is derived. Moreover, for a planning horizon within which the hazards may occur for multiple times, the incompletely informed performance function results in “time-dependent imprecise resilience,” which is dependent on the duration of the service period (e.g., life cycle) and can also be handled by applying the proposed method. Through examining the time-dependent resilience of a strip foundation in a coastal area subjected to groundwater intrusion in a changing climate, the applicability of the proposed resilience bounding method is demonstrated. The impact of imprecise probability information on resilience is quantified through sensitivity analysis.

The operation risks for deep-sea oil spill recovery are characterized by their multiplicity, fuzziness, and dynamic nature. To more properly and comprehensively assess the operational risks for deep-sea oil spill recovery, an assessment method has been proposed on the basis of combinatorial weighting and multi-element connection number. A full-domain risk assessment index system was built, which consists of four dimensions, namely personnel, equipment, management, and environment, and the combinatorial weighting was performed for the subjective and objective assessment indexes via the rough set improved analytical hierarchy process (AHP) and entropy method, respectively. Furthermore, in accordance with the set pair theory, the risk trend assessment model, built upon the multi-element connection number, was developed to comprehensively illustrate the current state of safety control and the developmental trajectory of these risk factors. Finally, a case study of oil spill recovery operations was carried out. Compared with the conventional methods, the proposed method delivers index weights more consistent with the actual situation, due to the applications of combinatorial weighting based on the improved AHP and entropy methods, as well as the multi-element connection number. The analysis showed that this method can deliver a thorough, scientific, and systematic assessment of the status quo. Therefore, the trend of risks and targeted management measures for key risk factors can be obtained.

Hurricanes can cause devastating damage to overhead distribution lines leading to large power outages in electric grids. Power outage prediction models can help utilities to plan for an expedited power recovery by identifying the extent of power disruptions before the arrival of a hurricane. These models often use multiple input parameters, including early warning forecasts of hurricane characteristics, environmental data, power system details, and demographic information. We propose a quasi-binomial regression model to advance power outage models and overcome their existing limitations, such as unbounded outage predictions, limited extrapolation, and high uncertainties at low and high winds. This paper shows that the quasi-binomial model allows us to better capture the mechanics of power system failures due to hurricanes. We fitted our model to power outage data across 2,322 cities for four historical hurricanes: Harvey (2017), Michael (2018), Isaias (2020), and Ida (2021). We validated our model for the outages in Florida during Hurricane Ian (2022). The quasi-binomial model outperformed existing random forest and negative binomial regression models with a 7% error versus 50% and 76%, respectively. To demonstrate the quasi-binomial model’s good performance more comprehensively, we also tested a new beta regression model for outages. We show the quasi-binomial model had a smaller cross-validation root-mean squared error of 0.23 compared with 0.28 for the beta model. Finally, we show that our model also captures that grids with more redundant components can be more resilient to hurricane-caused outages. Thus, our proposed quasi-binomial model advances the state of the art for power outage predictions.

The probability density function (PDF) of structural responses is one of the significant fundamentals for structural reliability analysis. Nonetheless, it is still challenging to compute the PDF, especially when the distribution tail is concerned for the structural reliability. Because an arbitrary PDF is the exact inverse Fourier transform of its corresponding characteristic function (CF), working with the CF provides an alternative to obtain the PDF. Recently, based on the inverse Fourier transform of CF, which can be numerically calculated with the complex fractional moments, a generalized distribution reconstruction (GDR) method was proposed in the literature. This paper aims to provide new improvements to the GDR method, including (1) giving a theoretical explanation to demonstrate why the GDR method could reconstruct the PDF in an accurate way; (2) proposing two new expressions to approximate the CF with fewer undetermined parameters, which strictly satisfy the mathematical properties of CF; and (3) deriving an analytical PDF such that possible errors from the numerical inverse Fourier transform can be avoided. Analytical distributions and practical applications are studied to illustrate the efficiency and accuracy of the proposed approach. Some open issues to be further investigated are outlined as well.

Abstract

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.

Practical Applications

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.

Stratigraphic variability contributes significantly to the deformation and stability of geotechnical structures. In this case study, the stratigraphic variability of a typical deep manually filled site in Lanzhou New District, Gansu Province, China is simulated. By applying Walther’s law, the relationship between vertical and horizontal transition counting matrices is established to calculate the vertical and horizontal state-transition probability matrices using a coupled Markov chain model. The Monte Carlo simulation method is used to predict the distribution of soil layers. Based on borehole data, the effects of different borehole layout schemes on the estimation of the transfer probability matrix and prediction of the soil layer distribution are investigated. The results indicate that to obtain accurate estimations for the state transition probability matrix and predictions for the soil layer distribution, more evenly distributed borehole data should be selected. When the values of the diagonal elements in the matrix are high, the sensitivity of the transition probability matrix estimation to the borehole layout scheme is low. Multiple predictions of soil layer distributions using the same borehole layout scheme yield different results primarily because of the significant spacing between the boreholes.