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Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.

The Logistics Performance Index (LPI), published by the World Bank, is a key measure of national-level logistics performance. It comprises six indicators: customs, infrastructure, international shipments, service quality, timeliness, and tracking and tracing. The objective of this study is to explore temporal dependencies among the six LPI indicators while operationalizing the World Bank’s LPI framework in terms of mapping the input indicators (customs, infrastructure, and service quality) to the outcome indicators (international shipments representing cost, timeliness, and tracking and tracing representing reliability).

A Bayesian Belief Network (BBN)-based methodology was adopted to effectively map temporal dependencies among variables in a probabilistic network setting. Using forward and backward propagation features of BBN inferencing, critical variables were also identified. A BBN model was developed using the World Bank’s LPI datasets for 2010, 2012, 2014, 2016, 2018, and 2023, covering the six LPI indicators for 118 countries.

The prediction accuracy of the model is 88.1%. Strong dependencies are found across the six LPI indicators over time. The forward propagation analysis of the model reveals that “logistics competence and quality” is the most critical input indicator that can influence all three outcome indicators over time. The backward propagation analysis indicates that “customs” is the most critical indicator for improving the performance on the “international shipments” indicator, whereas “logistics competence and quality” can significantly improve the performance on the “timeliness” and “tracking and tracing” indicators. The sensitivity analysis of the model reveals that “logistics competence and quality” and “infrastructure” are the key indicators that can influence the results across the three outcome indicators. These findings provide useful insights to researchers regarding the importance of exploring the temporal modeling of dependencies among the LPI indicators. Moreover, policymakers can use these findings to help their countries target specific input indicators to improve country-level logistics performance.

This paper contributes to the literature on logistics management by exploring the temporal dependencies among the six LPI indicators for 118 countries over the last 14 years. Moreover, this paper proposes and operationalizes a data-driven BBN modeling approach in this unique context.

Manual monitoring is a conventional method for monitoring and managing construction safety risks. However, construction sites involve risk coupling - a phenomenon in which multiple safety risk factors occur at the same time and amplify the probability of construction accidents. It is challenging to manually monitor safety risks that occur simultaneously at different times and locations, especially considering the limitations of risk manager’s expertise and human capacity.

To address this challenge, an automatic approach that integrates point cloud, computer vision technologies, and Bayesian networks for simultaneous monitoring and evaluation of multiple on-site construction risks is proposed. This approach supports the identification of risk couplings and decision-making process through a system that combines real-time monitoring of multiple safety risks with expert knowledge. The proposed approach was applied to a foundation project, from laboratory experiments to a real-world case application.

In the laboratory experiment, the proposed approach effectively monitored and assessed the interdependent risks coupling in foundation pit construction. In the real-world case, the proposed approach shows good adaptability to the actual construction application.

The core contribution of this study lies in the combination of an automatic monitoring method with an expert knowledge system to quantitatively assess the impact of risk coupling. This approach offers a valuable tool for risk managers in foundation pit construction, promoting a proactive and informed risk coupling management strategy.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

This study aimed to address a gap in subcontractor management by focusing on previously unexplored complexities surrounding subcontractor management in developing countries. While past studies concentrated on selection and relationships, this study delved into how effective subcontractor management impacts project success.

This study used the Bayesian Network analysis approach, through a meticulously developed questionnaire survey refined through a piloting stage involving experienced industry professionals. The survey was ultimately distributed among participants based in Accra, Ghana, resulting in a response rate of approximately 63%.

The research identified diverse components contributing to subcontractor disruptions, highlighted the necessity of a clear regulatory framework, emphasized the impact of financial and leadership assessments on performance, and underscored the crucial role of main contractors in Integrated Project and Labour Cost Management with Subcontractor Oversight and Coordination.

Previous studies have not considered the challenges subcontractors face in projects. This investigation bridges this gap from multiple perspectives, using Bayesian network analysis to enhance subcontractor management, thereby contributing to the successful completion of construction projects.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

This paper aims to capture the complex interdependences between supply chain disruptions (SCDs), SC risk mitigation strategies and firm performance in the context of disruptive events to enhance resilience for medium-sized and large firms coping with complex supply chain networks. The roles of digitalization, insurance and government support have also been addressed as potential strategies to counteract the impacts of disruptions on supply chains.

This study is based on an empirical investigation in an FMCG company – using a hybrid causal mapping technique based on the frameworks of interpretive structural modeling (ISM) and Bayesian networks (BN) – of 11 levels of relationships between SCDs (in supply, production, logistics, demand and finance), SC risk mitigation strategies (flexibility, efficiency, agility and responsiveness), insurance, government support, information and knowledge sharing, digitalization and finally the key firm performance measures (continuity, quality and financial performance).

The results of the empirical investigation reveal and describe: (1) the nature and probabilistic quantification of the lower-level relationships among the four SCDs, among the mitigation strategies and the three firm performance measures; (2) the nature and probabilistic quantification of the higher-level relationships among the impacts of SCDs, SC risk mitigation strategies and firm performance and (3) how to model and quantify the complex interdependences in single firms and their supply chains.

Our results can support managers in developing more effective decision-making models to assess and manage unfavorable events and cascade effects among different functions and processes in the context of risks and disruptions.

To optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. However, delay predictions often involve imprecise shifts based on known delay times. Real-time and accurate train delay predictions, facilitated by data-driven neural network models, can significantly reduce dispatcher stress and improve adjustment plans. Leveraging current train operation data, these models enable swift and precise predictions, addressing challenges posed by train delays in high-speed rail networks during unforeseen events.

This paper proposes CBLA-net, a neural network architecture for predicting late arrival times. It combines CNN, Bi-LSTM, and attention mechanisms to extract features, handle time series data, and enhance information utilization. Trained on operational data from the Beijing-Tianjin line, it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.

This study evaluates our model's predictive performance using two data approaches: one considering full data and another focusing only on late arrivals. Results show precise and rapid predictions. Training with full data achieves a MAE of approximately 0.54 minutes and a RMSE of 0.65 minutes, surpassing the model trained solely on delay data (MAE: is about 1.02 min, RMSE: is about 1.52 min). Despite superior overall performance with full data, the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals. For enhanced adaptability to real-world train operations, training with full data is recommended.

This paper introduces a novel neural network model, CBLA-net, for predicting train delay times. It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.

The present study is performed to identify the propagation mechanism of the ripple effect as well as examine the simultaneous impact of risks on supply chain (SC) performance.

A theoretical framework with many hypotheses regarding the relationships between SC risk types and performance is established. The data are collected from a large-scale survey supported by a project of the Japanese government to promote sustainable socioeconomic development for the Association of Southeast Asian Nations (ASEAN) region, with the participation of 207 firms. Structural equation modeling (SEM) is used to test the hypotheses of the theoretical framework.

It is indicated that human-made risk causes operational risk, while natural risk causes both supply risk and operational risk. Furthermore, the impacts of human-made risk and natural risk on performance are amplified through operational risk.

This study is one of the first attempts that identifies the propagation mechanism of the ripple effect and examines the simultaneous impact of risks on performance in construction SCs.

Although many studies on risk management in construction SCs have been carried out, they mainly focus on risk identification or quantification of risk impact. It is observed that research on the ripple effect of disruptions has been very scarce.

The purpose of this paper is to optimize the maintenance strategies for wind turbine (WT) gearboxes to minimize costs associated with PM actions, cooling, production loss and gearbox replacement. Two approaches, periodic imperfect maintenance and a novel design incorporating alternating gearboxes are compared to identify the most cost-effective solution.

This study employs mathematical modeling to analyze the design, operation and maintenance of WT gearboxes. Two maintenance strategies are investigated, involving periodic imperfect maintenance actions and the incorporation of two similar gearboxes operating alternately. The models determine optimal preventive maintenance (PM) and switching periods to minimize total expected costs over the operating time span.

The research findings reveal, for the considered case of a moroccan wind farm, that the use of two similar gearboxes operating alternately is more cost-effective than relying on a single gearbox. The mathematical models developed enable the determination and comparison of optimal strategies for various WT gearbox scenarios and associated maintenance costs.

Limitations may arise from simplifications in the mathematical models and assumptions about degradation, temperature monitoring and maintenance effectiveness. Future research could refine the models and incorporate additional factors for a more comprehensive analysis.

Practically, the study provides insights into optimizing WT gearbox maintenance strategies, considering the trade-offs between PM actions, cooling, production loss and gearbox replacement costs. The findings can inform decisions on maintenance planning and design modifications to enhance cost efficiency.

While the primary focus is on cost optimization, the study indirectly contributes to the broader societal goal of sustainable energy production. Efficient maintenance strategies for WTs help ensure reliable and cost-effective renewable energy, potentially benefiting communities relying on wind power.

This paper introduces two distinct strategies for WT gearbox maintenance, extending beyond traditional periodic maintenance. The incorporation of alternating gearboxes presents a novel design approach. The developed mathematical models offer a valuable tool for determining and comparing optimal strategies tailored to specific WT scenarios and associated maintenance costs.