High-speed trains (HSTs) have the advantages of comfort, efficiency, and convenience and have gradually become the mainstream means of transportation. As the operating scale of HSTs continues to increase, ensuring their safety and reliability has become more imperative. As the core component of HST, the reliability of the traction system has a substantially influence on the train. During the long-term operation of HSTs, the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures, thus threatening the running safety of the train. Therefore, performing fault monitoring and diagnosis on the traction system of the HST is necessary. In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis. Machine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensive systematic review is still lacking in this field. This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint. First, the structure and function of the HST traction system are briefly introduced. Then, the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed. Finally, the challenges for accurate fault diagnosis under actual operating conditions are revealed, and the future research trends of machine learning in traction systems are discussed.

This study explores the integration of ChatGPT and AI-generated content (AIGC) in engineering management. It assesses the impact of AIGC services on engineering management processes, mapping out the potential development of AIGC in various engineering functions. The study categorizes AIGC services within the domain of engineering management and conceptualizes an AIGC-aided engineering lifecycle. It also identifies key challenges and emerging trends associated with AIGC. The challenges highlighted are ethical considerations, reliability, and robustness in engineering management. The emerging trends are centered on AIGC-aided optimization design, AIGC-aided engineering consulting, and AIGC-aided green engineering initiatives.

Remanufacturing is widely recognized as beneficial to the environment and a circular economy. However, remanufacturing is more complex than traditional manufacturing due to the effects of government policy, uncertainty of consumer preferences, competition and cooperation among firms, and so on. These factors motivate academics to optimize remanufacturing outcomes, especially for product pricing and production. This study reviews the published literature on pricing and production strategies in remanufacturing from four perspectives of supply chain, namely, government policy, consumer characteristics, relationships among firms, and supply chain structures. Review results can benefit scholars/practitioners in the future by highlighting the challenges and opportunities in remanufacturing strategies.

In recent years, the architecture, engineering, construction, and facility management (FM) industries have been applying various emerging digital technologies to facilitate the design, construction, and management of infrastructure facilities. Digital twin (DT) has emerged as a solution for enabling real-time data acquisition, transfer, analysis, and utilization for improved decision-making toward smart FM. Substantial research on DT for FM has been undertaken in the past decade. This paper presents a bibliometric analysis of the literature on DT for FM. A total of 248 research articles are obtained from the Scopus and Web of Science databases. VOSviewer is then utilized to conduct bibliometric analysis and visualize keyword co-occurrence, citation, and co-authorship networks; furthermore, the research topics, authors, sources, and countries contributing to the use of DT for FM are identified. The findings show that the current research of DT in FM focuses on building information modeling-based FM, artificial intelligence (AI)-based predictive maintenance, real-time cyber–physical system data integration, and facility lifecycle asset management. Several areas, such as AI-based real-time asset prognostics and health management, virtual-based intelligent infrastructure monitoring, deep learning-aided continuous improvement of the FM systems, semantically rich data interoperability throughout the facility lifecycle, and autonomous control feedback, need to be further studied. This review contributes to the body of knowledge on digital transformation and smart FM by identifying the landscape, state-of-the-art research trends, and future needs with regard to DT in FM.

Amidst the inefficiencies of traditional job-seeking approaches in the recruitment ecosystem, the importance of automated job recommendation systems has been magnified. However, existing models optimized to maximize user clicks for general product recommendations prove inept in addressing the unique challenges of job recommendation, namely reciprocity and competition. Moreover, sparse data on online recruitment platforms can further negatively impact the performance of existing job recommendation algorithms. To counteract these limitations, we propose a bilateral heterogeneous graph-based competition iteration model. This model comprises three integral components: 1) two bilateral heterogeneous graphs for capturing multi-source information from people and jobs and alleviating data sparsity, 2) fusion strategies for synthesizing attributes and preferences to produce mutually beneficial job matches, and 3) a competition-enhancing strategy for dispersing competition realized through a two-stage optimization algorithm. Augmented by granular attention mechanisms for enhanced interpretability, the model’s efficacy, competition dispersion, and interpretability are validated through rigorous empirical evaluations on a real-world recruitment platform.

Driving safety and accident prevention are attracting increasing global interest. Current safety monitoring systems often face challenges such as limited spatiotemporal coverage and accuracy, leading to delays in alerting drivers about potential hazards. This study explores the use of edge computing for monitoring vehicle motion and issuing accident warnings, such as lane departures and vehicle collisions. Unlike traditional systems that depend on data from single vehicles, the cooperative vehicle-infrastructure system collects data directly from connected and automated vehicles (CAVs) via vehicle-to-everything communication. This approach facilitates a comprehensive assessment of each vehicle’s risk. We propose algorithms and specific data structures for evaluating accident risks associated with different CAVs. Furthermore, we examine the prerequisites for data accuracy and transmission delay to enhance the safety of CAV driving. The efficacy of this framework is validated through both simulated and real-world road tests, proving its utility in diverse driving conditions.

Understanding the influencing factors and the evolving trends of the Water-Sediment Regulation System (WSRS) is vital for the protection and management of the Yellow River. Past studies on WSRS have been limited in focus and have not fully addressed the complete engineering control system of the basin. This study takes a holistic view, treating sediment management in the Yellow River as a dynamic and ever-evolving complex system. It merges concepts from system science, information theory, and dissipative structure with practical efforts in sediment engineering control. The key findings of this study are as follows: between 1990 and 2019, the average Yellow River Sediment Regulation Index (YSRI) was 55.99, with the lowest being 50.26 in 1990 and the highest being 61.48 in 2019; the result indicates that the WSRS activity decreased, yet it fluctuated, gradually approaching the critical threshold of a dissipative structure.

This paper proposes a framework for evaluating the efficacy and suitability of maintenance programs with a focus on quantitative risk assessment in the domain of aircraft maintenance task transfer. The analysis is anchored in the principles of Maintenance Steering Group-3 (MSG-3) logic decision paradigms. The paper advances a holistic risk assessment index architecture tailored for the task transfer of maintenance programs. Utilizing the analytic network process (ANP), the study quantifies the weight interrelationships among diverse variables, incorporating expert-elicited subjective weighting. A multielement connection number-based evaluative model is employed to characterize decision-specific data, thereby facilitating the quantification of task transfer-associated risk through the appraisal of set-pair potentials. Moreover, the paper conducts a temporal risk trend analysis founded on partial connection numbers of varying orders. This analytical construct serves to streamline the process of risk assessment pertinent to maintenance program task transfer. The empirical component of this research, exemplified through a case study of the Boeing 737NG aircraft maintenance program, corroborates the methodological robustness and pragmatic applicability of the proposed framework in the quantification and analysis of mission transfer risk.

With the escalating complexity in production scenarios, vast amounts of production information are retained within enterprises in the industrial domain. Probing questions of how to meticulously excavate value from complex document information and establish coherent information links arise. In this work, we present a framework for knowledge graph construction in the industrial domain, predicated on knowledge-enhanced document-level entity and relation extraction. This approach alleviates the shortage of annotated data in the industrial domain and models the interplay of industrial documents. To augment the accuracy of named entity recognition, domain-specific knowledge is incorporated into the initialization of the word embedding matrix within the bidirectional long short-term memory conditional random field (BiLSTM-CRF) framework. For relation extraction, this paper introduces the knowledge-enhanced graph inference (KEGI) network, a pioneering method designed for long paragraphs in the industrial domain. This method discerns intricate interactions among entities by constructing a document graph and innovatively integrates knowledge representation into both node construction and path inference through TransR. On the application stratum, BiLSTM-CRF and KEGI are utilized to craft a knowledge graph from a knowledge representation model and Chinese fault reports for a steel production line, specifically SPOnto and SPFRDoc. The F1 value for entity and relation extraction has been enhanced by 2% to 6%. The quality of the extracted knowledge graph complies with the requirements of real-world production environment applications. The results demonstrate that KEGI can profoundly delve into production reports, extracting a wealth of knowledge and patterns, thereby providing a comprehensive solution for production management.

Urban rail transit (URT) disruptions present considerable challenges due to several factors: i) a high probability of occurrence, arising from facility failures, disasters, and vandalism; ii) substantial negative effects, notably the delay of numerous passengers; iii) an escalating frequency, attributable to the gradual aging of facilities; and iv) severe penalties, including substantial fines for abnormal operation. This article systematically reviews URT disruption management literature from the past decade, categorizing it into pre-disruption and post-disruption measures. The pre-disruption research focuses on reducing the effects of disruptions through network analysis, passenger behavior analysis, resource allocation for protection and backup, and enhancing system resilience. Conversely, post-disruption research concentrates on restoring normal operations through train rescheduling and bus bridging services. The review reveals that while post-disruption strategies are thoroughly explored, pre-disruption research is predominantly analytical, with a scarcity of practical pre-emptive solutions. Moreover, future research should focus more on increasing the interchangeability of transport modes, reinforcing redundancy relationships between URT lines, and innovating post-disruption strategies.

Reliability-redundancy allocation, preventive maintenance, and spare parts logistics are crucial for achieving system reliability and availability goal. Existing methods often concentrate on specific scopes of the system’s lifetime. This paper proposes a joint redundancy-maintenance-inventory allocation model that simultaneously optimizes redundant component, replacement time, spares stocking, and repair capacity. Under reliability and availability criteria, our objective is to minimize the system’s lifetime cost, including design, manufacturing, and operational phases. We develop a unified system availability model based on ten performance drivers, serving as the foundation for the establishment of the lifetime-based resource allocation model. Superimposed renewal theory is employed to estimate spare part demand from proactive and corrective replacements. A bisection algorithm, enhanced by neighborhood exploration, solves the complex mixed-integer, nonlinear optimization problem. The numerical experiments show that component redundancy is preferred and necessary if one of the following situations occurs: extremely high system availability is required, the fleet size is small, the system reliability is immature, the inventory holding is too costly, or the hands-on replacement time is prolonged. The joint allocation model also reveals that there exists no monotonic relation between spares stocking level and system availability.

Studies have demonstrated that advanced technology, such as smart contract applications, can enhance both pre- and post-contract administration within the built environment sector. Smart contract technology, exemplifying blockchain technologies, has the potential to improve transparency, trust, and the security of data transactions within this sector. However, there is a dearth of academic literature concerning smart contract applications within the construction industries of developing countries, with a specific focus on Nigeria. Consequently, this study seeks to explore the relevance of smart contract technology and address the challenges impeding its adoption, offering strategies to mitigate the obstacles faced by smart contract applications. To investigate the stakeholders, this research conducted 14 virtual interview sessions to achieve data saturation. The interviewees encompassed project management practitioners, senior management personnel from construction companies, experts in construction dispute resolution, professionals in construction software, and representatives from government construction agencies. The data obtained from these interviews underwent thorough analysis employing a thematic approach. The study duly recognizes the significance of smart contract applications within the sector. Among the 12 identified barriers, issues such as identity theft and data leakage, communication and synchronization challenges, high computational expenses, lack of driving impetus, excessive electricity consumption, intricate implementation processes, absence of a universally applicable legal framework, and the lack of a localized legal framework were recurrent impediments affecting the adoption of smart contract applications within the sector. The study also delves into comprehensive measures to mitigate these barriers. In conclusion, this study critically evaluates the relevance of smart contract applications within the built environment, with a specific focus on promoting their usage. It may serve as a pioneering effort, especially within the context of Nigeria.

Dynamic speed guidance for vehicles in on-ramp merging zones is instrumental in alleviating traffic congestion on urban expressways. To enhance compliance with recommended speeds, the development of a dynamic speed-guidance mechanism that accounts for heterogeneity in human driving styles is pivotal. Utilizing intelligent connected technologies that provide real-time vehicular data in these merging locales, this study proposes such a guidance system. Initially, we integrate a multi-agent consensus algorithm into a multi-vehicle framework operating on both the mainline and the ramp, thereby facilitating harmonized speed and spacing strategies. Subsequently, we conduct an analysis of the behavioral traits inherent to drivers of varied styles to refine speed planning in a more efficient and reliable manner. Lastly, we investigate a closed-loop feedback approach for speed guidance that incorporates the driver’s execution rate, thereby enabling dynamic recalibration of advised speeds and ensuring fluid vehicular integration into the mainline. Empirical results substantiate that a dynamic speed guidance system incorporating driving styles offers effective support for human drivers in seamless mainline merging.

Bus bunching has been a persistent issue in urban bus system since it first appeared, and it remains a challenge not fully resolved. This phenomenon may reduce the operational efficiency of the urban bus system, which is detrimental to the operation of fast-paced public transport in cities. Fortunately, extensive research has been undertaken in the long development and optimization of the urban bus system, and many solutions have emerged so far. The purpose of this paper is to summarize the existing solutions and serve as a guide for subsequent research in this area. Upon careful examination of current findings, it is found that, based on the different optimization objects, existing solutions to the bus bunching problem can be divided into five directions, i.e., operational strategy improvement, traffic control improvement, driver driving rules improvement, passenger habit improvement, and others. While numerous solutions to bus bunching are available, there remains a gap in research exploring the integrated application of methods from diverse directions. Furthermore, with the development of autonomous driving, it is expected that the use of modular autonomous vehicles could be the most potential solution to the issue of bus bunching in the future.

Numerous maintenance strategies have been proposed in the literature related to reliability. This paper focuses on the utilization of reliability importance measures to optimize maintenance strategies. We analyze maintenance strategies based on importance measures and identify areas lacking sufficient research. The paper presents principles and formulas for advanced importance measures within the context of optimizing maintenance strategies. Additionally, it classifies methods of maintenance strategy optimization according to importance measures and outlines the roles of these measures in various maintenance strategies. Finally, it discusses potential challenges that optimization of maintenance strategies based on importance measures may encounter with future technologies.

In the steelmaking industry, enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities. Thus, effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain, reducing management costs and complexities. This paper proposes, for the first time, the integration of Vision-Language Model (VLM) and Large Language Model (LLM) technologies in the steel manufacturing domain, creating a novel steelmaking process management system. The system facilitates data collection, analysis, visualization, and intelligent dialogue for the steelmaking process. The VLM module provides textual descriptions for slab defect detection, while LLM technology supports the analysis of production data and intelligent question-answering. The feasibility, superiority, and effectiveness of the system are demonstrated through production data and comparative experiments. The system has significantly lowered costs and enhanced operational understanding, marking a critical step toward intelligent and cost-effective management in the steelmaking domain.

There is notable variability in carbon emission reduction efforts across different provinces in China, underscoring the need for effective strategies to implement carbon emission allowance auctions. These auctions, as opposed to free allocations, could be more aligned with the principle of “polluter pays.” Focusing on three diverse regions — Ningxia, Beijing, and Zhejiang — this study employs a system dynamics simulation model to explore markets for carbon emissions and green certificates trading. The aim is to determine the optimal timing and appropriate policy intensities for auction introduction. Key findings include: (1) Optimal auction strategies differ among the provinces, recommending immediate implementation in Beijing, followed by Ningxia and Zhejiang. (2) In Ningxia, there’s a potential for a 6.20% increase in GDP alongside a 21.59% reduction in carbon emissions, suggesting a feasible harmony between environmental and economic objectives. (3) Market-related policy variables, such as total carbon allowances and Renewable Portfolio Standards, significantly influence the optimal auction strategies but have minimal effect on carbon auction prices.

In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.

The joint optimization of production, maintenance, and quality control has shown effectiveness in reducing long-term operational costs in production systems. However, existing studies often assume that changes in the mean value of product quality characteristics in a deteriorating system follow a specific distribution while keeping variance constant. To address this limitation, we propose an innovative method based on the continuous ranking probability score (CRPS). This method enables the simultaneous detection of changes in mean and variance in nonconformities, thus removing the assumption of a specific distribution for quality characteristics. Our approach focuses on developing optimal strategies for production, maintenance, and quality control to minimize cost per unit of time. Additionally, we employ a stochastic model to optimize the production time allocated to the inventory buffer, resulting in significant cost reductions. The effectiveness of our proposed joint optimization method is demonstrated through comprehensive numerical experiments, sensitivity analysis, and a comparative study. The results show that our method can achieve cost reductions compared to several other related methods, highlighting its practical applicability for manufacturing companies aiming to reduce costs.

In recent decades, interest in project-based learning within organizational learning has grown significantly. This study synthesizes principles that facilitate learning at the project level. Through a cross-case analysis of the Gaasperdammer Tunnel project in the Netherlands and the Hong Kong-Zhuhai-Macao Bridge in China, and validation via focus group discussions, we have identified five key principles: Owner Commitment, Social Environment Approach, Collaboration Vision, Value Orientation, and Open Mindset. These principles highlight the mindsets that guide the behavior and thinking of project practitioners beyond prescriptive processes and routines. Our research enhances the understanding of how project participants can learn from their involvement in unique, complex projects and improve their capabilities for future endeavors. We emphasize the critical role of learning in the development of project capabilities and suggest it be a focal point in future research on infrastructure development projects.

Automated driving has recently attracted significant attention. While considerable research has been conducted on the technologies and societal acceptance of autonomous vehicles, investigations into the control and scheduling of urban automated driving traffic are still nascent. As automated driving gains traction, urban traffic control logic is poised for substantial transformation. Presently, both manual and automated driving predominantly operate under a local decision-making traffic mode, where driving decisions are based on the vehicle’s status and immediate environment. This mode, however, does not fully exploit the potential benefits of automated driving, particularly in optimizing road network resources and traffic efficiency. In response to the increasing adoption of automated driving, it is essential for traffic bureaus to initiate proactive dialogs regarding urban traffic control from a global perspective. This paper introduces a novel global control mode for urban automated driving traffic. Its core concept involves the central scheduling of all autonomous vehicles within the road network through vehicle-infrastructure cooperation, thereby optimizing traffic flow. This paper elucidates the mechanism and process of the global control mode. Given the operational complexity of expansive road networks, the paper suggests segmenting these networks into multiple manageable regions. This mode is conceptualized as an autonomous vehicle global scheduling problem, for which a mathematical model is formulated and a modified A-star algorithm is developed. The experimental findings reveal that (i) the algorithm consistently delivers high-quality solutions promptly and (ii) the global scheduling mode significantly reduces traffic congestion and equitably distributes resources. In conclusion, this paper presents a viable and efficacious new control mode that could substantially enhance urban automated traffic efficiency.

This study explores urban air mobility (UAM) as a strategy for mitigating escalating traffic congestion in major urban areas as a consequence of a static transportation supply versus dynamic demand growth. It offers an in-depth overview of UAM development, highlighting its present state and the challenges of integration with established urban transport systems. Key areas of focus include the technological advancements and obstacles in electric vertical take-off and landing (eVTOL) aircrafts, which are essential for UAM operation in urban environments. Furthermore, it explores the infrastructure requirements for UAM, including vertiport deployment and the creation of adept air traffic control (ATC) systems. These developments must be integrated into the urban landscape without exacerbating land-use challenges. This paper also examines the regulatory framework for UAM, including existing aviation regulations and the necessity for novel policies specifically designed for urban aerial transport. This study presents a comprehensive perspective for various stakeholders, from policymakers to urban planners, highlighting the need for a thorough understanding of UAM’s potential and effective assimilation into urban mobility frameworks.

This study investigates the use of autonomous vehicles in bus rapid transit lanes during the initial phases of autonomous driving development. The aim is to accelerate the advancement of autonomous driving technologies and enhance the efficiency of bus lane usage. We first develop a dynamic joint optimization model that adjusts autonomous vehicle speeds and bus timetables to minimize vehicle travel times while reducing bus passenger waiting times. We account for random variables such as stochastic passenger arrivals at bus stations and variable demand for autonomous vehicle travel by constructing a stochastic dynamic model. To address the computational challenges of large-scale scenarios, we implement a simulation-based heuristic algorithm framework. This framework is designed to efficiently produce high-quality solutions within feasible time limits. Our numerical studies on an actual bus line show that our approach significantly improves system throughput compared to existing benchmarks. Moreover, by strategically managing the entry of autonomous vehicles into the lane and modifying bus timetables, we further enhance the operational efficiency of the system.

The widespread use of energy storage systems in electric bus transit centers presents new opportunities and challenges for bus charging and transit center energy management. A unified optimization model is proposed to jointly optimize the bus charging plan and energy storage system power profile. The model optimizes overall costs by considering battery aging, time-of-use tariffs, and capacity service charges. The model is linearized by a series of relaxations of the nonlinear constraints. This means that we can obtain the exact solution of the model quickly with a commercial solver that is fully adapted to the time scale of day-ahead scheduling. The numerical simulations demonstrate that the proposed method can optimize the bus charging time, charging power, and power profile of energy storage systems in seconds. Monte Carlo simulations reveal that the proposed method significantly reduces the cost and has sufficient robustness to uncertain fluctuations in photovoltaics and office loads.

Power grids play a crucial role in connecting electricity suppliers and consumers. They facilitate efficient power transmission and energy management, significantly contributing to the transition toward low-carbon practices across both upstream and downstream sectors. Effectively managing carbon reduction in the power industry is essential for enhancing carbon reduction efficiency and achieving dual-carbon goals. Recent studies have focused on the outcomes of carbon reduction efforts rather than the management process. However, when power grids prioritize the process of carbon reduction in their management, they are more likely to achieve better results. To address this gap, we propose an evaluation model for managing carbon reduction activities in power grids, comprising the carbon management efficiency (CME) module based on the maturity model and the carbon reduction efficiency (CRE) module based on the entropy method. The CME module provides a scorecard corresponding to a detailed and continuous evaluation model for carbon management processes to calculate its performance. Simultaneously, the CRE module relates carbon reduction results to the development direction of the government and power grid, allowing for effective adjustments and updates based on actual situations. The evaluation model was applied to provincial power grids within the China Central Power Grid. The results reveal that despite some fluctuations in carbon reduction performance, provincial power grids within the China Central Power Grid have made continuous progress in carbon reduction efforts. According to the synergy model, there is evidence suggesting that power grids are steadily improving their carbon reduction performance, and a more organized approach would lead to a greater degree of synergy. The evaluation model applies to power grids, and its framework can be extended to other industries, providing a theoretical reference for evaluating their carbon reduction efforts.