Yuming Liu

Against the backdrop of the advancing Transportation Power Strategy, long and large tunnels face critical challenges in ensuring the safety and efficiency of transportation scheduling due to their harsh environment, complex traffic network, and the need for coordination among multiple types of vehicles. Addressing the shortcomings of existing research—such as the disconnection between path planning and dynamic environments, insufficient coordination between timetables and paths, and incomplete conflict management—this paper constructs a comprehensive optimization model for the scheduling of construction vehicles in tunnel traffic networks. Firstly, integrating the improved social force model with the BPR function, an adaptive social force-BPR path planning model with a collision compensation mechanism is proposed, and the weights of sub-items are optimized using the improved AHP algorithm. Secondly, a constraint system covering paths, spatio-temporal logic, and three types of conflicts (crossing conflicts, head-on conflicts, and congestion conflicts) is established, and a bi-objective function of “minimum total scheduling time” and “minimum number of conflicts” is designed. Combined with the improved NSGA-II algorithm, the collaborative optimization of departure intervals and paths is realized. In particular, a conflict entropy repair operator is introduced to quantify the conflict chaos through node conflict entropy and vehicle conflict entropy, and the scheduling strategy is accurately adjusted based on the logic of “priority ranking-dynamic delay” to balance conflict resolution and efficiency loss. Finally, a case verification is carried out relying on a tunnel topological network with 30 nodes and 41 edges. The experimental results show that the optimal repulsion coefficient k_f of the social force model is 20, and the maximum departure interval of 8 min is the best configuration after introducing the repair operator. At this time, the total scheduling time is 136 min, and the total number of conflicts is only 2, completely avoiding high-risk head-on conflicts and congestion conflicts. The research outputs a vehicle scheduling scheme, enriches the theory of tunnel traffic scheduling, and provides scientific and feasible technical support for the coordinated scheduling of construction vehicles in long and large tunnels.

Current technological innovation in mega projects faces the problem of mismatch between supply and demand, where technology demand-side entities struggle to translate engineering problems into precise scientific research language, while technology supply-side entities fail to capture authentic scenario parameters from engineering sites. This study employs an evolutionary game model to thoroughly investigate behavioral interaction processes among governance entities, demand-side entities, and intermediary collaborative innovation platforms during technological innovation supply–demand matching. By constructing and deriving a tripartite evolutionary game model, this research analyzes the impacts of initial states, the matching effort coefficient, the innovation risk coefficient, and other factors on the evolution of scientific technological innovation supply–demand matching. Additionally, this study simulates the dynamic evolutionary processes of strategic selection. The findings reveal that the initial states of the three parties do not influence behavioral evolution. Furthermore, the subsidy coefficient, additional benefits, and risk coefficient emerge as the most significant parameters affecting tripartite strategy selection. The research outcomes and managerial implications provide crucial reference value for enhancing the alignment between scientific research supply and demand in mega projects, thereby promoting the transformation of scientific and technological achievements in major railway engineering projects.

Tunnel construction, a critical aspect of railway engineering, is a repetitive process with distinct linear characteristics. While the Linear Scheduling Method (LSM) is widely used for scheduling optimization in linear projects, it struggles to accommodate dynamic construction sequences, reverse construction, and flexible team allocation. Minimizing the project duration is a primary objective in tunnel construction scheduling optimization. To optimize tunnel construction, we propose a duration-shortening method using additional working surfaces, adaptable to multi-segment and multi-team scenarios. A dynamic optimization model is developed for tunnel construction scheduling, integrating LSM, soft logic, Work Breakdown Structure (WBS), and Resource Breakdown Structure (RBS) within a dynamic scheduling framework. This model analyzes logical relationships, work continuity, temporal and spatial constraints, and resource variation, focusing on reverse construction. The Mixed-Integer Programming (MIP) approach is used to build the mathematical model, solved with both exact algorithms and Genetic Algorithms (GA), and implemented in Python 3.12.7. Both algorithms perform well, with the GA excelling at handling complex constraints. Case studies confirm the method’s effectiveness in optimizing durations, devising flexible schedules, and improving efficiency and practicality. This research provides both theoretical insights and practical guidance for tunnel construction scheduling optimization in railway engineering.

This paper presents an in-depth study of the automated recognition and geometric information quantification of rebar meshes, proposing a deep learning-based method for rebar mesh detection and segmentation. By constructing a diverse rebar mesh image dataset, an improved Unet-based model was developed, incorporating residual modules to enhance the network’s feature extraction capabilities and training efficiency. The study found that the improved model maintains high segmentation accuracy and robustness even in the presence of complex backgrounds and noise. To achieve the precise measurement of rebar spacing, a rebar intersection detection algorithm based on convolution operations was designed, and the IQR (Interquartile Range) algorithm was applied to remove outliers, ensuring the accuracy and reliability of spacing calculations. The experimental results demonstrate that the proposed model and methods effectively and efficiently accomplish the automated recognition and geometric information extraction of rebar meshes, providing reliable technical support for the automated detection and geometric data analysis of rebar meshes in practical engineering applications.

Megaproject construction endeavors and technological innovation activities, led by industry–university–research (IUR) collaboration, demonstrate marked disparities in value orientations, implementing entities, and constituent components. These discrepancies lead to a mismatch between innovation demands and actual activities, as well as insufficient innovation motivation among construction entities, subsequently impacting innovation effectiveness and the commercialization of outcomes and failing to adequately support engineering construction needs. In response to this predicament, the academic community widely acknowledges IUR collaborative innovation as a solution. This research integrates fundamental theoretical analysis with a multi-case study approach and systematically dissects the distinctive features at the micro, meso, and macro levels, grounded in the collaborative innovation practices of IUR in three iconic railway engineering projects in China. Subsequently, it unravels the inherent operational mechanics of the IUR collaborative innovation system within large-scale projects. Specifically, at the micro level, the profound engagement of governments and project owners fosters a solid supportive environment and collaborative platform for IUR collaboration, while past successful cooperation experiences among key innovation entities enhance their technological and knowledge interactions. At the meso level, shared industry cognitions and values, hierarchical organizational structures, flexible institutional designs, and resource allocation strategies based on balancing risks and benefits collectively constitute the supporting system for megaproject collaborative innovation. At the macro level, the tight integration of the innovation chain and industrial chain promotes the formation of an open cooperation ecosystem, ensuring the continuity and systematic nature of innovation activities and accelerating the rapid commercialization and efficient utilization of innovation outcomes. This study not only enriches the theoretical connotations of IUR collaborative innovation in the context of major engineering projects but also provides theoretical foundations for strategy formulation and management practices for major project managers, holding significant value in guiding the innovation management of future major engineering projects.

Tunnel construction projects are a classic type of repetitive project, and hold a crucial position in the construction industry. The linear scheduling method (LSM) has been in the spotlight in scheduling optimization for repetitive construction projects since it was first proposed. However, the possibility of changing the construction sequence during application is very often overlooked, which is not in line with actual engineering practices. Incorporating soft logic into LSM and considering the characteristics of tunnel construction, we propose an optimization method for the tunnel construction schedule to shorten the duration by opening up an additional working surface. A mixed-integer programming (MIP) model is developed, which considers various constraints, such as construction sequence and work continuity. The exact algorithm and genetic algorithm (GA) are designed via Python, and the validity is verified through practical cases. A comparative analysis was conducted between the two algorithms and their outcomes demonstrates that the method can satisfy the restrictive constraints of tunnel construction projects. In addition, improvement strategies for tunnel construction management are recommended.

The construction of major railway projects poses significant risks, which present considerable challenges to construction management. To accurately assess the level of construction management for these projects, this study incorporated resilience theory into the field. The grounded theory method was utilized to establish a resilience evaluation indicator system for managing a major railway project construction. Additionally, a resilience evaluation model based on the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation method was proposed. This model was applied to evaluate the construction management resilience of a major railway project located in the mountainous region of southwest China. The results indicated that the project exhibits a very high overall level of construction management resilience. Specifically, it demonstrates high levels of ability to monitor and warn, an ability to resist absorption, and an ability to respond to emergencies. Additionally, it showcases high levels of ability to recover and rebuild, and an ability to learn to adapt. The evaluation results were consistent with the actual situation and verified the correctness and reliability of the method. Based on the aforementioned research findings, this paper puts forward recommendations on material redundancy and resource security from a resource perspective, and suggestions on organizational optimization and personnel capacity improvement from a subject perspective, thus indicating directions for enhancing the management level of major engineering railway constructions.

With the promotion of Engineering Procurement Construction mode in railway construction projects, collaborative management between design and construction has faced increasing challenges due to complexities of the participants and the various professional fields involved in railway EPC projects. This paper aims to analyze the issues present in the collaborative management of railway EPC project design and construction. Utilizing the social network analysis methodology combined with the Wuli–Shili–Renli (WSR) system methodology, a network model was established to explore relationships between design and construction management. This study investigates network-influencing factors and proposes targeted measures to enhance project management efficiency. The findings indicate that coordination and communication mechanisms, organization building, interface management systems and design management systems represent core network elements that significantly impact railway EPC collaborative management. These elements are closely related to other network factors. The research not only advances design and construction coordination theory, but it also introduces new ideas for engineering practice.

The construction of mega infrastructure projects has the characteristics of repeatability, long duration, and high complexity. Therefore, it is particularly important to implement dynamic decision-making in projects. This study takes data-driven decision-making mechanisms as the entry point and constructs a dynamic decision-making system for mega infrastructure projects consisting of an information collection subsystem, an information processing and transformation subsystem, a human–computer collaborative decision-making subsystem and an evaluation and feedback subsystem. On this basis, we established a system dynamics model of dynamic decision-making for mega infrastructure projects. Vensim PLE 9.3.5 software was used to simulate and analyze the operation law of dynamic decision-making for mega infrastructure projects from a data-driven perspective, and the sensitivity of the application rate of information management technology, the application rate of data analysis methods, the participation rate of experts in decision-making, the historical case information on this project, and the information on similar projects on the effectiveness of program implementation were simulated and analyzed. The results of the study showed that all five key influencing factors have a positive impact on the effectiveness of program implementation. In addition, the application rate of information management technology and the application rate of information analysis methods have a higher sensitivity to the effectiveness of program implementation, the participation rate of experts in decision-making and historical case information on this project have average sensitivity to the effectiveness of program implementation, and information on similar projects has lower sensitivity to the effectiveness of program implementation. This study provides some ideas and suggestions to promote the effective use of information technology and digital technology by each participant in the construction of mega infrastructure projects while improving their dynamic decision-making efficiency, scientificity, and accuracy.

With mega railway construction projects (MRCPs) facing various natural disasters and extremely difficult engineering construction tasks, there is an urgent need to improve the resilience management capability of engineering construction to cope with complex and changing internal and external risks. As a key element of the construction management system, the level of institutional resilience directly determines the adaptability of the construction system in the face of crisis. However, the research on the influencing factors and the formation mechanism of institutional resilience is slightly insufficient. Therefore, this paper defines the connotation of institutional resilience in MRCPs on the basis of existing research. Secondly, it codes authoritative journals and expert interview reports related to MRCPs in China by applying a rooting theory. Finally, the paper analyses the formation mechanism of institutional resilience based on an Interpretative Structural Modeling and proposes strategies to enhance it. The results show that the institutional environment is the fundamental influencing factor, institutional implementation, institutional supervision, institutional guarantee and institutional feedback are the direct influencing factors, and institutional stakeholders and institutional design are indirect influencing factors. The mechanism of interaction between the institution and the institutional environment, the mechanism of interaction between the institution and the institutional stakeholders, the mechanism of generation of institutional rules and the mechanism of operation of institutional rules work together to generate institutional resilience. This paper, as an exploratory study on institutional resilience in the field of engineering management, has sorted out the ideas for the subsequent research on institutional resilience in this field and provided the directions for project managers of MRCPs to improve their institutional systems.