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This study aims to construct a “contractual–relational–governmental” 3D governance framework for new infrastructure projects (NIPs) within China’s distinct institutional context. The primary objective is to explore the impact of multiple governance mechanisms on the NIP performance, thus identifying the key governance mechanisms and proposing targeted performance improvement strategies.

The research design follows a sequential mixed methodology of integrating qualitative and quantitative data collection and analysis. Firstly, project governance and performance indicators were collected from relevant literature and expert interviews. Secondly, a questionnaire was developed, and data were collected through on-site and online means. Finally, the partial least square structural equation model (PLS-SEM) was utilized to examine and analyze the relationships between governance mechanisms and NIP performance.

Contractual, relational and governmental governance all have a certain role in promoting the NIP performance. Specifically, contract stringency, trust and governmental decision are the core elements of contractual, relational and governmental governance, respectively, while commitment does not significantly affect NIP performance. Generally, relational and governmental governance exert a more substantial influence compared to contractual governance, with governmental decision and trust being the most effective.

This paper contributes to the field by introducing PLS-SEM as a measurement tool for exploring the impact of multiple governance mechanisms on governance performance in NIPs. The results offer valuable insights for project managers, enabling them to concentrate on core factors while refining and optimizing governance mechanisms and strategies.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

Megaproject knowledge innovation (MKI) is perceived as a critical strategy for engineering value co-creation and industrial chain upgrading. Ascertaining the impact mechanism of MKI is a crucial initial step towards improving management practices. Within the framework of complex systems in megaprojects, factors exhibit intricate interdependencies. However, the current domain of knowledge has either overlooked or oversimplified this relationship and therefore cannot propose pragmatic and efficacious strategies for enhancing MKI. To close this gap, this study develops a Bayesian network (BN) model aiming to investigate the interdependencies among MKI-related factors and their impact on MKI.

First, this study implements literature review, expert interview and field investigation to identify the influencing factor nodes for the network model development. Second, a Bayesian network was constructed by integrating the expert knowledge with Dempster-Shafer theory. Next, a MKI measurement model was established using 253 training samples. Finally, the factor significance and optimal MKI improvement strategies are identified from the sensitivity analysis and probabilistic reasoning within the BNs.

The results indicate that (1) the BN model exhibits significant reliability and holds promotion and application value in formulating MKI management strategies; (2) knowledge sharing, shared vision and leadership are the key influencing factors of MKI; and (3) simultaneously improving institutional pressure, leadership and knowledge sharing is the most optimal strategy to enhance MKI.

This study innovatively introduced the BN method into the domain of MKI management, providing an appropriate approach for modelling complex relationships among factors and investigate nonlinear influences. The developed model raises megaproject stakeholders’ awareness about factors influencing MKI and presents quantified strategies that increase the likelihood of maximising MKI levels. Its ease of generalisability positions it as a promising decision support tool, facilitating the implementation of sustainable MKI practices.

This study aims to build a framework of the influencing factors of construction workers' career promotion and identifies the critical determinants so as to propose suggestions for the government and enterprises to offer construction workers a path for career promotion.

In line with the theory of human resources, such as Herzberg's two-factor theory, this study constructs a theoretical framework that affects the career promotion of construction workers. Using evidence from Guangzhou city, valid data provided by 464 workers from 50 sites were collected by a questionnaire survey, and the significance test on the influencing factors of construction workers' career promotion was taken by binary logistic regression.

The overall career development of construction workers in Guangzhou is worrying. The binary logistic regression indicates that age, working years, type of work, career development awareness, legal awareness, professional mentality, vocational psychological training and career development path are critical factors that affect construction workers' career promotion.

This study for the first time explores the career promotion of frontline construction workers. Specifically, it identifies the critical factors that affect the career promotion of workers and thus lays a foundation for further research and the promotion and continuous and healthy development of the construction industry. Thus, this study is original and has theoretical and practical significance.

Public participation has been implemented with growing frequency as an instrument for dealing with the increased socio-economic and environmental disputes in public infrastructure and construction (PIC) projects in China. The purpose of this paper is to examine the perceptions of major stakeholders on the major aspects related to public participation practices in China's PIC projects and intends to convey what is presently happening in this segment of the construction industry.

Specific topics of the social effects, benefits, forms, and barriers were addressed through a recent survey of the major stakeholders involved in PIC projects. The survey results were used to perform a strength-weakness-opportunity-threat analysis for evaluating the status quo of public participation in PIC projects.

The survey results indicate that the development of public participation practices in China remains relatively slow despite the urgent need to promote this mechanism for solving socio-economic and environmental disputes in PIC projects. Thus, a four-step strategic plan is suggested to be established to overcome main barriers for the implementation of public participation and promote its development in China.

This study aims to evaluate the status quo of the public participation practices in China by conducting a national survey, which has never been conducted before. The findings of this paper provide a holistic view of the status quo of public participation in China's PIC projects and promote a better application of this mechanism in the construction industry.

To reduce the computational scale for quasi-magnetostatic problems, model order reduction is a good option. Reduced-order modelling techniques based on proper orthogonal decomposition (POD) and centroidal Voronoi tessellation (CVT) have been used to solve many engineering problems. The purpose of this paper is to investigate the computational principle, accuracy and efficiency of the POD-based and the CVT-based reduced-order method when dealing with quasi-magnetostatic problems.

The paper investigates computational features of the reduced-order method based on POD and CVT methods for quasi-magnetostatic problems. Firstly the construction method for the POD and the CVT reduced-order basis is introduced. Then, a reduced model is constructed using high-fidelity finite element solutions and a Galerkin projection. Finally, the transient quasi-magnetostatic problem of the TEAM 21^a model is studied with the proposed reduced-order method.

For the TEAM 21^a model, the numerical results show that both POD-based and CVT-based reduced-order approaches can greatly reduce the computational time compared with the full-order finite element method. And the results obtained from both reduced-order models are in good agreement with the results obtained from the full-order model, while the computational accuracy of the POD-based reduced-order model is a little higher than the CVT-based reduced-order model.

The CVT method is introduced to construct the reduced-order model for a quasi-magnetostatic problem. The computational accuracy and efficiency of the presented approaches are compared.

The purpose of this paper is to present a crack initiation mechanism of the external hydrogen effect on type 304 stainless steel, as well as on fatigue crack propagation in the presence of hydrogen gas.

The effects of external hydrogen on hydrogen-assisted crack initiation in type 304 stainless steel were discussed by performing fatigue crack growth rate and fatigue life tests in 5 MPa argon and hydrogen.

Hydrogen can reduce the incubation period of fatigue crack initiation of smooth fatigue specimens and greatly promote the fatigue crack growth rate during the subsequent fatigue cycle. During the fatigue cycle, hydrogen invades into matrix through the intrusion and extrusion and segregates at the boundaries of α′ martensite and austenite. As the fatigue cycle increased, hydrogen-induced cracks would initiate along the slip bands. The crack initiation progress would greatly accelerate in the presence of hydrogen.

To the best of the authors’ knowledge, this paper is an original work carried out by the authors on the hydrogen environment embrittlement of type 304 stainless steel. The effects of external hydrogen and argon were compared to provide understanding on the hydrogen-assisted crack initiation behaviors during cycle loading.

The car-sharing market has entered the mature stage, and consumers' demand shows a diversified increasing trend. This paper considers two modes of operation and two pricing strategies, which are business-to-consumer and consumer-to-consumer modes, market pricing and platform pricing. Under these conditions, the platform's revenue-sharing ratio will be different. The purpose of this paper is to explore this research question, and seeks an optimal pricing mechanism that can achieve a win–win situation between platform and automobile manufacturer in the two market modes.

The authors design different profit functions for platform under the two contexts. Of course, the platform's function is constrained to the manufacturer's function. By introducing a revenue-sharing contract a Stackelberg game model dominated by the platform is established and the equilibrium solutions under the two pricing models are derived.

The study found that even if only market pricing is executed, the scale of the car-sharing market will continue to expand. As the car-sharing market becomes more saturated, platform pricing is better for the automobile manufacturer; in most cases, the platform prefers platform pricing, but when the number of private cars is relatively small, if the cost of car operation and maintenance for the automobile manufacturer is lower or the revenue-sharing ratio of private cars is high, then market pricing will be more favorable to the platform.

With the cross-border integration of car service platforms and the automobile manufacturing industry, the key to achieving win–win cooperation and sustainable development in the car-sharing market will converge on the question of how to design a suitable pricing mechanism and revenue-sharing method.

Authors have determined how a car-sharing platform achieves a win–win order pricing strategy with the manufacturer and private car owners, respectively. And authors combined the supply chain revenue-sharing contract with the car-sharing market to explore the application of the revenue-sharing contract in the sharing economy.

A direct and unified approach is proposed toward simultaneously simulating large strain elastic behaviors of gellan gels with different gellan polymer concentrations. The purpose of this paper is to construct an elastic potential with certain parameters of direct physical meanings, based on well-designed invariants of Hencky’s logarithmic strain.

For each given value of the concentration, the values of the parameters incorporated may be determined in the sense of achieving accurate agreement with large strain uniaxial extension and compression data. By means of a new interpolating technique, each parameter as a function of the concentration is then obtained from a given set of parameter values for certain concentration values.

Then, the effects of gellan polymer concentrations on large strain elastic behaviors of gellan gels are studied in demonstrating how each parameter relies on the concentration. Plane-strain (simple shear) responses are also presented for gellan gels with different polymer concentrations.

A direct, unified approach was proposed toward achieving a simultaneous simulation of large elastic strain behaviors of gellan gels for different gellan polymer concentrations. Each parameter incorporated in the proposed elastic potential will be derived as a function of the polymer concentration in an explicit form, in the very sense of simultaneously simulating large strain data for different concentrations.

To remain competitive in an unpredictable environment where the complexity and frequency of cybercrime are rapidly increasing, a cyber resiliency strategy is vital for business continuity. However, one of the barriers to improving cyber resilience is that security defense and accident recovery do not combine efficaciously, as embodied by emphasizing cyber security defense strategies, leaving firms ill-prepared to respond to attacks. The present study thus develops an expected resilience framework to assess cyber resilience, analyze cyber security defense and recovery investment strategies and balance security investment allocation strategies.

Based on the expected utility theory, this paper presents an expected resilience framework, including an expected investment resilience model and an expected profit resilience model that directly addresses the optimal joint investment decisions between defense and recovery. The effects of linear and nonlinear recovery functions, risk interdependence and cyber insurance on defense and recovery investment are also analyzed.

According to the findings, increasing the defense investment coefficient reduces defense and recovery investment while increasing the expected resilience. The nonlinear recovery function requires a smaller defense investment and overall security investment than the linear one, reflecting the former’s advantages in lowering cybersecurity costs. Moreover, risk interdependence has positive externalities for boosting defense and recovery investment, meaning that the expected profit resilience model can reduce free-riding behavior in security investments. Insurance creates moral hazard for firms by lowering defensive investment, yet after purchasing insurance, expanded coverage and cost-effectiveness incentivize firms to increase defense and recovery spending, respectively.

The paper is innovative in its methodology as it offers an expected cyber resilience framework for integrating defense and recovery investment and their effects on security investment allocation, which is crucial for building cybersecurity resilience but receives little attention in cybersecurity economics. It also provides theoretical advances for cyber resilience assessment and optimum investment allocation in other fields, such as cyber-physical systems, power and water infrastructure – moving from a resilience triangle metric to an expected utility theory-based method.