Smart construction has become popular because it improves the efficiency of various activities on construction sites. Data sharing is crucial for multiple stakeholders to achieve smart construction management. However, a unified framework is absent for data sharing between stakeholders. This study aims to develop a unified framework for data sharing in smart construction at the project level. A two-pronged design method is adopted to develop a unified framework for data sharing in smart construction using text analysis and semi-structured interviews. Then, a case study is incorporated for the application of the smart construction management framework. The unified framework for data sharing is developed as a five-layer architecture, consisting of the primary layers of perception, transmission, database, application, and presentation. The function modules of the unified framework include on-site monitoring, personnel, safety, quality, material, digital file, environment, and supervision, supported by the implementation of BIM, cloud computing, and IoT techniques. As illustrated in the case study, the construction period can be saved by 50 days and the cost can be saved by 4.2% after implementing the proposed framework in the project. This study provides a benchmark for related studies on data management and collaborative innovation from various industries and stakeholders. Also, a useful framework has been developed by identifying the key elements, defining the primary functions, and developing the essential layers based on structural consistency.

The main paths of the knowledge diffusion network can reveal the important actors and diffusion process, which has an important significance in improving the efficiency of knowledge diffusion. Due to the independent path choice of project actors, knowledge diffusion networks show a dynamic characteristic in collaborative innovation projects. Taking into account this dynamic characteristic, dynamic main path analysis method for project context is proposed. The method, constructed by MATLAB simulation modeling, proposes calculation index and analysis strategies. Contrastive application of two main types of path analysis (the main path analysis method and dynamic main path analysis method) is carried out through a collaborative innovation project case, in order to verify the effectiveness and applicability of this method. The comparison results show that the main paths identified by the new method are more consistent with the actual main paths in knowledge diffusion practice, and the level of knowledge flow through main paths is higher. Therefore, our conclusion is that the dynamic main path analysis method proposed in this research has high applicability and accuracy for identifying the main paths in the collaborative innovation projects.

Traditional off-the-job training is becoming ineffective in high-end equipment research and development (R&D) projects due to the contradiction between rapid technological progress and the slow growth of newcomers, calling for “on-the-job mentoring” to enable synchronized advancement of project execution and newcomer cultivation. For this, we propose the multi-skilled project scheduling problem with newcomer cultivation under uncertain durations (MSPSP-NCU) and abstract it as a stochastic programming model. The model aims to minimize expected makespan and maximize newcomers’ skill efficiency by optimizing workforce assignment that enables experienced workers to mentor newcomers while simultaneously optimizing task scheduling. Solving the model is blocked by the inherently NP-hard nature of the project scheduling problem and the stochasticity of the durations. Therefore, we put forward an adaptive simulation–optimization approach featuring two-fold: a simulation module capable of dynamically adjusting sample sizes based on convergence feedback and evaluating solutions with improved efficiency and stable accuracy; a tailored non-dominated sorting genetic algorithm II (NSGA-II) with adaptive evolutionary operators that enhance search effectiveness and ensure the identification of a well-distributed Pareto front. By using data from an aerospace component R&D project, the proposed approach is validated for its performance in identifying Pareto-optimal solutions. Several personalized rules are designed by integrating workforce development strategies into the selection process, providing actionable guidelines for cultivating newcomers in technology-intensive projects.