Innovation and knowledge diffusion in megaprojects is one of the most complicated issues in project management. Compared with conventional projects, megaprojects typically entail large-scale investments, long construction periods, and conflicting stakeholder interests, which result in a distinctive pattern of innovation diffusion. However, traditional investigation of innovation diffusion relies on subjective feedback from experts and frequently neglects inter-organizational knowledge creation, which frequently emerges in megaprojects. Therefore, this study adopted project network theory and modeled innovation diffusion in megaprojects as intra- and inter-organizational learning processes. In addition, system dynamics and fuzzy systems were combined to interpret experts’ subject options as quantitative coefficients of the project network model. This integrated model will assist in developing an insightful understanding of the mechanisms of innovation diffusion in megaprojects. Three typical network structures, namely, a traditional megaproject procurement organization (TMO), the environ megaproject organization (EMO), and an integrated megaproject organization (IMO), were examined under six management scenarios to verify the proposed analytic paradigm. Assessment of project network productivity suggested that the projectivity of the TMO was insensitive to technical and administrative innovations, the EMO could achieve substantial improvement from technical innovations, and the IMO trended incompatibly with administrative innovations. Thus, industry practitioners and project managers can design and reform agile project coordination by using the proposed quantitative model to encourage innovation adoption and reduce productivity loss at the start of newly established collaborations.

Residual life estimation is essential for reliability engineering. Traditional methods may experience difficulties in estimating the residual life of products with high reliability, long life, and small sample. The Bayes model provides a feasible solution and can be a useful tool for fusing multisource information. In this study, a Bayes model is proposed to estimate the residual life of products by fusing expert knowledge, degradation data, and lifetime data. The linear Wiener process is used to model degradation data, whereas lifetime data are described via the inverse Gaussian distribution. Therefore, the joint maximum likelihood (ML) function can be obtained by combining lifetime and degradation data. Expert knowledge is used according to the maximum entropy method to determine the prior distributions of parameters, thereby making this work different from existing studies that use non-informative prior. The discussion and analysis of different types of expert knowledge also distinguish our research from others. Expert knowledge can be classified into three categories according to practical engineering. Methods for determining prior distribution by using the aforementioned three types of data are presented. The Markov chain Monte Carlo is applied to obtain samples of the parameters and to estimate the residual life of products due to the complexity of the joint ML function and the posterior distribution of parameters. Finally, a numerical example is presented. The effectiveness and practicability of the proposed method are validated by comparing it with residual life estimation that uses non-informative prior. Then, its accuracy and correctness are proven via simulation experiments.