This research carries out a comparative study to investigate a machine learning solution that employs the Gaussian Process Regression (GPR) for modeling compressive strength of high-performance concrete (HPC). This machine learning approach is utilized to establish the nonlinear functional mapping between the compressive strength and HPC ingredients. To train and verify the aforementioned prediction model, a data set containing 239 HPC experimental tests, recorded from an overpass construction project in Danang City (Vietnam), has been collected for this study. Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.

This multinational study of the contribution of the Project Management Body of Knowledge (PMBOK) to the success of construction engineering projects was performed to reveal cross-country similarities and differences. A survey questionnaire of professional engineers in the construction industry (project managers, stakeholders, and team members) was performed in Taiwan, Indonesia, and Vietnam. Structural equation modeling was used to determine the effects of PMBOK techniques/tools/skills (TTSs) on project success (PS). The contributions to the PMBOK include (1) a novel PS index (PSI) framework adapted from the American Customer Satisfaction Index and an importance-performance analysis (IPA) for prioritizing PMBOK TTS predictors of PS, (2) an IPA technique for identifying PMBOK TTS items that help construction practitioners improve PS by implementing effective managerial strategies, enhancing project performance, and improving the efficient use of management resources, and (3) guidance in the current...