Construction equipment is one of the most significant resources in every large construction project, accounting for a considerable portion of the project budget. Improving heavy machinery performance can increase efficiency and reduce costs. However, research on boosting machine efficiency is limited. This study adopts mix review methodology (systematic review and bibliometric analysis) and evaluates emerging technologies like Digital twin, Cyber physical systems, Geo-graphic information systems, Global navigation satellite system, Onboard instrumentation system, Radio frequency identification, Internet of things, Telematics, Machine learning, Deep learning, and Computer vision for machine productivity and provides insights into how they can be used to improve the performance of construction equipment. The article defined three major equipment operating areas-monitoring and control, tracking and navigation, and data-driven performance optimization-classified technologies respectively, and explored how they can increase machine productivity. Other circumstantial issues affecting machine operation as well as loopholes in existing innovative tools used in machine processes were also highlighted. This study contributes to the goals of deploying digital tools and outlines future directions for the development of automated machines to optimize project efficiency.

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Interactive learning environment usually offer a userfriendly interface. This allows inexperienced users fast access to the key point of knowledge, because they have experience in making decisions in simulators. Thus, they are not only able to examine the results of their decisions but also the causes of these results. This article will explain the principle and process of interactive learning environment using system dynamics for software project management training.

Blade icing detection is critical to maintaining the health of wind turbines, especially in cold climates. Rapid and accurate icing detection allows proper control of wind turbines, including shutting down and clearing the ice, thus ensuring turbine safety. This paper presents a wavelet-driven multiscale graph convolutional network (MWGCN), which is a supervised deep learning model for blade icing detection. The proposed model first uses wavelet decomposition to capture multivariate information in the time and frequency domains, then employs a temporal graph convolutional network to model the intervariable correlations of the decomposed multiscale wavelets, as well as their temporal dynamics. In addition, this paper introduces scale attention to the MWGCN for a further improvement of the model and proposes the method to address the class imbalance problem of the training data sets. Finally, the paper conducts comprehensive experiments to evaluate the proposed model, and the results demonstrate the effectiveness of the model in blade icing detection and its better performance over eight state-of-the-art algorithms, with 17.2% and 11.3% higher F1 scores over the best state-of-the-art baseline on the labeled datasets.

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