According to the analysis and prediction of engineering cost, a BIM-aided analysis method based on GA network model is proposed. First, we improve the neural network by genetic algorithm; second, according to the engineering feature vector, BIM software is used to train the GA network model; finally, the GA network model reaches a steady state, given prediction of Engineering cost. According to the experimental study of 20 high-rise residential buildings in YJW area, the experimental results show that the proposed GA model combined with BIM auxiliary analysis method can accurately and easily complete the project cost prediction.

With the purpose of clarifying the energy-saving influencing factors of the participants in the construction project, qualitative analysis and scientific measurement analysis were combined in this paper to define and identify the energy-saving influencing factors of the construction project lifecycle. First, a qualitative analysis of the research literature of the natural science index (SCI) and social science citation index (SSCI) databases was conducted through a combination of literature analysis and expert interviews, constructing a list of the energy-saving influencing factors in the lifecycle of construction projects. Second, the knowledge map of the energy-saving research literature of construction projects in the past 20 years from the Web of Science (WOS) database was drawn through Citespace. Then the hotspots of energy-saving research in international construction projects were identified through keyword co-occurrence map as energy saving, energy efficiency, and energy consumption. The main research subjects were divided into four categories: energy-saving management and incentive policies, project users’ energy-saving behaviors and awareness, various energy-saving new materials and technologies, and energy-saving management and control at the operation and maintenance stage. Next, nine main clusters on energy-saving research were identified through cluster analysis, mainly distributed in various energy-saving technology research, energy-saving behavior and power research of owners and residents, policy and regulation research, and energy-saving management and control research in the operation and maintenance stage. Finally, the knowledge mapping analysis results were classified and defined according to the action scope of each stage of the construction project, and the identification of the energy-saving influencing factors in the construction project lifecycle was completed.

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The aim of this paper is to objectively present the development and trend of international MOOC research. Metadata taken from 660 literature records is used to visualize the state of MOOC research. The records were published between 2008 and 2018, and Citespace software is employed to visualize key data about the research contained on the SCI and SSCI platform. It has the following findings. First, since 2012, the number of international MOOC research papers and cited frequency has shown an upward trend. The research force was mainly concentrated in North America, Europe and Asia, and the publishing institutions were mainly concentrated in Universities of various countries, and formed a more obvious international cooperation network. Second, journals such as [Formula: see text]INT REV RES OPEN DIS[Formula: see text] and [Formula: see text]COMPUTERS & EDUCATION[Formula: see text] are the foundational literatures of MOOC research. George Siemens, Stephen Downes, John Daniel and others have made important contributions to the foundation research of MOOC. Third, Breslow, Liyanagunawardena et al. were cited more frequently and formed eight research clusters such as teaching interaction, relativism/behaviorism theory, individualized learning and diversified education. Fourth, distance education, online learning, learning situation analysis, curriculum construction and platform construction are the hot topics of international MOOC research in the recent decade. Fifth, the development of international MOOC has spread to various disciplines and promoted the interdisciplinary research of environment, biology, medicine, philosophy, psychology and other disciplines.

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This paper presents the method and findings of a visual knowledge domain map used to explore the development and evolution of Building information modeling (BIM) technology. Metadata taken from 806 literature records is used to visualize the state of BIM research. The records were published between 1998 and 2017, and Citespace software is employed to visualize key data about the research contained on the Web of Science platform, including nationality of research institutions, research themes, co-citations of periodicals, authorship, literature sources, and the development of the discipline over time. The distribution of power in this field is also examined by interrogating the BIM knowledge base, identifying research hotspots to provide a reference for assessing the current state-of-the-art.

Abstract The displacement can be calculated based on the integrated value of the acceleration signal waveform obtained by the acceleration sensor or gyroscope. However, this method is not effective in accurate measurement. Although some studies have improved the method of calculating accurate distance values by overcoming the effects of sensor noise or integration delay, the evaluation is still affected by sensor accuracy and environment. However, there are some special displacements, such as the chest compression. The displacement is a reciprocating motion and will return to the starting point again. Therefore, the acceleration waveform changes have obvious characteristics in the two stages from moving to the equilibrium position and returning to the starting point. Therefore, we propose an embedded classification method based on one-dimensional Convolutional Neural Network (CNN), which directly learns from the data of chest compressions and performs the signal formed by the Classification, distinguish the signal waveform under the standard pressing distance, so as to replace the calculation of distance measurement, and is not affected by factors such as pressure occlusion and electromagnetic wave interference, and has certain practical value on site. We tagged compressions and collected data from the simulator. The experiment evaluates the proposed CNN structure, and compares the classification results of the sample data with several CNN networks and SVMs with different structures in the literature. The results show that with sufficient training, the proposed 1D-CNN method can achieve an accuracy rate of more than 95%, and balances the accuracy rate and the hardware requirements.

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