Suchergebnisse

Global hubs of STI are strategic nodes in the global science network and will lead the future of global scientific progress. This paper takes big data analysis and mining technology as an example, exploring the structures and characteristics of global science network of 15 representative global hubs of STI from 2000 to 2019. Based on the data of highly cited papers and methods of social network analysis, we find that the global science network has the structure of hierarchy, with an obvious characteristic of regionality. Based on the structures and characteristics of global science network, the knowledge flow and management model of global hubs has been proposed, which may be conducive to strengthening the knowledge management and network collaboration among global hubs of STI in other frontier technologies.

Abstract. Liquefaction-induced hazards such as sand boils, ground cracks, settlement, and lateral spreading are responsible for considerable damage to engineering structures during major earthquakes. Presently, there is no effective empirical approach that can assess different liquefaction-induced hazards in one model. This is because of the uncertainties and complexity of the factors related to seismic liquefaction and liquefaction-induced hazards. In this study, Bayesian networks (BNs) are used to integrate multiple factors related to seismic liquefaction, sand boils, ground cracks, settlement, and lateral spreading into a model based on standard penetration test data. The constructed BN model can assess four different liquefaction-induced hazards together. In a case study, the BN method outperforms an artificial neural network and Ishihara and Yoshimine's simplified method in terms of accuracy, Brier score, recall, precision, and area under the curve (AUC) of the receiver operating characteristic (ROC). This demonstrates that the BN method is a good alternative tool for the risk assessment of liquefaction-induced hazards. Furthermore, the performance of the BN model in estimating liquefaction-induced hazards in Japan's 2011 Tōhoku earthquake confirms its correctness and reliability compared with the liquefaction potential index approach. The proposed BN model can also predict whether the soil becomes liquefied after an earthquake and can deduce the chain reaction process of liquefaction-induced hazards and perform backward reasoning. The assessment results from the proposed model provide informative guidelines for decision-makers to detect the damage state of a field following liquefaction.