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Summary: The reconstruction of microstructure in porous media is crucial for evaluating material properties in various fields. Existing approaches mostly treat the porous medium as an ideal two-phase material, ignoring the existence of isolated solid clusters within pore domains. This inconsistency can lead to computational errors in determining transport properties. To address this issue, an efficient approach is proposed that utilizes the watershed algorithm to identify and eliminate isolated solid clusters, while employing various morphological descriptors to measure discrepancies between target structures and reconstructions. The proposed method allows for the reconstruction of microstructures without isolated solids and preserves valuable morphological information.
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Zhifeng Ren et al.
Summary: Artificial ground freezing is widely used in various projects and has achieved good engineering results. This study investigates the unfrozen water characteristics and compressive strength of soil at ultralow temperatures and discusses the applicability of a prediction model for unfrozen water content. The results show that there is a small amount of unfrozen water at extremely low temperatures, and the unfrozen water content is related to the compressive strength of the frozen soil through a power function.
COLD REGIONS SCIENCE AND TECHNOLOGY
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Song-Shun Lin et al.
Summary: This study proposes a hybrid model based on the PSO algorithm and LSTM neural network, exploring automatic data collection and model application in tunnel excavation. By analyzing the relationships between influential factors and predicted object, and testing with 1500 data sets, the hybrid model with all factors performed the best, providing guidance for coping with measured data from an automatic monitoring system in shield machines.
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Summary: In this study, a unit cell model of freeze pipes was established to examine the effects of uncertainties such as groundwater seepage, freeze pipe inclination, and spatial variability in thermal conductivity on the AGF system. Results show that considering these uncertainties can help evaluate the additional freezing time required and provide reasonable freeze pipe spacing and critical seepage velocity.
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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
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Summary: This paper presents the measurement and prediction of tunnelling-induced surface response in karst ground in Guangzhou, China. A predictive method called the expanding deep learning method is proposed, which uses expanding tunnelling data to predict ground settlement in real time. Results show that the expanding Conv1d model can accurately predict the tunnelling-induced ground settlement, and kinetic correlation analysis reflects the variable influence of geological conditions and tunnelling operation parameters on ground settlement.
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