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In view of the importance and difficulty of obtaining suitable rock mechanical parameters for rock, rotary penetration technology combines the advantages of the cone penetration test with rotary cutting, providing a new path towards collecting the relevant data. Indoor tests using a hollow drill have shown that differences in the sample material will not affect the results of rotary penetration tests. This paper introduces the theory that psychological factors will cause some deviation of the selection results and thus that the results need to be modified according to these psychological factors. A fast, intelligent integrated rotary penetration system contains the three modules of a psychological system, expert system, and neural network and can rapidly determine the mechanical parameters of rock and the level of engineering rock mass. The system is feasible and promising, providing detailed and accurate information for engineering predictions and design.
Guangyu Lei; Jichang Han; Qian Li. Intelligent Analysis on Introducing Psychological Factors into Rotary Penetration Technology. Iranian Journal of Science and Technology, Transactions of Civil Engineering 2020, 45, 373 -380.
AMA StyleGuangyu Lei, Jichang Han, Qian Li. Intelligent Analysis on Introducing Psychological Factors into Rotary Penetration Technology. Iranian Journal of Science and Technology, Transactions of Civil Engineering. 2020; 45 (1):373-380.
Chicago/Turabian StyleGuangyu Lei; Jichang Han; Qian Li. 2020. "Intelligent Analysis on Introducing Psychological Factors into Rotary Penetration Technology." Iranian Journal of Science and Technology, Transactions of Civil Engineering 45, no. 1: 373-380.
Quantification of soil organic carbon (SOC) and pH, and their spatial variations at regional scales, is a foundation to adequately assess agriculture, pollution control, or environmental health and ecosystem functioning, so as to establish better practices for land use and land management. In this study, we used the random forest (RF) model to map the distribution of SOC and pH in the topsoil (0–20 cm) and estimate SOC and pH changes from 1982 to 2012 in Liaoning Province, Northeast China. A total of 10 covariates (elevation, slope gradient, topographic wetness index (TWI), mean annual temperature (MAT), mean annual precipitation (MAP), visible-red band 3 (B3), near-infrared band 4 (B4), short-wave infrared band 5 (B5), normalized difference vegetation index (NDVI), and land-use data) and a set of 806 (in 1982) and 973 (in 2012) soil samples were selected. Cross-validation technology was used to test the performance and uncertainty of the RF model. We found that the prediction R2 of SOC and pH was 0.69 and 0.54 for 1982, and 0.63 and 0.48 for 2012, respectively. Elevation, NDVI, and land use are the main environmental variables affecting the spatial variability of SOC in both periods. Correspondingly, the topographic wetness index and mean annual precipitation were the two most critical environmental variables affecting the spatial variation of pH. The mean SOC and pH decreased from 18.6 to 16.9 kg−1 and 6.9 to 6.6, respectively, over a 30-year period. SOC distribution generated using the RF model showed a decreasing SOC trend from east to west across the city in the two periods. In contrast, the spatial distribution of pH showed an opposite trend in both periods. This study provided important information of spatial variations in SOC and pH to agencies and communities in this region, to evaluate soil quality and make decisions on remediation and prevention of soil acidification and salinization.
Li Qi; Shuai Wang; Qianlai Zhuang; Zijiao Yang; Shubin Bai; Xinxin Jin; Guangyu Lei. Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data. Sustainability 2019, 11, 3569 .
AMA StyleLi Qi, Shuai Wang, Qianlai Zhuang, Zijiao Yang, Shubin Bai, Xinxin Jin, Guangyu Lei. Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data. Sustainability. 2019; 11 (13):3569.
Chicago/Turabian StyleLi Qi; Shuai Wang; Qianlai Zhuang; Zijiao Yang; Shubin Bai; Xinxin Jin; Guangyu Lei. 2019. "Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data." Sustainability 11, no. 13: 3569.