A new model approach to predict the unloading rock slope displacement behavior based on monitoring data

Jiang, Ting,Shen, Zhenzhong,Yang, Meng,Xu, Liqun,Gan, Lei,Cui, Xinbo Techno-Press 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.67 No.2

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

A new model approach to predict the unloading rock slope displacement behavior based on monitoring data

Ting Jiang,Zhenzhong Shen,Meng Yang,Liqun Xu,Lei Gan,Xinbo Cui 국제구조공학회 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.67 No.2

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

Localized reliability analysis on a large-span rigid frame bridge based on monitored strains from the long-term SHM system

Zejia Liu,Liqun Tang,Yinghua Li,Yiping Liu,Zhenyu Jiang,Daining Fang 국제구조공학회 2014 Smart Structures and Systems, An International Jou Vol.14 No.2

With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years\' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy

Localized reliability analysis on a large-span rigid frame bridge based on monitored strains from the long-term SHM system

Liu, Zejia,Li, Yinghua,Tang, Liqun,Liu, Yiping,Jiang, Zhenyu,Fang, Daining Techno-Press 2014 Smart Structures and Systems, An International Jou Vol.14 No.2

With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy.

Inhalation of panaxadiol alleviates lung infl ammation via inhibiting TNFA/ TNFAR and IL7/IL7R signaling between macrophages and epithelial cells

Yifan Wang,Hao Wei,Zhen Song,Liqun Jiang,Mi Zhang,Xiao Lu,Wei Li,Yuqing Zhao,Lei Wu,Shuxian Li,Huijuan Shen,Qiang Shu,Yicheng Xie 고려인삼학회 2024 Journal of Ginseng Research Vol.48 No.1

Background: Lung inflammation occurs in many lung diseases, but has limited effective therapeutics. Ginseng andits derivatives have anti-inflammatory effects, but their unstable physicochemical and metabolic propertieshinder their application in the treatment. Panaxadiol (PD) is a stable saponin among ginsenosides. Inhalationadministration may solve these issues, and the specific mechanism of action needs to be studied. Methods: A mouse model of lung inflammation induced by lipopolysaccharide (LPS), an in vitro macrophageinflammation model, and a coculture model of epithelial cells and macrophages were used to study the effectsand mechanisms of inhalation delivery of PD. Pathology and molecular assessments were used to evaluate efficacy. Transcriptome sequencing was used to screen the mechanism and target. Finally, the efficacy andmechanism were verified in a human BALF cell model. Results: Inhaled PD reduced LPS-induced lung inflammation in mice in a dose-dependent manner, includinginflammatory cell infiltration, lung tissue pathology, and inflammatory factor expression. Meanwhile, the dose ofinhalation was much lower than that of intragastric administration under the same therapeutic effect, which maybe related to its higher bioavailability and superior pharmacokinetic parameters. Using transcriptome analysisand verification by a coculture model of macrophage and epithelial cells, we found that PD may act by inhibitingTNFA/TNFAR and IL7/IL7R signaling to reduce macrophage inflammatory factor-induced epithelial apoptosisand promote proliferation. Conclusion: PD inhalation alleviates lung inflammation and pathology by inhibiting TNFA/TNFAR and IL7/IL7Rsignaling between macrophages and epithelial cells. PD may be a novel drug for the clinical treatment of lunginflammation.