Relationship between joint roughness coefficient and fractal dimension of rock fracture surfaces

AbstractNumerous empirical equations have been proposed to estimate the joint roughness coefficient (JRC) of a rock fracture based on its fractal dimension (D). A detailed review is made on these various methods, along with a discussion about their usability and limitations. It is found that great variation exists among the previously proposed equations. This is partially because of the limited number of data points used to derive these equations, and partially because of the inconsistency in the methods for determining D. The 10 standard profiles on which most previous equations are based are probably too few for deriving a reliable correlation. Different methods may give different values of D for a given profile. The h–L method is updated in this study to avoid subjectivity involved in identifying the high-order asperities. The compass-walking, box-counting and the updated h–L method are employed to examine a larger population of 112 rock joint profiles. Based on these results, a new set of empirical equations are proposed, which indicate that the fractal dimension estimated from compass-walking and the updated h–L method closely relate to JRC, whereas the values estimated from box-counting do not relate as closely

Acceleration Characteristics of a Rock Slide Using the Particle Image Velocimetry Technique

The Particle Image Velocimetry (PIV) technique with high precision and spatial resolution is a suitable sensor for flow field experiments. In this paper, the PIV technology was used to monitor the development of a displacement field, velocity field and acceleration field of a rock slide. It was found that the peak acceleration of the sliding surface appeared earlier than the peak acceleration of the sliding body. The characteristics of the rock slide including the short failure time, high velocities, and large accelerations indicate that the sliding forces and energy release rate of the slope are high. The deformation field showed that the sliding body was sliding outwards along the sliding surface while the sliding bed moved in an opposite direction. Moving upwards at the top of the sliding bed can be one of the warning signs for rock slide failure

On the momentum flux of vertically-propagating orographic gravity waves excited in nonhydrostatic flow over three-dimensional orography

This work studies nonhydrostatic effects (NHE) on the momentum flux of orographic gravity waves (OGWs) forced by isolated three-dimensional orography. Based on linear wave theory, an asymptotic expression for low horizonal Froude number (Fr=sqrt(U^2+(gamma V)^2)/(Na)) where (U, V) is the mean horizontal wind, γ and a are the orography anisotropy and half-width and N is the buoyancy frequency) is derived for the gravity wave momentum flux (GWMF) of vertically-propagating waves. According to this asymptotic solution, which is quite accurate for any value of Fr, NHE can be divided into two terms (NHE1 and NHE2). The first term contains the high-frequency parts of the wave spectrum that are often mistaken as hydrostatic waves, and only depends on Fr. The second term arises from the difference between the dispersion relationships of hydrostatic and nonhydrostatic OGWs. Having an additional dependency on the horizontal wind direction and orography anisotropy, this term can change the GWMF direction. Examination of NHE for OGWs forced by both circular and elliptical orography reveals that the GWMF is reduced as Fr increases, at a faster rate than for two-dimensional OGWs forced by a ridge. At low Fr, the GWMF reduction is mostly attributed to the NHE2 term, whereas the NHE1 term starts to dominate above about Fr = 0.4. The behavior of NHE is mainly determined by Fr, while horizontal wind direction and orography anisotropy play a minor role. Implications of the asymptotic GWMF expression for the parameterization of nonhydrostatic OGWs in high-resolution and/or variable-resolution models are discussed

Integrative lipidomic features identify plasma lipid signatures in chronic urticaria

Chronic urticaria (CU) is a chronic inflammatory skin disease mainly mediated by mast cells. Lipids exert essential functions in biological processes; however, the role of lipids in CU remains unclear. Nontargeted lipidomics was performed to investigate the differential lipid profiles between CU patients and healthy control (HC) subjects. Functional validation studies were performed in vitro and in vivo including β-hexosaminidase release examination from mast cells and passive cutaneous anaphylaxis (PCA) mouse model. We detected dramatically altered glycerophospholipids in CU patients compared with HCs. Phosphatidylserine (PS), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG) were increased, while phosphatidylcholine (PC) was reduced in CU patients. The reduction in PC was related to a high weekly urticaria activity score (UAS7), while PS was positively associated with the dermatology life quality index (DLQI). We also identified the differential lipid profiles between chronic spontaneous urticaria (CSU), symptomatic dermographism (SD), and CSU coexist with SD. CU patients were classified into two subtypes (subtype 1 and subtype 2) based on consensus clustering of lipid profiling. Compared with patients in subtype 2, patients in subtype 1 had elevated levels of PC (18:0e/18:2) and PE (38:2), and lower urticaria control test (UCT) scores indicated worse clinical efficiency of secondary generation H1 antihistamines treatment. Importantly, we found that supplementation with PC could attenuate IgE-induced immune responses in mast cells. In general, We described the landscape of plasma lipid alterations in CU patients and provided novel insights into the role of PC in mast cells

The response of parameterized orographic gravity waves to rapid warming over the Tibetan Plateau

Using the ERA-Interim reanalysis during 1979–2017, this work for the first time investigates the climatology and long-term trend of orographic gravity waves (OGWs) in the Tibetan Plateau (TP). The linkage between the trends of OGWs and the rapid warming over the TP is also studied. Climatologically, the most prominent surface wave momentum flux (SWMF) of OGWs occurs in the western and southeastern TP, while it is weak in the central TP. The SWMF is stronger in winter and spring than in autumn and summer. Overall, the mean SWMF over the TP experienced a weak decreasing trend. The decrease of SWMF mainly took place in the western and southeastern TP in spring. However, increasing trends were found in the central TP in winter. Changes of SWMF are mainly caused by the changes of horizontal wind near the surface, while buoyancy frequency and air density play a minor role. In response to the inhomogeneous warming over the TP, the surface winds were adjusted through thermal wind balance. In spring (winter), the most remarkable warming occurred in the northern (southern) TP, which reduced (enhanced) the meridional temperature gradient across the plateau, and thus led to a deceleration (acceleration) of the horizontal wind

A hybrid machine-learning model to estimate potential debris-flow volumes

Empirical-statistical models of debris-flow are challenging to implement in environments where sedimentary and hydrologic triggering processes change through time, such as after a large earthquake. The flexible and adaptive statistical methods provided by machine learning algorithms may improve the quality of debris flow predictions where triggering conditions and the nature of sediment that can bulk flows varies with time. We developed a hybrid machine-learning model of future debris-flow volumes using a dataset of measured debris-flow volumes from 60 catchments that generated post-Wenchuan Earthquake (Mw 7.9) debris flows. We input topographic variables (catchment area, topographic relief, channel length, distance from seismic fault, and average channel gradient) and the total volume of co-seismic landslide debris into the PSO-ELM_AdaBoost machine-learning model, created by combining Extreme learning machine (ELM), particle swarm optimization (PSO) and adaptive boosting machine learning algorithm (AdaBoost). The model was trained and tested using post-2008 Mw 7.9 Wenchuan Earthquake debris flows, then applied to understand potential volumes of post-earthquake debris flows associated with other regional earthquakes (2013 Mw 6.6 Lushan Earthquake, 2010 Mw 6.9 Yushu Earthquake). We compared the PSO-ELM_Adaboost method with different machine learning methods, including back-propagation neural network (BPNN), support vector machine (SVM), ELM, PSO-ELM. The Comparative analysis demonstrated that the PSO-ELM_Adaboost method has a higher statistical validity and prediction accuracy with a mean absolute percentage error (MAPE) less than 0.10. The prediction accuracy of debris-flow volumes trigged by other earthquakes decreases to 0.11–0.16 (absolute percentage error), suggesting that once calibrated for a region this method can be applied to other regional earthquakes. This model may be useful for engineering design to mitigate the risk of large post-earthquake debris flows