scholarly journals The effect of chemical erosion on mechanical properties and fracture of sandstone under shear loading: an experimental study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Cancan Chen ◽  
Shoujian Peng ◽  
Shankang Wu ◽  
Jiang Xu
Keyword(s):  
Shear Strength ◽  
Fracture Surface ◽  
Root Mean Square ◽  
Shear Loading ◽  
Grain Structure ◽  
Maximum Height ◽  
Mean Square ◽  
Microscope Imaging ◽  
Coal Rock ◽  
Chemical Solutions

AbstractIn order to study the effect of water-rock interactions on shear strength characteristics, we performed shearing tests under varying hydrochemical environments. Moreover, a custom meso-shear test equipment for coal rock was used for the tests. Through 3D scanning of the shear fractures and scanning electron microscope imaging, we studied the effect of different pH chemical solutions on the shear strength and fracture characteristics of sandstones. We obtained three main results. With increasing solution acidity or alkalinity, water-hemical solution corrosivity increases. Moreover, the shear strength of sandstones reduces almost linearly and the fracture surfaces become smoother. The erosive effect is evidenced by the decrease in fracture surface fluctuations, roughness and the high-order microbulges, and scaling of the grain structure. A collection of characteristic parameters, including the maximum height Sh, the root mean square deviation Sq, the area ratio SA, and the slope root mean square S∆q, can be used to quantitatively describe the rough and irregular texture of the fracture surface.

Effect of grain angle on shear strength of Douglas-fir wood

Holzforschung ◽  
2012 ◽  
Vol 66 (5) ◽  
pp. 655-658 ◽  
Author(s):  
Rakesh Gupta ◽  
Arijit Sinha
Keyword(s):  
Shear Strength ◽  
Root Mean Square ◽  
Failure Mode ◽  
Shear Plane ◽  
Douglas Fir ◽  
Mean Square ◽  
Rolling Shear ◽  
Grain Angle ◽  
Shear Block ◽  
The Relationship

Abstract The effect of grain angle (GA) on shear strength of Douglas-fir has been evaluated. Shear block specimens with a GA varying from 0 to 90° was loaded in the shear plane, resulting in failure mode transitioning from parallel to grain shear to rolling shear. As expected, shear strength decreased as the GA increased from 0° to 90°. A root-mean-square equation was found to be suitable to predict the relationship between GA and shear strength. Traditional Hankinson formula and the Tsai-Wu criteria were less effective with this regard.

scholarly journals Resistência ao cisalhamento de vigas de concreto autoadensável com adição de fibras de aço

2021 ◽  
Vol 42 (1) ◽  
pp. 45
Author(s):  
Gustavo Savaris ◽  
Isabela de Gois Laufer
Keyword(s):  
Shear Strength ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Concrete Beams ◽  
Steel Fibers ◽  
Strength Prediction ◽  
Failure Behavior ◽  
Mean Square ◽  
Self Compacting Concrete

The use of self-compacting concrete has increased for several reasons over recent decades but, mainly due to its high fluidity, which dispenses of the need for concrete vibrators, ease of casting, higher quality and better compacting, allowing the production of slender pieces, with a higher reinforcement ratio. However, even self-compacting concrete exhibits brittle failure behavior and low tensile and shear strength, issues that can be mitigated with the use of steel fibers. Aiming to investigate the shear strength in self-compacting concrete beams with steel fibers, this study presents a database collected from 113 experimental tests in the literature. Using the Root Mean Square Error (RMSE) and the Collins’ Demerit Points Classification (DPC), five code-based equations and ten experimental based equations for the prediction of the shear capacity of SFRC beams were evaluated. The results show that, unlike concrete without the addition of fibers, increase in aggregate dimensions decreases the shear strength with the use of steel fibers in SCC beams. Additionally, the increase in fiber volume corresponds to an increase in concrete shear strength with a maximum compressive strength of 50 MPa. The results also demonstrate that the Root Mean Square Error (RMSE) is better for evaluating the precision but not the safety of the shear strength prediction equations, which are better determined by Collins’ Demerit Points Classification (DPC). Code-based equations for ultimate shear strength prediction of fiber reinforced concrete beams presented results with satisfactory safety and economy.

scholarly journals The Optimum Wavelet Base of Wavelet Analysis in Coal Rock Microseismic Signals

2014 ◽  
Vol 6 ◽  
pp. 537415
Author(s):  
Shoufeng Tang ◽  
Minming Tong ◽  
Xinmin He
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Signal To Noise Ratio ◽  
Wavelet Denoising ◽  
Mean Square ◽  
Wavelet Base ◽  
Wavelet Bases ◽  
Coal Rock ◽  
Microseismic Signal

Coal rock rupture microseismic signal is characterized by time-varying, nonstationary, unpredictability, and transient property. Wavelet transform is an important method in microseismic signals processing. However, different wavelet bases yield different results when analyzing the same signal. To study the comparability of different wavelet bases in analyzing microseismic signals, the current paper uses the microseismic signals released from coal rock bursting as the research subject. Through the analysis of the properties of commonly used wavelet basis functions and the characteristics of coal rock microseismic signals, the current study found that Coiflet and Symlet wavelets are suitable for analyzing coal rock microseismic signals. Sym 8 and Coif 2 wavelets were found to be suitable for analyzing and denoising coal rock microseismic signals. After Sym 8 wavelet denoising, signal-to-noise ratio (SNR) and the root mean square error were 30.4184 and 1.3109 E–07, respectively. After Coif 2 wavelet denoising, the SNR and the root mean square error values were 35.2176 and 1.0312 E–07, respectively. The results will aid in the analysis and extraction of coal rock microseismic signals.

The Relationship of Root Mean Square Value of Electromyography and Isometric Torque of Quadriceps in Normal Subjects

2016 ◽  
Vol 26 (1) ◽  
pp. 58
Author(s):  
Qiurong XIE ◽  
Zheng JIANG ◽  
Qinglu LUO ◽  
Jie LIANG ◽  
Xiaoling WANG ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Normal Subjects ◽  
Mean Square ◽  
Isometric Torque ◽  
Relationship Of ◽  
The Relationship

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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