Influence of displacement rate, stress orientation and stress history on birefringence properties of polyvinylidene chloride

2016 ◽  
Vol 82 (838) ◽  
pp. 15-00498-15-00498
Author(s):  
Kenji GOMI ◽  
Yasushi NIITSU
Keyword(s):  
Displacement Rate ◽  
Stress History ◽  
Polyvinylidene Chloride ◽  
Stress Orientation

scholarly journals Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽  
2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Progressive Failure ◽  
Principal Component ◽  
Cost Effective ◽  
Displacement Rate ◽  
Machine Learning Classification ◽  
Management Perspective ◽  
Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

scholarly journals Displacement Rate Effects on the Mode II Shear Delamination Behavior of Carbon Fiber/Epoxy Composites

Polymers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1881
Author(s):  
Kean Ong Low ◽  
Mahzan Johar ◽  
Haris Ahmad Israr ◽  
Khong Wui Gan ◽  
Seyed Saeid Rahimian Koloor ◽  
...  
Keyword(s):  
Fracture Toughness ◽  
Peak Load ◽  
Epoxy Composites ◽  
Displacement Rate ◽  
Interface Debonding ◽  
Mode Ii ◽  
Scanning Electron Micrographs ◽  
Unstable Crack ◽  
Mode Ii Fracture ◽  
Mode Ii Fracture Toughness

This paper studies the influence of displacement rate on mode II delamination of unidirectional carbon/epoxy composites. End-notched flexure test is performed at displacement rates of 1, 10, 100 and 500 mm/min. Experimental results reveal that the mode II fracture toughness GIIC increases with the displacement, with a maximum increment of 45% at 100 mm/min. In addition, scanning electron micrographs depict that fiber/matrix interface debonding is the major damage mechanism at 1 mm/min. At higher speeds, significant matrix-dominated shear cusps are observed contributing to higher GIIC. Besides, it is demonstrated that the proposed rate-dependent model is able to fit the experimental data from the current study and the open literature generally well. The mode II fracture toughness measured from the experiment or deduced from the proposed model can be used in the cohesive element model to predict failure. Good agreement is found between the experimental and numerical results, with a maximum difference of 10%. The numerical analyses indicate crack jump occurs suddenly after the peak load is attained, which leads to the unstable crack propagation seen in the experiment.

scholarly journals Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021 ◽  
Vol 11 (4) ◽  
pp. 1381
Author(s):  
Xiuzhen Li ◽  
Shengwei Li
Keyword(s):  
Support Vector Regression ◽  
Water Level ◽  
Large Scale ◽  
Displacement Rate ◽  
Support Vector ◽  
Single Factor ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Depth Analysis ◽  
Three Factor

Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.

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