In situ investigation and detection of opto-mechanical properties of polymeric fibres from their digital distorted microinterferograms using machine learning algorithms

2020 ◽  
Vol 129 ◽  
pp. 106295
Author(s):  
E.Z. Omar ◽  
T.Z.N. Sokkar ◽  
A.A. Hamza
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
In Situ Investigation

scholarly journals Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 557 ◽  
Author(s):  
Cristiano Fragassa ◽  
Matej Babic ◽  
Carlos Perez Bergmann ◽  
Giangiacomo Minak
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Pattern Recognition ◽  
Cast Iron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tensile Behaviour ◽  
Cast Alloys ◽  
Pattern Recognition Analysis

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

scholarly journals Characterizing Soil Stiffness Using Thermal Remote Sensing and Machine Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 2306
Author(s):  
Jordan Ewing ◽  
Thomas Oommen ◽  
Paramsothy Jayakumar ◽  
Russell Alger
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Predictor Variable ◽  
Thermal Inertia ◽  
Soil Strength ◽  
In Situ Measurements ◽  
Machine Learning Algorithms ◽  
Soil Stiffness

Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms.

scholarly journals Machine learning algorithms for the prediction of the mechanical properties of railways’ rail pads

2021 ◽  
Vol 1765 ◽  
pp. 012008
Author(s):  
D Ferreño ◽  
J A Sainz-Aja ◽  
I A Carrascal ◽  
M Cuartas ◽  
J Pombo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 870
Author(s):  
Josefina Mosre ◽  
Francisco Suárez
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Meteorological Data ◽  
Learning Algorithms ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Actual Evapotranspiration ◽  
Regression Equations ◽  
Cold Regions

Actual evapotranspiration (ETa) estimations in arid regions are challenging because this process is highly dynamic over time and space. Nevertheless, several studies have shown good results when implementing empirical regression formulae that, despite their simplicity, are comparable in accuracy to more complex models. Although many types of regression formulae to estimate ETa exist, there is no consensus on what variables must be included in the analysis. In this research, we used machine learning algorithms—through implementation of empirical linear regression formulae—to find the main variables that control daily and monthly ETa in arid cold regions, where there is a lack of available ETa data. Meteorological data alone and then combined with remote sensing vegetation indices (VIs) were used as input in ETa estimations. In situ ETa and meteorological data were obtained from ten sites in Chile, Australia, and the United States. Our results indicate that the available energy is the main meteorological variable that controls ETa in the assessed sites, despite the fact that these regions are typically described as water-limited environments. The VI that better represents the in situ ETa is the Normalized Difference Water Index, which represents water availability in plants and soils. The best performance of the regression equations in the validation sites was obtained for monthly estimates with the incorporation of VIs (R2 = 0.82), whereas the worst performance of these equations was obtained for monthly ETa estimates when only meteorological data were considered. Incorporation of remote-sensing information results in better ETa estimates compared to when only meteorological data are considered.

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