Coupling In Situ Microstructure Observation with Machine Learning Algorithms for Damage Diagnostics and Prognostics

2017 ◽  
Author(s):  
BRIAN J. WISNER ◽  
ANTONIOS KONTSOS
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Microstructure Observation ◽  
Damage Diagnostics

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 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.

scholarly journals Leak-Off Pressure Using Weakly Correlated Geospatial Information and Machine Learning Algorithms

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 181
Author(s):  
Jung Chan Choi ◽  
Zhongqiang Liu ◽  
Suzanne Lacasse ◽  
Elin Skurtveit
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Multivariate Regression ◽  
Learning Algorithms ◽  
Multivariate Regression Analysis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Grid Search ◽  
Geospatial Information

Leak-off pressure (LOP) is a key parameter to determine the allowable weight of drilling mud in a well and the in situ horizontal stress. The LOP test is run in situ and is frequently used by the petroleum industry. If the well pressure exceeds the LOP, wellbore instability may occur, with hydraulic fracturing and large mud losses in the formation. A reliable prediction of LOP is required to ensure safe and economical drilling operations. The prediction of LOP is challenging because it is affected by the usually complex earlier geological loading history, and the values of LOP and their measurements can vary significantly geospatially. This paper investigates the ability of machine learning algorithms to predict leak-off pressure on the basis of geospatial information of LOP measurements. About 3000 LOP test data were collected from 1800 exploration wells offshore Norway. Three machine learning algorithms (the deep neural network (DNN), random forest (RF), and support vector machine (SVM) algorithms) optimized by three hyperparameter search methods (the grid search, randomized search and Bayesian search) were compared with multivariate regression analysis. The Bayesian search algorithm needed fewer iterations than the grid search algorithms to find an optimal combination of hyperparameters. The three machine learning algorithms showed better performance than the multivariate linear regression when the features of the geospatial inputs were properly scaled. The RF algorithm gave the most promising results regardless of data scaling. If the data were not scaled, the DNN and SVM algorithms, even with optimized parameters, did not provide significantly improved test scores compared to the multivariate regression analysis. The analyses also showed that when the number of data points in a geographical setting is much smaller than that of other geographical areas, the prediction accuracy reduces significantly.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Sign in / Sign up

Export Citation Format

Share Document