scholarly journals Guided wave–based condition assessment of in situ timber utility poles using machine learning algorithms

2014 ◽  
Vol 13 (4) ◽  
pp. 374-388 ◽  
Author(s):  
Ulrike Dackermann ◽  
Bradley Skinner ◽  
Jianchun Li
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Condition Assessment ◽  
Guided Wave ◽  
Machine Learning Algorithms ◽  
Utility Poles

Spatial Roadway Condition-Assessment Mapping Utilizing Smartphones and Machine Learning Algorithms

2021 ◽  
pp. 036119812110061
Author(s):  
Charalambos Kyriakou ◽  
Symeon E. Christodoulou ◽  
Loukas Dimitriou
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Condition Assessment ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Geographical Information ◽  
Related Field ◽  
Pavement Surface ◽  
Automated Method ◽  
Smartphone Technology

The paper presents a data-driven framework and related field studies on the use of supervised machine learning and smartphone technology for the spatial condition-assessment mapping of roadway pavement surface anomalies. The study explores the use of data, collected by sensors from a smartphone and a vehicle’s onboard diagnostic device while the vehicle is in movement, for the detection of roadway anomalies. The research proposes a low-cost and automated method to obtain up-to-date information on roadway pavement surface anomalies with the use of smartphone technology, artificial neural networks, robust regression analysis, and supervised machine learning algorithms for multiclass problems. The technology for the suggested system is readily available and accurate and can be utilized in pavement monitoring systems and geographical information system applications. Further, the proposed methodology has been field-tested, exhibiting accuracy levels higher than 90%, and it is currently expanded to include larger datasets and a bigger number of common roadway pavement surface defect types. The proposed system is of practical importance since it provides continuous information on roadway pavement surface conditions, which can be valuable for pavement engineers and public safety.

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.

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