首页|Recent Research from Nazarbayev University Highlight Findings in Machine Learnin g (Predicting Disc Cutter Wear Using Two Optimized Machine Learning Techniques)
Recent Research from Nazarbayev University Highlight Findings in Machine Learnin g (Predicting Disc Cutter Wear Using Two Optimized Machine Learning Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Machine Learn ing have been published. According to newsoriginating from Astana, Kazakhstan, by NewsRx correspondents, research stated, “The estimation ofdisc cutter wear ( CW) remains a complex problem in mechanized tunneling using tunnel boring machin es(TBM), despite the development of numerous TBM performance models. This resea rch aimed to estimatethe cutter life index (CLI) as an index to predict the CW by developing predictive models based on twomachine learning algorithms, namely gradient boosting (GB) and random forest (RF), optimized by threeoptimization techniques: particle swarm optimization (PSO), differential evolution (DE), and simulatedannealing (SA).”Funders for this research include Nazarbayev University, Faculty Development Com petitive ResearchGrant program of Nazarbayev University in Kazakhstan.Our news journalists obtained a quote from the research from Nazarbayev Universi ty, “To gain theaim, a dataset consisting of four rock parameters-density (rho) , uniaxial compressive strength, Braziliantensile strength (BTS), and brittlene ss index-with 80 mechanized tunnel cases for each parameter hasbeen utilized by obtaining the sample and then relevant tests on them were conducted in the labo ratory.First, various parameter selection methods, such as mutual information, have been employed to reduce thedimensionality of the problem, and it has been revealed that rho and BTS have been the most influentialparameters to estimate the CLI. Then, by developing six optimized models, including GB-PSO, GB-DE,GB-S A, RF-PSO, RF-DE, and RF-SA, using the two mentioned parameters, their performan ce has beenassessed via three performance evaluation indices of coefficient of determination (r2), root mean squareerror (RMSE), and mean absolute percentage error (MAPE).”
AstanaKazakhstanAsiaCyborgsEmerging TechnologiesMachine LearningNazarbayev University
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