首页|Studies in the Area of Machine Learning Reported from University of Transport Te chnology (Developing interpretable machine learning model for evaluating young m odulus of cemented paste backfill)
Studies in the Area of Machine Learning Reported from University of Transport Te chnology (Developing interpretable machine learning model for evaluating young m odulus of cemented paste backfill)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting originating from Hanoi, Vietnam, b y NewsRx correspondents, research stated, "Cemented paste backfill (CPB), a mixt ure of wet tailings, binding agent, and water, proves cost-effective and environ mentally beneficial." The news correspondents obtained a quote from the research from University of Tr ansport Technology: "Determining the Young modulus during CPB mix design is cruc ial. Utilizing machine learning (ML) tools for Young modulus evaluation and pred iction streamlines the CPB mix design process. This study employed six ML models , including three shallow models Extreme Gradient Boosting (XGB), Gradient Boost ing (GB), Random Forest (RF) and three hybrids Extreme Gradient Boosting-Particl e Swarm Optimization (XGB-PSO), Gradient Boosting-Particle Swarm Optimization (G B-PSO), Random Forest- Particle Swarm Optimization (RF-PSO). The XGB-PSO hybrid m odel exhibited superior performance (coefficient of determination R2 = 0.906, ro ot mean square error RMSE = 19.535 MPa, mean absolute error MAE = 13.741 MPa) on the testing dataset. Shapley Additive Explanation (SHAP) values and Partial Dep endence Plots (PDP) provided insights into component influences."
University of Transport TechnologyHano iVietnamAsiaCyborgsEmerging TechnologiesMachine LearningParticle Swa rm Optimization
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