首页|Aeronautics Institute of Technology Researcher Yields New Study Findings on Mach ine Learning (A Machine Learning-Based Approach for Predicting Installation Torq ue of Helical Piles from SPT Data)
Aeronautics Institute of Technology Researcher Yields New Study Findings on Mach ine Learning (A Machine Learning-Based Approach for Predicting Installation Torq ue of Helical Piles from SPT Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting out of Sao Jos e dos Campos, Brazil, by NewsRx editors, research stated, "Helical piles are adv antageous alternatives in constructions subjected to high tractions in their fou ndations, like transmission towers." Funders for this research include Coordination of Superior Level Staff Improveme nt. The news journalists obtained a quote from the research from Aeronautics Institu te of Technology: "Installation torque is a key parameter to define installation equipment and the final depth of the helical pile. This work applies machine le arning (ML) techniques to predict helical pile installation torque based on info rmation from 707 installation reports, including Standard Penetration Test (SPT) data. It uses this information to build three datasets to train and test eight machine-learning techniques. Decision tree (DT) was the worst technique for comp aring performances, and cubist (CUB) was the best. Pile length was the most impo rtant variable, while soil type had little relevance for predictions. Prediction s become more accurate for torque values greater than 8 kNm."
Aeronautics Institute of TechnologySao Jose dos CamposBrazilSouth AmericaCyborgsEmerging TechnologiesMachine Learning
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