首页|Studies from De La Salle University Provide New Data on Machine Learning (Predic tion of Soil Liquefaction Triggering Using Rule- Based Interpretable Machine Lear ning)
Studies from De La Salle University Provide New Data on Machine Learning (Predic tion of Soil Liquefaction Triggering Using Rule- Based Interpretable Machine Lear ning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting originating from Manila, Phil ippines, by NewsRx correspondents, research stated, "Seismic events remain a sig nificant threat, causing loss of life and extensive damage in vulnerable regions ." Financial supporters for this research include Department of Science And Technol ogy Engineering Research And Development For Technology. Our news reporters obtained a quote from the research from De La Salle Universit y: "Soil liquefaction, a complex phenomenon where soil particles lose confinemen t, poses a substantial risk. The existing conventional simplified procedures, an d some current machine learning techniques, for liquefaction assessment reveal l imitations and disadvantages. Utilizing the publicly available liquefaction case history database, this study aimed to produce a rule-based liquefaction trigger ing classification model using rough set-based machine learning, which is an int erpretable machine learning tool. Following a series of procedures, a set of 32 rules in the form of IF-THEN statements were chosen as the best rule set. While some rules showed the expected outputs, there are several rules that presented a ttribute threshold values for triggering liquefaction. Rules that govern fine-gr ained soils emerged and challenged some of the common understandings of soil liq uefaction."
De La Salle UniversityManilaPhilippi nesAsiaCyborgsEmerging TechnologiesMachine Learning
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