首页|Study Data from Department of Petroleum Engineering Provide New Insights into Ma chine Learning (A Critical Review of Rock Failure Criteria: a Scope of Machine L earning Approach)
Study Data from Department of Petroleum Engineering Provide New Insights into Ma chine Learning (A Critical Review of Rock Failure Criteria: a Scope of Machine L earning Approach)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting out of Gujarat, India, by N ewsRx editors, research stated, “Understanding rock failure behaviors is crucial for various engineering applications, including geotechnical engineering, minin g, petroleum engineering, and underground construction. Rock failure criteria pr ovide essential tools for the prediction of mechanical response of rocks under d ifferent loading conditions.” Our news journalists obtained a quote from the research from the Department of P etroleum Engineering, “This review article overviews various rock failure criter ia, highlighting their underlying theories, modeling approaches, and application s. The paper discusses classical failure criteria and extended criteria based on them, such as Mohr -Coulomb (M -C), Hoek-Brown (H-B), and Griffith Criteria, as well as more advanced criteria incorporating rock fabric, anisotropy, and compl ex failure modes. The review also explores machine learning approaches for rock failure prediction and uniaxial compressive strength based on experimental and r eal-time well-log data to determine and validate rock failure criteria. The insi ghts provided in this article can assist researchers, engineers, and practitione rs in selecting appropriate failure criteria for specific rock types and enginee ring projects.”
GujaratIndiaAsiaCyborgsEmerging TechnologiesEngineeringMachine LearningDepartment of Petroleum Engineering
NETL
NSTL
