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基于随钻关键参数的岩体智能探测方法研究进展

Research progress on intelligent rock mass detection method based on drilling parameters

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【作者】 柏君王胜赖昆徐世毅张杰张洁

【Author】 BAI Jun;WANG Sheng;LAI Kun;XU Shiyi;ZHANG Jie;ZHANG Jie;College of Environmental and Civil Engineering, Chengdu University of Technology;State Key Laboratory of Geological Hazard Prevention and Geological Environment Protection;

【通讯作者】 王胜;

【机构】 成都理工大学环境与土木工程学院地质灾害防治与地质环境保护国家重点实验室

【摘要】 在钻探施工条件的极端化和新一代信息技术发展引起的产业变革的双重影响下,钻探技术的智能化成为了钻探技术发展的必然趋势。文章聚焦于基于随钻关键参数的岩体智能识别这个最为关键的智能钻探难题,对基于钻进过程的不取心原位岩体测试技术进行综合阐述,最后对钻探工程的智能化发展进行展望。通过综述目前在该领域的最新技术和研究进展,提出了随钻过程-物理多模态信息深度融合的全新识别体系,以期实现钻探过程的岩土体智能识别能力,提高重大基础设施工程勘察质量和效率。

【Abstract】 Under the dual influence of the extremization of drilling construction conditions and the industrial transformation brought about by the development of new-generation information technologies, the intelligence of drilling technology has become an inevitable trend in the development of drilling technology. Intelligent drilling, in line with the needs of the Fourth Industrial Revolution, utilizes the theories and methods of information technology to transform and enhance traditional drilling models and technical systems, achieving digital empowerment and digital transformation of drilling projects, thereby becoming the core driving force for the development of drilling technology.In this context, this paper focuses on the critical issue of intelligent rock mass recognition based on real-time drilling key parameters, providing a comprehensive overview of non-core sampling in-situ rock mass testing technology based on the drilling process. Finally, it offers an outlook on the intelligent development of drilling engineering. By reviewing the latest technologies and research progress in this field, a new recognition system for deep fusion of process-physical multimodal information during drilling is proposed, aiming to realize intelligent identification of rock and soil masses during the drilling process and improve the quality and efficiency of major infrastructure engineering investigation.

【基金】 珠峰科学研究计划(编号:80000-2020ZF11411)
  • 【文献出处】 钻探工程 ,Drilling Engineering , 编辑部邮箱 ,2024年05期
  • 【分类号】P634
  • 【下载频次】57
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