Challenges in data-driven site characterization

Kok Kwang Phoon, Jianye Ching, Takayuki Shuku


91 被引用数 (Scopus)


Site characterisation is a cornerstone of geotechnical and rock engineering. “Data-driven site characterisation” refers to any site characterisation methodology that relies solely on measured data, both site-specific data collected for the current project and existing data of any type collected from past stages of the same project or past projects at the same site, neighbouring sites, or beyond. It is an open question what data-driven site characterisation (DDSC) can achieve and how useful are the outcomes for practice, but this “value of data” question is of major interest given the rapid pace of digital transformation in many industries. The scientific aspects of this question are presented as three challenges in this paper: (1) ugly data, (2) site recognition, and (3) stratification. The practical aspect that cannot be ignored is how to scale any solution to a realistic 3D setting in terms of size and complexity at reasonable cost. No deployment in practice is possible otherwise. At this point, the practicing community at large has yet to be convinced what data, big or small, could do to transform current practice. The authors believe that we need a more purposeful agenda to hasten research in this direction that would include articulating clearer statements for the challenges, developing benchmarks to compare solutions, and bringing research to practice through software.

出版ステータスPublished - 2022

ASJC Scopus subject areas

  • 土木構造工学
  • 建築および建設
  • 安全性、リスク、信頼性、品質管理
  • 地盤工学および土木地質学
  • 地質学


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