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2026 Vol.42, Issue 2 Preview Page
Physics-informed Neural Network-based Inverse Analysis for Simultaneous Estimation of Consolidation Parameters from Settlement Monitoring Data

침하 계측 자료 기반 압밀 정수 동시 추정을 위한 물리정보 신경망(PINN) 기반 역해석 기법

Jeeyeon You1
,
Juhui Kim2
,
Young-Hoon Jung3*
유 지연1
,
김 주희2
,
정 영훈3*
1Member, Graduate Student, Dept. of Civil Engineering, Kyung Hee Univ.
2Member, Undergraduate Student, Dept. of Civil Engineering, Kyung Hee Univ.
3Member, Prof., Dept. of Civil Engineering, Kyung Hee Univ.
1정회원, 경희대학교 사회기반시스템공학과 석사과정
2학생회원, 경희대학교 사회기반시스템공학과 학사과정
3정회원, 경희대학교 사회기반시스템공학과 교수
*Corresponding Author
30 April 2026. pp. 135-153
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Abstract

This study introduces a physics-informed neural network (PINN)-based inverse analysis method for simultaneously estimating the coefficient of consolidation and final settlement using only settlement monitoring data. The approach utilizes a unified loss function that integrates the governing equation of one-dimensional consolidation with settlement-based constraints, allowing for stable parameter identification without the need for additional pore water pressure measurements. Results from synthetic data analyses show that the PINN model outperforms conventional hyperbolic and Asaoka methods across various drainage and loading conditions, particularly during the early stages of consolidation and under staged loading. In addition, applying the model to real field data indicates that predictions reliably converge toward long-term settlement behavior. This method enables accurate estimation of consolidation parameters at early stages and demonstrates significant potential as a practical inverse analysis tool for geotechnical engineering applications.

Keywords
Coefficient of consolidation
Final consolidation settlement
Incremental loading
Inverse analysis
Physics-informed neural network
Soft ground

본 연구에서는 침하 계측 자료만을 이용하여 압밀계수와 최종 압밀침하량을 동시에 추정할 수 있는 물리정보 신경망(PINN) 기반 역해석 기법을 제안하였다. 제안된 방법은 1차원 압밀 지배방정식과 침하량 제약조건을 통합한 손실함수를 활용하여, 추가적인 간극수압 계측 없이도 안정적인 매개변수 식별이 가능하도록 구성되었다. 합성 데이터에 대한 분석 결과, PINN 모델은 다양한 배수 및 하중 조건에서 기존 쌍곡선법 및 아사오카법 대비 높은 정확도를 보였으며, 특히 압밀 초기 단계와 점증재하 조건에서 우수한 성능을 나타냈다. 또한 실제 현장 데이터 적용 시에도 장기 침하 거동에 안정적으로 수렴하는 결과를 확인하였다. 본 기법은 압밀 초기 단계에서의 신뢰성 있는 지반정수 추정을 가능하게 하며, 실무 적용성이 높은 역해석 방법으로 활용될 수 있다.

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Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 42
  • No :2
  • Pages :135-153
  • Received Date : 2026-04-06
  • Accepted Date : 2026-04-16
  • DOI :https://doi.org/10.7843/kgs.2026.42.2.135
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