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Research Article

Proposal of a Predictive Algorithm for the Dry Unit Weight of Sandy Soils under Subzero Conditions

영하 온도 조건에서 사질토의 건조단위중량 예측 알고리즘 제안

Jaesub Lee1
,
Gigwon Hong2
,
Kicheol Lee3*
이 재섭1
,
홍 기권2
,
이 기철3*
1Non-Member, Graduate Student, School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea
2Member, Assistant Professor, Department of Urban Infra Engineering, Halla University, 28 Halladae-gil, Wonju-si 26404, Republic of Korea
3Member, Research Professor, RISE Project Group, Halla University, 28 Halladae-gil, Wonju-si 26404, Republic of Korea
*Corresponding Author
30 December 2025. pp. 9-18
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Abstract

This study experimentally demonstrates that the compaction behavior of sandy soils under sub-zero temperatures differs qualitatively from that at room temperature, and proposes a machine learning algorithm that predicts dry unit weight using only field input parameters(temperature and water content). For this purpose, standard compaction tests were conducted on four distinct sandy soils sampled from Songdo, Incheon, at laboratory temperature (20°C) and at sub-zero temperatures (0, -5, -10, -20°C). At zero-above temperature, the water content–density relationship exhibited a parabolic curve with a maximum dry unit weight at the optimal water content (OWC); under sub-zero conditions, however, the curve transitioned to a monotonically decreasing form toward the wet side. Based on these observations, Gradient Boosting Regressor were developed that inputs baseline compaction characteristics at zero above temperature and basic index properties, augmented with site temperature and water content, to output the dry unit weight for field conditions. Cross-validation showed strong agreement between predictions and measurements, with a coefficient of determination (R2=0.9587) and no systematic bias across the range. Accordingly, the proposed algorithm can provide immediate, realistic estimates of dry unit weight in winter even when an OWC peak is not observed, making it practical for cold-weather quality control.

Keywords
Sandy soil
Sub-zero temperature
Machine learning algorithm
Standard compaction test

본 연구는 영하 조건에서 사질토의 다짐거동이 상온과 질적으로 달라지는 현상을 실험적으로 규명하고, 현장 입력값(온도 및 함수비)만으로 건조단위중량을 추정할 수 있는 머신러닝 알고리즘을 제안한다. 이를 위해 인천 송도에서 채취한 서로 다른 4종의 사질토에 대하여 상온(20°C)과 영하(0, −5, −10, −20°C)에서 표준 다짐시험을 수행하였다. 상온에서는 함수비 증가에 따라 최적 함수비에서 최대 건조단위중량을 보이는 포물선형 곡선이 관찰되었으나, 영하 온도에서는 습윤측으로 갈수록 건조단위중량이 지속적으로 감소하는 하향형 곡선으로 전환되었다. 이를 바탕으로, 상온 기준 다짐 특성과 기본 물성을 입력하고, 현장 온도와 함수비를 추가하여 현장의 건조단위중량을 출력 할 수 있는 Gradient Boosting Regressor 모델을 구축하였다. 교차검증 결과, 예측 및 실측값의 결정계수(R2)은 0.9587로 전 구간에서 편향 없이 양호한 적합도를 보였다. 따라서, 제안된 알고리즘은 최적 함수비가 관측되지 않는 영하 조건에서도 현재 현장의 조건만으로 현실적인 건조단위중량을 즉시 산정할 수 있어 동절기 품질관리에 실용적일 것으로 판단된다.국문초록

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Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 24
  • No :4
  • Pages :9-18
  • Received Date : 2025-10-04
  • Revised Date : 2025-10-09
  • Accepted Date : 2025-10-13
  • DOI :https://doi.org/10.12814/jkgss.2025.24.4.009
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