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Research Article

UHPC Bond Strength Prediction in Precast Joints Using Machine Learning

머신러닝 기법을 활용한 프리캐스트 연결부 UHPC 부착강도 예측

Dong Kyu Lim1
,
Ho Bi Kang2
,
Young Jin Kim3
,
Myoung Sung Choi4*
임 동규1
,
강 호비2
,
김 영진3
,
최 명성4*
1Post-Doctoral Researcher, AI+KCIR Global Research Center, Seoul 16419, Korea
2M.S. Researcher, Department of Civil and Environment Engineering, Dankook University, Seoul 16890, Korea
3Korea Concrete Institute (KCI), Seoul 06130, Korea
4Professor, Department of Civil and Environment Engineering, Dankook University, Seoul 16890, Korea
1AI+KCIR 글로벌 연구 센터 박사후연구원
2단국대학교 토목환경공학과 석사
3한국콘크리트학회 연구소장
4단국대학교 토목환경공학과 교수
*Corresponding Author
31 December 2025. pp. 49-67
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Abstract

This study experimentally investigated the bond performance of precast bridge-deck joints using ultra-high-performance concrete (UHPC) to enhance structural efficiency and constructability. Conventional loop-splice joint details often require long splice lengths and may reduce construction productivity. UHPC was examined as a filling material to secure adequate structural performance while shortening the splice length. Fourteen splice specimens were fabricated with varying compressive strengths (80, 100, and 120 MPa) and splice lengths (8d_b, 10d_b, and 15d_b), and splice tests were conducted under four-point loading. The test results demonstrated that UHPC, due to its high compressive strength and fiber reinforcement, provided sufficient load transfer and crack control even at short splice lengths. However, existing design provisions such as Eurocode 2, the Korean Highway Bridge Design Code, ACI 318, and the Structural Design Guideline for Fiber-Reinforced SUPER Concrete were found to be limited in reflecting the distinct bond characteristics of UHPC. To address this, machine learning techniques were employed to develop data-driven bond strength and development-length prediction models. Random forest and SHAP analysis were used to identify influential parameters, and symbolic regression (PySR) was applied to derive interpretable equations. The proposed models showed high prediction accuracy and are expected to improve the design reliability and efficiency of precast UHPC joint systems.

Keywords
UHPC
precast
precast joint
bond performance
machine learning

본 연구는 프리캐스트 교량 바닥판 연결부에 초고성능 콘크리트(UHPC)를 적용하여 부착성능을 평가하고, 시공성과 구조적 효율을 향상시키는 것을 목적으로 한다. 기존 루프 이음 방식은 긴 이음길이를 요구하여 시공성과 구조 효율성이 저하되는 한계가 있다. 이에 UHPC를 충전재로 활용하여 이음길이를 단축하면서도 충분한 구조 성능 확보 가능성을 검토하였다. 압축강도(80, 100, 120 MPa)와 이음길이(8d_b, 10d_b, 15d_b)를 변수로 한 총 14개의 실험체를 제작하고, 4점 재하 실험을 수행하였다. 실험 결과, UHPC는 높은 압축강도와 섬유보강 효과로 인해 짧은 이음길이에서도 우수한 하중전달성능과 균열제어 능력을 확보할 수 있음을 확인하였다. 반면 Eurocode 2, 한국 도로교 설계기준, ACI 318 및 섬유보강 SUPER 콘크리트 설계지침 등 기존 기준은 UHPC의 특수한 부착거동을 충분히 반영하지 못하는 한계가 나타났다. 이러한 문제를 해결하기 위해 머신러닝 기반의 데이터 분석을 수행하였으며, 변수 영향도 분석(Random Forest 및 SHAP)과 상징회귀(PySR)를 통해 부착강도 및 정착길이 예측식을 제안하였다. 제안된 모델은 높은 예측 정확도를 보였으며, 프리캐스트 UHPC 연결부 설계의 신뢰성 향상과 효율적인 시공 계획 수립에 기여할 수 있을 것으로 기대된다.

키워드
초고성능 콘크리트(UHPC)
프리캐스트
프리캐스트 연결부
부착 거동
머신러닝
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Information
  • Publisher :Korean Institute of Bridge and Structural Engineers
  • Publisher(Ko) :한국교량및구조공학회
  • Journal Title :Journal of Structure Research and Practice
  • Journal Title(Ko) :한국교량및구조공학회 논문집
  • Volume : 3
  • No :2
  • Pages :49-67
  • Received Date : 2025-11-11
  • Accepted Date : 2025-11-18
  • DOI :https://doi.org/10.22725/JSRP.2025.3.2.49
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