Data-Driven Analysis of Retrofit Effects on RC Columns Under Blast Loading: A PDP-Based xAI Approach

Yeeun Kim; Jiuk Shin

doi:10.7734/COSEIK.2025.38.5.317

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2025 Vol.38, Issue 5 Preview Page Next Page

Research Paper

Data-Driven Analysis of Retrofit Effects on RC Columns Under Blast Loading: A PDP-Based xAI Approach PDP 기반 xAI 접근법을 활용한 철근콘크리트 기둥의 데이터 기반 내폭 보강 효과 분석

31 October 2025. pp. 317-324

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Abstract

This study introduces an integrated framework for the rapid evaluation of retrofit levels (RL) in reinforced concrete (RC) columns subjected to blast loading, employing machine learning and explainable artificial intelligence (xAI) techniques. A multi-stage machine learning approach was developed to classify failure modes and predict retrofit levels. Partial dependence plot (PDP) analysis was subsequently applied to extract data-driven insights on retrofit effectiveness. The proposed framework comprises two major components: (1) blast performance assessment employing a multi-stage ML model for failure mode classification and RL prediction, and (2) PDP-based analysis for systematic evaluation of input variable effects. The RL prediction models were trained on a blast damage dataset and validated across three damage conditions (severe, moderate, and minor) for both flexural and shear failure modes. PDP-based analysis effectively identified feasible and infeasible reinforcement ranges for both longitudinal and transverse reinforcement ratios, providing clear guidance for optimal retrofit strategies. The proposed framework offers a practical tool for blast-resistant design that enables rapid and informed retrofit decisions. It supports efficient evaluation of retrofit demands and goal-oriented reinforcement planning based on column detailing and specific blast load scenarios.

Keywords

machine learning

explainable AI

partial dependence plot

retrofit strategy

reinforced concrete columns

blast resistance performance

본 연구는 기계학습 및 설명 가능한 인공지능(xAI) 기법을 활용하여 폭발 하중을 받는 철근콘크리트 기둥의 보강 단계(Retrofit Level, RL)를 신속하게 평가하는 종합적 프레임워크를 제시한다. 파괴 유형와 보강 요구사항을 예측하기 위한 다단계 기계학습 접근법을 개발하였으며, 이후 부분 의존성 그래프(Partial Dependence Plot, PDP) 분석을 통해 데이터 기반 보강 전략을 수립하였다. 제안된 프레임워크는 두 가지 주요 프로세스로 구성된다: (1) 파괴유형 분류 및 RL 예측을 위한 다단계 기계학습 모델을 활용한 폭발 성능 평가, (2) 입력 변수 효과의 체계적 분석을 통한 PDP 기반 보강 전략 개발. RL 예측 모델은 광범위한 폭발 손상 평가 데이터를 바탕으로 학습되었으며, 휨 및 전단 파괴유형에 대해 세 가지 손상 조건(심각, 보통, 경미)에서 검증되었다. PDP 분석 결과, 파괴유형과 손상 조건에 따라 서로 다른 보강 특성이 나타남을 확인하였다. PDP 기반 분석을 통해 주철근비 및 전단철근비에 대한 보강 가능 구간과 불가능 구간을 성공적으로 식별하였다.

키워드

기계학습

설명가능한 인공지능

부분 의존성 그래프

보강 전략

철근콘크리트기둥

폭발저항성평가

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Information

Publisher :Computational Structural Engineering Institute of Korea
Publisher(Ko) :한국전산구조공학회
Journal Title :Journal of the Computational Structural Engineering Institute of Korea
Journal Title(Ko) :한국전산구조공학회 논문집
Volume : 38
No :5
Pages :317-324
Received Date : 2025-06-18
Revised Date : 2025-07-08
Accepted Date : 2025-07-08
DOI :https://doi.org/10.7734/COSEIK.2025.38.5.317

Journal of the Computational Structural Engineering Institute of Korea ISSN:1229-3059(Print) 2287-2302(Online) 한국전산구조공학회 논문집

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