ML-CCD:预测用 FRP 片材加固的钢筋混凝土梁中混凝土覆盖层分层破坏模式的机器学习模型

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software Impacts Pub Date : 2024-07-20 DOI:10.1016/j.simpa.2024.100685
Fahed H. Salahat , Hayder A. Rasheed , Huthaifa I. Ashqar
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引用次数: 0

摘要

ML-CCD 是一款开源 Python 软件,基于机器学习模型,用于预测使用纤维增强聚合物 (FRP) 加固的钢筋混凝土 (RC) 梁的过早失效。该模型通过一个数据库进行训练,该数据库由 70 个经过实验测试的因混凝土覆盖层分层(CCD)而过早失效的梁组成。影响 CCD 失效的重要梁参数被用于训练 ML-CCD。该软件可预测玻璃钢板材在失效时的极限应变,从而找出其极限抗拉强度和有效强化率,以用于设计目的。
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ML-CCD: machine learning model to predict concrete cover delamination failure mode in reinforced concrete beams strengthened with FRP sheets

ML-CCD is an open-source Python software based on a Machine-Learning model that was utilized to predict the premature failure of reinforced concrete (RC) beams strengthened with Fiber Reinforced Polymers (FRP). The model was trained using a database consisting of 70 experimentally tested beams that failed prematurely due to Concrete Cover Delamination (CCD). The significant beams parameters that influence the CCD failure were used in training the ML-CCD. This software predicts the ultimate strain in the FRP sheets at failure, thus finding its ultimate tensile strength and the effective strengthening ratio for design purposes.

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来源期刊
Software Impacts
Software Impacts Software
CiteScore
2.70
自引率
9.50%
发文量
0
审稿时长
16 days
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