New Model for the Lap-Splice Length of Tensile Reinforcement in Concrete Elements

被引：0

作者：

Karimipour, Arash ^{[1
]}

Ghalehnovi, Mansour ^{[2
]}

De Brito, Jorge ^{[3
]}

机构：

[1] Dept. of Civil Engineering at Texas, Member of Center for Transportation Infrastructure Systems, Univ. of Texas, El Paso,TX,79968, United States

[2] Dept. of Civil Engineering, Ferdowsi Univ. of Mashhad, Mashhad,91779-48974, Iran

[3] Dept. of Civil Engineering, Architecture and Georresources, Instituto Superior Técnico, Universidade de Lisboa, Lisbon,1649-004, Portugal

来源：

Journal of Structural Engineering (United States) | 2021年 / 147卷 / 12期

关键词：

Reinforced concrete - Compressive strength - Machine learning - High performance concrete - Genetic algorithms - Concrete beams and girders;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In this study, a model is proposed to determine the required lap-splice length in RC beams using a multilayer genetic programming machine learning (MLGPML) algorithm. This model was obtained from the study of the bond stress between concrete with normal and high compressive strength and tensile reinforcement. Previous models were used to estimate this bond stress, taking into account different parameters. In this study, using the results of 386 experimental tests on reinforced concrete beams, the lap-splice length and the required transverse reinforcement over that length were calculated. The proposed model to determine the bond stress is very well suited to the design purposes by comparison with various experimental campaigns' results. © 2021 American Society of Civil Engineers.

引用

共 50 条

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