Home Journals Articles Updates About
CN
image
收藏切换
Bearing capacity prediction of SPRC coupling beams with small span-to-height ratio based on machine learning method
收藏切换
PDF
Jianbo TIAN1, Wenjing ZHOU1, Huangjian CHEN2, Yong ZHAO1, Qin ZHAO1, Daguan HUANG1, Jingshuai YAN1
Earthquake Engineering and Engineering Dynamics | 2025, 45(1) : 85 - 94
Less
收藏切换
Earthquake Engineering and Engineering Dynamics | 2025, 45(1): 85-94
Bearing capacity prediction of SPRC coupling beams with small span-to-height ratio based on machine learning method
Full
Jianbo TIAN1, Wenjing ZHOU1, Huangjian CHEN2, Yong ZHAO1, Qin ZHAO1, Daguan HUANG1, Jingshuai YAN1
Affiliations
  • 1.School of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, China
  • 2.China Construction First Group Corporation Limited, Beijing 100161, China
Published: 2025-02-28 doi: 10.13197/j.eeed.2025.0109
Outline
收藏切换

In order to predict the bearing capacity of steel plate-concrete reinforced composite (SPRC) coupling beams more conveniently. In this paper, it is of great significance to study the bearing capacity prediction model of SPRC coupling beams by machine learning (ML) method. Firstly, the SPRC coupling beam database is established by collecting the existing experimental data. On this basis, six ML algorithms, including extreme learning machine (ELM) algorithm, back propagation neural network (BPNN) algorithm, support vector machine (SVM) algorithm, K-nearest neighbor (KNN) algorithm, random forest (RF) algorithm and extreme gradient boosting (XGBoost) algorithm were used for data regression training. Through the comparative analysis of model performance indicators, it is found that the prediction model based on XGBoost algorithm has the best robustness and generalization ability. Compared with the softened strut-and-tie model (SSTM), it has higher calculation accuracy and stability. A high-precision SPRC coupling beam bearing capacity prediction model based on ML method is proposed. In addition, the sensitivity analysis of the parameters affecting the bearing capacity of SPRC coupling beams is also carried out. The results show that the influence degree of each characteristic parameter on the bearing capacity of SPRC coupling beams is in descending order as follows: steel plate ratio (ρp), coupling beam section height (h), coupling beam section width (b), span-depth ratio (ln/h), stirrup yield strength (fvy), longitudinal reinforcement ratio (ρs), longitudinal reinforcement yield strength (fsy), stirrup ratio (ρt), steel plate yield strength (fpy), concrete compressive strength (fcu).

small span-depth ratio  /  SPRC coupling beam  /  machine learning  /  robustness  /  bearing capacity prediction
Jianbo TIAN, Wenjing ZHOU, Huangjian CHEN, Yong ZHAO, Qin ZHAO, Daguan HUANG, Jingshuai YAN. Bearing capacity prediction of SPRC coupling beams with small span-to-height ratio based on machine learning method[J]. Earthquake Engineering and Engineering Dynamics, 2025 , 45 (1) : 85 -94 . DOI: 10.13197/j.eeed.2025.0109
Appendix
Less
Year 2025 volume 45 Issue 1
PDF
48
25
Cite this Article
BibTeX
Article Info
doi: 10.13197/j.eeed.2025.0109
  • Receive Date：2024-05-28
  • Online Date：2026-03-20
  • Published：2025-02-28
Article Data
Affiliations
History
  • Received：2024-05-28
  • Revised：2024-06-27
Funding
Affiliations
    1.School of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, China
    2.China Construction First Group Corporation Limited, Beijing 100161, China
References
Share
https://castjournals.cast.org.cn/joweb/dzgcygczd/EN/10.13197/j.eeed.2025.0109
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House