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Hybrid physics-based and data-driven model for predicting flexural capacity of corroded prestressed concrete structures

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成果类型:
期刊论文
作者:
Yiming Yang;Ziwei Zhou;Jianxin Peng*
通讯作者:
Jianxin Peng
作者机构:
[Ziwei Zhou] School of Civil Engineering, Hunan City University, Yiyang, Hunan 413000, China
Hunan Engineering Research Center of Development and Application of Ceramsite Concrete Technology, Hunan City University, Yiyang 413000, China
[Jianxin Peng] School of Civil Engineering, Changsha University of Science and Technology, Changsha, Hunan 410114, China
[Yiming Yang] School of Civil Engineering, Hunan City University, Yiyang, Hunan 413000, China<&wdkj&>Hunan Engineering Research Center of Development and Application of Ceramsite Concrete Technology, Hunan City University, Yiyang 413000, China
通讯机构:
[Jianxin Peng] S
School of Civil Engineering, Changsha University of Science and Technology, Changsha, Hunan 410114, China
语种:
英文
期刊:
Materials Today Communications
ISSN:
2352-4928
年:
2025
页码:
112815
基金类别:
CRediT authorship contribution statement Zhou Ziwei: Writing – review & editing, Validation, Software. Peng Jianxin: Writing – review & editing, Supervision, acquisition. Yang Yiming: Writing – original draft, Methodology, Investigation, acquisition, Data curation, Conceptualization.
机构署名:
本校为第一机构
院系归属:
土木工程学院
摘要:
This study aims to propose a hybrid physics-based and data-driven model for predicting flexural capacity of corroded prestressed concrete (PC) structures, aiming to improve the rationality and applicability of flexural capacity assessment. First, a self-adaptive physics-informed deep neural network framework is developed. Then, based on a collected database containing 100 sets of measured data, the proposed model is compared with three deep learning (DL) models, two machine learning (ML) models and two traditional mechanism-based prediction models. After that, the score analysis, error analysi...

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