用于近表面安装纤维增强聚合物加固钢筋混凝土梁应变预测的高效改进梯度提升法

IF 2.9 3区 工程技术 Q2 ENGINEERING, CIVIL
Abdelwahhab Khatir, Roberto Capozucca, Samir Khatir, Erica Magagnini, Brahim Benaissa, Thanh Cuong-Le
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引用次数: 0

摘要

近年来,近表面贴装(NSM)加固技术已成为传统加固方法的一种有前途的替代方法。过去二十年来,研究人员对其潜力、优势和应用以及相关参数进行了广泛研究,旨在优化建筑系统。然而,无论是从静态角度(包括考虑纤维增强聚合物(FRP)棒材与树脂之间的粘结滑移效应所导致的接触部分的不保留,而现有的分析模型通常会忽略这一点),还是从动态角度(包括研究振动频率的变化趋势,以了解各种形式的损坏和加固效率的影响)来看,仍有进一步探索的必要。为了填补这一知识空白,本研究对使用 NSM 碳纤维增强聚合物 (CFRP) 和玻璃纤维增强聚合物 (GFRP) 杆件的简支钢筋混凝土 (RC) 梁进行了静态和动态测试。主要目的是检验各种加固方法的效果。这项研究进行了加载循环直至破坏的弯曲试验,有助于确定梁试件在不同破坏程度(包括混凝土开裂)下的行为。通过自由振动试验进行动态分析,可以跟踪加载过程中各个阶段不同破坏程度下的加固效果。此外,还提出应用粒子群优化(PSO)和遗传算法(GA)来优化梯度提升(GB)训练性能,以预测 NSM-FRP RC 中的混凝土应变。使用实验数据集对使用粒子群优化的 GB 系统(GBPSO)和使用遗传算法的 GB 系统(GBGA)进行了训练,其中输入数据为静态外加载荷,输出数据为随之产生的应变。结果表明,GBPSO 和 GBGA 混合模型可提供高度准确的应变预测结果。这些模型结合了两种优化技术的优势,是一种强大而高效的预测工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient improved Gradient Boosting for strain prediction in Near-Surface Mounted fiber-reinforced polymer strengthened reinforced concrete beam

The Near-Surface Mounted (NSM) strengthening technique has emerged as a promising alternative to traditional strengthening methods in recent years. Over the past two decades, researchers have extensively studied its potential, advantages, and applications, as well as related parameters, aiming at optimization of construction systems. However, there is still a need to explore further, both from a static perspective, which involves accounting for the non-conservation of the contact section resulting from the bond-slip effect between fiber-reinforced polymer (FRP) rods and resin and is typically neglected by existing analytical models, as well as from a dynamic standpoint, which involves studying the trends of vibration frequencies to understand the effects of various forms of damage and the efficiency of reinforcement. To address this gap in knowledge, this research involves static and dynamic tests on simply supported reinforced concrete (RC) beams using rods of NSM carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP). The main objective is to examine the effects of various strengthening methods. This research conducts bending tests with loading cycles until failure, and it helps to define the behavior of beam specimens under various damage degrees, including concrete cracking. Dynamic analysis by free vibration testing enables tracking of the effectiveness of the reinforcement at various damage levels at each stage of the loading process. In addition, application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is proposed to optimize Gradient Boosting (GB) training performance for concrete strain prediction in NSM-FRP RC. The GB using Particle Swarm Optimization (GBPSO) and GB using Genetic Algorithm (GBGA) systems were trained using an experimental data set, where the input data was a static applied load and the output data was the consequent strain. Hybrid models of GBPSO and GBGA have been shown to provide highly accurate results for predicting strain. These models combine the strengths of both optimization techniques to create a powerful and efficient predictive tool.

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来源期刊
CiteScore
5.20
自引率
3.30%
发文量
734
期刊介绍: Frontiers of Structural and Civil Engineering is an international journal that publishes original research papers, review articles and case studies related to civil and structural engineering. Topics include but are not limited to the latest developments in building and bridge structures, geotechnical engineering, hydraulic engineering, coastal engineering, and transport engineering. Case studies that demonstrate the successful applications of cutting-edge research technologies are welcome. The journal also promotes and publishes interdisciplinary research and applications connecting civil engineering and other disciplines, such as bio-, info-, nano- and social sciences and technology. Manuscripts submitted for publication will be subject to a stringent peer review.
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