基于 GRNN 的级联集合模型用于非破坏性损伤状态识别:小数据方法

IF 8.7 2区 工程技术 Q1 Mathematics
Ivan Izonin, Athanasia K. Kazantzi, Roman Tkachenko, Stergios-Aristoteles Mitoulis
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引用次数: 0

摘要

评估受气候应力影响的老化结构的结构完整性是对传统工程方法的挑战。原因在于,结构退化通常是在没有任何明显警告的情况下开始和发展的,直到出现明显的严重损坏或灾难性故障。例如,预应力混凝土桥梁的传统检测方法无法解释巨大的永久性挠度,因为其原因(通常是肌腱脱落)几乎不可见或无法测量。在许多情况下,传统检测方法无法发现这些潜在的缺陷和损坏,因此需要进行昂贵的连续结构健康监测,以进行知情评估,从而采取适当的结构干预措施。这种能力上的差距导致了人员伤亡和巨大损失,因为操作人员几乎没有时间做出反应。本研究针对这一差距,提出了一种新颖的机器学习方法,以可测量的结构挠度为基础,对桥梁损坏状态进行快速非破坏性评估。首先,通过模拟与不同程度和模式的肌腱损失相关联的各种可信桥梁损坏情况,建立了一个全面的训练数据集,肌腱的完整性对桥面的健康至关重要。其次,开发了基于通用回归神经网络(GRNN)的新型级联集合模型,利用有限的数据集预测三个相互依存的输出属性。利用差分进化法对所提出的级联模型进行了优化。对一座真实的大跨度桥梁进行了建模和验证。结果证实,与现有方法相比,所提出的模型在准确识别桥梁损伤状态方面非常有效。所开发的模型显示出卓越的预测准确性和可靠性,突出了其在无损桥梁损伤评估中的实用价值,有助于制定有效的修复规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach

GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach

Assessing the structural integrity of ageing structures that are affected by climate-induced stressors, challenges traditional engineering methods. The reason is that structural degradation often initiates and advances without any notable warning until visible severe damage or catastrophic failures occur. An example of this, is the conventional inspection methods for prestressed concrete bridges which fail to interpret large permanent deflections because the causes—typically tendon loss—are barely visible or measurable. In many occasions, traditional inspections fail to discern these latent defects and damage, leading to the need for expensive continuous structural health monitoring towards informed assessments to enable appropriate structural interventions. This is a capability gap that has led to fatalities and extensive losses because the operators have very little time to react. This study addresses this gap by proposing a novel machine learning approach to inform a rapid non-destructive assessment of bridge damage states based on measurable structural deflections. First, a comprehensive training dataset is assembled by simulating various plausible bridge damage scenarios associated with different degrees and patterns of tendon losses, the integrity of which is vital for the health of bridge decks. Second, a novel General Regression Neural Network (GRNN)-based cascade ensemble model, tailored for predicting three interdependent output attributes using limited datasets, is developed. The proposed cascade model is optimised by utilising the differential evolution method. Modelling and validation were conducted for a real long-span bridge. The results confirm the efficacy of the proposed model in accurately identifying bridge damage states when compared to existing methods. The model developed demonstrates exceptional prediction accuracy and reliability, underscoring its practical value in non-destructive bridge damage assessment, which can facilitate effective restoration planning.

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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
期刊介绍: Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.
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