基于 GA-SMO-SVM 算法的节段桥梁施工过程中的变形预测:以 CFST 拱桥为例

IF 3.6 2区 工程技术 Q1 ENGINEERING, CIVIL Journal of Civil Structural Health Monitoring Pub Date : 2024-07-06 DOI:10.1007/s13349-024-00825-6
Kaizhong Xie, Jiecai Ning, Quanguo Wang, Hongxin Yao
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引用次数: 0

摘要

关于大跨度混凝土填充钢管(CFST)拱桥建造过程中拱桥变形预测的现有优化模型研究有限。此外,CFST 拱桥是桥梁工程分段结构领域的典型实例。本研究以 CFST 拱桥为案例,进行变形预测。为了精确预测 CFST 拱桥在施工过程中的变形,我们的研究制定了拱桁变形预测模型和计算方法。此外,通常利用高精度测量机器人(全站仪)来监测拱桥的变形,以获得精确的变形数据,用于后续预测研究。该模型采用遗传算法(GA)、顺序最小优化算法(SMO)和优化支持向量机(SVM)。在构建模型时,采用了斜拉索紧固-悬臂组装方法中使用的五个输入参数。通过采用自适应遗传算法,确定了 SMO-SVM 模型的最佳参数配置,以确保高效、精确的优化过程。随后,SMO-SVM 模型使用确定的最佳参数集进行了训练。预测结果随后通过测试得到验证,从而有助于预测 CFST 拱桥施工过程中的拱桁变形。为了证明所提模型的适用性,我们将其应用于平南三桥,该桥被公认为世界上跨度最大的 CFST 拱桥(竣工时),主跨达 575 米。我们还从三个不同的维度对 GA-SMO-SVM 模型进行了比较研究:不同的核函数、不同的优化算法和不同的回归模型。研究结果表明,GA-SMO-SVM 模型利用自适应遗传算法实现了高效的模型优化,实现了最精确的变形预测,最大绝对误差为 8.86 毫米,优于其他模型,达到了毫米级精度。此外,GA-SMO-SVM 模型运行效率高,所需的时间约为通过网格搜索(GS)优化的 SMO-SVM 模型的 1/64。本研究通过全面的模型解释和分析,验证了基于 GA-SMO-SVM 算法的 CFST 拱桥施工变形预测模型的机理,为大跨度 CFST 拱桥施工中的拱桥变形预测提供了一种开创性的方法。此外,它还为预测其他分段结构的变形奠定了基础。
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Deformation prediction during the construction of segmental bridges based on GA-SMO-SVM algorithm: an example of CFST arch bridge

Limited research has been undertaken on the extant optimization models pertaining to the prediction of arch deformation in the course of erecting long-span concrete-filled steel tube (CFST) arch bridges. Moreover, CFST arch bridges stand as prototypical instances within the realm of bridge engineering’s segmental structures. This study focuses on the CFST arch bridge as a case study for deformation prediction. In pursuit of precise CFST arch bridge deformation prediction during construction, our investigation has formulated an arch-truss deformation prediction model and computational approach. Moreover, high-precision measuring robots (total stations) are usually utilized to monitor the deformation of arch bridges to obtain accurate deformation data for subsequent prediction studies. This model relies on the employment of the genetic algorithm (GA), sequential minimal optimization (SMO) algorithm, and an optimized support vector machine (SVM). Five input parameters, as utilized in the cable-stayed fastening-hanging cantilever assembly method, have been employed in constructing the model. The optimal parameter configuration for the SMO-SVM model was ascertained by employing an adaptive genetic algorithm to ensure an efficient and precise optimization process. Subsequently, the SMO-SVM model underwent training with the identified optimal parameter set. The prediction outcomes were subsequently verified through testing, facilitating the prediction of arch truss deformations during the construction of CFST arch bridges. To demonstrate the applicability of the proposed model, it was applied to the Pingnan Third Bridge, which is recognized as the world's longest-span CFST arch bridge (at the time of completion) and has a main span of 575 m. We also conducted a comparative study of the GA-SMO-SVM model in three distinct dimensions: various kernel functions, differing optimization algorithms, and alternative regression models. Our findings indicate that the GA-SMO-SVM model, which harnesses an adaptive genetic algorithm for efficient model optimization, achieves the most precise deformation predictions with a maximum absolute error of 8.86 mm, outperforming other models and achieving millimeter-level accuracy. Furthermore, the GA-SMO-SVM model operates with high efficiency, requiring approximately 1/64th of the time consumed by the SMO-SVM model optimized via a grid search (GS). This study validates the mechanism of the CFST arch bridge construction deformation prediction model founded on the GA-SMO-SVM algorithm through thorough model interpretation and analysis, presenting a pioneering approach to predicting arch deformations in the construction of extensive-span CFST arch bridges. Additionally, it offers a foundation for predicting deformations in other segmental structures.

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来源期刊
Journal of Civil Structural Health Monitoring
Journal of Civil Structural Health Monitoring Engineering-Safety, Risk, Reliability and Quality
CiteScore
8.10
自引率
11.40%
发文量
105
期刊介绍: The Journal of Civil Structural Health Monitoring (JCSHM) publishes articles to advance the understanding and the application of health monitoring methods for the condition assessment and management of civil infrastructure systems. JCSHM serves as a focal point for sharing knowledge and experience in technologies impacting the discipline of Civionics and Civil Structural Health Monitoring, especially in terms of load capacity ratings and service life estimation.
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