Digital twins-boosted identification of bridge vehicle loads integrating video and physics

IF 4.4 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Structures Pub Date : 2024-11-04 DOI:10.1016/j.compstruc.2024.107578
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Abstract

Traffic loads are very critical in bridge digital twins for assessing the deterioration state and structural integrity of road bridges. The existing load rating methods are complicated and time-consuming, necessitating more efficient and intelligent approaches to identify and evaluate safe load capacities. This paper presents a digital twins-boosted approach to identify vehicle loads on road bridges by integrating video records and related physic information. The convolutional neural network (CNN) is adapted with a proposed pixel scale factor (PSF) method to track the motion and dimension of vehicles crossing the bridge. Based on the tracked vehicle data, the time-dependent traffic flow is regenerated via traffic simulation models. Due to the correlation in vehicle loads within a road network, the detailed weight of each vehicle in the traffic flow is inferred using related vehicle load models, e.g., the model established from nearby tollgate data in the case study. After a preliminary verification in the laboratory, a field trial test is carried out to validate the proposed approach in identifying the traffic flow. Then, finite element (FE) simulations are integrated into the approach to predict the vehicle-inducted structural response of an urban arch bridge. The prediction shows a satisfying agreement with the measurement by sensors, which validates the proposed approach in identifying traffic loads. Moreover, compared with purely data-driven methods, the proposed approach demands less training effort and provides more details due to the integration of physics. In general, the output not only offers a promising solution for the digital twins of traffic loads at low costs, but also highlights the integration of visual data and physics in solving engineering issues.
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整合视频和物理的数字双胞胎增强型桥梁车辆荷载识别技术
在桥梁数字双胞胎中,交通荷载对于评估道路桥梁的老化状态和结构完整性至关重要。现有的荷载分级方法复杂且耗时,因此需要更高效、更智能的方法来识别和评估安全荷载能力。本文提出了一种数字孪生增强方法,通过整合视频记录和相关物理信息来识别道路桥梁上的车辆荷载。卷积神经网络(CNN)与提议的像素比例因子(PSF)方法相匹配,可跟踪过桥车辆的运动和尺寸。根据跟踪的车辆数据,通过交通仿真模型重新生成随时间变化的交通流。由于道路网络中车辆负载的相关性,交通流中每辆车的详细重量是通过相关车辆负载模型推断出来的,例如案例研究中通过附近收费站数据建立的模型。在实验室进行初步验证后,我们进行了实地试验,以验证所提出的交通流识别方法。然后,将有限元(FE)模拟纳入该方法,以预测城市拱桥的车辆感应结构响应。预测结果与传感器的测量结果显示出令人满意的一致性,从而验证了所提出的交通荷载识别方法。此外,与纯粹的数据驱动方法相比,所提出的方法对训练的要求更低,而且由于融入了物理学原理,还能提供更多细节。总之,该成果不仅为低成本的交通负荷数字双胞胎提供了一个有前途的解决方案,而且还突出了在解决工程问题时视觉数据与物理学的结合。
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来源期刊
Computers & Structures
Computers & Structures 工程技术-工程:土木
CiteScore
8.80
自引率
6.40%
发文量
122
审稿时长
33 days
期刊介绍: Computers & Structures publishes advances in the development and use of computational methods for the solution of problems in engineering and the sciences. The range of appropriate contributions is wide, and includes papers on establishing appropriate mathematical models and their numerical solution in all areas of mechanics. The journal also includes articles that present a substantial review of a field in the topics of the journal.
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Digital twins-boosted identification of bridge vehicle loads integrating video and physics Offline iteration-based real-time hybrid simulation for high-fidelity fluid-structure dynamic interaction in structures subjected to seismic excitation Complete dispersion characteristics of elastic waves in periodically multilayered arbitrarily-anisotropic media A novel modular origami strategy: Achieving adjustable Poisson’s ratio and tunable distinctive mechanical properties for versatile applications Stress-constrained topology optimization using the velocity field level set method
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