Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method

Xiao, Feng; Yan, Yu; Meng, Xiangwei; Mao, Yuxue; Chen, Gang S.

doi:10.3390/s24061884

Sensors

2024

DOI: 10.3390/s24061884

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Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method

Feng Xiao,

Yu Yan,

Xiangwei Meng

et al.

Abstract: Identifying the parameters of multispan rigid frames is challenging because of their complex structures and large computational workloads. This paper presents a stiffness separation method for the static response parameter identification of multispan rigid frames. The stiffness separation method segments the global stiffness matrix of the overall structure into the stiffness matrices of its substructures, which are to be computed, thereby reducing the computational workload and improving the efficiency of para… Show more

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Cited by 3 publications

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Real-time estimation of bridge displacements under vehicle loads using physical mechanism-based attention-LSTM network with partial strain measurements

Lei,

Zhang,

Wang

2024

Advances in Structural Engineering

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Dynamic displacement of bridges under moving vehicle loads is a critical safety indicator for bridges, yet directly measuring these displacements poses practical challenges. Although computer vision-based techniques for measuring displacements have gained application, they are influenced by environmental conditions. Alternatively, indirect estimation of bridge displacements has attracted great attention. Currently, some deep learning methods have been developed to estimate displacements at positions deployed with accelerometers or strain sensors. In this paper, a method for real-time estimation of moving vehicle load-induced bridge displacements in interested regions is proposed utilizing an attention-Long Short-Term Memory (LSTM) network with partial strain measurements. It is based on the physical mechanisms of strain-displacement relationship of beam-type structures and bridge modal responses can be estimated from partial strain measurements to reconstruct structural displacements through the superposition of modal responses. For network training, the physical mapping between partial strains and the displacements in interested regions is learned by an attention-based LSTM network. Then, bridge displacements in interested regions can be estimated by the trained network using only partial strain measurements. Numerical examples of estimating the displacements of a simply supported beam bridge and the Hainan Haiwen bridge under random moving loads validate that the proposed method can estimate bridge displacements in any interested regions in real-time using only partial strain measurements.

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Real-time estimation of bridge displacements under vehicle loads using physical mechanism-based attention-LSTM network with partial strain measurements

Lei,

Zhang,

Wang

2024

Advances in Structural Engineering

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Physically Guided Estimation of Vehicle Loading-Induced Low-Frequency Bridge Responses with BP-ANN

Lu,

Qu,

Sun

et al. 2024

Buildings

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The intersectional relationship in bridge health monitoring refers to the mapping function that correlates bridge responses across different locations. This relationship is pivotal for estimating structural responses, which are then instrumental in assessing a bridge’s service status and identifying potential damage. The current research landscape is heavily focused on high-frequency responses, especially those associated with single-mode vibration. When it comes to low-frequency responses triggered by multi-mode vehicle loading, a prevalent strategy is to regard these low-frequency responses as “quasi-static” and subsequently apply time-series prediction techniques to simulate the intersectional relationship. However, these methods are contingent upon data regarding external loading, such as traffic conditions and air temperatures. This necessitates the collection of long-term monitoring data to account for fluctuations in traffic and temperature, a task that can be quite daunting in real-world engineering contexts. To address this challenge, our study shifts the analytical perspective from a static analysis to a dynamic analysis. By delving into the physical features of bridge responses of the vehicle–bridge interaction (VBI) system, we identify that the intersectional relationship should be inherently time-independent. The perceived time lag in quasi-static responses is, in essence, a result of low-frequency vibrations that are aligned with driving force modes. We specifically derive the intersectional relationship for low-frequency bridge responses within the VBI system and determine it to be a time-invariant transfer matrix associated with multiple mode shapes. Drawing on these physical insights, we adopt a time-independent machine learning method, the backpropagation–artificial neural network (BP-ANN), to simulate the intersectional relationship. To train the network, monitoring data from various cross-sections were input, with the responses at a particular section designated as the output. The trained network is now capable of estimating responses even in scenarios where time-related traffic conditions and temperatures deviate from those present in the training data set. To substantiate the time-independent nature of the derived intersectional relationship, finite element models were developed. The proposed method was further validated through the in-field monitoring of a continuous highway bridge. We anticipate that this method will be highly effective in estimating low-frequency responses under a variety of unknown traffic and air temperature conditions, offering significant convenience for practical engineering applications.

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Stability Analysis of Construction Factors for Partially Cable-Stayed Bridges with Multiple Towers and High Piers

Zhang,

Wang,

Kang

et al. 2024

Buildings

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Partially cable-stayed bridges have the characteristics of continuous rigid-frame bridges and cable-stayed bridges, making them a novel composite bridge system. This study focuses on the construction project of a multi-tower high-pier curved partially cable-stayed bridge to investigate the bridge’s stability during construction. The Midas/Civil software was used to establish a model for key construction stages of the bridge, considering structural linear elasticity and geometric nonlinearity. The study examines the impact of static wind loads, asymmetric construction of the main girder, closure sequence, and the load and detachment of the hanging basket on the bridge’s stability during construction. The results indicate that static wind loads have a significant impact on structural geometric nonlinearity, with a maximum reduction of 4.99%. Asymmetric construction at both ends of the main girder can cause structural instability and should be avoided. The geometric nonlinearity stability coefficient for the hanging basket load decreased by 10.83% during the maximum no-cable stage and by 7.84% during the cable stage, significantly affecting the stability during construction. A bridge closure sequence of side-span, secondary midspan, and midspan provides the most stable condition during the construction phase. The results of this study can inform the construction of similar partially cable-stayed bridges.

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Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method

Cited by 3 publications

References 42 publications

Real-time estimation of bridge displacements under vehicle loads using physical mechanism-based attention-LSTM network with partial strain measurements

Real-time estimation of bridge displacements under vehicle loads using physical mechanism-based attention-LSTM network with partial strain measurements

Physically Guided Estimation of Vehicle Loading-Induced Low-Frequency Bridge Responses with BP-ANN

Stability Analysis of Construction Factors for Partially Cable-Stayed Bridges with Multiple Towers and High Piers

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