Seismic Response Analysis and Evaluation of Laminated Rubber Bearing Supported Bridge Based on the Artificial Neural Network

Zhang, Bingzhe; Wang, Kehai; Lu, Guanya; Guo, Weizuo

doi:10.1155/2021/5566874

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…In this regard, this study utilizes the Artificial Neural Networks for ground motion prediction using the global database. ANN is a strong tool for addressing complex issues, and it is particularly effective for civil engineering challenges [28][29][30][31][32]. Here, the prediction equation was built utilizing 12,706 data points obtained from 283 earthquakes from the revised NGA-West2 database using neural networks.…”

Section: Introductionmentioning

confidence: 99%

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Challagulla,

Suluguru,

Noroozinejad Farsangi

et al. 2023

The Journal of Engineering

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The seismic resilience of a structure has been evaluated using peak ground acceleration (PGA). Ground motion parameters such as source characteristics, local site conditions are used to forecast the PGA of the ground motion. This paper aims to develop an Artificial Neural Network (ANN) based model to predict the PGA. Here, hypocentral distance (Rhypo${R}_{hypo}$), shear wave velocity (Vs30${V}_{s30}$), and moment magnitude (Mw${M}_w$), are used as input parameters. The model uses 12,706 ground motion recordings from 283 earthquakes from the revised NGA‐West2 database supplied by Pacific Engineering Research Centre. Among the whole data, 70% of the data is set for training, 15% for validation, and 15% for testing the network. The R value derived from the testing dataset is 0.952, indicating the excellent performance of a network. An extensive parametric study is conducted with the PGA values, and the results indicate that the PGA increases with the magnitude and decreases with the hypocentral distance. The predicted PGA values from the present study are comparable with those from the existing relationships in the global database. The generated ANN model is further verified by comparing the predicted and recorded PGA values of an actual recorded event.

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Section: Introductionmentioning

confidence: 99%

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Challagulla,

Suluguru,

Noroozinejad Farsangi

et al. 2023

The Journal of Engineering

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Experimental and Numerical Investigations on the Spherical Steel Bearing Capacity of New Anti-Separation Design

Yan,

Gou,

Zhang

et al. 2023

KSCE J Civ Eng

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Mastering seismic time series response predictions using an attention-Mamba transformer model for bridge bearings and piers across varied testing conditions

Yazdanpanah,

Park,

Chang

et al. 2024

Sci Rep

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This research introduces an advanced method for predicting seismic responses and hysteresis curves of instrumented bridge piers and bearings under various loading conditions, leaning solely on a single deep learning architecture and the same hyperparameters tuning. Test specimens are subjected to ground accelerations including vertical seismic loads and axial forces. To accurately capture peak values, particularly on the negative side of the hysteresis loop (unloading region), the model employs a stacked deep architecture. A key component to overcome the challenges is the self-attention-Mambadriven transformer layer, which enhances the model's ability to capture long-range dependencies in seismic data. This layer works in conjunction with other deep learning techniques to ensure robust and precise predictions. Implemented with Python's Keras functional API, the model processes inputs like ground accelerations, actuator loads, effective height, moment of inertia, and superstructure mass. The model is evaluated with a dataset of 95 real-time hybrid simulation (RTHS) tests for lead rubber bearings, 29 RTHS tests for bridge piers, and 17 cyclic tests (10 fast and 7 slow). Extensive hyperparameter tuning demonstrates the model's proficiency to capture hysteresis and residual deformations accurately. Achieving an impressive correlation with experimentally measured values, ranging from 88.1 to 98.9%, and a reasonable dissipated energy error ratio are notable. The deep learning model reduces the need for additional tests, offering time and cost savings, and provides rapid, and accurate insights into bridge behavior. This supports timely and precise bridge design and aids decision-makers during emergencies.

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Seismic Response Analysis and Evaluation of Laminated Rubber Bearing Supported Bridge Based on the Artificial Neural Network

Cited by 5 publications

References 26 publications

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Experimental and Numerical Investigations on the Spherical Steel Bearing Capacity of New Anti-Separation Design

Mastering seismic time series response predictions using an attention-Mamba transformer model for bridge bearings and piers across varied testing conditions

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