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Traffic-induced bridge displacement reconstruction using a physics-informed convolutional neural network
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Design of an FIR filter for the displacement reconstruction using measured acceleration in low‐frequency dominant structures
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Feasibility of displacement monitoring using low-cost GPS receivers
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Lost data recovery for structural health monitoring based on convolutional neural networks
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Seismic response prediction method for building structures using convolutional neural network
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A convolutional neural network‐based full‐field response reconstruction framework with multitype inputs and outputs
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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
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Hybrid physics-informed neural networks for main bearing fatigue prognosis with visual grease inspection
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Deflection estimation of a full scale prestressed concrete girder using long-gauge fiber optic sensors
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The determination of bridge displacement using measured acceleration
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Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling
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Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning
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Physics informed machine learning: Seismic wave equation
inferredcites →https://doi.org/10.1016/j.jsv.2007.04.037openalex-snapshot
Estimation of dynamic structural displacements using fiber Bragg grating strain sensors
inferredcites →https://doi.org/10.1016/j.measurement.2004.04.005openalex-snapshot
Numerical double integration of acceleration measurements in noise
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Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration
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A vision-based system for remote sensing of bridge displacement
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Errors in velocities and displacements deduced from accelerographs: An approach based on the theory of error propagation
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Dynamic Testing Procedures for Highway Bridges Using Traffic Loads
inferredcites →https://doi.org/10.1061/(asce)em.1943-7889.0001556openalex-snapshot
Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification
inferredcites →https://doi.org/10.1063/5.0032402openalex-snapshot
Physics-guided deep learning framework for predictive modeling of bridge vortex-induced vibrations from field monitoring
inferredcites →https://doi.org/10.1089/big.2020.0071openalex-snapshot
Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
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Physics-Constrained Deep Learning of Geomechanical Logs
inferredcites →https://doi.org/10.1109/tmech.2013.2275187openalex-snapshot
Displacement Estimation Using Multimetric Data Fusion
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
inferredcites →https://doi.org/10.1111/j.1467-8667.2007.00536.xopenalex-snapshot
A State‐Space Approach for Deriving Bridge Displacement from Acceleration
inferredcites →https://doi.org/10.1115/1.4047173openalex-snapshot
Physics-Informed Neural Networks for Missing Physics Estimation in Cumulative Damage Models: A Case Study in Corrosion Fatigue
inferredcites →https://doi.org/10.1177/1475921719897571openalex-snapshot
Convolutional neural network–based data recovery method for structural health monitoring
inferredcites →https://doi.org/10.1177/1475921720916881openalex-snapshot
Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks
inferredcites →https://doi.org/10.1177/1475921720959226openalex-snapshot
Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks
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Measuring and testing dependence by correlation of distances
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Estimation of bridge displacement responses using FBG sensors and theoretical mode shapes
inferredcites →https://doi.org/10.12989/sss.2015.15.3.645openalex-snapshot
Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model
inferredcites →https://doi.org/10.2749/ghent.2021.0526openalex-snapshot
Design and construction of a three-span continuous box girder model
inferredcites →https://doi.org/10.3141/2028-15openalex-snapshot
Bridge Displacement Estimates from Measured Acceleration Records
inferredcites →https://doi.org/10.32604/cmc.2019.06641openalex-snapshot
Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems
inferredcites →https://doi.org/10.32604/cmc.2019.06660openalex-snapshot
A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate
inferredcites →https://doi.org/10.3390/s17122888openalex-snapshot
A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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