Traffic-induced bridge displacement reconstruction using a physics-informed convolutional neural network
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[1] Hui Li,et al. Physics-guided deep learning framework for predictive modeling of bridge vortex-induced vibrations from field monitoring , 2021, Physics of Fluids.
[2] T. Rabczuk,et al. A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate , 2021, Computers, Materials & Continua.
[3] P. Tahmasebi,et al. Physics informed machine learning: Seismic wave equation , 2020, Geoscience Frontiers.
[4] Ye Xia,et al. Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks , 2020, Structural Health Monitoring.
[5] Naif Alajlan,et al. Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates , 2020, European Journal of Mechanics - A/Solids.
[6] Nikhil Muralidhar,et al. Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems , 2020, Big Data.
[7] Dongxiao Zhang,et al. Physics-Constrained Deep Learning of Geomechanical Logs , 2020, IEEE Transactions on Geoscience and Remote Sensing.
[8] Arinan Dourado,et al. Physics-Informed Neural Networks for Missing Physics Estimation in Cumulative Damage Models: A Case Study in Corrosion Fatigue , 2020, J. Comput. Inf. Sci. Eng..
[9] Gao Fan,et al. Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks , 2020, Structural Health Monitoring.
[10] Hyo Seon Park,et al. Seismic response prediction method for building structures using convolutional neural network , 2020, Structural Control and Health Monitoring.
[11] Fuh-Gwo Yuan,et al. Machine learning for structural health monitoring: challenges and opportunities , 2020, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.
[12] Hyo Seon Park,et al. Convolutional neural network–based data recovery method for structural health monitoring , 2020, Structural Health Monitoring.
[13] Yang Liu,et al. Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling , 2019, Engineering Structures.
[14] Timon Rabczuk,et al. An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications , 2019, Computer Methods in Applied Mechanics and Engineering.
[15] Gao Fan,et al. Lost data recovery for structural health monitoring based on convolutional neural networks , 2019, Structural Control and Health Monitoring.
[16] Luning Sun,et al. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data , 2019, Computer Methods in Applied Mechanics and Engineering.
[17] Si-Da Zhou,et al. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network , 2017, Sensors.
[18] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Sung-Han Sim,et al. Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model , 2015 .
[20] Sung-Han Sim,et al. Feasibility of displacement monitoring using low‐cost GPS receivers , 2013 .
[21] Sung-Han Sim,et al. Displacement Estimation Using Multimetric Data Fusion , 2013, IEEE/ASME Transactions on Mechatronics.
[22] Nam-Sik Kim,et al. Estimation of bridge displacement responses using FBG sensors and theoretical mode shapes , 2012 .
[23] H. Lee,et al. Design of an FIR filter for the displacement reconstruction using measured acceleration in low‐frequency dominant structures , 2010 .
[24] Stathis C. Stiros,et al. Errors in velocities and displacements deduced from accelerographs: An approach based on the theory of error propagation , 2008 .
[25] Richard J. Vaccaro,et al. A State‐Space Approach for Deriving Bridge Displacement from Acceleration , 2008, Comput. Aided Civ. Infrastructure Eng..
[26] Nam-Sik Kim,et al. Deflection estimation of a full scale prestressed concrete girder using long-gauge fiber optic sensors , 2008 .
[27] Maria L. Rizzo,et al. Measuring and testing dependence by correlation of distances , 2007, 0803.4101.
[28] Jae-Hung Han,et al. Estimation of dynamic structural displacements using fiber Bragg grating strain sensors , 2007 .
[29] Hani Nassif,et al. Bridge Displacement Estimates from Measured Acceleration Records , 2007 .
[30] Jong-Jae Lee,et al. A vision-based system for remote sensing of bridge displacement , 2006 .
[31] Hani Nassif,et al. Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration , 2005 .
[32] Ki-Tae Park,et al. The determination of bridge displacement using measured acceleration , 2005 .
[33] John A. Crowe,et al. Numerical double integration of acceleration measurements in noise , 2004 .
[34] Patrick Paultre,et al. Dynamic Testing Procedures for Highway Bridges Using Traffic Loads , 1995 .
[35] Xudong Jian,et al. Design and construction of a three-span continuous box girder model , 2021, IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs.
[36] Rih-Teng Wu,et al. Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification , 2019, Journal of Engineering Mechanics.
[37] Naif Alajlan,et al. Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems , 2019, Computers, Materials & Continua.
[38] Yigit A. Yucesan,et al. Computers in Industry , 2022 .