Detection of cracks using neural networks and computational mechanics
From MaRDI portal
Publication:1600794
DOI10.1016/S0045-7825(02)00221-9zbMath1131.74316OpenAlexW2169303706MaRDI QIDQ1600794
Publication date: 16 June 2002
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0045-7825(02)00221-9
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (20)
A spline-based FE approach to modelling of high frequency dynamics of 1-D structures ⋮ Calibrating a J2 plasticity material model using a 2D inverse finite element procedure ⋮ A new tool based on artificial neural networks for the design of lightweight ceramic-metal armour against high-velocity impact of solids ⋮ A data-driven multi-flaw detection strategy based on deep learning and boundary element method ⋮ Damage detection in cracked structure rotating under the fluid medium through radial basis function neural network technique ⋮ Genetic fuzzy system for damage detection in beams and helicopter rotor blades. ⋮ Damage detection on crates of beverages by artificial neural networks trained with finite-element data. ⋮ A 2.5D TRACTION BOUNDARY ELEMENT METHOD FORMULATION APPLIED TO THE STUDY OF WAVE PROPAGATION IN A FLUID LAYER HOSTING A THIN RIGID BODY ⋮ A GENETIC ALGORITHM BASED PROCEDURE FOR AUTOMATIC CRACK PROFILE IDENTIFICATION ⋮ Estimation of degraded composite laminate properties using acoustic wave propagation model and a reduction‐prediction network ⋮ Computer-aided design of the effects of CR\(_2\)O\(_3\) nanoparticles on split tensile strength and water permeability of high strength concrete ⋮ Detection of holes in a plate using global optimization and parameter identification techniques ⋮ A DNN-based data-driven modeling employing coarse sample data for real-time flexible multibody dynamics simulations ⋮ A LOCAL-PATCH BASED MULTI-STAGE ARTIFICIAL-NEURAL-NETWORK TRAINING PROCEDURE AND ITS APPLICATION TO MATERIAL CHARACTERIZATION ⋮ Detection of defective pile geometries using a coupled FEM/SBFEM approach and an ant colony classification algorithm ⋮ A computationally efficient approach for inverse material characterization combining Gappy POD with direct inversion ⋮ Computational mechanics enhanced by deep learning ⋮ Tubenet: A Special Trumpetnet for Explicit Solutions to Inverse Problems ⋮ Vibratory Characteristics of Euler-Bernoulli Beams with an Arbitrary Number of Cracks Subjected to Axial Load ⋮ An uncertainty inversion technique using two-way neural network for parameter identification of robot arms
Cites Work
- Transient scattering of SH waves by surface-breaking and sub-surface cracks
- Transient scattering of Rayleigh waves by surface-breaking and sub-surface cracks
- Neural crack identification in steady state elastodynamics
- A neural network approach for damage detection and identification of structures
- Quantitative nondestructive evaluation
- Use of neural networks in detection of structural damage
- Unnamed Item
This page was built for publication: Detection of cracks using neural networks and computational mechanics