Detection of cracks using neural networks and computational mechanics

From MaRDI portal
Publication:1600794

DOI10.1016/S0045-7825(02)00221-9zbMath1131.74316OpenAlexW2169303706MaRDI QIDQ1600794

R. Smith

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 structuresCalibrating a J2 plasticity material model using a 2D inverse finite element procedureA new tool based on artificial neural networks for the design of lightweight ceramic-metal armour against high-velocity impact of solidsA data-driven multi-flaw detection strategy based on deep learning and boundary element methodDamage detection in cracked structure rotating under the fluid medium through radial basis function neural network techniqueGenetic 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 BODYA GENETIC ALGORITHM BASED PROCEDURE FOR AUTOMATIC CRACK PROFILE IDENTIFICATIONEstimation of degraded composite laminate properties using acoustic wave propagation model and a reduction‐prediction networkComputer-aided design of the effects of CR\(_2\)O\(_3\) nanoparticles on split tensile strength and water permeability of high strength concreteDetection of holes in a plate using global optimization and parameter identification techniquesA DNN-based data-driven modeling employing coarse sample data for real-time flexible multibody dynamics simulationsA LOCAL-PATCH BASED MULTI-STAGE ARTIFICIAL-NEURAL-NETWORK TRAINING PROCEDURE AND ITS APPLICATION TO MATERIAL CHARACTERIZATIONDetection of defective pile geometries using a coupled FEM/SBFEM approach and an ant colony classification algorithmA computationally efficient approach for inverse material characterization combining Gappy POD with direct inversionComputational mechanics enhanced by deep learningTubenet: A Special Trumpetnet for Explicit Solutions to Inverse ProblemsVibratory Characteristics of Euler-Bernoulli Beams with an Arbitrary Number of Cracks Subjected to Axial LoadAn uncertainty inversion technique using two-way neural network for parameter identification of robot arms



Cites Work


This page was built for publication: Detection of cracks using neural networks and computational mechanics