Nondestructive Evaluation of Crack Depth in Concrete Using PCA-compressed Wave Transmission Function and Neural Networks

Abstract

Cracks in concrete are common defects that may enable rapid deterioration and failure of structures. Determination of a crack's depth using surface wave transmission measurement and the cut-off frequency in the transmission function (TRF) is difficult, in part due to variability of the measurement data. In this study, use of complete TRF data as features for crack depth assessment is proposed. A principal component analysis (PCA) is employed to generate a basis for the measured TRFs for various simulated crack (notch) cases in concrete. The measured TRFs are represented by their projections onto the most significant PCs. Then neural networks (NN), using the PCA-compressed TRFs, are applied to estimate the crack depth. An experimental study is carried out for five different artificial crack (notch) cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can effectively estimate the artificial crack depth in concrete structures, even with incomplete NN training.

Surface-Wave Based Model for Estimation of Discontinuity Depth in Concrete

“…From a broad viewpoint, nondestructive methods developed for the evaluation of the damage condition of concrete to date include visual and optical inspections and stress-wave based methods, as well as nuclear, magnetic, and electronic methods [ 3 ]. Of these methods, ultrasonic techniques involve measurements of pulse velocity [ 9 , 10 , 11 ], characteristics of guided waves [ 12 ], surface-wave transmission [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ], acoustic emission [ 5 ], diffuse ultrasound [ 25 , 26 , 27 , 28 ], coda wave interferometry [ 28 , 29 ], and nonlinear ultrasound [ 30 , 31 ]. Each of the ultrasonic techniques makes use of different characteristics of ultrasound to detect the location and propagation of cracks, to estimate the width and/or depth of cracks, or to evaluate the durability and residual mechanical properties of concrete [ 32 ].…”

“…In et al [ 22 ] tried to estimate crack depth using air-coupled transducers as both transmitter and receiver. Shin et al [ 23 ] estimated crack depth based on transmission of surface wave combined with principal component analysis (PCA) and neural network (NN) techniques. Kee and Nam [ 24 ] tried to estimate crack depth automatically using transmission of surface wave measured through piezoelectric (PZT) sensors.…”

Neural networks and principle component analysis approaches to predict pile capacity in sand

“…It was also successfully applied as a technique for reducing the dimensionality of ANN inputs in a variety of engineering applications [10][11][12][13]. Mathematically, PCA is an orthogonal projection technique that projects multidimensional observations represented in a subspace of dimension m (m is the number of observed variables) in a subspace of lower dimension (L < m) by maximizing the variance of the projections.…”

Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

“…It was also successfully applied as a technique for reducing the dimensionality of ANN inputs in a variety of engineering applications (e.g., Harkat 2003;Kuniar, Waszczyszyn 2006;Shin et al 2008;Boukhatem et al 2012). Mathematically, PCA is an orthogonal projection technique that projects multidimensional observations represented in a subspace of dimension m (m is the number of observed variables) in a subspace of lower dimension (L < m) by maximizing the variance of the projections.…”