Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network

Abstract This paper deals with the combination of non-linear ultrasonic and artificial neural networks (ANNs) for the non-destructive evaluation of the damages in concrete under stressed state. Two networks, one using raw variables and another using dimensionless variables were trained and tested to predict concrete damages. Input data to the neural network is the time-domain signals of the received ultrasonic waves, obtained from the experimental studies carried out as reported in the earlier literature involving experimental data base of 75 ultrasonic measurements performed on concrete cubes with water–cement ( w / c ) ratios of 0.40, 0.50 and 0.60 respectively. Both networks were two-layer-perceptrons trained according to back-propagation algorithm. The results of this research highlight the potential of artificial neural networks for solving the problem of concrete damage evaluation using non-linear ultrasonic measurements. It was found that the proposed ANN models predict the strength of concrete laboratory cubes with low absolute errors. The performance of ANN model for predicting the residual strength of concrete using the raw data is better than the prediction using grouped dimensionless variables.

[1]  Antonio Gliozzi,et al.  Monitoring evolution of compressive damage in concrete with linear and nonlinear ultrasonic methods , 2010 .

[2]  Wimal Suaris,et al.  ULTRASONIC PULSE ATTENUATION AS A MEASURE OF DAMAGE GROWTH DURING CYCLIC LOADING OF CONCRETE , 1987 .

[3]  Chia-Chi Cheng,et al.  Detecting flaws in concrete blocks using the impact-echo method , 2008 .

[4]  B. Stawiski,et al.  Non-destructive strength characterization of concrete using surface waves , 2000 .

[5]  R. Gr. Maev,et al.  Review / Sythèse Nonlinear acoustic applications for material characterization: A review , 1999 .

[6]  Jerzy Hoła,et al.  Application of artificial neural networks to determine concrete compressive strength based on non‐destructive tests , 2005 .

[7]  Mustafa Saridemir,et al.  Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks , 2009, Adv. Eng. Softw..

[8]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[9]  I. Marie,et al.  The use of USPV to anticipate failure in concrete under compression , 2003 .

[10]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[11]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[12]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[13]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[14]  Sohichi Hirose,et al.  Nondestructive evaluation of damaged concrete using nonlinear ultrasonics , 2009 .

[15]  Igor Yu. Solodov,et al.  Ultrasonics of non-linear contacts: propagation, reflection and NDE-applications , 1998 .

[16]  G. Arliguie,et al.  Non-destructive evaluation of concrete physical condition using radar and artificial neural networks , 2009 .

[17]  Jan Drewes Achenbach,et al.  ULTRASONIC INVESTIGATION OF CONCRETE WITH DISTRIBUTED DAMAGE , 1998 .

[18]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[19]  Sung Quek,et al.  EXTRACTING DIMENSIONAL INFORMATION FROM STEEL REINFORCING BARS IN CONCRETE USING NEURAL NETWORKS TRAINED ON DATA FROM AN INDUCTIVE SENSOR , 2004 .

[20]  Hong-Guang Ni,et al.  Prediction of compressive strength of concrete by neural networks , 2000 .

[21]  Hosein Naderpour,et al.  Prediction of FRP-confined compressive strength of concrete using artificial neural networks , 2010 .

[22]  M. A. Bhatti,et al.  Predicting the compressive strength and slump of high strength concrete using neural network , 2006 .

[23]  Kyung-Young Jhang,et al.  Applications of nonlinear ultrasonics to the NDE of material degradation , 2000 .

[24]  Sohichi Hirose,et al.  Nonlinear Ultrasonic Investigation of Concrete Damaged under Uniaxial Compression Step Loading , 2010 .

[25]  David R. Martinelli,et al.  Radar signal interpretation using neural network for defect detection in concrete , 1996 .

[26]  Dan M. Frangopol,et al.  Improved assessment of mass concrete dams using acoustic travel time tomography. Part II — application , 2000 .

[27]  Mary Sansalone,et al.  Impact-echo : nondestructive evaluation of concrete and masonry , 1997 .