Design and Performance Analysis of an Ultrasonic System for Health Monitoring of Concrete Structure

Abstract

The development and research of an ultrasonic-based concrete structural health monitoring system encounters a variety of problems, such as demands of decreasing complexity, high accuracy, and extendable system output. Aiming at these requirements, a low-cost extendable system based on FPGA with adjustable system output has been designed, and the performance has been evaluated by different assessment parameters set in this paper. Besides the description of the designed system and the experiments in air medium, the residual similarity and Pearson correlation coefficients of experimental and theoretical data have been used to evaluate the submodules’ output. The output performance of the overall system is evaluated by the Pearson correlation coefficient, root-mean-square error (RMSE), and magnitude-squared coherence with 40 experimental data. The maximum, median, minimum, and mean values in three-parameter datasets are analyzed for discussing the working condition of the system. The experimental results show that the system works stably and reliably with tunable frequency and amplitude output.

Surface Properties of Concrete and Bonding Strength of Pine Resin-Based Adhesives to Concrete Surfaces

“…Among the most important parts of this evaluation are accurate investigations and reasonable assessments of structures [2]. Studies of various sensors and evaluation and diagnosis systems along with proper assessment procedures are actively conducted for accurate evaluations and assessments of structures [3][4][5][6][7][8][9][10]. Te investigation of concrete structures is performed by permanently attaching measurement devices or sensors to concrete surfaces or by employing noncontact methods.…”

A Wavelet Packet Transform and Convolutional Neural Network Method Based Ultrasonic Detection Signals Recognition of Concrete

“…Zhao [ 12 ] used the stochastic configuration network to identify the ultrasonic detection signals of concrete defects and the accuracy is around 95%. Their experimental data were collected from concrete blocks of C30 class containing hole defects using the ultrasonic testing system [ 13 ]. These machine learning methods do not easily devise higher recognition accuracy for complex ultrasonic detection signals of either small or medium-sized defects in concrete [ 14 ].…”