Detection of structural changes in concrete using embedded ultrasonic sensors based on autoregressive model

Abstract

Embedded ultrasonic transmission measurements can be a cost effective and more user-friendly alternative in comparison to commonly used structural health monitoring systems used in civil engineering to detect operational or environmental changes in structure. They can be used to detect small structural changes in large concrete structures without necessity of placing a sensor on the spot where the changing is taking place. This paper presents the investigations on the possibility of utilising autoregressive model, where the velocity of ultrasonic wave in a medium is dependent on the operational state. The goal is to use the model for localization of operational changes in the large concrete structure by means of embedded ultrasonic transducer networks. In this study, several static load tests and dynamic test on large reinforced concrete beams have been performed using embedded ultrasonic sensors. Using the autoregressive model it is possible to localize operational changes in the concrete structure. The proposed approach of diagnostic signal processing allows for precise evaluation of structural changes in concrete.

Early Crack Detection of Reinforced Concrete Structure Using Embedded Sensors

“…Figure 14 indicates that increment of the level of nonlinearities (cracking) in the beam subjected to external stress, and simultaneously, the amplitude of the AR parameters (increasing the residual error) tends to decrease. The AR parameters were calculated by fitting the AR model to a baseline signal from one transducer pair (top) before the start of the experiment using the RMSE technique (see [43] for more details). If cracks are present in the structure, the residual errors will increase due to higher attenuation (decreasing the AR parameters amplitude), which is caused by the increased crack width under loading.…”

“…Finally, the short-time Fourier transform (STFT) is capable of providing information about signal behavior by performing a time-frequency analysis, from which it is possible to determine the time instants at which specific harmonics are present in the signal, as well as the power spectrum analysis. More information on feature extraction methods used in this study is available in [30,37,43,44,45,46]. The above-described features are summarized in Table 1.…”

Sensors for Structural Health Monitoring of Agricultural Structures

“…The link between compressive strength and ultrasonic sensitivity is calculated using a linear mathematical relationship presented by Chakraborty and Katunin [ 84 ]. The formula suggests that the measurement of the skimming longitudinal wave velocities and Rayleigh waves are reliable predictors of material damage; this is because the speed of sound is reduced or enhanced by the microscopic arrangement of the atomic particles and elasticity.…”

“…The relationship between the two core variables is derived using Equations (1) and (2), where E is the material modulus, rho- density. At the same time, the Poisson’s ratio is denoted by n. The sound velocity for the Rayleigh and the longitudinal waves is represented by V r and V l , respectively [ 84 ]. The two equations draw a relationship between the material elastic properties, wave velocities, and the density of the material, which contributes to the estimation of the attenuation and the diffraction of the waves.…”

Addressing the detection capability for scalable energy consumption using primary data acquisition system of embedded ultrasonic sensors in SHM

“…A simple but valuable methods to relate the structural changes, is calculating the autoregressive (AR) model coefficient between a predicted signal from initial measurement and all consecutive measurement under different operational effects. The AR model methods composed of using the parameters estimated from the baseline signal, and computing the experimental data obtained from the structure [16]. This AR model residual error can be written as:…”