Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

Giulio Siracusano,Carmelo Scuro,Francesca Garescì,Giovanni Finocchio,Riccardo Tomasello,Francesco Lamonaca,Aurelio La Corte,Mario Carpentieri,Massimo Chiappini

doi:10.3390/app112412059

Abstract

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

Highlights

One hydraulic press with a closed loop governing system with 5000 kN connected to the AS to control and record the load-displacement diagram; Piezoelectric transducers, R15α, with a peak sensitivity of 69 V/(m/s), resonant frequency 150 kHz, and directionality ±1.5 dB [50]; Controlling hardware appliance constituted by multiple Logic Flat Amplifier Trigger generator (L-FAT) and DAta acQuisition boards (DAQ) NI-6110 with four input channels each, 12-bit resolution, and sampling frequency f AS = 5 Msample/s wherein a channel (Ch) is directly associated to each transducer
In order to avoid over-fitting during the learning process and improve classification accuracy, the dataset was divided into training and validation quotas with an 80/20 ratio
The key ingredient is a proper selection of event descriptors (ED) we found to be the instantaneous frequency, spectral entropy and spectral kurtosis

Summary

Introduction

The area of the SHM receiving most attention in the literature [1,2,3,4,5,6,7,8,9] is related to the extraction of data features allowing to distinguish between the undamaged and damaged structures. Acoustic emission (AE) monitoring [10,11] is becoming an established method for feature extraction. It is based on the simultaneous analysis of multiple parameters, such as vibration amplitude and frequency, with the characteristics of acoustic wave as originated during a crack. A recent study [14] has pointed out that data quality can be compromised by untreated disturbances related to the measuring system (i.e., the sensor-induced distortion) or the environment (

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