Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

Applied Sciences

Aurelio La Corte¹  Giulio Siracusano¹  Riccardo Tomasello²  Mario Carpentieri²  Francesca Garescì³  Francesco Lamonaca⁴  Giovanni Finocchio⁵  Carmelo Scuro⁶  Massimo Chiappini⁷ 

[1] Department of Electric, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy;Department of Electrical and Information Engineering, Politecnico di Bari, Via E. Orabona 4, 70125 Bari, Italy;Department of Engineering, University of Messina, C. di Dio, S. Agata, 98166 Messina, Italy;Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, Via P. Bucci, 87036 Rende, Italy;Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina, C. di Dio, S. Agata, 98166 Messina, Italy;Department of Physics, University of Calabria, Via P. Bucci, 87036 Rende, Italy;Istituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata 605, 00143 Rome, Italy;

关键词: acoustic emission;    damage classification;    structural health monitoring;    deep learning;    bidirectional long short term memory;   

DOI  :  10.3390/app112412059

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown