Meso Mechanical Study of Cracking Process in Concrete Subjected to Tensile Loading

Flansbjer, Mathias; Lindqvist, Jan Erik

doi:10.2478/ncr-2018-0012

Cited by 2 publications

(2 citation statements)

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“…Along with their presence everywhere, there is the inevitable need for a maintenance plan in order to ensure in-service safety and to prolong the lifetime of concrete structures. Numerous studies [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] have been conducted by laboratories, companies, and individuals racing to identify solutions regarding the performance amelioration of structural health monitoring (SHM). By employing an effective monitoring scheme, it is possible to provide the user with more insight into the in-service system/structure, avoid near-future failures, and lessen downtime by a significant amount.…”

Section: Introductionmentioning

confidence: 99%

“…As nondestructive methods for SHM have become dominant in recent years, different methods have been investigated for concrete SHM such as ultrasonic [ 5 , 17 , 18 ], vibration [ 19 , 20 ], image processing [ 3 , 21 , 22 ], and acoustic emission (AE) [ 4 , 12 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Even though ultrasonic testing can detect internal defects and their sizes, it is susceptible to complicated part geometry and certain materials, such as austenitic steel, which can mask defects by causing attenuation.…”

Section: Introductionmentioning

confidence: 99%

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A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

Nguyen

Ahmad

Kim

2022

Sensors

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This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen’s useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback–Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen’s work.

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