Investigation of tensile fracture of rubberized self‐compacting concrete by acoustic emission and digital image correlation

Nowadays, the feature extraction method of multichannel acoustic emission (AE) signal provides a solid research foundation for digital and intelligent fault diagnosis of the hoisting system. More specifically, AE signal collected from the hoisting system is generally characterized by nonlinear and non-stationary, thus making the traditional intelligent fault diagnosis methods cannot accurately extract the inherent fault features. To alleviate this problem and improve

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multichannel intelligent fault diagnosis of hoisting system using differential search algorithm‐variational mode decomposition and improved deep convolutional neural network

Lee

2022

“…11,12 There are different methods to monitoring the cracks propagation such as acoustic emission. 13,14 The cracks in concrete introduce an additional impact within the transport properties of concrete, namely, the permeability of concrete, which is likely to significantly increase the transport of chloride ions inside concrete. 15 These results are not fully in agreement with the results obtained in Sheo-Feng et al and Kwon et al 15,16 due to natural formation of cracks in the sample (width, depth and tortuosity) or the method of assessing the diffusion coefficient of chloride.…”

Section: Introductionmentioning

confidence: 99%

Modelling chloride ingress into in‐service cracked reinforced concrete structures exposed to de‐icing salt environment and climate change: Part 1

AL-Ameeri

Rafiq

Τσιούλου

et al. 2022

The greenhouse gases (GHGs), in terms of CO 2 emissions, have influenced the climate system by altering the planet's temperature and relative humidity (RH) patterns creating global warming. These changes in temperature and RH could be increased the penetration rate of chloride in existing concrete structures causing an acceleration of degradation processes that have the tendency to influence the serviceability and safety of these structures. There is a flurry of models to predict chloride penetration; however, only very limited models aim to predict the chloride concentration profiles as a function of time, temperate and RH in cracked concrete members which is the characteristic of any existing concrete structures. The factors affecting chloride concentration and penetration in concrete are categorised as 'internal' (relating to the characteristic material properties such as porosity and crack width) and 'external' (relating to the environmental parameters, such as temperature and RH). The fundamental aim of this research is to develop an integrated deterioration prediction model of chloride concentration and penetration in concrete structures considering the impacts of variations in the internal and external factors. The model is based on simultaneous solutions of diffusivity of chloride ions, and it is validated using the data obtained from accelerated chloride penetration experiments. It is then used to investigate the chloride concentrations in existing concrete structures due to the impact of climate change scenarios based on the IPCC, 2014 and the UKCP'09 climate projections.

“…As a nondestructive testing method, AE is often used for damage detection and evaluation 2,3 . Compared to other nondestructive testing methods, AE is more suitable for recording temporal behavior of structures and can provide data for analysis of dynamic characteristics and real‐time state of cracks 4–6 . Traditional analysis involves AE parameters such as amplitude, frequency, signal strength, rise time, and hits of the detected AE signal.…”

Section: Introductionmentioning

confidence: 99%

“…2,3 Compared to other nondestructive testing methods, AE is more suitable for recording temporal behavior of structures and can provide data for analysis of dynamic characteristics and real-time state of cracks. [4][5][6] Traditional analysis involves AE parameters such as amplitude, frequency, signal strength, rise time, and hits of the detected AE signal. With the development of AE applications, the traditional analysis has been combined with methods from other fields to study damage mechanisms and failure modes in materials; examples include the b-value method, 7,8 cluster analysis, 9 and machine learning.…”

mentioning

confidence: 99%

Assessment of damage in hydraulic concrete by gray wolf optimization‐support vector machine model and hierarchical clustering analysis of acoustic emission

Chen

Jivkov

et al. 2021

Self Cite

Summary Acoustic emission (AE) is a useful method for recording fracture processes in concrete. In this work, AE data are recorded during three‐point bending tests to fracture of hydraulic concrete. First, AE data are used to analyze concrete's damage development using hits distribution, b‐value, Ib‐value, and average frequency versus RA value. Second, clustering analysis of AE signals is performed by hierarchical clustering. Third, a support vector machine model based on the gray wolf optimization algorithm is proposed to quantify the degree of damage. Via b‐value analysis it is shown that the fracture process of hydraulic concrete can be divided into three stages: microcracks nucleation; microcracks coalescence into macrocracks (macrocrack nucleation); and macrocrack propagation. Further, it is shown that rise time, ringdown counts, energy, duration, amplitude, and central frequency can be used to characterize the failure modes. Specifically, it is found that microcrack nucleation stage is dominated by tensile failures, macrocrack nucleation stage is characterized by rapid increase of shear failures, which become dominant over tensile failures, and macrocrack propagation stage is dominated by shear failures. Via hierarchical cluster analysis, it is found that the fracture process can be divided into three clusters, which corresponds to the three stages obtained via b‐value analysis. Finally, the proposed support vector machine model based on gray wolf optimization is found to predict the degree of damage in excellent agreement with experiment. This offers an effective practical method for damage assessment by combining AE with machine learning.