Study on Multiparameter Precursory Information Identification of the Fracture of Yellow Sandstone

Abstract

In order to explore the disaster caused by uncontrollable instability of coal and rock mass, a multiparameter fusion system is constructed to predict and predict disasters more accurately by identifying the mechanical and acoustic precursors of coal and rock fracture. In order to explore the precursor information of yellow sandstone rupture, the damage evolution process of yellow sandstone is analyzed from the four aspects of rock mechanics, acoustic emission time domain, frequency domain, and characteristic parameters, and the body strain, dissipated energy and acoustic emission counting, acoustic emission energy, average frequency, peak frequency, b value, and entropy value precursor information identification points are obtained, and 8 parameters are analyzed by time series fusion. The specific conclusions are as follows: body strain in the violent stage of damage evolution, the slope is zero, the zero end point is the precursor information identification point, the dissipative energy curve overall shows the “s” type, the early growth rate is faster—the medium-term stability—the later period is slowed down, and the upper slope boundary point of the “s” type curve is used as the precursor information identification point. In the violent stage of damage evolution, the layered features of the acoustic emission count are obvious, the specific gravity shift is more obvious, and the high count appears as the precursor information identification point; the acoustic emission energy accumulates the high-energy signal and is accompanied by the steady and rapid growth of energy as the precursor information identification point. The effects of shearing main cracks, shear microcracks, tensile cracks, and composite cracks on the acoustic emission count and energy in the damage evolution process are analyzed. The increase of medium- and high-frequency signals and the reduction of high-frequency signals predict the rupture. The average frequency signal change law is continuous high frequency-blank-continuous high frequency, with the blank period end point as the damage precursor identification point; the b value damage evolution stage shows a continuously steady increase to a rapid increase, with the continuous stable growth starting point as the crack identification point. In the process of damage evolution, the sample entropy presents an orderly, chaotic, disordered, and orderly process. The end of chaos and the beginning of disorder are used as the prejudging demarcation points. Based on the time sequence, an eight-parameter comprehensive early warning system is constructed. The indicators are classified into five levels for early warning in the stage of severe damage evolution. The identification of multiparameter precursory information of yellow sandstone provides a new research idea and analysis angle and method for the failure of other coal and rock masses.

Calibration Method for Monitoring Hygro-Mechanical Reactions of Pine and Oak Wood by Acoustic Emission Nondestructive Testing

“…When considering the oak specimens, the majority of the signals fall in the amplitude group including signals between 50 and 60 dB, but again the most populated energy range is the class with events with less than 500 A.U. From the literature [ 23 , 27 ] it is known that real AE signals have low amplitude emission with short duration; in addition and as introduced in Section 1 , it is expected that excess energy is emitted during a DL mechanism, mostly as heat/radiation of very low amplitude [ 1 ] and wide bandwidth frequency [ 27 ].…”

“…This confirms that which was stated in [ 2 ], i.e., the detection of AE single events with higher counts in brittle versus ductile materials. In the time domain, a BL rupture crack shows a combination of phenomena as follows: an increase in the number of events with high-counting signals, while the number of events for low-counting signal is decreasing; and an accelerated growth of the crack [ 1 ]. From the analysis of frequency spectra of events collected during the ductile state ( Figure 5 b,d), differences emerged between pine and oak, since, in the former, the frequency with the highest amplitude falls at about 23 KHz, whereas, for the latter, it falls at 126 KHz.…”

“…From the analysis of frequency spectra of events collected during the ductile state ( Figure 5 b,d), differences emerged between pine and oak, since, in the former, the frequency with the highest amplitude falls at about 23 KHz, whereas, for the latter, it falls at 126 KHz. Reference [ 1 ] reported that BL fracture, considered to be a severe stage in damage evolution, can be predicted by more compact high frequency peaks and by a decrease in high frequency signals progressing from intermediate damage stages (DL) towards a severe stage (BL), i.e., cell wall cracks [ 6 ]. These statements are clearly confirmed by the experimental results shown in Figure 5 .…”

“…The comparison between the distributions (irrespective of the full-scale) reported in Figure 4 and Figure 6 (displaying the distribution of the selected AE parameters in the ductile and brittle state intervals), clearly shows that a DL behavior exhibits a peak in the AE amplitude distribution at lower values than those detected during BL fracture mechanisms. Similarly, the difference in count distributions indicates a pattern for recognizing DL fracture in wood [ 1 ]. In support of the above statements, literature results have assumed that AE behavior with high energy signals in term of energy accumulation during DL fracture indicates slow crack growths that are continuous and steady (e.g., cell separation mechanism) [ 1 , 28 ].…”

“…Mechanical damage in materials is procured under the action of stress and deformation. During laboratory tests, a continuously supplied tensile load to a material sample provides energy to the material, and the specimen continuously stores this energy until the ultimate energy storage capacity is reached and energy release occurs [ 1 ]. It is well known that the conditions under which a crack can grow when applied a stress ( σ 2 in Equation (1)) can be explained by Equation (1) correlating the elastic energy of the material ( E El ) to the square of the crack length ( l 2 ) proportional to the inverse of the elasticity modulus ( Y ) as follows: …”