Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine

Yang, Liu; Ye, Yicheng; Wang, Qihu; Liu, Xiaoyun; Wang, Weiqi

doi:10.3390/app9102064

Cited by 17 publications

(10 citation statements)

References 59 publications

(59 reference statements)

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“…Passive detection is based on the response of the sensor embedded in the structure or bonded on its surface without involving the actuation function. Often, piezoelectric sensors are used to directly measure structural acceleration, strain, and acoustic emission [33][34][35]. The active damage-detection technology uses an actuator to induce the stress wave signal, and a sensor to detect the travelling stress signal.…”

Section: Piezoelectric Transducers and Active Sensing Based On Piezoementioning

confidence: 99%

Piezoceramic-Based Damage Monitoring of Concrete Structure for Underwater Blasting

Zhong

Xiong

2020

Sensors

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This paper developed a piezoelectric-transducer-based damage detection of concrete materials after blasting. Two specimens (with or without an energy-relieving structure) were subjected to a 40 m deep-underwater blasting load in an underwater-explosion vessel, and their damage was detected by a multifunctional piezoelectric-signal-monitoring and -analysis system before and after the explosion. Statistical-data analysis of the piezoelectric signals revealed four zones: crushing, fracture, damage, and safe zones. The signal energy was analyzed and calculated by wavelet-packet analysis, and the blasting-damage index was obtained after the concrete specimen was subjected to the impact load of the underwater explosion. The damage of the two specimens gradually decreased from the blast hole to the bottom of the specimen. The damage index of the specimen with the energy-relieving structure differed for the fracture area and the damage area, and the damage protection of the energy-relieving structure was prominent at the bottom of the specimen. The piezoelectric-transducer-based damage monitoring of concrete materials is sensitive to underwater blasting, and with wavelet-packet-energy analysis, it can be used for postblasting damage detection and the evaluation of concrete materials.

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Section: Piezoelectric Transducers and Active Sensing Based On Piezoementioning

confidence: 99%

Piezoceramic-Based Damage Monitoring of Concrete Structure for Underwater Blasting

Zhong

Xiong

2020

Sensors

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“…Xue [20] combined the genetic algorithm (GA) and backpropagation (BP) neural network to analyze an 18 datasets database and found that the predictive performance of the new proposed GA-BP is much better than the original BP model. Liu et al [21] utilized a wavelet relevance vector machine in broken rock zone (BRZ) prediction, and the proposed model is showing a square correlation coefficient of 0.95.…”

Section: Introductionmentioning

confidence: 99%

Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

Shi

Zhou

et al. 2020

Applied Sciences

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A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (BRZT), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (GP) and particle swarm optimization algorithm (PSO)-GP method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (ED), drift span (DS), surrounding rock mass strength (RMS) and joint index (JI) were selected to train the intelligence model, while broken rock zone thickness (BRZT) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (VAF), determination coefficient (R2) and root mean squared error (RMSE) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (BRZT). In addition, the sensitive effect of collected variables on broken rock zone thickness (BRZT) can be listed as JI, ED, DS and RMS, and JI was found to be the most sensitive factor.

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“…The feasibility and reliability of the piezoelectric active sensing method, with respect to the monitoring of the shear failure of composite rocks, was therefore experimentally demonstrated in this study.Sensors 2020, 20, 1376 2 of 17 the damage, and issue timely warnings [6,7]. It is therefore necessary to conduct further research on the shear failure monitoring of composite rocks.At present, studies have been conducted with respect to the stability evaluation of rocks [8,9] and rock failure monitoring [10][11][12]. With respect to rock failure monitoring, there are four main methods:(1) the monitoring of micro-seismic signals to conduct a preliminary analysis of the rock failure characteristics [13,14]; (2) the monitoring of acoustic emission signals to investigate the characteristics and surface degradation of soft rock, in addition to the development of a roughness damage model based on the acoustic emission time characteristics [15]; (3) the use of acoustic waves for the evaluation of the rock mass characteristics in the damaged area of the rock slope excavation [16]; and (4) the use of fiber Bragg grating (FBG) sensors for the monitoring of the surrounding rock stability of tunnels [17].…”

mentioning

confidence: 99%

“…At present, studies have been conducted with respect to the stability evaluation of rocks [8,9] and rock failure monitoring [10][11][12]. With respect to rock failure monitoring, there are four main methods:…”

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confidence: 99%

Experimental Research on Shear Failure Monitoring of Composite Rocks Using Piezoelectric Active Sensing Approach

Liu

Wang

et al. 2020

Sensors

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Underground space engineering structures are generally subject to extensive damages and significant deformation. Given that composite rocks are prone to shear failure, which cannot be accurately monitored, the piezoelectric active sensing method and wavelet packet analysis method were employed to conduct a shear failure monitoring test on composite rocks in this study. For the experiment, specimens were prepared for the simulation of the composite rocks using cement. Two pairs of piezoelectric smart aggregates (SAs) were embedded in the composite specimens. When the specimens were tested using the direct shear apparatus, an active sensing-based monitoring test was conducted using the embedded SAs. Moreover, a wavelet packet analysis was conducted to compute the energy of the monitoring signal; thus allowing for the determination of the shear damage index of the composite specimens and the quantitative characterization of the shear failure process. The results indicated that upon the shear failure of the composite specimens, the amplitudes and peak values of the monitoring signals decreased significantly, and the shear failure and damage indices of the composite specimens increased abruptly and approached a value of 1. The feasibility and reliability of the piezoelectric active sensing method, with respect to the monitoring of the shear failure of composite rocks, was therefore experimentally demonstrated in this study.

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Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine

Cited by 17 publications

References 59 publications

Piezoceramic-Based Damage Monitoring of Concrete Structure for Underwater Blasting

Piezoceramic-Based Damage Monitoring of Concrete Structure for Underwater Blasting

Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

Experimental Research on Shear Failure Monitoring of Composite Rocks Using Piezoelectric Active Sensing Approach

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