Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Li, Guichen; Kumar, Deepak; Samui, Pijush; Rad, Hima Nikafshan; Roy, Bappadittya; Hasanipanah, Mahdi

doi:10.3390/app10020434

Cited by 19 publications

(3 citation statements)

References 54 publications

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“…The rest of the world uses approaches formulated by USBM and others [12][13][14][15]. Another more modern approach involving ECM (Error Correction Model) focuses on the application of forecast models [16][17][18]. These models are based on artificial neural networks (ANNs) and appear to be more precise, easily adjustable, and more predictable, and they avoid strictly formalized mathematical relations.…”

Section: Introductionmentioning

confidence: 99%

Digital Processing of Seismic Data from Open-Pit Mining Blasts

Koteleva

Frenkel

2021

Applied Sciences

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This article describes an approach of mathematical processing of signals (seismograms) from five blasthole charges from experimental blasting, each 3 m deep, with equal explosive weight (1 kg), and equidistant (3 m) from one other. The seismic explosive waves were measured at a 13 to 25 m distance. This article provides spectral analysis, wavelet analysis, and fractal analysis results. It defines the dependence of dominant frequency and amplitude on the distance to the blast center. According to the experimental data, the dominant frequency is calculated as y = 1.0262x0.2622 and the amplitude dependency as y = 18.139x−2.276. Furthermore, the analysis shows that 80% of the entire signal is concentrated in half the area of frequency range, i.e., the low frequency zone is of the most interest. This research defines the dependence of distance on the energy value of signal wavelet analysis. It is demonstrated that, according to the experimental data, the 12th frequency range is closely correlated with the distance values. This article gives the definitions of entropy, correlation dimension, and predictability time. This experiment shows that entropy and correlation dimension decrease but predictability time increases when the distance to the blast center increases. This article also describes the method for determining optimal drilling and blasting parameters, and concludes with the possibility of applying the analytical results to predicting and enhancing drilling and blasting operations.

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Section: Introductionmentioning

confidence: 99%

Digital Processing of Seismic Data from Open-Pit Mining Blasts

Koteleva

Frenkel

2021

Applied Sciences

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“…Monjezi et al [21] used an artificial neural network (ANN) model which includes an input layer, two hidden layers, and an output layer to evaluate the ground vibration based on the Siahbisheh project, which demonstrates the effectiveness of using an ANN to predict the blast-induced ground vibration. Li et al [22] proposed two hybrid models using biogeography-based optimization (BBO), deterministic optimization algorithm (DIRECT), and artificial neural network, namely, BBO-ANN and DIRECT-ANN, and the generalization capability was found to be better than other prediction models. is study shows the use of an optimization algorithm to improve the prediction performance of the prediction model.…”

Section: Introductionmentioning

confidence: 99%

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Jiang¹,

Xu²,

Chen

et al. 2021

Advances in Civil Engineering

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The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.

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“…It then uses the smallest norm least square to arrive at the global solution. Within the recent years, the ELM approach has been extensively applied in several fields of sciences and engineering [27][28][29] including a few notable studies in blast-induced ground vibration prediction [30,31]. Prediction results from these studies have shown how well the ELM approach is able to generalise across the entire testing data.…”

Section: Introductionmentioning

confidence: 99%

A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

2020

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Blast-induced ground vibration is still an adverse impact of blasting in civil and mining engineering projects that need much consideration and attention. This study proposes the use of Self-Adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) for the prediction of ground vibration due to blasting using 210 blasting data points from an open pit mine in Ghana. To ascertain the predictive performance of the proposed SaDE-ELM approach, several artificial intelligence and empirical approaches were developed for comparative purposes. The performances of various developed models were assessed using model performance indicators of mean squared error (MSE), Nash-Sutcliffe Efficiency Index (NSEI) and correlation coefficient (R). Furthermore, the Bayesian Information Criterion (BIC) was applied to select the best performing approach. The obtained prediction results based on the performance indicators showed that the SaDE-ELM outperformed all the competing models as it had the lowest MSE value of 0.01942, respectively. The SaDE-ELM also achieved the highest R and NSEI values of 0.8711 and 0.7537, respectively. The other artificial intelligent approaches had MSE, R and NSEI in the ranges of (0.02166-0.03006), (0.8012-0.8537) and (0.6188-0.7254), respectively. The empirical approaches performed poorly relative to the artificial intelligence approaches by having had MSE, R and NSEI in the ranges of (0.03419-0.06587), (0.7466-0.7833) and (0.1649-0.5665), respectively. The prediction superiority of SaDE-ELM was confirmed when it is achieved the lowest BIC value of − 293.40. Therefore, the proposed SaDE-ELM has demonstrated great potential to be used for on-site prediction, control and management of blast-induced ground vibration to prevent unwanted effects on the environment.

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Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Cited by 19 publications

References 54 publications

Digital Processing of Seismic Data from Open-Pit Mining Blasts

Digital Processing of Seismic Data from Open-Pit Mining Blasts

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

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