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

Abstract

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.

The effect of EGR and hydrogen addition to natural gas on performance and exhaust emissions in a diesel engine by AVL fire multi-domain simulation, GPR model, and multi-objective genetic algorithm

“…By dividing the available data set into two training data sets (X, y) and a set of test data (X * ; f * ) [46]:…”

Advances in Computational Intelligence Applications in the Mining Industry

“…To overcome the limitations associated with ANN in predicting blast-induced ground vibrations, studies have also applied other ML algorithms that are without these shortcomings. Some of the algorithms applied included SVM [104][105][106][107][108][109][110][111], relevance vector regression [112], particle swarm optimization [113,114] Bayesian network and random forest [108], Gaussian process regression [115], classification and regression trees, chi-square automatic interaction detection, random forest [1,116,117], hybrid artificial bee colony algorithm [118], fuzzy Delphi method and hybrid ANN-based systems [119], cuckoo search algorithm [120], extreme learning machine [121], extreme gradient boosting (XGBoost) [122], and the firefly algorithm [123][124][125][126].…”

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

“…To overcome the limitations associated with ANN in predicting blast-induced ground vibrations, studies have also applied other ML algorithms that are without these shortcomings. Some of the algorithms applied included SVM [104][105][106][107][108][109][110][111], relevance vector regression [112], particle swarm optimization [113,114] Bayesian network and random forest [108], Gaussian process regression [115], classification and regression trees, chi-square automatic interaction detection, random forest [1,116,117], hybrid artificial bee colony algorithm [118], fuzzy Delphi method and hybrid ANN-based systems [119], cuckoo search algorithm [120], extreme learning machine [121], extreme gradient boosting (XGBoost) [122], and the firefly algorithm [123][124][125][126].…”