Assessment of the ground vibration during blasting in mining projects using different computational approaches

Hosseini, Shahab; Khatti, Jitendra; Taiwo, Blessing Olamide; Fissha, Yewuhalashet; Grover, Kamaldeep Singh; Ikeda, Hajime; Pushkarna, Mukesh; Berhanu, Milkias; Ali, Mujahid

doi:10.1038/s41598-023-46064-5

Cited by 37 publications

(7 citation statements)

References 91 publications

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“…In this paper, the performance of the estimation is presented in the terms of the coefficient of determination ( R 2 ), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the Mean Absolute Error (MAE), Variance Accounted For (VAF), Index of Scatter (IOS), agreement index (IOA), Mean absolute percentage error (MAPE), Weighted Mean absolute percentage error (WMAPE), Performance Index (PI), and a20index are computed. These metrics are frequently employed to assess regression issues [ 63 , 64 ]. RMSE, MSE, MAE, MAPE, WMAPE, and IOS indicated the error prediction.…”

Section: Model Development Results and Discussionmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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The uniaxial compressive strength (UCS) and elasticity modulus (E) of intact rock are two fundamental requirements in engineering applications. These parameters can be measured either directly from the uniaxial compressive strength test or indirectly by using soft computing predictive models. In the present research, the UCS and E of intact carbonate rocks have been predicted by introducing two stacking ensemble learning models from non-destructive simple laboratory test results. For this purpose, dry unit weight, porosity, P‐wave velocity, Brinell surface harnesses, UCS, and static E were measured for 70 carbonate rock samples. Then, two stacking ensemble learning models were developed for estimating the UCS and E of the rocks. The applied stacking ensemble learning method integrates the advantages of two base models in the first level, where base models are multi-layer perceptron (MLP) and random forest (RF) for predicting UCS, and support vector regressor (SVR) and extreme gradient boosting (XGBoost) for predicting E. Grid search integrating k-fold cross validation is applied to tune the parameters of both base models and meta-learner. The results demonstrate the generalization ability of the stacking ensemble method in the comparison of base models in the terms of common performance measures. The values of coefficient of determination (R2) obtained from the stacking ensemble are 0.909 and 0.831 for predicting UCS and E, respectively. Similarly, the stacking ensemble yielded Root Mean Squared Error (RMSE) values of 1.967 and 0.621 for the prediction of UCS and E, respectively. Accordingly, the proposed models have superiority in the comparison of SVR and MLP as single models and RF and XGBoost as two representative ensemble models. Furthermore, sensitivity analysis is carried out to investigate the impact of input parameters.

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Section: Model Development Results and Discussionmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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“…To comprehensively assess the efficiency of the proposed models in the present research, various metrics, including the mean average error (MAE), root mean square error (RMSE), coefficient of determination (R 2 ), and a 20 index [60,101,102], are employed. These indicators serve to depict the correlations between the measured CSGePoCo values and the estimated CSGePoCo value [105][106][107][108][109]. The mathematical formulas for calculation of these indices are as follows:…”

Section: Performance Evaluation Of the Modelsmentioning

confidence: 99%

Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Zhang,

Wang,

et al. 2024

Buildings

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Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges with its intricate cementitious matrix and a vague mix design, where the components and their relative amounts can influence the compressive strength. In response to these challenges, the application of accurate and applicable soft computing techniques becomes imperative for predicting the strength of such a composite cementitious matrix. This research aimed to predict the compressive strength of GePoCo using waste resources through a novel ensemble ML algorithm. The dataset comprised 156 statistical samples, and 15 variables were selected for prediction. The model employed a combination of the RF, GWO algorithm, and XGBoost. A stacking strategy was implemented by developing multiple RF models with different hyperparameters, combining their outcome predictions into a new dataset, and subsequently developing the XGBoost model, termed the RF–XGBoost model. To enhance accuracy and reduce errors, the GWO algorithm optimized the hyperparameters of the RF–XGBoost model, resulting in the RF–GWO–XGBoost model. This proposed model was compared with stand-alone RF and XGBoost models, and a hybrid GWO–XGBoost system. The results demonstrated significant performance improvement using the proposed strategies, particularly with the assistance of the GWO algorithm. The RF–GWO–XGBoost model exhibited better performance and effectiveness, with an RMSE of 1.712 and 3.485, and R2 of 0.983 and 0.981. In contrast, stand-alone models (RF and XGBoost) and the hybrid model of GWO–XGBoost demonstrated lower performance.

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“…R 2 ), Nash-Sutcliffe efficiency (NS), Mean Relative Error (MRE), Willmott's Index of agreement (WI), Performance Index (PI), root mean square error (RMSE), Mean Absolute Percentage Error (MAPE), and Variance Account For (VAF), BIAS, SI, and ρ for evaluating the capacity of constructed models were determined. These performance evaluation indicators are calculated as follows [27,[144][145][146][147][148][149][150][151][152][153]:…”

Section: Model Validation and Evaluationmentioning

confidence: 99%

Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete

Shi,

Chen,

Wang

et al. 2024

Gels

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As an environmentally responsible alternative to conventional concrete, geopolymer concrete recycles previously used resources to prepare the cementitious component of the product. The challenging issue with employing geopolymer concrete in the building business is the absence of a standard mix design. According to the chemical composition of its components, this work proposes a thorough system or framework for estimating the compressive strength of fly ash-based geopolymer concrete (FAGC). It could be possible to construct a system for predicting the compressive strength of FAGC using soft computing methods, thereby avoiding the requirement for time-consuming and expensive experimental tests. A complete database of 162 compressive strength datasets was gathered from the research papers that were published between the years 2000 and 2020 and prepared to develop proposed models. To address the relationships between inputs and output variables, long short-term memory networks were deployed. Notably, the proposed model was examined using several soft computing methods. The modeling process incorporated 17 variables that affect the CSFAG, such as percentage of SiO2 (SiO2), percentage of Na2O (Na2O), percentage of CaO (CaO), percentage of Al2O3 (Al2O3), percentage of Fe2O3 (Fe2O3), fly ash (FA), coarse aggregate (CAgg), fine aggregate (FAgg), Sodium Hydroxide solution (SH), Sodium Silicate solution (SS), extra water (EW), superplasticizer (SP), SH concentration, percentage of SiO2 in SS, percentage of Na2O in SS, curing time, curing temperature that the proposed model was examined to several soft computing methods such as multi-layer perception neural network (MLPNN), Bayesian regularized neural network (BRNN), generalized feed-forward neural networks (GFNN), support vector regression (SVR), decision tree (DT), random forest (RF), and LSTM. Three main innovations of this study are using the LSTM model for predicting FAGC, optimizing the LSTM model by a new evolutionary algorithm called the marine predators algorithm (MPA), and considering the six new inputs in the modeling process, such as aggregate to total mass ratio, fine aggregate to total aggregate mass ratio, FASiO2:Al2O3 molar ratio, FA SiO2:Fe2O3 molar ratio, AA Na2O:SiO2 molar ratio, and the sum of SiO2, Al2O3, and Fe2O3 percent in FA. The performance capacity of LSTM-MPA was evaluated with other artificial intelligence models. The results indicate that the R2 and RMSE values for the proposed LSTM-MPA model were as follows: MLPNN (R2 = 0.896, RMSE = 3.745), BRNN (R2 = 0.931, RMSE = 2.785), GFFNN (R2 = 0.926, RMSE = 2.926), SVR-L (R2 = 0.921, RMSE = 3.017), SVR-P (R2 = 0.920, RMSE = 3.291), SVR-S (R2 = 0.934, RMSE = 2.823), SVR-RBF (R2 = 0.916, RMSE = 3.114), DT (R2 = 0.934, RMSE = 2.711), RF (R2 = 0.938, RMSE = 2.892), LSTM (R2 = 0.9725, RMSE = 1.7816), LSTM-MPA (R2 = 0.9940, RMSE = 0.8332), and LSTM-PSO (R2 = 0.9804, RMSE = 1.5221). Therefore, the proposed LSTM-MPA model can be employed as a reliable and accurate model for predicting CSFAG. Noteworthy, the results demonstrated the significance and influence of fly ash and sodium silicate solution chemical compositions on the compressive strength of FAGC. These variables could adequately present variations in the best mix designs discovered in earlier investigations. The suggested approach may also save time and money by accurately estimating the compressive strength of FAGC with low calcium content.

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Assessment of the ground vibration during blasting in mining projects using different computational approaches

Cited by 37 publications

References 91 publications

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete

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