scholarly journals A novel soft computing model for predicting blast - induced ground vibration in open - pit mines using gene expression programming

2020 ◽  
Vol 61 (5) ◽  
pp. 107-116
Author(s):  
Hoang Nguyen . ◽  
Nam Xuan Bui . ◽  
Hieu Quang Tran . ◽  
Giang Huong Thi Le ◽  
Keyword(s):  
Gene Expression ◽  
Empirical Equation ◽  
Gene Expression Programming ◽  
Ground Vibration ◽  
Open Pit ◽  
Open Pit Mines ◽  
Computing Model ◽  
Non Linear Equation ◽  
Influential Parameters ◽  
Better Than

The efforts of this study are to develop and propose a state - of - the - art model for predicting blast - induced ground vibration in open - pit mines with high accuracy anf ability based on the gene expression programming (GEP) technique. 25 blasts were conducted in the Tan Dong Hiep quarry mines with a total of 83 blasting events that were collected for this study. The GEP method was then applied to develop a non - linear equation for predicting blast - induced ground vibration based on a variety of influential parameters. A traditional empirical equation, namely Sadovski, was also applied to compare with the proposed GEP model. The results indicated that the GEP model can predict blast - induced ground vibration in open - pit mines better than the Sadovski model with an RMSE of 0.986 and R2 of 0.867. Meanwhile, the traditional empirical model (Sadovski) only provided an accuracy with an RMSE of 1.850 và R2 of 0.767.

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

2019 ◽  
Vol 29 (2) ◽  
pp. 711-721 ◽  
Author(s):  
Xiliang Zhang ◽  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Quang-Hieu Tran ◽  
Dinh-An Nguyen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Ground Vibration ◽  
Open Pit ◽  
Swarm Optimization ◽  
Open Pit Mines ◽  
Computing Model

scholarly journals Exploring the Relation between Seismic Coefficient and Rock Properties Through Field Measurements and Empirical Model for Evaluating the Effect of Blast-Induced Ground Vibration in Open- Pit Mines: A Case Study at the Thuong Tan III Quarry (Vietnam)

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Quang Hieu TRAN
Keyword(s):  
Empirical Model ◽  
Empirical Equation ◽  
Ground Vibration ◽  
Field Measurements ◽  
Open Pit ◽  
Rock Properties ◽  
Open Pit Mines ◽  
Surrounding Environment ◽  
The Stability

Blasting is one of the most effective methods for fragmenting rock in quarries. Nevertheless, itsadverse effects are significant, especially blast-induced ground vibration. Field measurement andempirical equations are simple methods to determine and estimate the intensity of blast-induced groundvibration. However, we cannot evaluate the effects of blast-induced ground vibration on the surroundingenvironment based on these outcomes. Therefore, this study explores the relation between seismiccoefficient and rock properties through field measurements and an empirical model for evaluating theeffect of blast-induced ground vibration in open-pit mines. Accordingly, the seismic coefficient (K) isconsidered the main objective in this study. Firstly, it was determined based on the rock properties.Subsequently, an empirical model for estimating blast-induced ground vibration was developed based onfield measurements. This empirical equation was then expanded to determine K to check whether itmatches the determined K by the rock properties. Finally, it was used as the threshold to determine themaximum explosive charged per delay to ensure the safety of the surrounding environment from blastinducedground vibration. For this aim, the Thuong Tan III quarry (in Binh Duong province, Vietnam)was selected as a case study. Fifth-teen blasting events with a total of 75 blast-induced ground vibrationvalues were recorded and collected. An empirical equation for estimating blast-induced ground vibrationwas then developed based on the collected dataset, and K was determined in the range of 539 to 713 forthe Thuong Tan III quarry. Based on the measured blast-induced ground vibrations, developed empiricalmodel, and K values, the Phase 2 software was applied to simulate the effects of blast-induced groundvibration on the stability of slopes as one of the impacts on the surrounding environment. From thesimulation results, we can determine the maximum explosive charged per delay for each type of rock toensure the stability of the slope.

scholarly journals Application of the k - nearest neighbors algorithm for predicting blast - induced ground vibration in open - pit coal mines: a case study

2020 ◽  
Vol 61 (6) ◽  
pp. 22-29
Author(s):  
Hoang Nguyen . ◽  
Keyword(s):  
Artificial Intelligence ◽  
Coal Mine ◽  
Empirical Model ◽  
Ground Vibration ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
Open Pit ◽  
K Nearest Neighbors ◽  
Open Pit Mines

Blasting is considered as one of the most effective methods for rock fragmentation in open - pit mines. However, its side effects are significant, especially blast - induced ground vibration. Therefore, this study aims to develop and apply artificial intelligence in predicting blast - induced ground vibration in open - pit mines. Indeed, the k - nearest neighbors (KNN) algorithm was taken into account and developed for predicting blast - induced ground vibration at the Deo Nai open - pit coal mine (Vietnam) as a case study. An empirical model (i.e., USBM) was also developed to compare with the developed KNN model aiming to highlight the advantage of the KNN model. Accordingly, 194 blasting events were collected and analyzed for this aim. This database was then divided into two parts, 80% for training and 20% for testing. The MinMax scale and 10 - fold cross - validation techniques were applied to improve the accuracy, as well as avoid overfitting of the KNN model. Root - mean - squared error (RMSE) and determination coefficient (R2) were used as the performance metrics for models’ evaluation and comparison purposes. The results indicated that the KNN model yielded better superior performance than those of the USBM empirical model with an RMSE of 1.157 and R2 of 0.967. In contrast, the USBM model only provided a weak performance with an RMSE of 4.205 and R2 of 0.416. With the obtained results, the KNN can be introduced as a potential artificial intelligence model for predicting and controlling blast - induced ground vibration in practical engineering, especially at the Deo Nai open - pit coal mine.

Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

2019 ◽  
Vol 29 (2) ◽  
pp. 771-790 ◽  
Author(s):  
Xuan-Nam Bui ◽  
Yosoon Choi ◽  
Victor Atrushkevich ◽  
Hoang Nguyen ◽  
Quang-Hieu Tran ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Ground Vibration ◽  
Open Pit ◽  
Open Pit Mines ◽  
Vibration Intensity ◽  
Intelligence System ◽  
Aerial Vehicle

scholarly journals Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1106 ◽  
Author(s):  
Mohsin Ali Ali Khan ◽  
Adeel Zafar ◽  
Arslan Akbar ◽  
Muhammad Faisal Javed ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Empirical Equation ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Regression Equations ◽  
Soft Computing Techniques ◽  
Water Ratio

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength fc′ of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 fc′ experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F/AG), the percentage of total aggregate by volume ( %AG), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (AL/FA), the sodium oxide (Na2O) to water ratio (N/W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (Ns/No). A GEP empirical equation is proposed to estimate the fc′ of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

scholarly journals Experimental investigation and predictive modeling of compressive strength of pozzolanic geopolymer concrete using gene expression programming

2020 ◽  
Author(s):  
Amir Ali Shahmansouri ◽  
Habib Akbarzadeh Bengar ◽  
Saeed Ghanbari
Keyword(s):  
Gene Expression ◽  
Experimental Investigation ◽  
Compressive Strength ◽  
Gene Expression Programming ◽  
Solution Concentration ◽  
Partial Replacement ◽  
Geopolymer Concrete ◽  
Naoh Solution ◽  
High Prediction ◽  
Influential Parameters

With regard to the adverse environmental impacts of cement production, the use of geopolymer concrete (GPC) can be considered as a more environmentally friendly approach for concreting. This study deals with an experimental investigation on the effects of partial replacement of the GGBS (replaced with 5, 10, 15, 20, 25, and 30%) used in GPC with natural zeolite (NZ) and silica fume (SF) simultaneously with different concentration (4, 6 and 8 M) of sodium hydroxide (NaOH) together with sodium silicate (water glass) solution on the compressive strength. Results indicate that increasing concentration of NaOH yields decreases the compressive strength of the concrete. In contrast, adding NZ and SF into concrete results in increasing the compressive strength. In addition, gene expression programming (GEP) was employed to develop mathematical models for predicting the compressive strength of GPC based on GGBS. Using the experimental results, an extensive and reliable database of compressive strength of GGBS-based GPC was obtained. The database comprises the compressive strength results of 351 specimens produced from 117 different mixtures. The five most influential parameters i.e., age of specimens, NaOH solution concentration, NZ, SF and GGBS content of GPC, were considered as the input parameters for modeling. The results reflected that the proposed models are accurate and possess a high prediction capability. The findings of this study can enhance the re-use of GGBS for the development of GPC leading to environmental protection and monetary benefits.

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