Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network

2021 ◽  
Author(s):  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Quang-Hieu Tran ◽  
Dinh-An Nguyen ◽  
Le Thi Thu Hoa ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Optimization Algorithms ◽  
Ground Vibration ◽  
Open Pit ◽  
Open Pit Mines

scholarly journals Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

2020 ◽  
Vol 10 (5) ◽  
pp. 1657 ◽  
Author(s):  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Training Data ◽  
Open Pit ◽  
Percentage Error ◽  
Open Pit Mines ◽  
Determination Coefficient ◽  
Packet Data ◽  
Hidden Layer

This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively.

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 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.

scholarly journals Rock Strain Prediction Using Deep Neural Network and Hybrid Models of ANFIS and Meta-Heuristic Optimization Algorithms

2021 ◽  
Vol 6 (9) ◽  
pp. 129
Author(s):  
T. Pradeep ◽  
Abidhan Bardhan ◽  
Avijit Burman ◽  
Pijush Samui
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Optimization Algorithms ◽  
Grey Wolf Optimizer ◽  
Statistical Parameters ◽  
Unconfined Compression Test ◽  
Rock Stratum ◽  
Inference System ◽  
Prediction Approach

The majority of natural ground vibrations are caused by the release of strain energy accumulated in the rock strata. The strain reacts to the formation of crack patterns and rock stratum failure. Rock strain prediction is one of the significant works for the assessment of the failure of rock material. The purpose of this paper is to investigate the development of a new strain prediction approach in rock samples utilizing deep neural network (DNN) and hybrid ANFIS (adaptive neuro-fuzzy inference system) models. Four optimization algorithms, namely particle swarm optimization (PSO), Fireflies algorithm (FF), genetic algorithm (GA), and grey wolf optimizer (GWO), were used to optimize the learning parameters of ANFIS and ANFIS-PSO, ANFIS-FF, ANFIS-GA, and ANFIS-GWO were constructed. For this purpose, the necessary datasets were obtained from an experimental setup of an unconfined compression test of rocks in lateral and longitudinal directions. Various statistical parameters were used to investigate the accuracy of the proposed prediction models. In addition, rank analysis was performed to select the most robust model for accurate rock sample prediction. Based on the experimental results, the constructed DNN is very potential to be a new alternative to assist engineers to estimate the rock strain in the design phase of many engineering projects.

scholarly journals Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Karrar Raoof Kareem Kamoona ◽  
Cenk Budayan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Deep Neural Network ◽  
Optimization Algorithms ◽  
Support Vector ◽  
Project Cost ◽  
Final Project ◽  
Input Optimization ◽  
Intelligent Models ◽  
Intelligent Model

In construction project management, there are several factors influencing the final project cost. Among various approaches, estimate at completion (EAC) is an essential approach utilized for final project estimation. The main merit of EAC is including the probability of the project performance and risk. In addition, EAC is extremely helpful for project managers to define and determine the critical throughout the project progress and determine the appropriate solutions to these problems. In this research, a relatively new intelligent model called deep neural network (DNN) is proposed to calculate the EAC. The proposed DNN model is authenticated against one of the predominated intelligent models conducted on the EAC prediction, namely, support vector regression model (SVR). In order to demonstrate the capability of the model in the engineering applications, historical project information obtained from fifteen projects in Iraq region is inspected in this research. The second phase of this research is about the integration of two input algorithms hybridized with the proposed and the comparable predictive intelligent models. These input optimization algorithms are genetic algorithm (GA) and brute force algorithm (BF). The aim of integrating these input optimization algorithms is to approximate the input attributes and investigate the highly influenced factors on the calculation of EAC. Overall, the enthusiasm of this study is to provide a robust intelligent model that estimates the project cost accurately over the traditional methods. Also, the second aim is to introduce a reliable methodology that can provide efficient and effective project cost control. The proposed GA-DNN is demonstrated as a reliable and robust intelligence model for EAC calculation.

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
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