Simulating the peak particle velocity in rock blasting projects using a neuro-fuzzy inference system

Jiang, Wenchao; Arslan, Chelang A.; Tehrani, Mehran Soltani; Khorami, Majid; Hasanipanah, Mahdi

doi:10.1007/s00366-018-0659-6

Cited by 39 publications

(9 citation statements)

References 45 publications

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“…e neural reasoning system [25] is a system composed of three components, which mainly include (1) rule base (2) database (3) reasoning system. In the fuzzy neural inference system, the input parameters consider different fuzzification and defuzzification methods and strategies and have various rules.…”

Section: Prediction Based On Fuzzy Neural Inference Systemmentioning

confidence: 99%

Big Data Analysis and Modeling of Higher Education Reform Based on Cloud Computing Technology

Tang

2022

Security and Communication Networks

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In view of the current educational environment, the development of higher education cannot be separated from the support of information technology. The era of big data has had various impacts on higher education and accelerated the development of higher education. Cloud computing technology has good application prospects for the data analysis and mathematical modeling content involved in the reform of higher education. This study analyzes and calculates the concerns of the four main contents involved in the educational reform of a higher education institution as the research object. In order to quantify the application effects of different cloud computing technologies, the paper introduces three typical cloud computing technologies to analyze the research data and establish related mathematical models. The results show that the prediction accuracy based on the immune cloning algorithm is the best. Based on the calculation results, it can be found that the corresponding data of the two main contents of teaching scene setting and teaching quality detection show an obvious polynomial function relationship.

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Section: Prediction Based On Fuzzy Neural Inference Systemmentioning

confidence: 99%

Big Data Analysis and Modeling of Higher Education Reform Based on Cloud Computing Technology

Tang

2022

Security and Communication Networks

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“…Mining activities and civil projects are carried out using one of the most important operations, namely rock blasting, as a wide and economical way to rock breakage and displacement of them 1 . In this regard, the rock mass is drilled (drilling operations), and then many blast-holes are charged using explosive materials (blasting operations).…”

Section: Introductionmentioning

confidence: 99%

“…Inevitably, blasting operations are caused several side environmental consequences/issues such as flyrock, back-break, dust pollution, air-overpressure, and ground vibration 2 – 7 . The blast-induced, air over-pressure, ground vibration, and flyrock are the most adverse phenomenon among them 1 , 8 , 9 . Therefore, the blasting sites and mine environment must be safe by monitoring and eliminating the adverse effects of the aforementioned consequences.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that the accurate amount of each phenomenon should be determined/predicted before conducting the operations. The pre-split and power-deck methods can be used to minimization adverse effects 1 .…”

Section: Introductionmentioning

confidence: 99%

“…The ground vibrations can be measured/recorded based on two different terms: peak particle velocity (PPV) and frequency 12 – 15 . According to various standards, the PPV is the most famous representative for estimating and evaluating blast-induced ground vibration in surface mines 1 , 16 , 17 . The most significant parameters on PPV are the number of blast-holes, hole depth, burden, spacing, powder factor, the charge per delay, and the distance between installed seismograph and blasting bench 18 – 21 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques

Hosseini

Pourmirzaee

Armaghani

et al. 2023

Sci Rep

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Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in the surface mines. In this regard, two ensemble systems, i.e., the ensemble of artificial neural networks and the ensemble of extreme gradient boosting (EXGBoosts) were developed for PPV prediction in one of the largest lead–zinc open-pit mines in the Middle East. For ensemble modeling, several ANN and XGBoost base models were separately designed with different architectures. Then, the validation indices such as coefficient determination (R2), root mean square error (RMSE), mean absolute error (MAE), the variance accounted for (VAF), and Accuracy were used to evaluate the performance of the base models. The five top base models with high accuracy were selected to construct an ensemble model for each of the methods, i.e., ANNs and XGBoosts. To combine the outputs of the top base models and achieve a single result stacked generalization technique, was employed. Findings showed ensemble models increase the accuracy of PPV predicting in comparison with the best individual models. The EXGBoosts was superior method for predicting of the PPV, which obtained values of R2, RMSE, MAE, VAF, and Accuracy corresponding to the EXGBoosts were (0.990, 0.391, 0.257, 99.013(%), 98.216), and (0.968, 0.295, 0.427, 96.674(%), 96.059), for training and testing datasets, respectively. However, the sensitivity analysis indicated that the spacing (r = 0.917) and number of blast-holes (r = 0.839) had the highest and lowest impact on the PPV intensity, respectively.

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Advanced Analytics for Rock Blasting and Explosives Engineering in Mining

Mariz

Soofastaei

2022

Advanced Analytics in Mining Engineering

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Simulating the peak particle velocity in rock blasting projects using a neuro-fuzzy inference system

Cited by 39 publications

References 45 publications

Big Data Analysis and Modeling of Higher Education Reform Based on Cloud Computing Technology

Big Data Analysis and Modeling of Higher Education Reform Based on Cloud Computing Technology

Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques

Advanced Analytics for Rock Blasting and Explosives Engineering in Mining

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