Rock strength assessment based on regression tree technique

Liang, Minxian; Mohamad, Edy Tonnizam; Faradonbeh, Roohollah Shirani; Armaghani, Danial Jahed; Ghoraba, Saber

doi:10.1007/s00366-015-0429-7

Cited by 67 publications

(22 citation statements)

References 35 publications

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“…This approach has successfully been utilized in numerous areas of rock mechanics and geotechnical engineering [35][36][37]. Tiryaki [35] and Liang et al [34] used this method for predicting strength of the rock and observed that CART is a powerful tool for solving rock strength problem. Rock cuttability values were simulated using CART approach in an investigation carried out by Tiryaki [36].…”

Section: Literature Reviewmentioning

confidence: 97%

“…Classification and regression tree (CART) analysis technique is considered as an innovative, powerful and accurate approach for approximating science and engineering problems [34]. CART technique can be defined according to a decision tree where several parameters are considered as inputs of the system to determine the influence of them on output(s) of the system.…”

Section: Literature Reviewmentioning

confidence: 99%

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Classification and regression tree technique in estimating peak particle velocity caused by blasting

Khandelwal

Armaghani

Faradonbeh

et al. 2016

Engineering with Computers

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Section: Literature Reviewmentioning

confidence: 97%

Section: Literature Reviewmentioning

confidence: 99%

Classification and regression tree technique in estimating peak particle velocity caused by blasting

Khandelwal

Armaghani

Faradonbeh

et al. 2016

Engineering with Computers

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“…Many scholars have attempted to develop the MLR in various fields in rock and geotechnical engineering [45][46][47][48]. As an example, Enayatollahi et al [49] developed MLR to predict rock fragmentation at Gol-e-Gohar mine, Iran.…”

Section: Ppv Prediction Using Mlrmentioning

confidence: 99%

Prediction of blast-produced ground vibration using particle swarm optimization

Hasanipanah

Naderi

Kashir

et al. 2016

Engineering with Computers

124

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work, a database including 80 data sets was collected, and the values of the maximum charge weight used per delay (W), distance between blast-point and monitoring station (D) and peak particle velocity (PPV) were measured. To develop the PSO models, PPV was used as output parameter, while W and D were used as input parameters. To check the performance of the proposed PSO models, multiple linear regression (MLR) model and United States Bureau of Mines (USBM) equation were also developed. Accuracy of models established was evaluated using statistical criteria, i.e., coefficient of correlation (R 2 ) and root mean square error (RMSE), variance absolute relative error (VARE) and Nash & Sutcliffe (NS). Finally, it was found that the PSO power form provided more accurate predictions in comparison with PSO linear form, MLR and USBM models.

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“…Although this approach uses simple techniques, it can be performed even sometimes better than complicated approaches such as ANN [88]. The DT has a graph the same as a tree and is composed of roots, branches, leaves, and nodes [65,89]. In this approach, a variable is assigned as root or root node, and each node is then divided into sub-node according to the question about the interval of that variable which is either "Yes" or "No" which in turn indicates an input parameter that includes a prediction of output parameter in itself.…”

Section: Classification and Regression Tree (Cart)mentioning

confidence: 99%

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Moghaddam

Monjezi

et al. 2020

Applied Sciences

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Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.

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Rock strength assessment based on regression tree technique

Cited by 67 publications

References 35 publications

Classification and regression tree technique in estimating peak particle velocity caused by blasting

Classification and regression tree technique in estimating peak particle velocity caused by blasting

Prediction of blast-produced ground vibration using particle swarm optimization

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

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