Support vector machine (SVM) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion

Onyelowe, Kennedy C.; Mahesh, C.; Srikanth, Bandela; Nwa-David, Chidobere; Obimba-Wogu, Jesuborn; Shakeri, Jamshid

doi:10.1016/j.clet.2021.100290

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…The performance of the models obtained in this study has been compared using the most widely used evaluation criteria in engineering including R 2 , RMSE and MSE. Ideally, RMSE and MSE values should be close to zero while R 2 should be close to 1 (Onyelowe et al, 2021.…”

Section: Model Analysismentioning

confidence: 99%

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

Sorabi

Ataei

Jazi

et al. 2023

Preprint

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One of the adverse consequences of the blasting in the mineral extraction process in mines is back-break (BB) so that development of many fractures and cracks at large distances behind the last row of blast pits reduces the safety of the benches and increases operating costs. Since various parameters affect the BB, various techniques have been developed to predict and optimize its values. In this study, 48 blasts were investigated in Gol Gohar Mine No. 1 in the tailings section of the mine to predict BB based on the Whale Optimization Algorithm (WOA), Multiverse Optimizer (MVO), Sine Cosine Algorithm (SCA), and Ant Lion Optimizer (ALO). The parameters of bench height, hole length, burden, spacing, specific charge, the number of blasting rows, hole diameter, stemming, uniaxial compressive strength, joint spacing, and geological strength index (GSI) were evaluated as inputs to the models to predict back-breaks in the blasts. The comparison of the results of four BB prediction models suggested that the MVO-based model with a coefficient of determination (R2) of 0.9802, root-mean-square error (RMSE) of 0.2161, and mean squared error (MSE) of 0.1127 had the highest accuracy and the lowest error. So, it was introduced as the most appropriate model for predicting BB.

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Section: Model Analysismentioning

confidence: 99%

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

Sorabi

Ataei

Jazi

et al. 2023

Preprint

Self Cite

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“…(3) Artificial intelligence model: Now, with the development of artificial intelligence and other prediction methods, a variety of new models for wind speed and wind power prediction have been proposed. These include Support Vector Machines (SVM) [17], Fuzzy Logic methods [18], Artificial Neural Networks (ANN) [19], and hybrid prediction methods. Monhandes et al, used SVM for wind speed prediction and compared it with multi-layer Perceptron neural networks (MLP) [20].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Canyon Wind Speed Prediction Based on CNN—GRU Transfer Learning

Zheng

et al. 2022

Atmosphere

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Due to the particularity of the site selection of hydropower stations, the canyon wind with large fluctuations often occurs during the construction of the hydropower station, which will seriously affect the safety of construction personnel. Especially in the early stage of the construction of the hydropower station, the historical data and information on the canyon wind are scarce. Short-term forecasting of canyon wind speed has become extremely important. The main innovation of this paper is to propose a time series prediction method based on transfer learning. This method can achieve short-term prediction when there are few wind speed sample data, and the model is relatively simple while ensuring the accuracy of prediction. Considering the temporal and nonlinear characteristics of canyon wind speed data, a hybrid transfer learning model based on a convolutional neural network (CNN) and gated recurrent neural network (GRU) is proposed to predict short-term canyon wind speed with fewer observation data. In this method, the time sliding window is used to extract time series from historical wind speed data and temperature data of adjacent cities as the input of the neural network. Next, CNN is used to extract the feature vector from the input, and the feature vector can form time series. Then, the GRU network is used for short-term wind speed prediction by the time series. Experimental results show that the proposed method improves MAE and RMSE by nearly 20%, which will provide new ideas for the application of wind speed forecasting in canyons under complex terrain. The research contents of this paper contribute to the actual construction of hydropower stations.

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Utilizing heuristic strategies for predicting the backbreak occurrences in open-pit mines, Gol Gohar Mine, Iran

Sorabi,

Ataei,

Jazi

et al. 2024

Soft Comput

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Support vector machine (SVM) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion

Cited by 9 publications

References 15 publications

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

Short-Term Canyon Wind Speed Prediction Based on CNN—GRU Transfer Learning

Utilizing heuristic strategies for predicting the backbreak occurrences in open-pit mines, Gol Gohar Mine, Iran

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