Influence of geological conditions on the powder factor for tunnel blasting

Jong, Yong-Hun; Lee, Chung-In

doi:10.1016/j.ijrmms.2004.03.095

Cited by 78 publications

(8 citation statements)

References 8 publications

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“…In the current paper two methods known as the Cosine Amplitude (Jong and Lee, 2004) and PaD (Gevrey et al, 2003) were used that both of them showed similar results but with different values (Table 5).…”

Section: Resultsmentioning

confidence: 89%

Prediction of Site Response Spectrum Under Earthquake Vibration Using an Optimized Developed Artificial Neural Network Model

Esmaeilabadi¹,

Shahri²

2016

Adv. Sci. Technol. Res. J.

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Site response spectrum is one of the key factors to determine the maximum acceleration and displacement, as well as structure behavior analysis during earthquake vibrations. The main objective of this paper is to develop an optimized model based on artificial neural network (ANN) using five different training algorithms to predict nonlinear site response spectrum subjected to Silakhor earthquake vibrations is. The model output was tested for a specified area in west of Iran. The performance and quality of optimized model under all training algorithms have been examined by various statistical, analytical and graph analyses criteria as well as a comparison with numerical methods. The observed adaptabilities in results indicate a feasible and satisfactory engineering alternative method for predicting the analysis of nonlinear site response.

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Section: Resultsmentioning

confidence: 89%

Prediction of Site Response Spectrum Under Earthquake Vibration Using an Optimized Developed Artificial Neural Network Model

Esmaeilabadi¹,

Shahri²

2016

Adv. Sci. Technol. Res. J.

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“…The sensitive features of available inputs may be of assistance in the process of modeling because it allows handling such contributing features with great care and precision [21]. The results obtained in the sensitivity analysis are depicted in Fig.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Malaria Severity Classification Through Jordan-Elman Neural Network Based on Features Extracted From Thick Blood Smear

Chiroma¹,

Kareem²,

Ibrahim³

et al. 2015

NNW

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This article presents an alternative approach useful for medical practitioners who wish to detect malaria and accurately identify the level of severity. Malaria classifiers are usually based on feed forward neural networks. In this study, the proposed classifier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, confusion matrix, mean square error, determinant coefficient, and reliability. The effectiveness of the classifier is compared to a support vector machine and multiple regression models. The results of the comparative analysis demonstrate a superior performance level of the Jordan-Elman neural network model. Further comparison of the classier with previous literature indicates performance improvement over existing results. The Jordan-Elman neural networks classifier can assist medical practitioners in the fast detection of malaria and determining its severity, especially in tropical and subtropical regions where cases of malaria are prevalent.

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“…To that end, cosine amplitude method was selected. This method is one of the sensitivity analysis methods that is used to express similarity relations between the related parameters [15,35]. In this method, all the relevant data pairs are expressed in a common X-space.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Prediction of suspended sediment concentration from water quality variables

Bayram

Kankal

Tayfur

et al. 2013

Neural Comput & Applic

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This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time-and cost-saving implications.

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Influence of geological conditions on the powder factor for tunnel blasting

Cited by 78 publications

References 8 publications

Prediction of Site Response Spectrum Under Earthquake Vibration Using an Optimized Developed Artificial Neural Network Model

Prediction of Site Response Spectrum Under Earthquake Vibration Using an Optimized Developed Artificial Neural Network Model

Malaria Severity Classification Through Jordan-Elman Neural Network Based on Features Extracted From Thick Blood Smear

Prediction of suspended sediment concentration from water quality variables

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