A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method

Keshtegar, Behrooz; Hasanipanah, Mahdi; Bakhshayeshi, Ivan; Sarafraz, Mehdi Esfandi

doi:10.1016/j.measurement.2018.08.052

Cited by 48 publications

(9 citation statements)

References 42 publications

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“…They indicated the performance of multiple regression was better than simple regression ones in forecasting the BTS. Researchers of this field of study have proposed not only several empirical models, but also lots of intelligent systems to solve science and engineering problems (e.g., Singh et al [34]; Yılmaz and Yuksek [35]; Hajihassani et al [36]; Koopialipoor et al [37]; Nilashi et al [38]; Milovančević et al [39]; Roy et al [40]; Roy and Singh [41]; Keshtegar et al [42]), particularly the geotechnical engineering ones (e.g., Mohamad et al [43]; Nanda et al [44]). Furthermore, right to the point we are focused in this study, a number of studies can be found in literature.…”

Section: Introductionmentioning

confidence: 99%

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

et al. 2020

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Tensile strength (TS) of rock is one of the important properties in design process of construction civil works such as foundations and tunnels. Brazilian tensile strength (BTS) or splitting test is considered as a well-known method in evaluating TS. The present study attempts to propose a novel metaheuristic approach for the indirect measurement of BTS. This new approach is based on the firefly algorithm (FA) for training and optimizing the consequent parameters of the adaptive neuro-fuzzy inference system (ANFIS). The rock samples collected from a tunnel in Malaysia were tested in the laboratory for the purpose of providing a database consisting of totally 80 samples for analysis. Then, the statistical metrics such as coefficient of determination (R 2) were used to examine the acceptability of the proposed ANFIS-FA and ANFIS models. Finally, it was concluded that the ANFIS-FA (with the R 2 of 0.982) can be effectively used as a robust model to predict BTS. INDEX TERMS Tensile strength, brazilian tensile strength, ANFIS, firefly algorithm.

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Section: Introductionmentioning

confidence: 99%

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

et al. 2020

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“…By computing RMSE and MAE, the lower values represent the more accurate models, while higher NSE and d values indicate higher agreement predictions for a model. Therefore, the lowest values for error comparative factors, i.e., RMSE and MAE, and the highest agreement factor, i.e., d and NSE coefficients, can be used to select a model with superior prediction from among the other modeling approaches (77)(78)(79)(80)(81)(82)(83)(84)(85)(86)(87).…”

Section: Resultsmentioning

confidence: 99%

Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data

Keshtegar

Piri

Abdullah

et al. 2023

Front. Public Health

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Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.

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“…This approach has been employed in several natural catastrophe circumstances including the estimation of groundwater potentiality (Manap et al, 2014), to estimate the blastinduced air-blast (Keshtegar et al 2019), landslide susceptibility mapping (Lee and Pradhan 2007;Mondal and Maiti 2013), and flood susceptibility mapping (Lee et al, 2012;Rahamti et al 2015;Khosravi et al 2016). The advantage of this model is that it is easy to apply and gives totally understood outcomes (Ozdemir and Altural 2013).…”

Section: Frequency Ratio Methods (Fr)mentioning

confidence: 99%

Efficiency Exploration of Frequency Ratio, Entropy, and Weights of Evidence-Information Value Models in Flood Susceptibility Assessment: A Study of Raiganj Subdivision, Eastern India

Saha

Sarkar

Mondal

2021

Preprint

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The primary objective of this research was to assess the efficiency of RS and GIS for predicting the flood risk using the Frequency ratio, Entropy index, and Weight of evidence-information Value models. Consequently, for spatial analyses fourteen flood conditioning variables are constructed. The research region is experiencing floods consequently in the past with moderate to high intensities. The assessment demonstrates that parameters such as elevation, LULC, rainfall, distance from rivers, and drainage density contributed significantly to the occurrence of floods. The validation of the results indicates that the success rate of the presently constructed models. Around 33 per cent to 47 per cent of the total area of each block in the subdivision is projected to be in danger of floods. The report's findings will aid planners in designing flood prevention measures as part of regional flood risk management programs and will serve as a platform for future research in the study area.

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A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method

Cited by 48 publications

References 42 publications

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data

Efficiency Exploration of Frequency Ratio, Entropy, and Weights of Evidence-Information Value Models in Flood Susceptibility Assessment: A Study of Raiganj Subdivision, Eastern India

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