In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To pre...In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To predict the UCS,simple regression(SRA),multiple regression(MRA),artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and genetic expression programming(GEP)have been utilized.The obtained UCS values were compared with the actual UCS values with the help of various graphs.Datasets were modeled using different methods and compared with each other.In the study where the performance indice PIat was used to determine the best performing method,MRA method is the most successful method with a small difference.It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA,while these values are 2.44,2.33,and 2.22 for GEP,ANFIS,and ANN techniques,respectively.The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others.According to the performance index assessment,the weakest model among the nine model is P7,while the most successful models are P2,P9,and P8,respectively.展开更多
Coastal defenses such as the breakwaters are important structures to maintain the navigation conditions in a harbor.The estimation of their hydrodynamic characteristics is conventionally done using physical models,sub...Coastal defenses such as the breakwaters are important structures to maintain the navigation conditions in a harbor.The estimation of their hydrodynamic characteristics is conventionally done using physical models,subjecting to higher costs and prolonged procedures.Soft computing methods prove to be useful tools,in cases where the data availability from physical models is limited.The present paper employs adaptive neuro-fuzzy inference system(ANFIS)and artificial neural network(ANN)models to the data obtained from physical model studies to develop a novel methodology to predict the reflection coefficient(Kr)of seaside perforated semicircular breakwaters under low wave heights,for which no physical model data is available.The prediction was done using the input parameters viz.,incident wave height(Hi),wave period(T),center-to-center spacing of perforations(S),diameter of perforations(D),radius of semicircular caisson(R),water depth(d),and semicircular breakwater structure height(hs).The study shows the prediction below the available data range of wave heights is possible by ANFIS and ANN models.However,the ANFIS performed better with R^2=0.9775 and the error reduced in comparison with the ANN model with R2=0.9751.Study includes conventional data segregation and prediction using ANN and ANFIS.展开更多
In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wuton...In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.展开更多
Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind e...Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.展开更多
The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters. Because rocks have...The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters. Because rocks have complicated structure, direct determination of this parameter takes time, spends expenditure and requires accuracy. On the other hand, there are no precise equations for indirect determination of it; most of them are empirical. By using data sets of several dams of Iran and neuro-genetic, adaptive neuro-fuzzy inference system （ANFIS）, and gene expression programming （GEP） methods, models are rendered for prediction of shear wave velocity in limestone. Totally, 516 sets of data has been used for modeling. From these data sets, 413 ones have been utilized for building the intelligent model, and 103 have been used for their performance evaluation. Compressional wave velocity （Vp）, density （7） and porosity （.n）, were considered as input parameters. Respectively, the amount of R for neuro-genetic and ANFIS networks was 0.959 and 0.963. In addition, by using GEP, three equations are obtained; the best of them has 0.958R. ANFIS shows the best prediction results, whereas GEP indicates proper equations. Because these equations have accuracy, they could be used for prediction of shear wave velocity for limestone in the future.展开更多
In order to seek the economical, practical and effective method of obtaining the thickness of broken rock zone, an emerging intelligent prediction method with adaptive neuro-fuzzy inference system (ANFIS) was introduc...In order to seek the economical, practical and effective method of obtaining the thickness of broken rock zone, an emerging intelligent prediction method with adaptive neuro-fuzzy inference system (ANFIS) was introduced into the thickness prediction. And the software with functions of creating and applying prediction systems was devel- oped on the platform of MATLAB6.5. The software was used to predict the broken rock zone thickness of drifts at Li- angbei coal mine, Xinlong Company of Coal Industry in Xuchang city of Henan province. The results show that the predicted values accord well with the in situ measured ones. Thereby the validity of the software is validated and it provides a new approach to obtaining the broken zone thickness.展开更多
In general,submerged pipes passing over the sedimentary bed of seas are installed for transmitting oil and gas to coastal regions.The stability of submerged pipes can be threatened with waves and coastal flows occurri...In general,submerged pipes passing over the sedimentary bed of seas are installed for transmitting oil and gas to coastal regions.The stability of submerged pipes can be threatened with waves and coastal flows occurring at coastal regions.In this study,for the first time,the adaptive neuro-fuzzy inference system(ANFIS)is optimized using the particle swarm optimization(PSO)algorithm,and a meta-heuristic artificial intelligence model is developed for simulating the scour pattern around submerged pipes located in sedimentary beds.Afterward,six ANFIS-PSO models are developed by means of parameters affecting the scour depth.Then,the superior model is detected through sensitivity analysis.This model has the function of all input parameters.The calculated correlation coefficient and scatter index for this model are 0.993 and 0.047,respectively.The ratio of the pipe distance from the sedimentary bed to the submerged pipe diameter is introduced as the most effective input parameter.PSO significantly improves the performance of the ANFIS model.Approximately 36% of the scour depths simulated using the ANFIS model have an error less than 5%,whereas the value for ANFIS-PSO is roughly 72%.展开更多
文摘In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To predict the UCS,simple regression(SRA),multiple regression(MRA),artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and genetic expression programming(GEP)have been utilized.The obtained UCS values were compared with the actual UCS values with the help of various graphs.Datasets were modeled using different methods and compared with each other.In the study where the performance indice PIat was used to determine the best performing method,MRA method is the most successful method with a small difference.It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA,while these values are 2.44,2.33,and 2.22 for GEP,ANFIS,and ANN techniques,respectively.The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others.According to the performance index assessment,the weakest model among the nine model is P7,while the most successful models are P2,P9,and P8,respectively.
文摘Coastal defenses such as the breakwaters are important structures to maintain the navigation conditions in a harbor.The estimation of their hydrodynamic characteristics is conventionally done using physical models,subjecting to higher costs and prolonged procedures.Soft computing methods prove to be useful tools,in cases where the data availability from physical models is limited.The present paper employs adaptive neuro-fuzzy inference system(ANFIS)and artificial neural network(ANN)models to the data obtained from physical model studies to develop a novel methodology to predict the reflection coefficient(Kr)of seaside perforated semicircular breakwaters under low wave heights,for which no physical model data is available.The prediction was done using the input parameters viz.,incident wave height(Hi),wave period(T),center-to-center spacing of perforations(S),diameter of perforations(D),radius of semicircular caisson(R),water depth(d),and semicircular breakwater structure height(hs).The study shows the prediction below the available data range of wave heights is possible by ANFIS and ANN models.However,the ANFIS performed better with R^2=0.9775 and the error reduced in comparison with the ANN model with R2=0.9751.Study includes conventional data segregation and prediction using ANN and ANFIS.
基金financially supported by the National Science and Technology Major Demonstration Project 19 (2011ZX05062-008)
文摘In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.
文摘Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.
文摘The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters. Because rocks have complicated structure, direct determination of this parameter takes time, spends expenditure and requires accuracy. On the other hand, there are no precise equations for indirect determination of it; most of them are empirical. By using data sets of several dams of Iran and neuro-genetic, adaptive neuro-fuzzy inference system （ANFIS）, and gene expression programming （GEP） methods, models are rendered for prediction of shear wave velocity in limestone. Totally, 516 sets of data has been used for modeling. From these data sets, 413 ones have been utilized for building the intelligent model, and 103 have been used for their performance evaluation. Compressional wave velocity （Vp）, density （7） and porosity （.n）, were considered as input parameters. Respectively, the amount of R for neuro-genetic and ANFIS networks was 0.959 and 0.963. In addition, by using GEP, three equations are obtained; the best of them has 0.958R. ANFIS shows the best prediction results, whereas GEP indicates proper equations. Because these equations have accuracy, they could be used for prediction of shear wave velocity for limestone in the future.
基金Projects 50474063 and 50490273 supported by National Natural Science Foundation of China
文摘In order to seek the economical, practical and effective method of obtaining the thickness of broken rock zone, an emerging intelligent prediction method with adaptive neuro-fuzzy inference system (ANFIS) was introduced into the thickness prediction. And the software with functions of creating and applying prediction systems was devel- oped on the platform of MATLAB6.5. The software was used to predict the broken rock zone thickness of drifts at Li- angbei coal mine, Xinlong Company of Coal Industry in Xuchang city of Henan province. The results show that the predicted values accord well with the in situ measured ones. Thereby the validity of the software is validated and it provides a new approach to obtaining the broken zone thickness.
文摘In general,submerged pipes passing over the sedimentary bed of seas are installed for transmitting oil and gas to coastal regions.The stability of submerged pipes can be threatened with waves and coastal flows occurring at coastal regions.In this study,for the first time,the adaptive neuro-fuzzy inference system(ANFIS)is optimized using the particle swarm optimization(PSO)algorithm,and a meta-heuristic artificial intelligence model is developed for simulating the scour pattern around submerged pipes located in sedimentary beds.Afterward,six ANFIS-PSO models are developed by means of parameters affecting the scour depth.Then,the superior model is detected through sensitivity analysis.This model has the function of all input parameters.The calculated correlation coefficient and scatter index for this model are 0.993 and 0.047,respectively.The ratio of the pipe distance from the sedimentary bed to the submerged pipe diameter is introduced as the most effective input parameter.PSO significantly improves the performance of the ANFIS model.Approximately 36% of the scour depths simulated using the ANFIS model have an error less than 5%,whereas the value for ANFIS-PSO is roughly 72%.
