Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic...Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.展开更多
With the increasing researches on geotechnical properties of the diesel contaminated soil( DCS),the water content measured is indispensable part during the early period. In this study,the relative error of water conte...With the increasing researches on geotechnical properties of the diesel contaminated soil( DCS),the water content measured is indispensable part during the early period. In this study,the relative error of water content measurement using the traditional method is as high as 20. 78%,which is no longer suitable for contaminated soil. Through a series of tests to measure the loss coefficient of diesel in the drying time,the authors finally proposed a modified calculation formula for test samples. The results show that the maximum relative error calculated by using the modified formula is 0. 96%,far lower than that of traditional formula,which can provide accurate data for further study of diesel contaminated soil.展开更多
基金Project(2013CB036004)supported by the National Basic Research Program of ChinaProject(51378510)supported by the National Natural Science Foundation of China
文摘Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.
文摘With the increasing researches on geotechnical properties of the diesel contaminated soil( DCS),the water content measured is indispensable part during the early period. In this study,the relative error of water content measurement using the traditional method is as high as 20. 78%,which is no longer suitable for contaminated soil. Through a series of tests to measure the loss coefficient of diesel in the drying time,the authors finally proposed a modified calculation formula for test samples. The results show that the maximum relative error calculated by using the modified formula is 0. 96%,far lower than that of traditional formula,which can provide accurate data for further study of diesel contaminated soil.
