Taking the practical reinforced engineering of a reinforced soil retaining wall as an example, which located in Shandong Province and set on 104 national highway, the stress spread behaviors of the anchor bars in the ...Taking the practical reinforced engineering of a reinforced soil retaining wall as an example, which located in Shandong Province and set on 104 national highway, the stress spread behaviors of the anchor bars in the preforced proceeding were tested. According to the test data, and by use of the update backpropagation (BP) algorithm neural network(NN), the test method and it’s mechanism were studied by the network, then the learning results show the mean square error(MSE) only at the 2 55% level, and the proof testing results show the MSE at 4 38% level (the main aim is to build a NN directly from the in situ test results (the learning phase)). Ipso facto, the learning and adjustment abilities of the NN permit us to develop the test data, subsequently, 36 test data were acquired from the NN. By use of the provide data, as well as the failure situation and carried loading capacity of the retaining wall, finally, the choice the reasonable range interval distance of prestress cement grouting anchor bars were carried out, and the result was 2 m×2 m.展开更多
The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived usi...The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.展开更多
Based on the basic formula of the confidence interval and the sampling error of mathematical statistics, the mathematical statistics method of evaluating application effects of a new type of gas anchor was given in th...Based on the basic formula of the confidence interval and the sampling error of mathematical statistics, the mathematical statistics method of evaluating application effects of a new type of gas anchor was given in this paper. By the method mentioned above, the confidence interval and the sampling errors of the relevant mean value differences of Daqing Oilfield S block’s 150 wells, according to the mean value differences of the liquid producing capacity per day, the oil production per day, the submergence depth of the 10 sampling test wells, in which before and after a new type of gas anchor were laid down, were calculated. The calculation results show that a new type of gas anchor has a better effect of increasing oil production of oil well and enhancing pump efficiency. Through the real value differences analysis of the liquid producing capacity per day, the oil production per day, the submergence depth of 150 wells mentioned above, in which before and after a new type of gas anchor were laid down, it was verified. By using the confidence interval and the sampling errors of the liquid producing capacity per day, the oil production per day, the submergence depth mentioned above, in which before and after a new type of gas anchor were laid down, the application effects of a new type of gas anchor could be evaluated. And a mathematical statistics method of evaluation application effects of a new type of gas anchor is presented.展开更多
文摘Taking the practical reinforced engineering of a reinforced soil retaining wall as an example, which located in Shandong Province and set on 104 national highway, the stress spread behaviors of the anchor bars in the preforced proceeding were tested. According to the test data, and by use of the update backpropagation (BP) algorithm neural network(NN), the test method and it’s mechanism were studied by the network, then the learning results show the mean square error(MSE) only at the 2 55% level, and the proof testing results show the MSE at 4 38% level (the main aim is to build a NN directly from the in situ test results (the learning phase)). Ipso facto, the learning and adjustment abilities of the NN permit us to develop the test data, subsequently, 36 test data were acquired from the NN. By use of the provide data, as well as the failure situation and carried loading capacity of the retaining wall, finally, the choice the reasonable range interval distance of prestress cement grouting anchor bars were carried out, and the result was 2 m×2 m.
基金The National Natural Science Foundation of China(No.51778485).
文摘The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.
文摘Based on the basic formula of the confidence interval and the sampling error of mathematical statistics, the mathematical statistics method of evaluating application effects of a new type of gas anchor was given in this paper. By the method mentioned above, the confidence interval and the sampling errors of the relevant mean value differences of Daqing Oilfield S block’s 150 wells, according to the mean value differences of the liquid producing capacity per day, the oil production per day, the submergence depth of the 10 sampling test wells, in which before and after a new type of gas anchor were laid down, were calculated. The calculation results show that a new type of gas anchor has a better effect of increasing oil production of oil well and enhancing pump efficiency. Through the real value differences analysis of the liquid producing capacity per day, the oil production per day, the submergence depth of 150 wells mentioned above, in which before and after a new type of gas anchor were laid down, it was verified. By using the confidence interval and the sampling errors of the liquid producing capacity per day, the oil production per day, the submergence depth mentioned above, in which before and after a new type of gas anchor were laid down, the application effects of a new type of gas anchor could be evaluated. And a mathematical statistics method of evaluation application effects of a new type of gas anchor is presented.
