Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods
Abstract:This paper deals with the estimation of crest settlement in a concrete face rockfill dam (CFRD), utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent method… Show more
“…In order to obtain the optimal relative membership degree, the optimal clustering feature sih * , and optimal weight vector w * , we use weighted generalized index weight distance with relative membership degree uhj as weight. Finally, we get the weighted generalized distance d hj = u hj , where d hj is the distance concept; it contains the variables u, s, w. An objective function is created which is shown in formula (8) [12][13][14].…”
“…The flow chart of multiobjective fuzzy decision method is shown in Figure 1. We choose 25 suitable schemes in multiobjective decision-making system and judge them by five target eigenvalues; the formula is shown as (14). In formula (14), x ij is the special value for the target i and the scheme j, i = 1, 2, .…”
The design tries to solve the problem of low pass rate of platinum wire production and the waste of platinum in company. The paper uses multiobjective decision system fuzzy optimization theory to analyze five parameters, which are tensile strength, ductility, fracture load, filling aperture, and resistance. Besides, MATLAB software is used to write programs and calculate. To sum up the above analysis, the weight vector of five parameters is obtained and that can be used to determine which parameter has the greatest influence on the pass rate of the wire winding process.
“…In order to obtain the optimal relative membership degree, the optimal clustering feature sih * , and optimal weight vector w * , we use weighted generalized index weight distance with relative membership degree uhj as weight. Finally, we get the weighted generalized distance d hj = u hj , where d hj is the distance concept; it contains the variables u, s, w. An objective function is created which is shown in formula (8) [12][13][14].…”
“…The flow chart of multiobjective fuzzy decision method is shown in Figure 1. We choose 25 suitable schemes in multiobjective decision-making system and judge them by five target eigenvalues; the formula is shown as (14). In formula (14), x ij is the special value for the target i and the scheme j, i = 1, 2, .…”
The design tries to solve the problem of low pass rate of platinum wire production and the waste of platinum in company. The paper uses multiobjective decision system fuzzy optimization theory to analyze five parameters, which are tensile strength, ductility, fracture load, filling aperture, and resistance. Besides, MATLAB software is used to write programs and calculate. To sum up the above analysis, the weight vector of five parameters is obtained and that can be used to determine which parameter has the greatest influence on the pass rate of the wire winding process.
“…On the basis of the analysis in Section 3.1 for model construction, the preset factor sets are selected as follows. Temperature subset: T 0-1 , T 1-2 , T 3-7 , T [8][9][10][11][12][13][14][15] , and T [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] ; Aging subset: On the basis of the qualitative analysis of the process line of measured internal settlement at TA1-5, the variation is consistent with the characteristics of the combination of linear change and logarithmic change. Therefore, t 1 and ln(t 1 + 1) are selected as the preset aging factors from the six types of factors in Equation 7.…”
Section: Validating the Improved Statistical Modelmentioning
confidence: 99%
“…The first category includes traditional methods of statistical modeling, deterministic modeling, and mixed modeling. [9][10][11][12] In this category, the factors that influence deformation are generalized into water pressure, temperature, and aging factors. [13,14] The second category involves finite element calculations combined with monitoring data analysis.…”
SummaryMonitoring data collected during dam construction are important in complete series of monitoring data. These data play a significant role in dam safety monitoring and the analysis of structural conditions. The traditional statistical model of the deformation of a concrete face rockfill dam (CFRD) with filling height and time factors is associated with serious multicollinearity issues during the construction phase. This study uses the Longbeiwan CFRD as an
| INTRODUCTIONA concrete face rockfill dam (CFRD) is a type of dam that uses rockfill as the support structure and an upstream surface concrete face as the anti-seepage structure. CFRDs are effective because of their adaptability to poor topographical, geological, and climatic conditions. In addition, they have excellent safety features and economic efficiency. Currently, CFRDs are one of the most commonly used and cost competitive dam types. [1,2] Deformation and seepage control are two key technical problems in CFRD construction. Deformation (such as that of the surface, interior, and foundation of dams) and various joint deformations can be monitored using various technologies. [3] For example, the horizontal displacement of the dam surface can be monitored by line of sight or torsion, whereas the surface settlement (vertical displacement) of the dam can be monitored using the geometric method. Moreover, the horizontal displacement of the rockfill body can be monitored by a meter or inclinometer, whereas theThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Abstract. In recent years, controlled blasting has turned into an e cient method for evaluation of soil liquefaction on a real scale and of ground improvement techniques. Predicting blast-induced soil liquefaction using collected information can be an e ective step in the study of blast-induced liquefaction. In this study, to estimate residual pore pressure ratio, rst, a multi-layer perceptron neural network is used in which error (RMS) for the network was calculated as 0.105. Next, a neuro-fuzzy network, ANFIS, was used for modeling. Di erent ANFIS models are created using Grid Partitioning (GP), subtractive clustering (SCM), and Fuzzy C-Means clustering (FCM). Minimum error is obtained using FCM at about 0.081. Finally, Radial Basis Function (RBF) network is used. Error of this method was about 0.06. Accordingly, RBF network has better performance. Variables, including ne-content, relative density, e ective overburden pressure, and SPT value, are considered as input components, and residual pore pressure ratio, Ru, was used as the only output component for designing prediction models. In the next stage, the network output is compared with the results of a regression analysis. Finally, sensitivity analysis for RBF network is tested, and its results reveal that 0 v0 and SPT are the most e ective factors for determining Ru.
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