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 methods to estimate the subsidence of dams at the design stage. Due to dam zonification and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods. In addition, dam height (H), shape factor (S f ), and time (t, time after first operation) are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values of R 2 for both equations of GEP (with and without constant) were 0.9603 and 0.9734, and for the three approaches of ANFIS (grid partitioning (GP), subtractive clustering method (SCM), and fuzzy c-means clustering (FCM)) were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs.

Design of Winding Parameters Based on Multiobjective Decision-Making and Fuzzy Optimization Theory

“…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, .…”

A statistical model of deformation during the construction of a concrete face rockfill dam

“…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.…”

“…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.…”

Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system

“…In this method, data are grouped based on their degree of membership. FCM has improved SCM performance [51].…”