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 b...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 zonifieation 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 ex- pression programming （GEP） and adaptive neuro-fuzzy inference system （ANFIS） methods. In addition, dam height （H）, shape factor （St）, 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 ofR2 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.展开更多
Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical propertie...Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.展开更多
Estimating the cracking capacity of the face slab and recommending effective crack-control measures are important for the anti-seismic safety of concrete-faced rockfill dams(CFRDs). In this paper, two-dimensional anal...Estimating the cracking capacity of the face slab and recommending effective crack-control measures are important for the anti-seismic safety of concrete-faced rockfill dams(CFRDs). In this paper, two-dimensional analyses of CFRDs are performed to simulate the seismic cracking behavior of conventional reinforced concrete(RC) face slab and a type of composite face slab. The composite face slab is composed of a ductile fiber-reinforced cement-based composite(DFRCC) layer and an RC substrate. For this purpose, a co-axial rotating smeared crack model for concrete and DFRCC is coupled with the generalized plasticity model for the rockfill material, and then implemented in a finite element program. The results show that during strong earthquakes,an RC slab is more likely to develop a penetrating macro-crack in its thickness dimension. In contrast, the crack-controlling composite slab demonstrates excellent resistance to seismic cracking, and no penetrating macro-cracks are observed. Major harmful cracks that form in the concrete substrate are stopped by the DFRCC layer in composite slabs.展开更多
文摘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 zonifieation 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 ex- pression programming （GEP） and adaptive neuro-fuzzy inference system （ANFIS） methods. In addition, dam height （H）, shape factor （St）, 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 ofR2 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.
基金supported by the National Natural Science Foundation of China(Grant Nos.51379161&51509190)China Postdoctoral Science Foundation(Grant No.2015M572195)the Fundamental Research Funds for the Central Universities
文摘Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.
基金supported by the National Natural Science Foundation of China(Grant Nos.51379028,51421064&51279025)
文摘Estimating the cracking capacity of the face slab and recommending effective crack-control measures are important for the anti-seismic safety of concrete-faced rockfill dams(CFRDs). In this paper, two-dimensional analyses of CFRDs are performed to simulate the seismic cracking behavior of conventional reinforced concrete(RC) face slab and a type of composite face slab. The composite face slab is composed of a ductile fiber-reinforced cement-based composite(DFRCC) layer and an RC substrate. For this purpose, a co-axial rotating smeared crack model for concrete and DFRCC is coupled with the generalized plasticity model for the rockfill material, and then implemented in a finite element program. The results show that during strong earthquakes,an RC slab is more likely to develop a penetrating macro-crack in its thickness dimension. In contrast, the crack-controlling composite slab demonstrates excellent resistance to seismic cracking, and no penetrating macro-cracks are observed. Major harmful cracks that form in the concrete substrate are stopped by the DFRCC layer in composite slabs.
