A fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO 3 , and C 3 S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If -Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.

Sheet sediment transport was modelled by artificial neural networks (ANNs). A three-layer feed-forward artificial neural network structure was constructed and a back-propagation algorithm was used for the training of ANNs. Event-based, runoffdriven experimental sediment data were used for the training and testing of the ANNs. In training, data on slope and rainfall intensity were fed into the network as inputs and data on sediment discharge were used as target outputs. The performance of the ANNs was tested against that of the most commonly used physically-based models, whose transport capacity was based on one of the dominant variables-flow velocity (V), shear stress (SS), stream power (SP), and unit stream power (USP). The comparison results revealed that the ANNs performed as well as the physically-based models for simulating nonsteady-state sediment loads from different slopes. The performances of the ANNs and the physically-based models were also quantitatively investigated to estimate mean sediment discharges from experimental runs. The investigation results indicated that better estimations were obtained for V over mild and steep slopes, under low rainfall intensity; for USP over mild and steep slopes, under high rainfall intensity; for SP and SS over very steep slopes, under high rainfall intensity; and for ANNs over steep and very steep slopes, under very high rainfall intensities. Key words sediment transport; artificial neural networks; transport capacity Application des réseaux de neurones artificiels pour le transport sédimentaire en nappe Résumé Le transport sédimentaire en nappe a été modélisé par des réseaux de neurones artificiels. Une structure de réseau de neurones artificiel à trois couches et progressif a été construite, et un algorithme de rétro-propagation a été utilisé pour l'apprentissage. Des données expérimentales événementielles de transport de sédiment par ruissellement ont été utilisées pour l'apprentissage et le test des réseaux de neurones artificiels. Lors de l'apprentissage, des données de pente et d'intensité de précipitation ont été fournies au réseau en guise de données d'entrée et des données de débit solide ont servi de cible pour les sorties. La performance des réseaux de neurones artificiels a été testée par comparaison avec celles des modèles à bases physiques les plus communs, dont la capacité de transport est basée sur l'une des variables dominantes-vitesse d'écoulement, contrainte tangentielle, puissance du cours d'eau et puissance unitaire du cours d'eau. La comparaison des résultats a révélé que les réseaux de neurones ont des performances semblables à celles des modèles à bases physiques pour simuler les charges sédimentaires en état non-stationnaire, pour diverses pentes. Cette comparaison des performances a également été menée de manière quantitative pour l'estimation des débits solides moyens expérimentaux. Les meilleures simulations ont été obtenues sur la base de la vitesse d'écoulement pour des pentes douces à fortes et pour de faibles intensités de précipitat...

The dynamic behavior of bed-load sediment transport under unsteady flow conditions is experimentally and numerically investigated. A series of experiments are conducted in a rectangular flume (18 m in length, 0.80 m in width) with various triangular and trapezoidal shaped hydrographs. The flume bed of 8 cm in height consists of scraped uniform small gravel of D 50 ¼ 4:8 mm. Analysis of the experimental results showed that bed-load transport rates followed the temporal variation of the triangular and trapezoidal hydrographs with a time lag on the average of 11 and 30 s, respectively. The experimental data were also qualitatively investigated employing the unsteady-flow parameter and total flow work index. The analysis results revealed that total yield increased exponentially with the total flow work. An original expression which is based on the net acceleration concept was proposed for the unsteadiness parameter. Analysis of the results then revealed that the total yield increased exponentially with the increase in the value of the proposed unsteadiness parameter. Further analysis of the experimental results revealed that total flow work has an inverse exponential variation relation with the lag time. A onedimensional numerical model that employs the governing equations for the conservation of mass for water and sediment and the momentum was also developed to simulate the experimental results. The momentum equation was approximated by the diffusion wave approach, and the kinematic wave theory approach was employed to relate the bed sediment flux to the sediment concentration. The model successfully simulated measured sedimentographs. It predicted sediment yield, on the average, with errors of 7% and 15% of peak loads for the triangular and trapezoidal hydrograph experiments, respectively.

An artificial neural network ͑ANN͒ model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables ͓flow discharge ͑Q͒, flow depth ͑H͒, flow velocity ͑U͒, shear velocity ͑u * ͒, and relative shear velocity ͑U / u * ͔͒ and geometric characteristics ͓channel width ͑B͒, channel sinuosity ͑ ͒, and channel shape parameter ͑␤͔͒ constituted inputs to the ANN model, whereas the dispersion coefficient ͑K x ͒ was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient ͑K x Ͻ 100 m 2 /s͒. For narrower channels ͑B / H Ͻ 50͒ using only U / u* data would be sufficient to predict the coefficient. If ␤ and were used along with the flow variables, the prediction capability of the ANN model would be significantly improved.