Experimental investigation of steel fiber reinforced concrete box beams under bending

Reduction of dead weight of a reinforced-concrete (RC) structure without too much concession in its load carrying capacity has always been an attractive study subject because it engenders (1) a decrease in dimensions of the members, (2) a decrease in the reinforcement steel, and (3) a decrease in lateral inertia forces during severe earthquakes. In this study, nine RC beams of outer dimensions of 300 × 300 × 2000 mm, six of which are box beams, designed and produced using a C20 class steel fiber concrete, (SFRC) with the commonly used steel fiber type of Dramix-RC-80/0.60-BN at a dosage of 30 kg/m³, are tested under bending. The mechanical behaviours of all these nine beams under bending are recorded from the beginning of the test till the ultimate failure of the tensile reinforcement in a two-point beam-loading setup. The proportions of (1) loss in ultimate load versus reduction in dead weight and (2) (ultimate experimental load)/(ultimate theoretical load) of the SFRC box beams are determined for two different box thicknesses. Dimensionless behaviour relationships of all the SFRC beams are determined, and the experimentally obtained relationship between the ratio of (actual ultimate load)/(theoretical ultimate load) and the ratio of (wall thickness)/(beam height) for the SFRC box beams is expressed diagrammatically.     

Adaptive neuro-fuzzy computing technique for suspended sediment estimation

This paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in suspended sediment estimation. The monthly streamflow and suspended sediment data from two stations, Kuylus and Salur Koprusu, in Kizilirmak Basin in Turkey are used as case studies. The estimation results obtained by using the neuro-fuzzy technique are tested and compared with those of the artificial neural networks and sediment rating curves. Root mean squared errors, mean absolute errors and correlation coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neuro-fuzzy models can be employed successfully in monthly suspended sediment estimation.     

Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Monthly mean reference evapotranspiration (ET ₀ ) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET ₀ given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET ₀ values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET ₀ , because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN.     

Application of ANN techniques for estimating backwater through bridge constrictions in Mississippi River basin

Bridge backwater data were collected for 92 different floods at 35 bridge sites in the Mississippi River basin in 1960s [Neely BL. Hydraulic performance of bridges, hydraulic efficiency of bridges—analysis of field data. Unpublished Report Conducted by US Geological Survey, June 30; 1966]. This major field data showed that the backwater computed both by the United States Geological Survey’s method (USGS) and the United States Bureau of Public Roads’ method (USBPR) averaged approximately 50% less than the measured backwater. Therefore, in the current work, a new bridge backwater formula based on the three different artificial neural network approaches (ANNs), namely FFBP (Feed-Forward Back Propagation), RBNN (Radial Basis Function-Based Neural Network), and GRNN (Generalized Regression Neural Networks) are proposed and compared with the methods mentioned above. The results showed that the FFBP produced slightly better estimations than those of the RBNN and these two was significantly superior to the GRNN, USGS and USBPR methods when applied to Neely’s field data.     

Forecasting backwater through bridge constrictions in Mississippi River Basin

Hydraulic data collected in the 1960s during 92 distinct floods at 35 different bridge sites in the Mississippi River Basin revealed that the water surface profiles of these real‐life cases were distinctly different from those observed in laboratory models of the comprehensive experimental studies of the 1950s by the U.S. Geological Survey (USGS) and by U.S. Bureau of Public Roads (USBPR). The laboratory‐developed methods of USGS and USBPR yielded only about half of the field backwaters when applied to the comprehensive field data. In the current work, using the same field data and accepting a profile like that observed in the field, a new regression‐based formula for estimating bridge backwater is proposed and compared with the methods of USGS and USBPR, which yields more accurate results than these two methods with the advantage of requiring a smaller load of arithmetic operations. Copyright © 2008 John Wiley & Sons, Ltd.     

Comparison of Artificial Neural Network Methods with L-moments for Estimating Flood Flow at Ungauged Sites: the Case of East Mediterranean River Basin, Turkey

A regional flood frequency analysis based on the index flood method is applied using probability distributions commonly utilized for this purpose. The distribution parameters are calculated by the method of L-moments with the data of the annual flood peaks series recorded at gauging sections of 13 unregulated natural streams in the East Mediterranean River Basin in Turkey. The artificial neural networks (ANNs) models of (1) the multi-layer perceptrons (MLP) neural networks, (2) radial basis function based neural networks (RBNN), and (3) generalized regression neural networks (GRNN) are developed as alternatives to the L-moments method. Multiple-linear and multiple-nonlinear regression models (MLR and MNLR) are also used in the study. The L-moments analysis on these 13 annual flood peaks series indicates that the East Mediterranean River Basin is hydrologically homogeneous as a whole. Among the tried distributions which are the Generalized Logistic, Generalized Extreme Vaules, Generalized Normal, Pearson Type III, Wakeby, and Generalized Pareto, the Generalized Logistic and Generalized Extreme Values distributions pass the Z statistic goodness-of-fit test of the L-moments method for the East Mediterranean River Basin, the former performing yet better than the latter. Hence, as the outcome of the L-moments method applied by the Generalized Logistic distribution, two equations are developed to estimate flood peaks of any return periods for any un-gauged site in the study region. The ANNs, MLR and MNLR models are trained and tested using the data of these 13 gauged sites. The results show that the predicting performance of the MLP model is superior to the others. The application of the MLP model is performed by a special Matlab code, which yields logarithm of the flood peak, Ln(Q_T), versus a desired return period, T.     

Experimental and finite element analysis on the steel fiber-reinforced concrete (SFRC) beams ultimate behavior

Steel fiber-added reinforced concrete (SFRC) applications have become widespread in areas such as higher upper layers, tunnel shells, concrete sewer pipes, and slabs of large industrial buildings. Usage of SFRC in load-carrying members of buildings having conventional reinforced concrete (RC) frames is also gaining popularity recently because of its positive contribution to both energy absorption capacity and concrete strength.This paper presents experimental and finite element analysis of three SFRC beams. For this purpose, three SFRC beams with 250 × 350 × 2000 mm dimensions are produced using a concrete class of C20 with 30 kg/m³ dosage of steel fibers and steel class S420 with shear stirrups. SFRC beams are subjected to bending by a four-point loading setup in certified beam-loading frame, exactly after having been moist-cured for 28 days. The tests are with control of loads. The beams are loaded until they are broken and the loadings are stopped when the tensile steel bars are broken into two pieces. Applied loads and mid-section deflections are carefully recorded at every 5 kN load increment from the beginning till the ultimate failure.One of the SFRC beams modeled by using nonlinear material properties adopted from experimental study is analyzed till the ultimate failure cracks by ANSYS. Eight-noded solid brick elements are used to model the concrete. Internal reinforcement is modeled by using 3D spar elements. A quarter of the full beam is taken into account in the modeling process.The results obtained from the finite element and experimental analyses are compared to each other. It is seen from the results that the finite element failure behavior indicates a good agreement with the experimental failure behavior.     

Pattern-reversal visual evoked potentials in migraineurs with or without visual aura

To determine the possible electrophysiologic changes in migraineurs with or without visual aura, we investigated pattern-reversal visual evoked potentials in 39 patients. We compared the mean P100 latency and amplitude of 16 patients with aura, 23 patients without aura, and 17 age- and sex-matched normal subjects. There were no significant differences between groups. There was no correlation between age and the parameters in any group. However, in 7 of 23 patients without aura, the P100 latency was longer than the mean control value +2 SD. The mean disease duration in this subgroup was significantly longer than the means of the remaining 16 patients without aura or the patients with aura (P < 0.05 for each). This suggests the possibility that P100 latency prolongation is a consequence, but not an entity caused by the pathogenetic mechanism of the disease from the beginning.     

The complementary functions method for the element stiffness matrix of arbitrary spatial bars of helicoidal axes

The statical behaviour of a spatial bar of an elastic and isotropic material under arbitrary distributed loads having a non-circular helicoidal axis and cross-section supported elastically by single and/or continuous supports is studied by the stiffness matrix method based on the complementary functions approach. By considering the geometrical compatibility conditions together with the constitutive equations and equations of equilibrium, a set of 12 first-order differential equations having variable coefficients is obtained for spatial elements of helicoidal axes. The stiffness matrix and the element load vector of a helicoidal bar with a non-circular axis and arbitrary cross-section are obtained taking into consideration both the presence of an elastic support and the effects of the axial and shear deformations. For helicoidal staircases, the significance of both axial and shear deformations and eccentricities existing in wide and shallow sections are also investigated. The developed model has been coded in Fortran-77, which has been applied to various example problems available in the relevant literature, and the results have been compared.     

Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam

Recently, artificial neural networks (ANNs) have been used successfully for many engineering problems. This paper presents a practical way of predicting the hydropower energy potential using ANNs for the feasibility of adding a hydropower plant unit to an existing irrigation dam. Because the cost of energy has risen considerably in recent decades, addition of a suitable capacity hydropower plant (HPP) to the end of the pressure conduit of an existing irrigation dam may become economically feasible. First, a computer program to realistically calculate all local, frictional, and total head losses (THL) throughout any pressure conduit in detail is coded, whose end-product enables determination of the C coefficient of the highly significant model for total losses as: THL = C·Q ². Next, a computer program to determine the hydroelectric energies produced at monthly periods, the present worth (PW) of their monetary gains, and the annual average energy by a HPP is coded, which utilizes this simple but precise model for quantification of total energy losses from the inlet to the turbine. Inflows series, irrigation water requirements, evaporation rates, turbine running time ratios, and the C coefficient are the input data of this program. This model is applied to randomly chosen 10 irrigation dams in Turkey, and the selected input variables are gross head and reservoir capacity of the dams, recorded monthly inflows and irrigation releases for the prediction of hydropower energy. A single hidden-layered feed forward neural network using Levenberg–Marquardt algorithm is developed with a detailed analysis of model design of those factors affecting successful implementation of the model, which provides for a realistic prediction of the annual average hydroelectric energy from an irrigation dam in a quick-cut manner without the excessive operation studies needed conventionally. Estimation of the average annual energy with the help of this model should be useful for reconnaissance studies.