Rutting is a crucial concern impacting asphalt concrete pavements’ stability and long-term performance, negatively affecting vehicle drivers’ comfort and safety. This research aims to evaluate the permanent deformation of pavement under different traffic and environmental conditions using an Artificial Neural Network (ANN) prediction model. The model was built based on the outcomes of an experimental uniaxial repeated loading test of 306 cylindrical specimens. Twelve independent variables representing the materials’ properties, mix design parameters, loading settings, and environmental conditions were implemented in the model, resulting in a total of 3214 data points. The network accomplished high prediction accuracy with an R2 of 0.93 and a mean squared error (MSE) of 0.0039. Results based on the sensitivity analysis and variable importance techniques showed that the percentage of aggregate passing the 4.75 mm sieve and the (rice) theoretical maximum specific gravity (Gmm) were the most significant factors in predicting axial permanent strain (εp). Furthermore, the connection weight method highlighted input variables’ distinct positive and negative impacts on permanent deformation.
Background: Any child with Down's syndrome does not develop in the same manner as normal child. Therefore, the child should not be viewed as being like everyone else. Developmental enamel defects in primary teeth have been found at least twice as frequently in disabled children as in control children. Down's syndrome consumed protein more than the recommended daily allowance compared to other disabled groups. Therefore, the aim of this study was to investigate developmental defects of enamel and their relations to nutrient intake among Down's syndrome children in comparison to normal children. Materials and Methods: A sample consisted of fifty institutionalized Down's syndrome children (study group) and 50 normal children (control group)
... Show MoreEstimation of trip attraction and analyzing its main influencing factors are powerful for offering different classifications for business districts and presenting recommendations for improving attractiveness in long term. This is beneficial for designing transportation facilities and infrastructures. The paper presents the prediction of trip attraction using an artificial intelligence technology due to the profits that the technology can possess in shortening time, lowering expenses and saving effort. The new model has utilized six input parameters that have not been considered previously within the area of Nasiriyah city including; age and educational level of the passengers, mode of transport that the passengers use, purpose of the trip,
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The grey system model GM(1,1) is the model of the prediction of the time series and the basis of the grey theory. This research presents the methods for estimating parameters of the grey model GM(1,1) is the accumulative method (ACC), the exponential method (EXP), modified exponential method (Mod EXP) and the Particle Swarm Optimization method (PSO). These methods were compared based on the Mean square error (MSE) and the Mean Absolute percentage error (MAPE) as a basis comparator and the simulation method was adopted for the best of the four methods, The best method was obtained and then applied to real data. This data represents the consumption rate of two types of oils a he
... Show MoreIn this research we been estimated the survival function for data suffer from the disturbances and confusion of Iraq Household Socio-Economic Survey: IHSES II 2012 , to data from a five-year age groups follow the distribution of the Generalized Gamma: GG. It had been used two methods for the purposes of estimating and fitting which is the way the Principle of Maximizing Entropy: POME, and method of booting to nonparametric smoothing function for Kernel, to overcome the mathematical problems plaguing integrals contained in this distribution in particular of the integration of the incomplete gamma function, along with the use of traditional way in which is the Maximum Likelihood: ML. Where the comparison on the basis of the method of the Cen
... Show MoreFluid-structure interaction method is performed to predict the dynamic characteristics of axial fan system. A fluid-structure interface physical environment method (monolithic method) is used to couple the fluid flow solver with the structural solver. The integration of the three-dimensional Navier-Stokes equations is performed in the time Doman, simultaneously to the integration of the three dimensional structural model. The aerodynamic loads are transfer from the flow to structure and the coupling step is repeated within each time step, until the flow solution and the structural solution have converged to yield a coupled solution of the aeroelastic set of equations. Finite element method is applied to solve numerically
... Show MoreDeep submicron technologies continue to develop according to Moore’s law allowing hundreds of processing elements and memory modules to be integrated on a single chip forming multi/many-processor systems-on-chip (MPSoCs). Network on chip (NoC) arose as an interconnection for this large number of processing modules. However, the aggressive scaling of transistors makes NoC more vulnerable to both permanent and transient faults. Permanent faults persistently affect the circuit functionality from the time of their occurrence. The router represents the heart of the NoC. Thus, this research focuses on tolerating permanent faults in the router’s input buffer component, particularly the virtual channel state fields. These fields track packets f
... Show More In this paper the research represents an attempt of expansion in using the parametric and non-parametric estimators to estimate the median effective dose ( ED50 ) in the quintal bioassay and comparing between these methods . We have Chosen three estimators for Comparison. The first estimator is
( Spearman-Karber ) and the second estimator is ( Moving Average ) and The Third estimator is ( Extreme Effective Dose ) . We used a minimize Chi-square as a parametric method. We made a Comparison for these estimators by calculating the mean square error of (ED50) for each one of them and comparing it with the optimal the mean square