Many agencies use the AASHTO Guide for Design of Pavement Structures to design their pavement systems. The limitation inherent in this method is the empirical nature of the decision process, which was derived from a road test conducted almost 45 years ago in Ottawa, Illinois. The newly released Mechanistic-Empirical Pavement Design Guide (MEPDG), based on NCHRP Study 1-37A, has adopted a mechan...

First, a process of building of the neural network for events forecasting is presented, that is the selection of networks’ architecture and parameters. Next, the effect of adding data calibrated by nonlinear input function to input data calibrated linearly is described. The nonlinear input function hyperbolic tangent was accepted. Hyperbolic tangent sigmoid transfer function and log sigmoid tra...

In a previous study by the same authors, a new modified Jarvis model (NMJ-model) was developed, calibrated, and validated to estimate stomatal resistance (rs) for maize canopy on an hourly time step. The NMJ-model’s unique subfunctions, different from the original Jarvis model (J-model), include a photosynthetic photon flux density (PPFD)-rs response subfunction developed from field measurement...

In catchments characterized by spatially varying hydrological processes and responses, the optimal parameter values or regions of attraction in parameter space may differ with location-specific characteristics and dominating processes. This paper evaluates the value of semi-distributed calibration parameters for large-scale streamflow simulation using the spatially distributed LISFLOOD model. W...

a subsurface drainage network mainly carries unsteady flow and data are not usually available for model parameters calibration in such networks. in the present research, the finite volume method using the time splitting scheme was employed to develop a computer code for solving the one dimensional unsteady flow equations. using corrugated sub drainage pipes, an experimental prototype setup was ...

The predictive performance equation (PPE) is a mathematical model of learning and retention that attempts to capitalize on the regularities seen in human learning to predict future performance. To generate predictions, PPE’s free parameters must be calibrated to a minimum amount of historical performance data, leaving PPE unable to generate valid predictions for initial learning events. We exam...

this study aims to undertake a statistical study to evaluate the accuracy of nine models that have been previously proposed for estimating the ultimate resistance of plate girders subjected to patch loading. for each model, mean errors and standard errors, as well as the probability of underestimating or overestimating patch load resistance, are estimated and the resultant values are compared o...

A key limitation of all existing algorithms for shape and motion from image sequences under orthographic, weak perspective and para-perspective projection is that they require the calibration parameters of the camera. We present in this paper a new approach that allows the shape and motion to be computed from image sequences without having to know the calibration parameters. This approach is de...