Runway Stiffness Evaluation Using an Artificial Neural Systems Approach
نویسنده
چکیده
A critical issue concerning the deterioration of ageing road infrastructure all around the world is the need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure. Non-Destructive Test (NDT) and evaluation methods are well-suited for characterizing materials and determining structural integrity of pavement systems. The Falling Weight Deflectometer (FWD) is a Nondestructive Test (NDT) equipment used to assess the structural condition of airfield pavement systems and to determine the moduli of pavement layers which are not only good condition indicators, but are also necessary inputs for conducting mechanistic based pavement structural analysis. In this study, Artificial Neural Systems (ANSs) based models were used to predict flexible airport pavement layer moduli from realistic FWD deflection basins acquired at the U.S. Federal Aviation Administration’s (FAA’s) National Airport Pavement Test Facility (NAPTF). A finite-element pavement structural model, which can account for nonlinear, stress-dependent behavior of pavement geomaterials, was used to generate the ANS training and testing database. The pavement stiffness uniformity characteristics of a highstrength flexible test section at the NAPTF were successfully mapped from FWD deflection data using the ANS based backcalculation models. Keywords—ANN, ANS, non-destructive test, flexible pavement, NAPTF
منابع مشابه
A hybrid approach to supplier performance evaluation using artificial neural network: a case study in automobile industry
For many years, purchasing and supplier performance evaluation have been discussed in both academic and industrial circles to improve buyer-supplier relationship. In this study, a novel model is presented to evaluate supplier performance according to different purchasing classes. In the proposed method, clustering analysis is applied to develop purchasing portfolio model using available data in...
متن کاملLearning Curve Consideration in Makespan Computation Using Artificial Neural Network Approach
This paper presents an alternative method using artificial neural network (ANN) to develop a scheduling scheme which is used to determine the makespan or cycle time of a group of jobs going through a series of stages or workstations. The common conventional method uses mathematical programming techniques and presented in Gantt charts forms. The contribution of this paper is in three fold. First...
متن کاملPrediction the Return Fluctuations with Artificial Neural Networks' Approach
Time changes of return, inefficiency studies performed and presence of effective factors on share return rate are caused development modern and intelligent methods in estimation and evaluation of share return in stock companies. Aim of this research is prediction of return using financial variables with artificial neural network approach. Therefore, the statistical population of this study incl...
متن کاملRainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding
In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under in...
متن کاملFlood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique
Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that th...
متن کامل