New concept in analysis of floating piers for ship berthing impact

In this paper, special behavior of floating piers in the berthing event is assessed, and a new approach is presented for the analysis of these kinds of piers for berthing impact. Accurate estimation of berthing force is an important matter for appropriate design of mooring piles in these piers. In the pier design references, the traditional approach used in the design of fixed piers is extended to floating piers, ignoring distinctive response of these piers to berthing impact. In this paper, the fact of mobility and flexibility of structural system of floating piers are taken into consideration, and it is proved that energy absorption mechanism in these piers is different from that of fixed piers. Thus a new analytical approach and corresponding closed form formulations are presented in this paper for evaluating impact energy and induced berthing forces in these piers. The validity of presented method is shown by numerical simulations. Comparison of results of analysis by new and traditional methods in two typical piers shows that the traditional method underestimates berthing force in floating piers.     

An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing.