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Determination of the Most Important Parameters on Scour at Coastal Structures Original Article, B12 Yeganeh-Bakhtiary A, Ghorbani MA and Pourzangbar A. Journal. Civil Eng. Urban. 2(2): 68-71. 2012. ABSTRACT: Scour at coastal structures is one of the major problems that may lead to their failure. Therefore, predicting accurate scour depth at coastal structures is important. Extensive laboratory studies have been conducted predicting the maximum scour depth. These studies have developed their formulas using the limited set of effective input parameters. This study presents an alternative to the conventionally regression-based equations in the form of genetic programming (GP) in order to predict the maximum scour depth at coastal structures under the action of breaking waves. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as reflection coefficient, relative water depth at the toe of the structure, the serf similarity parameter, Shields parameter and breaking wave steepness and the wave breaking depth were found to be best inputs. 46 data sets compiled from published literatures were used to train and test the networks or evolve the models. Statistical parameters including the root mean square error, determination coefficient, scatter index and BIAS are used to measure their performance. The results indicate that relative water depth at the toe of the structure plays a crucial role in the scour process. Keywords: Coastal structures; breaking waves; Genetic programming; Scour depth; Regression-based equations. منبع: [Hidden Content] دانلود: [Hidden Content].,B12-68-71.pdf J. Civil Eng. Urban.,B12-68-71.pdf

A Wavelet-Genetic Programming Model for Predicting Short-Term and Long-Term Air Temperatures Original Article, A5 Kisi O, Shiri J and Nazemi AH. Journal. Civil Eng. Urban. 1(1): 26-38.2011. ABSTRACT: A new conjunction wavelet-gene expression programming (WGEP) method for predicting air temperature values is proposed in this paper. The conjunction method combines the discrete wavelet and genetic programming methods. The daily and monthly air temperature data from two weather stations of Mahabad and Urmieh in Iran were used as case studies and the accuracy of the single gene expression programming (GEP) and wavelet-gene expression programming (WGEP) models were compared with each other. First, the daily air temperatures were used as inputs to the GEP and WGEP models to forecast one-, two- and three day as well as thirty-day ahead air temperatures. Then, the monthly air temperatures were used as inputs to the GEP and WGEP models to forecast one-month ahead air temperatures. The comparison results indicated that the WGEP model significantly increased the accuracy of single GEP model especially in forecasting long-term (thirty-day and one-month ahead) air temperatures. The thirty-day and one-month ahead air temperatures of the Mahabad Station were also estimated using the data of nearby Urmieh Station. It was found that the WGEP model performed much better than the single GEP model in cross-station application. Keywords: Air temperature, discrete wavelet, genetic programming, cross application منبع: [Hidden Content] دانلود: [Hidden Content],%20A5,.pdf JCEU, A5,.pdf