Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques

Compound broad-crested weir is a typical hydraulic structure that provides flow control and measurements at different flow depths. Compound broad-crested weir mainly consists of two sections; first, relatively small inner rectangular section for measuring low flows, and a wide rectangular section at higher flow depths. In this paper, series of laboratory experiments was performed to investigate the potential effects of length of crest in flow direction, and step height of broad-crested weir of rectangular compound cross-section on the discharge coefficient. For this purpose, 15 different physical models of broad-crested weirs with rectangular compound cross-sections were tested for a wide range of discharge values. The results of examination for computing discharge coefficient were yielded by using multiple regression equations based on the dimensional analysis. Then, the results obtained were also compared with genetic programming (GP) and artificial neural network (ANN) techniques to investigate the applicability, ability, and accuracy of these procedures. Comparison of results from the GP and ANN procedures clearly indicates that the ANN technique is less efficient in comparison with the GP algorithm, for the determination of discharge coefficient. To examine the accuracy of the results yielded from the GP and ANN procedures, two performance indicators (determination coefficient (R ²) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of GP technique sound satisfactory regarding the performance indicators (R ²?=?0.952 and RMSE?=?0.065) with less deviation from the numerical values.     

An integrated framework for linear pattern extraction in the building group generalization process

Building pattern extraction is an essential step in building generalization. Although many studies have already been conducted, there is a lack of a framework for extracting building patterns. To overcome this problem, an integrated framework for extracting building linear patterns is presented. First, an aggregation function is presented based on the TOPSIS method, which determines the similarity index in terms of area, shape, rectangularity and distance similarities. This results in the extraction of straight and perpendicular patterns using the similarity index and orientation difference criteria. Second, a refinement strategy is proposed, which refines the extracted patterns using a novel definition of the pattern interaction index. To evaluate the proposed model, the complete building group generalization process is implemented using a data-set at 1:25 k scale. The evaluation results allowed us to conclude that the proposed model produces meaningful results, and therefore it would be beneficial in the generalization process.     

Is μCT irradiation nondestructive? A noble gas study on matrix samples from the CV3 chondrite Allende

Micro-computed tomography (μCT) is a fast and powerful technology for studying textural, physical, and chemical properties of solid objects in three dimensions. While regularly used for sample documentation and curation, it is often assumed that μCT techniques are essentially nondestructive or at least very little destructive. However, there are very few studies proving or rejecting the assumption of nondestructiveness. Here we study whether X-ray tomographic imaging affects the noble gas budget of matrix samples from the CV3 carbonaceous chondrite Allende. We irradiated powdered and homogenized matrix samples in the Bruker SkyScan 1272 μCT instrument at three different X-ray tube acceleration voltages of 30, 70, and 100 keV. By comparing the noble gas concentrations and especially the elemental and isotopic ratios of the irradiated samples with data for two non-irradiated aliquots, we found no significant differences. Our study therefore demonstrates that X-ray tomographic imaging has no measurable effect on the noble gas budget and can therefore safely be used for sample characterization prior to noble gas studies.