Abstract. The objective of this project was to compare two non-parametric classification methods (“Support Vector Machine” – SVM and “Artificial Neural Networks” – ANN) of road regions in high spatial resolution images and associated with data from Airborne Laser Scanning. The study aims to verify what kind of influence the layers of attributes have on the performance from respective classifiers: SVM and RNA. Our method based on tests of this classifiers on 4 bands of airborne images and normalization of the digital surface model (DSM) for showing only information on objects height in relation to ground and not of these in relation to the ground and relief, generating band 5. The samples were used to train chosen non-parametric classifiers (training sets for each different input image/landscape). All classifications had the same set of training samples and the same classification parameters. The optimal parameters for classifications were obtained through the existing library in the Weka mining package: LibSVM and LibMultiLayerPerceptron. Our results demonstrated the existence of a direct relationship between the elevation band of the targets in relation to the terrain (band 05) with the improvement of their performance and lower degree of between bands correlation can also be considered a factor that has a positive influence. As for Neural Networks, the experiment results demonstrate that the presence of the near infrared band (band 04) was decisive for the performance improving of certain combinations in relation to others.
Current studies indicate that it is possible to obtain soil-cement products with similar performances to high performance concrete (HPC). In this context, this work aimed to evaluate the flexural rupture parameters of high-performance soil-cement specimens (HPSC) in relation to the cement percentage. Eighteen HPSC specimens were analyzed, produced with compaction moisture of 13% and pressure of 1.2 MPa. The average flexural-tensile strength of the specimens was 4.6 MPa with 30% cement, 5.6 MPa with 40% cement and 5.8 MPa with 50% cement. The results indicate the possibility of using a new product (HPSC) with flexural-tensile strength equivalent to HPC, but without the need to use mining materials, such as sand, gravel and other additives like superplasticizers. This opens up the opportunity to produce new soil-cement artifacts, such as plates and/or floors, for which values of flexural-tensile strength higher than those currently obtained in concrete and/or soil-cement products are required.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.