Measurement methods and analysis tools for rail irregularities. A case study for urban tram track

Rail irregularities, in particular for urban railway infrastructures, are one of the main causes for the generation of noise and vibrations. In addition, repetitive loading may also lead to decay of the structural elements of the rolling stock. This further causes an increase in maintenance costs and reduction of service life. Monitoring these defects on a periodic basis enables the network rail managers to apply proactive measures to limit further damage. This paper discusses the measurement methods for rail corrugation with particular regard to the analysis tools for evaluating the thresholds of acceptability in relation to the tramway Italian transport system. Furthermore, a method of analysis has been proposed: an application of the methodology used for treating road profiles has been also utilized for the data processing of rail profilometric data

Procedure for measurement of surficial soil strength via bevameter

2020 Summer.Includes bibliographical references.Spatial prediction of moisture-variable soil strength is critical for forecasting the trafficability of vehicles across terrain. The Strength of Surface Soils (STRESS) model calculates soil strength properties as a function of soil texture from SSURGO data (or locally available data) and soil moisture from the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model. The STRESS model yields soil strength properties (friction angle and moisture-variable cohesion) that vary with soil texture and moisture conditions. However, the STRESS model is hindered by a lack of surficial soil strength data linked directly to soil texture. The objective of this study is to develop and validate a bevameter procedure to improve measurement of near-surface moisture-variable soil strength. The bevameter is a test apparatus that measures in-situ surficial soil strength properties by rotational shearing of a shear annulus under a constant normal force at a constant rate. The bevameter allows for lab or field determination of Mohr-Coulomb surficial soil strength properties at a given moisture content in a manner that approximates how vehicles interact with surficial soils. Experimental variables evaluated include the shearing surface (grousers, sandpaper, or bonded angular sand) and the use of interior and exterior annular surcharge weights to minimize slip sinkage of the shear annulus. Based on the results of this study, a bevameter procedure is recommended that uses a coarse sandpaper as the shear interface with an internal and external surcharge of 2 kPa during shear testing. Using the revised bevameter procedure for field testing, the performance of predicted moisture-variable soil strength by the STRESS model is evaluated. Field validation illustrates the need to develop surficial-soil specific pedotransfer functions for use in the STRESS model

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Producción CientíficaSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively

Polarised light stress analysis and laser scatter imaging for non-contact inspection of heat seals in food trays

This paper introduces novel non-contact methods for detecting faults in heat seals of food packages. Two alternative imaging technologies are investigated; laser scatter imaging and polarised light stress images. After segmenting the seal area from the rest of the respective image, a classifier is trained to detect faults in different regions of the seal area using features extracted from the pixels in the respective region. A very large set of candidate features, based on statistical information relating to the colour and texture of each region, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating faults from non-faults. With this approach, different features can be selected and optimised for the different imaging methods. In experiments we compare the performance of classifiers trained using features extracted from laser scatter images only, polarised light stress images only, and a combination of both image types. The results show that the polarised light and laser scatter classifiers achieved accuracies of 96\% and 90\%, respectively, while the combination of both sensors achieved an accuracy of 95\%. These figures suggest that both systems have potential for commercial development