sedimaging for soil classification
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This document summarizes a seminar on determining the particle size distribution of coarse-grained soils using a new method called sedimaging. Sedimaging uses image processing techniques like wavelet transformation to analyze images of soil particles accumulated over time during sedimentation. It provides a quick, automated method to obtain around 5000 data points for the particle size distribution curve between 0.075mm to 2mm, compared to only 10-11 points from traditional sieve analysis. While sedimaging mimics results from sieving, it has advantages of reduced testing time, lower energy use, and improved work environment. Further research is still needed to address limitations like particle texture effects and to develop the method for finer soils.
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ZEB1 Mobile Hand-held laser scanner for mining applications
ZEB1 Mobile Hand-held laser scanner for mining applications
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This document provides instructions for performing a sieve analysis test to determine the particle size distribution of fine aggregates or sand. The key steps include: 1) preparing a representative sample, 2) arranging sieves in order of decreasing size, 3) sieving the sample and weighing the material retained on each sieve, 4) calculating the percentage retained, cumulative percentage retained, and cumulative percentage passing through each sieve. The results are used to evaluate whether the sand is well graded or poorly graded and to calculate metrics like the uniformity coefficient.
Correction for construction Settlement
This document discusses boundary conditions for solving 1D consolidation equations and correcting settlement calculations for construction periods. It presents the basic 1D consolidation differential equation and describes the initial and boundary conditions used to solve it. These include an initial excess pore pressure condition and no flow boundary conditions. It also provides an empirical method to correct settlement calculations by accounting for load being applied over a construction period rather than instantaneously. Two example problems demonstrate applying the equations to calculate degree of consolidation and settlement with and without corrections for construction time.
hydrometer ( flow fluid lab ) chemical Eng
This document is a student laboratory report that details using an aerometer or hydrometer to determine the density of various liquids. It includes an introduction to the theory behind how aerometers measure density, the experimental procedure, a table of density readings for oil, water, and a saltwater solution, and a brief discussion section. The aim was to use appropriately calibrated aerometers to ascertain properties like alcohol content, fat content, concentration, and antifreeze content by measuring density.
Void Ratio Correlations
This document discusses correlations between various geotechnical properties and the void ratio of soils. It defines void ratio as the ratio of volume of voids to volume of solids. Typical void ratio ranges are provided for different soil types. Relationships are presented between void ratio and properties such as unit weight, moisture content, maximum and minimum void ratios, relative density, shear modulus, hydraulic conductivity, preconsolidation pressure, and compression index. Graphs illustrate how properties such as shear strength and hydraulic conductivity vary with changes in void ratio.
Artificial neural network ann prediction of one-dimensional consolidation
This document describes using artificial neural networks (ANN) to predict one-dimensional consolidation in homogeneous clay soils. It discusses:
1) Traditionally, the finite difference method is used to numerically solve consolidation equations in a step-wise manner, requiring significant computational resources.
2) The study develops two ANN models (net1 for single drainage and net2 for double drainage) to directly predict consolidation results like excess pore pressure and settlement over time.
3) A sensitivity analysis showed the key input for the ANN models is the time factor, with other inputs like thickness, coefficient of consolidation, and load having negligible effects. This allows simplifying the ANN input to just the time factor.
Solution for radial consolidation
The document discusses the three-dimensional form of the consolidation equation for soils with different properties in the horizontal and vertical directions. It provides the equation in polar coordinates. It also includes a table showing time factor (Tr) values for various coefficients (n) between 5 and 25 and degrees of consolidation (Ur) from 0.1 to 0.99. The document lists several references on topics related to compressibility, consolidation, and the 3D consolidation equation.
Mechanical analysis of soil
This document discusses methods for determining the particle size distribution of soils. It describes sieve analysis and hydrometer analysis, which are used to measure particle sizes above and below 0.075 mm, respectively. It explains how sieve analysis works by sieving dry soil through a stack of sieves and measuring the mass retained on each sieve. It also provides definitions and applications of key terms used to characterize particle size distributions, such as effective size and uniformity coefficient.
Sieve analysis test of sand (video version check https://www.youtube.com/watc...
Sieve analysis test of sand (video version check https://www.youtube.com/watc...John G. Luwalaga (P.E, A.M.ASCE, MEngCivil, MPH)
This document describes the sieve analysis test of sand. The test involves sieving various sizes of sand particles through a series of sieves and calculating the percentage of sand retained on each sieve. This determines the particle size distribution. Well graded sand has a wide range of particle sizes, uniformly graded sand has similar sized particles, and gap graded sand is missing some sizes. The test procedure, equipment used, and results/conclusions are explained.Sieve analysis test report
1. The objective of the experiment is to determine the grain size distribution of a soil sample using sieves and comparing the results to BS 410 standards.
2. The procedure involves sieving soil samples through a series of sieves with decreasing pore sizes, weighing the material retained on each sieve, and calculating the percentage retained and passing through each sieve.
3. The results show the weight and percentage retained and passing for each sieve size. A distribution curve is analyzed and compared to grading standards to evaluate the quality of the soil sample.
تجربة الضغط على نقطة Point load test 2013 full copy
تجربة الضغط على نقطة Point load test 2013 full copy
هذه التجربة من اكثر التجارب سهولة واقلها تكلفة , وتستخدم في الوصف والتصنيف بعيدا تماما عن كل مايتعلق بالتصميم الهندسي وذلك كله يعود الى نتائجها المعيارية وغير المباشرة.
Consolidation & Drainage properties
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Index property of soil
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Micromeritics
Micromeritics refers to the science and technology of small particles. It deals with particle size, size distribution, shape, surface area, and pore size. Knowledge of these properties is important in pharmacy because particle size affects drug release from dosage forms and stability of suspensions, emulsions, and tablets. It also influences flow properties and uniformity of drug fill in tablets and capsules. Smaller particle sizes increase dissolution and absorption rates for some drugs. Common methods to determine particle size include sieving, sedimentation, light scattering, and electrical sensing using a Coulter Counter.
Consolidation Properties
Consolidation is the process where water drains from saturated soil pores, transferring the load from water to soil particles and causing volume change. There are three types of consolidation: immediate, primary, and secondary. One-dimensional consolidation assumes vertical drainage, making the process primarily vertical. Terzaghi's theory of one-dimensional consolidation models this using parameters like permeability, compressibility, and effective stress. The coefficient of consolidation describes the rate of compression, while compression and swelling indices characterize the void ratio-effective stress relationship. The oedometer test experimentally determines consolidation properties from soil specimen compression under incremental loads.
Schmidt's Hammer Rebound Value Analysis for finding Uniaxial Compressive Stre...
Schmidt's Hammer Rebound Value Analysis for finding Uniaxial Compressive Stre...Jasmeet Singh Saluja
An innovative & easy way of establishing a relationship ship between Schmidt's Hammer Rebound value & Uniaxial Compressive Strength, Point Load Index & Density of rocks to obtain the value of UCS on site.
This method helps in eliminating the need of laboratory analysis of rock for UCS, Point Load Index & density determination.Solution of One Dimentional Consolidation
The document summarizes the solution of one-dimensional consolidation. It presents Terzaghi's consolidation equation and describes how the variables of depth, excess pore water pressure, and time can be used to model consolidation. Graphical solutions are presented using non-dimensional terms and isochrones, which represent degrees of consolidation at different times and locations throughout the consolidating soil layer. The average degree of consolidation over the entire depth is also defined using an equation.
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Artificial neural network ann prediction of one-dimensional consolidation
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تجربة الضغط على نقطة Point load test 2013 full copy
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Schmidt's Hammer Rebound Value Analysis for finding Uniaxial Compressive Stre...
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This document provides a survey of single image dehazing approaches. It begins with an introduction to the problem of haze in images and how it degrades quality. It then summarizes several existing single image dehazing methods, including those based on the atmospheric scattering model, dark channel prior, color attenuation prior, and deep learning approaches. The survey covers the key assumptions and limitations of each approach. Overall, the document reviews the progress that has been made in developing techniques to remove haze from a single input image.
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Images of outdoor scenes are degraded by absorption and scattering by the suspended particles and water droplets in the atmosphere. The light coming from a scene towards the camera is attenuated by fog and is blended with the airlight which adds more whiteness into the scene. Fog removal is highly desired in computer vision applications. Removing fog from images can
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This document summarizes the design and fabrication of a 3-inch diameter diffractive corrector element for use in a head-up display operating at 545 nanometers. A 14-level diffractive pattern was etched onto a fused silica lens using a 4-mask photolithography process, achieving an efficiency of over 96%. Fabrication errors including mask misalignment, etch depth uniformity, and diffraction efficiency were all within tolerances needed for the high optical demands of the head-up display application. Measurements showed good agreement with the theoretical diffraction performance of the designed element.
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Abstract— This paper demonstrates overcoming of the Abbe diffraction limit (ADL) on image resolution. Here, terahertz multispectral reconstructive imaging has been described and used for analyzing nanometer size metal lines fabricated on a silicon wafer. It has also been demonstrated that while overcoming the ADL is a required condition, it is not sufficient to achieve sub-nanometer image resolution with longer wavelengths. A nanoscanning technology has been developed that exploits the modified Beer-Lambert’s law for creating a measured reflectance data matrix and utilizes the ‘inverse distance to power equation’ algorithm for achieving 3D, sub-nanometer image resolution. The nano-lines images reported herein, were compared to SEM images. The terahertz images of 70 nm lines agreed well with the TEM images. The 14 nm lines by SEM were determined to be ~15 nm. Thus, the wavelength dependent Abbe diffraction limit on image resolution has been overcome. Layer-by-layer analysis has been demonstrated where 3D images are analyzed on any of the three orthogonal planes. Images of grains on the metal lines have also been analyzed. Unlike electron microscopes, where the samples must be in the vacuum chamber and must be thin enough for electron beam transparency, terahertz imaging is non-destructive, non-contact technique without laborious sample preparation.Nanoscale metrology of line patterns on semiconductor by continuous wave tera...
Nanoscale metrology of line patterns on semiconductor by continuous wave tera...Applied Research and Photonics, Inc.
Abstract:
This paper demonstrates overcoming of the Abbe diffraction limit (ADL) on image resolution. Here, terahertz multispectral reconstructive imaging has been described and used for analyzing nanometer size metal lines fabricated on a silicon wafer. It has also been demonstrated that while overcoming the ADL is a required condition, it is not sufficient to achieve sub-nanometer image resolution with longer wavelengths. A nanoscanning technology has been developed that exploits the modified Beer-Lambert’s law for creating a measured reflectance data matrix and utilizes the ‘inverse distance to power equation’ algorithm for achieving 3D, sub-nanometer image resolution. The nano-lines images reported herein, were compared to SEM images. The terahertz images of 70 nm lines agreed well with the TEM images. The 14 nm lines by SEM were determined to be 15 nm. Thus, the wavelength dependent Abbe diffraction limit on image resolution has been overcome. Layer-by-layer analysis has been demonstrated where 3D images are analyzed on any of the three orthogonal planes. Images of grains on the metal lines have also been analyzed. Unlike electron microscopes, where the samples must be in the vacuum chamber and must be thin enough for electron beam transparency, terahertz imaging is non-destructive, non-contact technique without laborious sample preparation.IRJET- A Novel Hybrid Image Denoising Technique based on Trilateral Filtering...
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Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
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Stereo matching based on absolute differences for multiple objects detection
This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
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- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
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4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
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- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
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12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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sedimaging for soil classification
- 1. SEMINAR ON by SUDARSHAN BAROLE 15CE64R07 DEPARTMENT OF CIVIL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR SIZE DISTRIBUTION OF COARSE- GRAINED SOIL BY SEDIMAGING
- 2. 1. INTRODUCTION 1.1 PARTICLE SIZE DISTRIBUTION Most fundamental property of coarse grained soil Traditional method for particle size distribution is sieve analysis. Fig 1.1- Apparatus for Performing Sieve analysis test on soil.(a) Set of Sieve (b) mechanical sieve shaker
- 3. 2.SEDIMAGING A new method recently develop to determine the particle size distribution of coarse grain soil by using imaging technique. practically implemented by Hyon-shok Ohm and Roman D. Hryciw it gives the particle size distribution for particles between 0.075mm and 2.0 mm This test also help to determine the percentage finer than .075mm 2.1 INTRODUCTION
- 4. In 2004 shin and Hryciw developed an image processing technique based on wavelet transformation. But due to absence of high resolution camera and laboratory testing system at that time, the above concept was only a theoretical concept Ohm and hryciw then tracked the development of camera technology until 20-40 megapixel camera became available in early 2010. 2.2 EARLY ATTEMPS TOWARD SEDIMAGING
- 5. 2.3 SEDIMAGING HARDWARE Sedimentation column Sediment accumulator Pre segregation tube
- 9. Pixel- It is the smallest controllable element of a picture represented on the screen. Pixels per diameter(PPD)- It is the most important and useful in sedimaging. It is define as average number of pixel per particle diameter. 2.5 THEORETICAL CONCEPT
- 10. Grey scale -The intensity of a pixel is expressed within a given range between a minimum and a maximum, inclusive. This range is represented in an abstract way as a range from 0 (total absence, black) and 1 (total presence, white), with any fractional values in between.
- 11. WAVELET TRANSFORMATION In sedimaging wavelet transformation is used for image processing and to determine it particle size distribution. The sedimaging program performs all the wavelet operations in the background with no user input require. Wavelet transformation decompose a image of 2ⁱ x 2ⁱ pixel size image into i decomposition level and calculate energy correspond to each level. Energy is the measure of the magnitude of the difference between average gray scale of adjacent regions in a image.
- 12. Seven levels of downscaling beginning with a 128 x 128 image at PPD=13.2
- 13. As the concentration of energy shifts to higher energy level. Wavelet index – The centroid of area beneath a normalize energy line with respect to vertical axis They gave the relation between PPD and CA based on experiments on pre- seived soil IMPORTANT POINT BASED ON ABOVE CONCEPTS
- 14. They produced the calibration curve as shown. The relationship between CA and PPD is given as Where A= 5.1 for saturated soil behind a glass window(A=5.9 for dry soil)
- 15. Once you determine CA for a portion, you can easily determine the PPD by using equation. The actual grain size can be computed as Where D- diameter of soil particle (mm) and M = camera magnification(pixels/mm)
- 16. Fig showing particle sorted by size This image is analyzed incrementally with height to produce the particle size distribution colorful squares represent area under analysis(128x128 pixels). It gives about 5500 values of D from a 4520 x 1280 pixel image
- 17. Random values of D(only 20% of total value obtained After sorting all the data points by particle size, final particle size distribution Software for analyzing image was coded using MATLAB R2013a
- 18. comparison between sieve and sedimaging results of different soil COMPARISON
- 19. Comparison of particle size distribution curve
- 20. 5.ADVANTAGES OF SEDIMAGING It is a quick test and takes 5-10 minutes. Installation, operation and maintenance is easy. It gives about 5000 points but in case of sieve analysis we get only 10-11 data points for plotting the curve In sedimaging energy consumption is low. It help in improving work environment.
- 21. 6.LIMITATIONS As this test uses calibration curve which is developed using sieving results and so this test just mimics sieving and yield comparable results. It shows variation in results due to internal particle texture. It does not redefine the particle.
- 22. 7.CONCLUSION As we discussed earlier, sedimaging has many advantages over sieving like reducing test time, low energy consumption, less equipment maintenance and improvement in work environment, But before completely adopting this method, a little more research on sedimaging to overcome the limitation is required. Future research and advances in camera will make it possible to develop sedimaging for fine soil like silt Finally there is a need to put attention toward development of sedimaging.
- 23. 8. REFERENCES Ohm. H.S.(2013) “image based soil particle size and shape characterization” Shin, S., and Hryciw, R.D.(2004). “wavelet analysis of soil mass images for particle size determination.” J. comput. Civ. Eng. 10.1061/(ASCE) 0887-3801(2004)18:1(19),19-27 Ohm, H S., and Hryciw, R.D.(2013b). “the translucent segregation table test for sand and gravel particle size distribution.” J. ASTM Geotech Test.,36(4),592-605
- 24. THANK YOU