Column set gill net fishing in Gobindsagar reservoir

Previous authors observed the influence of temperature variations in movement of fishes and noticed better catches in bottom set nets during summer in Tungabhadra reservoir. The present account reports on similar observations in Gobindsagar reservoir.

Discrete element calculations of the impact of a sand column against rigid structures

Discrete particle simulations of column of an aggregate of identical particles impacting a rigid, fixed target and a rigid, movable target are presented with the aim to understand the interaction of an aggregate of particles upon a structure. In most cases the column of particles is constrained against lateral expansion. The pressure exerted by the particles upon the fixed target (and the momentum transferred) is independent of the co-efficient of restitution and friction co-efficient between the particles but are strongly dependent upon the relative density of the particles in the column. There is a mild dependence on the contact stiffness between the particles which controls the elastic deformation of the densified aggregate of particles. In contrast, the momentum transfer to a movable target is strongly sensitive to the mass ratio of column to target. The impact event can be viewed as an inelastic collision between the sand column and the target with an effective co-efficient of restitution between 0 and 0.35 depending upon the relative density of the column. We present a foam analogy where impact of the aggregate of particles can be modelled by the impact of an equivalent foam projectile. The calculations on the equivalent projectile are significantly less intensive computationally and yet give predictions to within 5% of the full discrete particle calculations. They also suggest that "model" materials can be used to simulate the loading by an aggregate of particles within a laboratory setting. © 2012 Elsevier Ltd. All rights reserved.

Concrete column recognition in images and videos

The automated detection of structural elements (e.g., columns and beams) from visual data can be used to facilitate many construction and maintenance applications. The research in this area is under initial investigation. The existing methods solely rely on color and texture information, which makes them unable to identify each structural element if these elements connect each other and are made of the same material. The paper presents a novel method of automated concrete column detection from visual data. The method overcomes the limitation by combining columns’ boundary information with their color and texture cues. It starts from recognizing long vertical lines in an image/video frame through edge detection and Hough transform. The bounding rectangle for each pair of lines is then constructed. When the rectangle resembles the shape of a column and the color and texture contained in the pair of lines are matched with one of the concrete samples in knowledge base, a concrete column surface is assumed to be located. This way, one concrete column in images/videos is detected. The method was tested using real images/videos. The results are compared with the manual detection ones to indicate the method’s validity.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.

Improvements to Concrete Column Detection in Live Video

The automated detection of structural elements (e.g. concrete columns) in visual data is useful in many construction and maintenance applications. The research in this area is under initial investigation. The authors previously presented a concrete column detection method that utilized boundary and color information as detection cues. However, the method is sensitive to parameter selection, which reduces its ability to robustly detect concrete columns in live videos. Compared against the previous method, the new method presented in this paper reduces the reliance of parameter settings mainly in three aspects. First, edges are located using color information. Secondly, the orientation information of edge points is considered in constructing column boundaries. Thirdly, an artificial neural network for concrete material classification is developed to replace concrete sample matching. The method is tested using live videos, and results are compared with the results obtained with the previous method to demonstrate the new method improvements.

Wave propagation due to sinusoidal excitation in nonlinear one-dimensional soil column

The objective of the author's on-going research is to explore the feasibility of determining reliable in situ curves of shear modulus as a function of strain using the dynamic test. The purpose of this paper is limited to investigating what material stiffness is measured from a dynamic test, focusing on the harmonic excitation test. A one-dimensional discrete model with nonlinear material properties is used for this purpose. When a sinusoidal load is applied, the cross-correlation of signals from different depths estimates a wave velocity close to the one calculated from the secant modulus in the stress-strain loops under steady-state conditions. The variables that contributed to changing the average slope of the stress-strain loop also influence the estimate of the wave velocity from cross-correlation. Copyright ASCE 2007.

Sand column impact onto a Kolsky pressure bar

A laboratory-based methodology to launch cylindrical sand slugs at high velocities is developed. The methodology generates well-characterised soil ejecta without the need for detonation of an explosive; this laboratory-based tool thereby allows for the experimental investigation of the soil-structure events. The experimental set-up comprises a launcher with a cylindrical cavity and a piston to push out the sand slug. The apparatus is used to launch both dry and water-saturated sand slugs. High speed photography is used to characterise the evolution of the sand slugs after launch. We find that the diameter of the slugs remains unchanged, and the sand particles possess only an axial component of velocity. However, the sand particles have a uniform spatial gradient of axial velocity and this results in lengthening of the slugs as they travel towards their target. Thus, the density of the sand slugs remains spatially homogenous but decreases with increasing time. The velocity gradient is typically higher in the dry sand slugs than that of the water-saturated slugs. The pressure exerted by the slugs on a rigid-stationary target is measured by impacting the slugs against a direct impact Kolsky bar. After an initial high transient pressure, the pressure reduces to a value of approximately ρv 2 where ρ is the density of the impacting sand slug and v is the particle velocity. This indicates that loading due to the sand is primarily inertial in nature. The momentum transmitted to the Kolsky bar was approximately equal to the incident momentum of the sand slugs, regardless of whether they are dry or water-saturated. © 2013 Elsevier Ltd. All rights reserved.

Extraction of genomic DNA using a new amino silica monolithic column

A new amino silica monolithic column was developed for DNA extraction in a miniaturized format. The monolithic column was prepared in situ by polymerization of tetraethoxysilane (TEOS) and N-(beta-aminoethyl)-gamma-aminopropylmethyldimethoxysilane (AEAPMDMS). DNA was loaded in 50 mM tris(hydroxylmethyl)aminomethane-EDTA buffer at pH 7.0 and eluted with 300 mM potassium phosphate solution at pH 10.0. Under optimal condition, a 6.0-cm monolithic column provided a capacity of 56 ng DNA with an extraction efficiency of 71 +/- 5.2% (X +/- RSD). When the amino silica monolithic column was applied to extract genomic DNA from the whole blood of crucian carp, an extraction efficiency of 52 +/- 5.6% (X +/- SD) was obtained by three extractions. Since the chaotropic-based sample loading and organic solvent wash steps were avoided in this procedure, the purified DNA was suitable for downstream processes such as PCR. This amino silica monolithic column was demonstrated to allow rapid and efficient DNA purification in microscale.