Detection of large-scale concrete columns for automated bridge inspection

There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame. This is because manually inspecting bridges is a time-consuming and costly task, and some state Departments of Transportation (DOT) cannot afford the essential costs and manpower. In this paper, a novel method that can detect large-scale bridge concrete columns is proposed for the purpose of eventually creating an automated bridge condition assessment system. The method employs image stitching techniques (feature detection and matching, image affine transformation and blending) to combine images containing different segments of one column into a single image. Following that, bridge columns are detected by locating their boundaries and classifying the material within each boundary in the stitched image. Preliminary test results of 114 concrete bridge columns stitched from 373 close-up, partial images of the columns indicate that the method can correctly detect 89.7% of these elements, and thus, the viability of the application of this research.

Automated Detection of Concrete Columns from Visual Data

After earthquakes, licensed inspectors use the established codes to assess the impact of damage on structural elements. It always takes them days to weeks. However, emergency responders (e.g. firefighters) must act within hours of a disaster event to enter damaged structures to save lives, and therefore cannot wait till an official assessment completes. This is a risk that firefighters have to take. Although Search and Rescue Organizations offer training seminars to familiarize firefighters with structural damage assessment, its effectiveness is hard to guarantee when firefighters perform life rescue and damage assessment operations together. Also, the training is not available to every firefighter. The authors therefore proposed a novel framework that can provide firefighters with a quick but crude assessment of damaged buildings through evaluating the visible damage on their critical structural elements (i.e. concrete columns in the study). This paper presents the first step of the framework. It aims to automate the detection of concrete columns from visual data. To achieve this, the typical shape of columns (long vertical lines) is recognized using edge detection and the Hough transform. The bounding rectangle for each pair of long vertical lines is then formed. When the resulting rectangle resembles a column and the material contained in the region of two long vertical lines is recognized as concrete, the region is marked as a concrete column surface. Real video/image data are used to test the method. The preliminary results indicate that concrete columns can be detected when they are not distant and have at least one surface visible.

Preliminary laboratory-scale model auger installation and testing of carbonated soil-MgO columns

This paper presents details of the installation and performance of carbonated soil-MgO columns using a laboratory-scale model auger setup. MgO grout was mixed with the soil using the auger and the columns were then carbonated with gaseous CO2 introduced in two different ways: one using auger mixing and the other through a perforated plastic tube system inserted into the treated column. The performance of the columns in terms of unconfined compressive strength (UCS), stiffness, strain at failure and microstructure (using X-ray diffraction and scanning electron microscopy) showed that the soil-MgO columns were carbonated very quickly (in under 1 h) and yielded relatively high strength values, of 2.4-9.4 MPa, which on average were five times that of corresponding 28-day ambient cured uncarbonated columns. This confirmed, together with observations of dense microstructure and hydrated magnesium carbonates, that a good degree of carbonation had taken place. The results also showed that the carbonation method and period have a significant effect on the resulting performance, with the carbonation through the perforated pipe producing the best results. Copyright © 2013 by ASTM International.

Distribution and bioaccumulation of microcystins in water columns: A systematic investigation into the environmental fate and the risks associated with microcystins in Meiliang Bay, Lake Taihu

For the purpose of understanding the environmental fate of microcystins (MCs) and the potential health risks caused by toxic cyanobacterial blooms in Lake Taihu, a systematic investigation was carried out from February 2005 to January 2006. The distribution of MCs in the water column, and toxin bioaccumulations in aquatic organisms were surveyed. The results suggested that Lake Taihu is heavily polluted during summer months by toxic cyanobacterial blooms (with a maximum biovolume of 6.7 x 10(8) cells/L) and MCs. The maximum concentration of cell-bound toxins was 1.81 mg/g (DW) and the dissolved MCs reached a maximum level of 6.69 mu g/L. Dissolved MCs were always found in the entire water column at all sampling sites throughout the year. Our results emphasized the need for tracking MCs not only in the entire water column but also at the interface between water and sediment. Seasonal changes of MC concentrations in four species of hydrophytes (Eichhornic crassipes, Potamogeton maackianus, Alternanthera philoxeroides and Myriophyllum spicatum) ranged from 129 to 1317, 147 to 1534, 169 to 3945 and 124 to 956 ng/g (DW), respectively. Toxin accumulations in four aquatic species (Carassius auratus auratu, Macrobrachium nipponensis, Bellamya aeruginosa and Cristaria plicata) were also analyzed. Maximum toxin concentrations in the edible organs and non-edible visceral organs ranged from 378 to 730 and 754 to 3629 ng/g (DW), respectively. Based on field studies in Lake Taihu, risk assessments were carried out, taking into account the WHO guidelines and the tolerable daily intake (TDI) for MCs. Our findings suggest that the third largest lake in China poses serious health threats when serving as a source of drinking water and for recreational use. In addition, it is likely to be unsafe to consume aquatic species harvested in Lake Taihu due to the high-concentrations of accumulated MCs. (C) 2007 Elsevier Ltd. All rights reserved.

The optimization of high performance liquid chromatographic method for analysis of trace microcystins in natural water bodies

The ratio of methanol., water and trifluoroacetic acid ( TFA) was regulated to change the polarity and the pH of the rinse solution and the eluent, so as to improve the high performance liquid chromatography HPLC) detection method for trace microcystines (MCs) in natural water bodies. The results showed that 40 % similar to 45 % methanol-water solution containing 0. 1 % TFA could get good effects on the rinse of impurity, and 70% methanol-water solution containing 0. 1% TFA could elute all the MCs in solid phase extraction ( SPE) cartridge ( C-18), In this way. it is suggested that, in analysis of environmental samples with high concentration of impurity, impurity should be washed with 40% similar to 45% methanol-water solution containing 0. 1% TFA, and MCs should be eluted with 70% similar to 100% methanol-water solution containing 0. 1% TFA.

Quantitative relationship between chromatographic retentions and molecular structures of polychlorinated dibenzo-p-dioxins (PCDDs)

A new approach to study the quantitative relationships between chromatographic retentions and molecular structures of polychlorinated dibenzo-p-dioxins (PCDDs) is described. The retention equations of PCDDs log k' = A + B/T in gas chromatography (GC) are used to evaluate the properties of the regression coefficients A and B, which have been widely accepted as highly reliable chromatographic retentions. The quantitative relationships between the A, B values and the molecular structures are found. The molecular descriptors given for the first time in this article are very effective. As a result, the regression equations are derived with correlation coefficients greater than 0.9995. The A, B values of PCDDs with no standards available have been predicted according to these relationships. They are very useful in chromatographic identification. The retention times of all PCDDs can be conveniently predicted at any temperature program. Compared with the data obtained from the relevant experiments, the results of prediction are very accurate. (C) 2000 Elsevier Science Ltd. All rights reserved.