Thermal-creep analysis of concrete bridges

The creep effects on sequentially built bridges are analysed by the theory of thermal creep. Two types of analysis are used: time dependent and steady state. The traditional uniform creep analysis is also introduced briefly. Both simplified and parabolic normalising creep-temperature functions are used in the analysis for comparison. Numerical examples are presented, calculated by a computer program based on the theory of thermal creep and using the displacement method. It is concluded that different assumptions within thermal creep can lead to very different results when compared with uniform creep analysis. The steady-state analysis of monolithically built structures can serve as a limit to evaluate total creep effects for both monolithically and sequentially built structures. The importance of the correct selection of the normalising creep-temperature function is demonstrated.

Test on prestressed concrete beam with AFRP spiral confinement and external aramid tendons

This paper describes an experimental study of a new form of prestressed concrete beam. Aramid Fiber Reinforced Polymers (AFRPs) are used to provide compression confinement in the form of interlocking circular spirals, while external tendons are made from parallel-lay aramid ropes. The response shows that the confinement of the compression flange significantly increases the ductility of the beam, allowing much better utilization of the fiber strength. The failure of the beam is characterized by rupture of spiral confinement reinforcement.

Magnetic Resonance Imaging of concrete

http://www-civ.eng.cam.ac.uk/cjb/papers/cp88.pdf

Analysis of CFRP strap-strengthened reinforced concrete beams using the modified compression field theory (MCFT)

External, prestressed carbon fiber reinforced polymer (CFRP) straps can be used to enhance the shear strength of existing reinforced concrete beams. In order to effectively design a strengthening system, a rational predictive theory is required. The current work investigates the ability of the modified compression field theory (MCFT) to predict the behavior of rectangular strap strengthened beams where the discrete CFRP strap forces are approximated as a uniform vertical stress. An unstrengthened control beam and two strengthened beams were tested to verify the predictions. The experimental results suggest that the MCFT could predict the general response of a strengthened beam with a uniform strap spacing < 0.9d. However, whereas the strengthened beams failed in shear, the MCFT predicted flexural failures. It is proposed that a different compression softening model or the inclusion of a crack width limit is required to reflect the onset of shear failures in the strengthened beams.

Redundancy quantification in the safety assessment of existing concrete beam-and-slab bridges

In current practice the strength evaluation of a bridge system is typically based on firstly using elastic analysis to determine the distribution of load effects in the elements and then checking the ultimate section capacity of those elements. Ductility of the components in most bridge structures permits local yield and subsequent redistribution of the applied loads from the most heavily loaded elements. As a result a bridge can continue to carry additional loading even after one member has yielded, which has conventionally been adopted as the "failure criterion" in bridge strength evaluation. This means that a bridge with inherent redundancy has additional reserves of strength such that the failure of one element does not result in the failure of the complete system. For these bridges warning signs will show up and measures can be undertaken before the ultimate collapse is happening. This paper proposes a rational methodology for calculating the ultimate system strength and including in bridge evaluation the warning level due to redundancy. © 2004 Taylor & Francis Group, London.

Precracked reinforced concrete T-Beams repaired in shear with bonded carbon Fiber-Reinforced Polymer sheets

This study investigates the structural behavior of precracked reinforced concrete (RC) T-beams strengthened in shear with externally bonded carbon fiber-reinforced polymer (CFRP) sheets. It reports on seven tests on unstrengthened and strengthened RC T-beams, identifying the influence of load history, beam depth, and percentage of longitudinal steel reinforcement on the structural behavior. The experimental results indicate that the contributions of the external CFRP sheets to the shear force capacity can be significant and depend on most of the investigated variables. This study also investigates the accuracy of the prediction of the fiber-reinforced polymer (FRP) contribution in ACI 440.2R-08, UK Concrete Society TR55, and fib Bulletin 14 design guidelines for shear strengthening. A comparison of predicted values with experimental results indicates that the guidelines can overestimate the shear contribution of the externally bonded FRP system. © 2012, American Concrete Institute.

Analysis of asphalt concrete permeability data using representative pore size

The permeability of asphalt concrete has been the subject of much study by pavement engineers over the last decade. The work undertaken has tended to focus on high air voids as the primary indicator of permeable asphalt concrete. This paper presents a simple approach for understanding the parameters that affect permeability. Principles explained by Taylor in 1956 in channel theory work for soils are used to derive a new parameter-representative pore size. Representative pore size is related to the air voids in the compacted mix and the D75 of the asphalt mix grading curve. Collected Superpave permeability data from published literature and data collected by the writers at the Queensland Department of Transport and Main Roads is shown to be better correlated with representative pore size than air voids, reducing the scatter considerably. Using the database of collected field and laboratory permeability values an equation is proposed that pavement engineers can use to estimate the permeability of in-place pavements. © 2011 ASCE.

Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation

The safety of post-earthquake structures is evaluated manually through inspecting the visible damage inflicted on structural elements. This process is time-consuming and costly. In order to automate this type of assessment, several crack detection methods have been created. However, they focus on locating crack points. The next step, retrieving useful properties (e.g. crack width, length, and orientation) from the crack points, has not yet been adequately investigated. This paper presents a novel method of retrieving crack properties. In the method, crack points are first located through state-of-the-art crack detection techniques. Then, the skeleton configurations of the points are identified using image thinning. The configurations are integrated into the distance field of crack points calculated through a distance transform. This way, crack width, length, and orientation can be automatically retrieved. The method was implemented using Microsoft Visual Studio and its effectiveness was tested on real crack images collected from Haiti.

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.

Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

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.

Machine vision based concrete surface quality assessment

Manually inspecting concrete surface defects (e.g., cracks and air pockets) is not always reliable. Also, it is labor-intensive. In order to overcome these limitations, automated inspection using image processing techniques was proposed. However, the current work can only detect defects in an image without the ability of evaluating them. This paper presents a novel approach for automatically assessing the impact of two common surface defects (i.e., air pockets and discoloration). These two defects are first located using the developed detection methods. Their attributes, such as the number of air pockets and the area of discoloration regions, are then retrieved to calculate defects’ visual impact ratios (VIRs). The appropriate threshold values for these VIRs are selected through a manual rating survey. This way, for a given concrete surface image, its quality in terms of air pockets and discoloration can be automatically measured by judging whether their VIRs are below the threshold values or not. The method presented in this paper was implemented in C++ and a database of concrete surface images was tested to validate its performance. Read More: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CO.1943-7862.0000126?journalCode=jcemd4