Magnetic Resonance Imaging of concrete

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

An assessment of the blast resistance of sandwich beams

An assessment of the underwater blast resistance of sandwich beams with a prismatic Y-truss core is presented, utilizing three-dimensional finite element calculations. Results show a significant performance benefit for sandwich construction when compared to a monolithic plate of the same mass when the sandwich core combines high shear strength with low compressive strength.

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.

The development of a hot rolling process for variable cross-section I-beams

To meet targeted reductions in CO 2 emissions by 2050, demand for metal must be cut, for example through the use of lightweight technologies. However, the efficient production of weight optimized components often requires new, more flexible forming processes. In this paper, a novel hot rolling process is presented for forming I-beams with variable cross-section, which are lighter than prismatic alternatives. First, the new process concept is presented and described. A detailed computational and experimental analysis is then conducted into the capabilities of the process. Results show that the process is capable of producing defect free I-beams with variations in web depth of 30-50%. A full analysis of the process then indicates the likely failure modes, and identifies a safe operating window. Finally, the implications of these results for producing lightweight beams are discussed. © 2012 Elsevier B.V. All rights reserved.

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.

The soft impact of composite sandwich beams with a square-honeycomb core

The dynamic response of end-clamped monolithic beams and sandwich beams of equal areal mass have been measured by loading the beams at mid-span with metal foam projectiles to simulate localised blast loading. The sandwich beams were made from carbon fibre laminate and comprised identical face sheets and a square-honeycomb core. The transient deflection of the beams was determined as a function of projectile momentum, and the measured response was compared with finite element simulations based upon a damage mechanics approach. A range of failure modes were observed in the sandwich beams including core fracture, plug-type shear failure of the core, debonding of the face sheets from the core and tensile tearing of the face sheets at the supports. In contrast, the monolithic beams failed by a combination of delamination of the plies and tensile failure at the supports. The finite element simulations of the beam response were accurate provided the carbon fibre properties were endowed with rate sensitivity of damage growth. The relative performance of monolithic and sandwich beams were quantified by the maximum transverse deflection at mid-span for a given projectile momentum. It was found that the sandwich beams outperformed both monolithic composite beams and steel sandwich beams with a square-honeycomb core. However, the composite beams failed catastrophically at a lower projectile impulse than the steel beams due to the lower ductility of the composite material. © 2011 Elsevier Ltd. All rights reserved.

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.

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

Detecting air pockets for architectural concrete quality assessment using visual sensing

Air pockets, one kind of concrete surface defects, are often created on formed concrete surfaces during concrete construction. Their existence undermines the desired appearance and visual uniformity of architectural concrete. Therefore, measuring the impact of air pockets on the concrete surface in the form of air pockets is vital in assessing the quality of architectural concrete. Traditionally, such measurements are mainly based on in-situ manual inspections, the results of which are subjective and heavily dependent on the inspectors’ own criteria and experience. Often, inspectors may make different assessments even when inspecting the same concrete surface. In addition, the need for experienced inspectors costs owners or general contractors more in inspection fees. To alleviate these problems, this paper presents a methodology that can measure air pockets quantitatively and automatically. In order to achieve this goal, a high contrast, scaled image of a concrete surface is acquired from a fixed distance range and then a spot filter is used to accurately detect air pockets with the help of an image pyramid. The properties of air pockets (the number, the size, and the occupation area of air pockets) are subsequently calculated. These properties are used to quantify the impact of air pockets on the architectural concrete surface. The methodology is implemented in a C++ based prototype and tested on a database of concrete surface images. Comparisons with manual tests validated its measuring accuracy. As a result, the methodology presented in this paper can increase the reliability of concrete surface quality assessment

Defects Detection and Assessment in Concrete Surfaces

Aside from cracks, the impact of other surface defects, such as air pockets and discoloration, can be detrimental to the quality of concrete in terms of strength, appearance and durability. For this reason, local and national codes provide standards for quantifying the quality impact of these concrete surface defects and owners plan for regular visual inspections to monitor surface conditions. However, manual visual inspection of concrete surfaces is a qualitative (and subjective) process with often unreliable results due to its reliance on inspectors’ own criteria and experience. Also, it is labor intensive and time-consuming. This paper presents a novel, automated concrete surface defects detection and assessment approach that addresses these issues by automatically quantifying the extent of surface deterioration. According to this approach, images of the surface shot from a certain angle/distance can be used to automatically detect the number and size of surface air pockets, and the degree of surface discoloration. The proposed method uses histogram equalization and filtering to extract such defects and identify their properties (e.g. size, shape, location). These properties are used to quantify the degree of impact on the concrete surface quality and provide a numerical tool to help inspectors accurately evaluate concrete surfaces. The method has been implemented in C++ and results that validate its performance are presented.