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

Real Time Concrete Damage Visual Assessment for First Responders

First responders are in danger when they perform tasks in damaged buildings after earthquakes. Structural collapse due to the failure of critical load bearing structural members (e.g. columns) during a post-earthquake event such as an aftershock can make first responders victims, considering they are unable to assess the impact of the damage inflicted in load bearing members. The writers here propose a method that can provide first responders with a crude but quick estimate of the damage inflicted in load bearing members. Under the proposed method, critical structural members (reinforced concrete columns in this study) are identified from digital visual data and the damage superimposed on these structural members is detected with the help of Visual Pattern Recognition techniques. The correlation of the two (e.g. the position, orientation and size of a crack on the surface of a column) is used to query a case-based reasoning knowledge base, which contains apriori classified states of columns according to the damage inflicted on them. When query results indicate the column's damage state is severe, the method assumes that a structural collapse is likely and first responders are warned to evacuate.

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.

Comprehensive Property Retrieval and Measurement of Concrete Spalling Using Machine Vision for Post-Earthquake Safety Assessments

Active vibration control (AVC) is a relatively new technology for the mitigation of annoying human-induced vibrations in floors. However, recent technological developments have demonstrated its great potential application in this field. Despite this, when a floor is found to have problematic floor vibrations after construction the unfamiliar technology of AVC is usually avoided in favour of more common techniques, such as Tuned Mass Dampers (TMDs) which have a proven track record of successful application, particularly for footbridges and staircases. This study aims to investigate the advantages and disadvantages that AVC has, when compared with TMDs, for the application of mitigation of pedestrian-induced floor vibrations in offices. Simulations are performed using the results from a finite element model of a typical office layout that has a high vibration response level. The vibration problems on this floor are then alleviated through the use of both AVC and TMDs and the results of each mitigation configuration compared. The results of this study will enable a more informed decision to be made by building owners and structural engineers regarding suitable technologies for reducing floor vibrations.

Shear transfer across a diagonal crack in reinforced concrete strengthened with externally bonded FRP fabric

Aging concrete infrastructure in developed economies and more recently constructed concrete infrastructure in the developing world are frequently found to be deficient in structural strength relative to current needs. This can be attributed to a variety of factors including deterioration, construction defects, accidental damage, changes in understanding and failure to design for future loading requirements. Strengthening existing concrete structures can be a cost and carbon effective alternative to replacement. A competitive option for the strengthening of concrete slab-on-beam structures that are deficient in shear capacity is the U-wrapping of the down-stand beam portion of the shear span with externally bonded FRP fabric. While guidance exists for the strengthening of reinforced concrete by U-wrapping, the interaction between internal steel reinforcement, concrete and external FRP in the presence of a dominant diagonal shear crack is not well understood. An approach adopted in previous work has been to explore this interaction through conventional push-off testing. In conventional push-off testing, unlike in a beam, the shear plane is parallel to the direction of loading and perpendicular to the principal fibre orientation. This paper presents a novel push-off test variation in which the shear plane is inclined at 45° to the direction of loading and the principal fibre orientation. A variety of reinforcement ratios, FRP thicknesses and FRP end conditions are modelled. The implications of inclined cracking on debonding of FRP are investigated. The suitability and relevance of inclined push-off tests for further work in this area is also assessed. © 2013, NetComposite Limited.

Durability of similar self-compacting concrete batches produced in two different EU laboratories

The present study intends to evaluate the sensitivity of self-compacting concrete (SCC) mixtures, cast in two different laboratories of the European Union, with a focus on rheological parameters, mechanical characteristics and durability properties. Six SCC mixtures with different water-to-binder ratios and silica fume levels of cement replacement and two normally vibrated concrete (NVC) mixtures have been compared. It has been found that the reproducibility of similar mixtures is possible, when using different constituent materials that conform to the European Standards. Comparable rheological, mechanical and durability properties can be achieved. Open porosity and sorptivity appear to be more sensitive than chloride penetrability. © 2012 Elsevier Ltd. All rights reserved.

Performance of joints in reinforced concrete slabs for two-way spanning action

A series of tests on filigree slab joints was performed with the aim of assessing whether such joints can be reliably used in the construction of two-way spanning reinforced concrete slabs. The test results were compared with code requirements. Adequate joint performance is shown to be achievable when the joints are appropriately detailed. Further research is recommended for the formulation of a more generic understanding when the design parameters are varied from those studied in this work.

MgO expansive cement and concrete in China: Past, present and future

Uniquely, China employs MgO already contained in cement clinker or as an expansive additive to compensate for the thermal shrinkage of mass concrete, particularly dam concrete, with almost 40 years' experience in both research activities and industrial applications. Compensating shrinkage with expansion produced by MgO has been proved to effectively prevent thermal cracking of mass concrete, and reduce the cost of temperature control measures and speed up the construction process. Moreover, the expansion properties of MgO could be designed flexibly, through adjusting its microstructure by changing the calcination conditions (calcining temperature and residence time). The collective knowledge and experience of MgO expansive cement and concrete is worthy of sharing with relevant engineers and researchers globally but dissemination has been hindered as most of the relevant literature is published in Chinese. This paper reviews the history, state-of-the-art progress and future research needs in the field of MgO expansive cement and concrete. © 2013 Elsevier Ltd.