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

Automated On-Site Retrieval of Project Information

Among several others, the on-site inspection process is mainly concerned with finding the right design and specifications information needed to inspect each newly constructed segment or element. While inspecting steel erection, for example, inspectors need to locate the right drawings for each member and the corresponding specifications sections that describe the allowable deviations in placement among others. These information seeking tasks are highly monotonous, time consuming and often erroneous, due to the high similarity of drawings and constructed elements and the abundance of information involved which can confuse the inspector. To address this problem, this paper presents the first steps of research that is investigating the requirements of an automated computer vision-based approach to automatically identify “as-built” information and use it to retrieve “as-designed” project information for field construction, inspection, and maintenance tasks. Under this approach, a visual pattern recognition model was developed that aims to allow automatic identification of construction entities and materials visible in the camera’s field of view at a given time and location, and automatic retrieval of relevant design and specifications information.

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.

Improving Pothole Recognition through Vision Tracking for Automated Pavement Assessment

Pavement condition assessment is essential when developing road network maintenance programs. In practice, pavement sensing is to a large extent automated when regarding highway networks. Municipal roads, however, are predominantly surveyed manually due to the limited amount of expensive inspection vehicles. As part of a research project that proposes an omnipresent passenger vehicle network for comprehensive and cheap condition surveying of municipal road networks this paper deals with pothole recognition. Existing methods either rely on expensive and high-maintenance range sensors, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In our previous work we created a pothole detection method for pavement images. In this paper we present an improved recognition method for pavement videos that incrementally updates the texture signature for intact pavement regions and uses vision tracking to track detected potholes. The method is tested and results demonstrate its reasonable efficiency.

Automated On-Site Retrieval of Project Information

Among several others, the on-site inspection process is mainly concerned with finding the right design and specifications information needed to inspect each newly constructed segment or element. While inspecting steel erection, for example, inspectors need to locate the right drawings for each member and the corresponding specifications sections that describe the allowable deviations in placement among others. These information seeking tasks are highly monotonous, time consuming and often erroneous, due to the high similarity of drawings and constructed elements and the abundance of information involved which can confuse the inspector. To address this problem, this paper presents the first steps of research that is investigating the requirements of an automated computer vision-based approach to automatically identify “as-built” information and use it to retrieve “as-designed” project information for field construction, inspection, and maintenance tasks. Under this approach, a visual pattern recognition model was developed that aims to allow automatic identification of construction entities and materials visible in the camera’s field of view at a given time and location, and automatic retrieval of relevant design and specifications information.

Flaw sizing in pipes using long-range guided wave testing

The absence of adequate inspection data from difficult-to-access areas on pipelines, such as cased-road crossings, makes determination of fitness for continued service and compliance with increasingly stringent regulatory requirements problematic. Screening for corrosion using long-range guided wave testing is a relatively new inspection technique. The complexity of the possible modes of vibration means the technique can be difficult to implement effectively but this also means that it has great potential for both detecting and characterizing flaws. The ability to determine flaw size would enable the direct application of standard procedures for determining fitness-for-service, such as ASME B31G, RSTRENG, or equivalent for tens of metres of pipeline from a single inspection location. This paper presents a new technique for flaw sizing using guided wave inspection data. The technique has been developed using finite element models and experimentally validated on 6'' Schedule 40 steel pipe. Some basic fitness-for-service assessments have been carried out using the measured values and the maximum allowable operating pressure was accurately determined. © 2011 American Institute of Physics.

Thermal material with low curie temperature in a thermally actuated superconducting flux pump system

A thermally actuated flux pump is an efficient method to magnetize the high-temperature superconductor (HTS) bulk without applying a strong magnetic field. A thermal material is employed as a magnetic switch, which decides the efficiency of the system. To measure the Curie temperatures of those samples without destroying them, the nondestructive Curie temperature (NDT) measurement was developed. The Curie temperature of gadolinium (Gd) was measured by the NDT method and compared to the results from superconducting quantum interference device (SQUID). Because the SQUID tests require the sample to be cut into small piece, a constant shape of the testing sample could not be guaranteed. The demagnetizing effect was considered to remove the shape effect. The intrinsic permeability was modified from the apparent susceptibility by considering demagnetization. A thermal material with low Curie temperature, Mg 0.15Cu0.15Zn0.7Ti0.04Fe 1.96O4, was synthesized and its performance was tested and compared with previous thermal materials. Comparisons of three thermal materials, including the Curie temperature and the permeability, will be detailed in the paper. © 2002-2011 IEEE.

Non-destructive characterization of structural hierarchy within aligned carbon nanotube assemblies

Understanding and controlling the hierarchical self-assembly of carbon nanotubes (CNTs) is vital for designing materials such as transparent conductors, chemical sensors, high-performance composites, and microelectronic interconnects. In particular, many applications require high-density CNT assemblies that cannot currently be made directly by low-density CNT growth, and therefore require post-processing by methods such as elastocapillary densification. We characterize the hierarchical structure of pristine and densified vertically aligned multi-wall CNT forests, by combining small-angle and ultra-small-angle x-ray scattering (USAXS) techniques. This enables the nondestructive measurement of both the individual CNT diameter and CNT bundle diameter within CNT forests, which are otherwise quantified only by delicate and often destructive microscopy techniques. Our measurements show that multi-wall CNT forests grown by chemical vapor deposition consist of isolated and bundled CNTs, with an average bundle diameter of 16 nm. After capillary densification of the CNT forest, USAXS reveals bundles with a diameter 4 m, in addition to the small bundles observed in the as-grown forests. Combining these characterization methods with new CNT processing methods could enable the engineering of macro-scale CNT assemblies that exhibit significantly improved bulk properties. © 2011 American Institute of Physics.

Flow physics of a normal-hole bled supersonic turbulent boundary layer

An experimental study on normal hole bleed in a supersonic turbulent boundary layer has been conducted. A combination of LDV, Schlieren imagery and oil flow visualization were used to provide a better understanding of the three-dimensional flow field surrounding a supersonic bleed array. Experiments were performed at Mach numbers of 1.8 and 2.5, while previously published results at Mach numbers of 1.3 and 1.5 were also incorporated. The bleed system was capable of removing up to approximately 10% of the incoming boundary layer through a tunnel-spanning array of discrete holes with diameters the same order of magnitude of boundary layer displacement thickness. Inspection of boundary layer profiles downstream of the bleed region indicates that vorticity generated by the discrete holes can have a substantial influence on changes to the boundary layer shape factor and skin friction coefficient, through modification of the lower 20% of the boundary layer. This vorticity was visualized through oil-flow visualization, and LDV measurements, showing the development of two vortices off each bleed hole, and corresponding upwash and downwash regions with far-reaching three dimensional effects. © 2013 by J. M. Oorebeek and H. Babinsky.

A comparison of methods currently used in inclusive design.

Inclusive design has unique challenges because it aims to improve usability for a wide range of users. This typically includes people with lower levels of ability, as well as mainstream users. This paper examines the effectiveness of two methods that are used in inclusive design: user trials and exclusion calculations (an inclusive design inspection method). A study examined three autoinjectors using both methods (n=30 for the user trials). The usability issues identified by each method are compared and the effectiveness of the methods is discussed. The study found that each method identified different kinds of issues, all of which are important for inclusive design. We therefore conclude that a combination of methods should be used in inclusive design rather than relying on a single method. Recommendations are also given for how the individual methods can be used more effectively in this context.

Effects of tetrabrominated diphenyl ether and hexabromocyclododecanes in single and complex exposure to hepatoma HepG2 cells

This study was designed to determine cytotoxic effects of PBDE-47 and HBCDs individually or with a mixture of both compounds exposure to Hep G2 cells. The results showed PBDE-47 and HBCDs induced increase of nitric oxide synthase (NOS) activity, release of NO. dissipation of mitochondria membrane potential and cell apoptosis. Exposure to HBCDs induced ROS formation. Moreover, preincubation with PTIO (NO scavanger) and N-acetylcysteine (ROS scavanger) partially reversed cytotoxic effects of these compounds. The possible mechanism is that PBDE-47 and HBCDs could boost generation of NO and/or ROS, impact mitochondria, and result in start-ups of apoptosis program. Cells exposed to mixture of both compounds and each of them showed non-apoptotic rate significant difference, but the combination of them caused more adverse effects on cells. These results Suggest that PBDE-47 and HBCDs in single and complex exposure have the cytotoxic activity of anti-proliferation and induction of apoptosis in tumor cells in vitro. (C) 2008 Elsevier B.V. All rights reserved.