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This paper presents an innovative computer vision method for condition assessments of bridges with multiple defects in bridge elements using digital images. This work utilizes 3D model of existing bridges and overlays digital images on 3D model to simulate on-site visual inspection. The analysis of element condition index (ECI) of bridges requires information about the severity and extent of defects in elements. In general, ECI is evaluated manually during routine bridge inspection considering the severity of dominant defects. The evaluation of ECI with multiple defects needs to be addressed with consideration of dominant defect as well as the interaction among defects. However, Image-based quantification techniques largely depend on geometry of objects (i.e. shapes). Shape vectors of a given object change as they are translated, rotated, and scaled with different magnitudes. This work considers shape preserving algorithms such as, affine and projective transformation for proper image alignment. Semi-automated approach for detection and quantification of concrete distress such as cracks and spalling are considered for the defects analysis. The proposed methodology ensures the consistency in reporting ECI and eliminates the shortcoming of traditional approaches.

Purpose This paper presents a new method to retrieve concrete crack properties based on image processing techniques. Method Detection and quantification of cracks in concrete bridges pose various challenges. Cracks have fewer pixels compared to their background. For effective visualization, the objects need to be captured from near field. But it is not always possible to capture the complete cracked surface in a single frame while taking the image from near field. Hence image stitching is required before pre-processing of images for further analysis. Usually retrieved images have low contrast due to environmental and equipment limitations which add another difficulty in image visualization. State-ofthe-art image pre-processing as suggested in the literature may not be suitable for images captured in different environmental conditions. This paper discusses various techniques for image enhancement using point processing, histogram equalization and mask processing. Furthermore, a binary image is required to obtain a skeleton of an object. However, the pre-processing techniques cause discontinuity in crack alignment. Morphological techniques (e.g. dilation) are used in this work through successive iteration to ensure connectivity. Then the object skeleton which is unaffected by expanded boundaries is obtained by using skeleton algorithm to retrieve concrete crack properties such as length, bounding rectangle, and major and minor principal axes lengths. Results & Discussion The preliminary results obtained using this methodology is capable of retrieving length, orientation and bounding box of the identified cracks. This method is aimed at assisting in obtaining automated prediction of condition state (CS) rating of cracks in bridges. It can be also used as a tool for post-earthquake damage evaluation purposes.

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Purpose This paper presents a new automated method to predict condition state rating in bridge inspection. The method is designed to identify proper risk-based inspection interval by neural networks and image processing techniques. Method The surface defect considered in this research work is the loss of surface portion (scaling) of concrete due to freezethaw action based on Ontario Structure Inspection Manual (OSIM). Earlier, digital camera has been effectively used for identification of cracks in concrete bridge inspection. The research presented in this paper uses digital camera and artificial neural networks (ANN) for defects identification and rating purposes. The problem associated with scale calibration while zooming of the camera to capture the details of defects is solved either by known dimension of existing nearby element s of the bridge or via artificial objects with known dimensions in the picture frame. Determination of depth of defects, however, poses another challenge when 2D picture frames are used in this process. Red, green and blue (RGB) color profile is used to estimate the depth of defects. Various image processing techniques are used to extract the feature vectors to characterise and quantify defects. Subsequently, an ANN model is developed to predict the depth of defects based on 7 attributes obtained from the image processing. Condition state rating of scaling defects is then modelled using a developed back propagation neural network model (BPNN). Results & Discussion The developed model is capable of predicting condition state (CS) rating of scaling defects as light, medium, and severe with correlation coefficient (CR) of 99%. The proposed method is aimed to identify the proper risk-based bridge inspection interval which can significantly shorten the inspection interval and can assist in planning and executing necessary maintenance and rehabilitation work.

It is necessary to assess the physical and functional conditions of highway bridges at regular intervals to ensure they still meet their service requirements. Currently, this condition assessment is mainly performed through visual inspection, which has been identified with several limitations (e.g. the timeconsuming assessment process and heavy reliance on inspectors' personal experience). In order to overcome these limitations and enhance the current inspection practice, this paper presents a novel method for automated bridge condition assessment using a hybrid sensing system. Under the method, existing conditions of bridge components are first captured with a stream of point clouds and color images. Then, the bridge components and the defects inflicted on the components are detected utilizing their visual patterns. The detection results are mapped to the point cloud. This way, the 3D information of the components and defects can be retrieved. The bridge condition assessment can be made effectively through the 3D visualization of this information before carrying out any on-site detailed evaluations.

The changes in defects patterns or in element condition index during visual inspection of bridges are primary concerns for inspectors. This paper presents a new approach for change detection of defects in bridges by identifying changes in texture patterns through spectral analysis of digital images. The commonly used method for change detection is image differentiation. This subtraction method requires images to be of same size, scale, and rotation. However, no two images are same in real practices. Thus, image registration is required to align images and to produce change maps. This process is tedious and it is difficult often to achieve a good registered image. But, the change detection task can be readily modeled in frequency domain for texture patterns discrimination and also for quantifying their properties. This paper proposes a novel approach for change detection by transforming digital images into Fourier spectrum. In new coordinate system, 1-D signature functions can be drawn which facilitates easy comparison of textures in different directions. The proposed methodology provides useful tools for comparison of inspection history graphically and quantitatively. In practice, expensive sensors are used to detect subtle change in defect patterns. The proposed method can be used to detect any subtle change in defect patterns using digital images at much lower cost.