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• W2301913109 abstract "Inspection and repair maintenance or replacement of bridges at regular intervals of time is essential for long term safety and sustainability of bridge infrastructure. Visual Inspection method has been commonly used by transportation agencies for condition assessment of bridges but it has not proved to be totally reliable because of its inability to detect internal defects. Various Nondestructive Evaluation (NDE) Techniques for different types of bridges have been proposed but Ground Penetrating Radar (GPR) method has been extensively used and is preferred due to its distinct advantages of identifying major subsurface detects in a short interval of time with few limitations. However, interpretation of data obtained from the GPR profiles have been ambiguous due to lack of correlation of its results with the actual bridge condition. The commonly utilized numerical analysis approach of evaluating data yield inconclusive results as it does not encompass several factors like reinforcing bar depth, surface anomalies, reinforcement bar spacing and others into consideration. A novel approach based on visual image analysis identified in the literature involves an experienced analyst reading through GPR profiles and marking attenuated areas all along the GPR profile while considering structural anomalies and other several parameters, which are generally ignored in numerical analysis. Thus, such a holistic method can prove to be very efficient in decision making regarding replacement or repair of bridge elements. The shortcomings of the visual image analysis of GPR profiles include: subjectivity of analyst interpretation leading to differing results obtained by different experts; and to manually profile through all data profiles by the analyst is time consuming and prone to error. This research aims to overcome these limitations by automating the visual image approach using MATLAB® image processing tools. A condition rating algorithm of GPR profiles is developed in a sequential step-by-step procedure to incorporate defects and anomalies like corrosion, expansion joints, concrete cut & repair and presence of structural member. An ‘X-ray’ type image is developed based on texture segmentation and is found to be most useful in identifying corrosion through scaling it into color zones according to the severity of condition. The algorithm is further modified to incorporate other anomalies. A corrosion map can be generated for the complete bridge element by systematically combining all GPR data pro- files. However, the authors wish to further improve this algorithm by considering all remaining major defects and anomalies. The results obtained will be unique and more reliable in nature, and can be efficient in making informed decisions regarding repair and rehabilitation of concrete bridges." @default.
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• W2301913109 date "2016-01-01" @default.
• W2301913109 modified "2023-09-24" @default.
• W2301913109 title "Automating Visual Image Analysis of GPR Profiles for Reinforced Concrete Bridges" @default.
• W2301913109 hasPublicationYear "2016" @default.
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