S German

Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments

The current procedures in post-earthquake safety and structural assessment are performed manually by a skilled triage team of structural engineers/certified inspectors. These procedures, and particularly the physical measurement of the damage properties, are time-consuming and qualitative in nature. This paper proposes a novel method that automatically detects spalled regions on the surface of reinforced concrete columns and measures their properties in image data. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the exposure of longitudinal reinforcement (depth of spalling into the column) and length of spalling along the column are measured using a novel global adaptive thresholding algorithm in conjunction with image processing methods in template matching and morphological operations. The method was tested on a database of damaged RC column images collected after the 2010 Haiti earthquake, and comparison of the results with manual measurements indicate the validity of the method.

Machine Vision-Enhanced Postearthquake Inspection

AbstractCurrent postearthquake inspection of structures relies on certified inspectors to make an assessment of the existing safety of the structure based primarily on qualitative measures. Completing the required inspection takes weeks to complete, which has adverse economic and societal impacts on the affected population. This paper proposes an automated framework for rapid postearthquake building evaluation. Under the framework, the visible damage (cracks and spalling) inflicted on RC members (columns) is detected using machine vision. The damage properties are then measured in relationship to the column’s dimensions and orientation, so that the existing state of the column can be approximated as a damage index. The column damage index is then used to query fragility curves of similar buildings, constructed from the analyses of existing and ongoing experimental data. The framework is expected to automate the collection of building damage data, to provide a quantitative assessment of the building damage...

Comprehensive property retrieval and measurement of concrete spalling using machine vision for post-earthquake safety assessments

Current procedures in post-earthquake safety and structural assessment are performed by a skilled triage team of structural engineers/certified inspectors. These procedures, in particular the physical measurement of the damage properties, are time-consuming and qualitative in nature. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake, and thus provides a sound springboard for a model in machine vision automated assessment procedures as is proposed in this research. Thus, a novel method that automatically detects regions of spalling on reinforced concrete columns and measures their properties in image data is the specific focus of this work. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the properties of the spalled region are depicted by way of classification of the extent of spalling on the column. The region of spalling is sorted into one of three categories by way of a novel global entropy-based adaptive thresholding algorithm in conjunction with well-established image processing methods in template matching and morphological operations. These three categories are specified as the following: (1) No spalling; (2) Spalling of cover concrete; and (3) Spalling of the core concrete (exposing reinforcement). In addition, the extent of the spalling along the length of the column is quantified. The method was tested on a database of damaged RC column images collected after the 2010 Haiti Earthquake, and comparison of the results with manual measurements indicate the validity of the method.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.