Thermographic Laplacian-pyramid filtering to enhance delamination detection in concrete structure

Despite decades of efforts using thermography to detect delamination in concrete decks, challenges still exist in removing environmental noise from thermal images. The performance of conventional temperature-contrast approaches can be significantly limited by environment-induced non-uniform temperature distribution across imaging spaces. Time-series based methodologies were found robust to spatial temperature non-uniformity but require the extended period to collect data. A new empirical image filtering method is introduced in this paper to enhance the delamination detection using blob detection method that originated from computer vision. The proposed method employs a Laplacian of Gaussian filter to achieve multi-scale detection of abnormal thermal patterns by delaminated areas. Results were compared with the state-of-the-art methods and benchmarked with time-series methods in the case of handling the non-uniform heat distribution issue. To further evaluate the performance of the method numerical simulations using transient heat transfer models were used to generate the 'theoretical' noise-free thermal images for comparison. Significant performance improvement was found compared to the conventional methods in both indoor and outdoor tests. This methodology proved to be capable to detect multi-size delamination using a single thermal image. It is robust to the non-uniform temperature distribution. The limitations were discussed to refine the applicability of the proposed procedure

Bridge Deck Delamination Segmentation Based on Aerial Thermography Through Regularized Grayscale Morphological Reconstruction and Gradient Statistics

Environmental and surface texture-induced temperature variation across the bridge deck is a major source of errors in delamination detection through thermography. This type of external noise poises a significant challenge for conventional quantitative methods such as global thresholding and k-means clustering. An iterative top-down approach is proposed for delamination segmentation based on grayscale morphological reconstruction. A weight-decay function was used to regularize the reconstruction for regional maxima extraction. The mean and coefficient of variation of temperature gradient estimated from delamination boundaries were used for discrimination. The proposed approach was tested on a lab experiment and an in-service bridge deck. The results demonstrated the improved capability of the framework to handle the non-uniform background, and thus eliminates the need of inferencing the background temperature which is often required by existing methods. That the results also suggested that the gradient statistics of the delamination boundary in the thermal image could be valid criterions to refine the segmentation under the proposed framework. Therefore, the authors concluded that the proposed method is a valid delamination segmentation approach for processing field concrete deck thermal images. The parameter selection and the limitation of this approach were also discussed. Further work will be carried out in more field cases to fine tune the parameter selection of the framework

Early Detection of Near-Surface Void Defects in Concrete Pavement Using Drone Based Thermography and GPR Methods

The goal of this research is to evaluate the feasibility and the performance of using UAV-mounted infrared thermography (IRT) and ground penetration radar (GPR) to detect sub-surface voids caused by consolidation issues in concrete pavement. The motivation of the study is to identify the consolidation defects as early as the initial set of concrete to avoid having this problem in large pavement sections, which is costly and time consuming to repair. Using the two technologies in combination to detect subsurface voids in the concrete initial set stage is new and aims to take advantage of the strengths and minimize the limitations of each method. UAV-based IRT can cover large areas of the pavements in a short amount of time, while GPR can provide higher accuracy in locating the defects horizontally and vertically. Therefore, the combination of the two technologies can allow detection of small voids in large areas with improved confidence. In this project, both laboratory and field tests were conducted with both methods, and coring samples were used for validation of results. The results from multiple specimens and multiple experiments suggested that both technologies performed well in detecting the subsurface voids in the concrete pavement’s initial set stage. Despite some limitations discussed in the report, the outcomes of the project provided evidence that these technologies can be used separately or together on the field as efficient and economical quality control tools in concrete pavement construction