CONCRETE CRACK EVALUATION FOR CIVIL INFRASTRUCTURE USING COMPUTER VISION AND DEEP LEARNING

Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)Surface cracks of civil infrastructure are one of the important indicators for structural durability and integrity. Concrete cracks are typically investigated by manual visual observation on the surface, which is intrinsically subjective because it highly depends on the experience of inspectors. Furthermore, manual visual inspection is time-consuming, expensive, and often unsafe when inaccessible structural components need to be assessed. Computer vision-based approach is recognized as a promising alternative that can automatically extract crack information from images captured by the digital camera. As texts and cracks are similar in terms of consisting distinguishable lines and curves, image binarization developed for text detection can be appropriate for crack identification purposes. However, although image binarization is useful to separate cracks and backgrounds, the crack assessment is difficult to standardize owing to the high dependence of binarization parameters determined by users. Another critical challenge in digital image processing for crack detection is to automatically distinguish cracks from an image containing actual cracks and crack-like noise patterns (e.g., stains, holes, dark shadows, and lumps), which are often seen on the surface of concrete structures. In addition, a tailored camera system and the corresponding strategy are necessary to effectively address the practical issues in terms of the skewed angle and the process of the sequential crack images for efficient measurement. This research develops a computer vision-based approach in conjunction with deep learning for accurate crack evaluation of for civil infrastructure. The main contribution of the proposed approach can be summarized as follows: (1) a deep learning-based approach for crack detection, (2) a hybrid image processing for crack quantification, and (3) camera systems for the practical issues on civil infrastructure in terms of a skewed angle problem and an efficient measurement with the sequential crack images. The proposed research allows accurate crack evaluation to provide a proper maintenance strategy for civil infrastructure in practice.clos

AZB Rectangle Shrinkage Method and Heterogeneous Computing Accelerated Full Image Theory Method Ray Tracing Enabling Complex and Massive Outdoor 6G Propagation Modeling

Until now, despite their high accuracy, the utilization of the conventional image theory method ray tracers was limited to simple simulation environments with small number of field observation points and low maximum ray bouncing order due to their poor computational efficiency. This study presents a novel full-3D AZB rectangle shrinkage method and heterogeneous computing accelerated image theory method ray tracing framework for complex and massive outdoor propagation modeling. The proposed framework is divided into three parts: 1. Visibility preprocessing part. 2. Visibility tree generation part: in this part, a novel AZB rectangle shrinkage method that accelerates and reduces generation speed and size of visibility tree is proposed. 3. Shadow testing and field calculation part: in this part, a heterogeneous computing algorithm that can make possible to handle a large amount of field observation points is proposed. It is demonstrated that the proposed framework is faster more than 651 times than the image theory method solver of WinProp. Also, it is confirmed that the proposed ray tracing framework can handle 1km x 1km wide and dense urban outdoor simulation with up to the maximum ray bouncing order of 6 and thousands of field observation points. The proposed ray tracing framework would be a cornerstone of future image theory method ray tracing techniques for complex and massive scenarios that was exclusive to the shooting and bouncing rays method ray tracers

Incorporating Language-Driven Appearance Knowledge Units with Visual Cues in Pedestrian Detection

Large language models (LLMs) have shown their capability in understanding contextual and semantic information regarding appearance knowledge of instances. In this paper, we introduce a novel approach to utilize the strength of an LLM in understanding contextual appearance variations and to leverage its knowledge into a vision model (here, pedestrian detection). While pedestrian detection is considered one of crucial tasks directly related with our safety (e.g., intelligent driving system), it is challenging because of varying appearances and poses in diverse scenes. Therefore, we propose to formulate language-driven appearance knowledge units and incorporate them with visual cues in pedestrian detection. To this end, we establish description corpus which includes numerous narratives describing various appearances of pedestrians and others. By feeding them through an LLM, we extract appearance knowledge sets that contain the representations of appearance variations. After that, we perform a task-prompting process to obtain appearance knowledge units which are representative appearance knowledge guided to be relevant to a downstream pedestrian detection task. Finally, we provide plentiful appearance information by integrating the language-driven knowledge units with visual cues. Through comprehensive experiments with various pedestrian detectors, we verify the effectiveness of our method showing noticeable performance gains and achieving state-of-the-art detection performance.Comment: 11 pages, 4 figures, 9 table

The Go Wild with Whole Grains! school-based program: Positive impacts among children

Whole grain foods have been associated with health benefits, yet are underconsumed by youth compared to recommendations. This study evaluated impacts of a school-based curriculum among children in grades 3-5 to address barriers to intake (2018-2019) (n = 1,748). Surveys before and after the program indicated youth were more willing to try and better able to identify whole grain foods. Open-ended responses confirmed findings regarding increased ability to identify whole grain foods, increased preferences and perceptions of availability. Together, these impacts could increase the likelihood that youth can meet whole grain intake recommendations to improve diet quality and health

Applicability of Diffuse Ultrasound to Evaluation of the Water Permeability and Chloride Ion Penetrability of Cracked Concrete

This study aims to explore the applicability of diffuse ultrasound to the evaluation of water permeability and chloride ion penetrability of cracked concrete. Lab-scale experiments were conducted on disk-shaped concrete specimens, each having a different width of a penetrating crack that was generated by splitting tension along the centerline. The average crack width of each specimen was determined using an image binarization technique. The diffuse ultrasound test employed signals in the frequency range of 200 to 440 kHz. The water flow rate was measured using a constant water-head permeability method, and the chloride diffusion coefficient was determined using a modified steady-state migration method. Then, the effects of crack width on the diffusion characteristics of ultrasound (i.e., diffusivity, dissipation), water flow rate, and chloride diffusion coefficient are investigated. The correlations between the water flow rate and diffuse ultrasound parameters, and between the chloride diffusion coefficient and diffuse ultrasound parameters, are examined. The results suggest that diffuse ultrasound is a promising method for assessing the water permeability and chloride ion penetrability of cracked concrete

Learning to Discriminate Information for Online Action Detection

From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as well as the action of interest. For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others. Our unit, named Information Discrimination Unit (IDU), decides whether to accumulate input information based on its relevance to the current action. This enables our recurrent network with IDU to learn a more discriminative representation for identifying ongoing actions. In experiments on two benchmark datasets, TVSeries and THUMOS-14, the proposed method outperforms state-of-the-art methods by a significant margin. Moreover, we demonstrate the effectiveness of our recurrent unit by conducting comprehensive ablation studies.Comment: To appear in CVPR 202

Principles and applications of ultrasonic-based nondestructive methods for self-healing in cementitious materials

Recently, self-healing technologies have emerged as a promising approach to extend the service life of social infrastructure in the field of concrete construction. However, current evaluations of the self-healing technologies developed for cementitious materials are mostly limited to lab-scale experiments to inspect changes in surface crack width (by optical microscopy) and permeability. Furthermore, there is a universal lack of unified test methods to assess the effectiveness of self-healing technologies. Particularly, with respect to the self-healing of concrete applied in actual construction, nondestructive test methods are required to avoid interrupting the use of the structures under evaluation. This paper presents a review of all existing research on the principles of ultrasonic test methods and case studies pertaining to self-healing concrete. The main objective of the study is to examine the applicability and limitation of various ultrasonic test methods in assessing the self-healing performance. Finally, future directions on the development of reliable assessment methods for self-healing cementitious materials are suggested.ope

Probabilistic Integrated Urban Inundation Modeling Using Sequential Data Assimilation

Urban inundation due to climate change and heavy rainfall is one of the most common natural disasters worldwide. However, it is still insufficient to obtain accurate urban inundation predictions due to various uncertainties coming from input forcing data, model parameters, and observations. Despite of numerous sophisticated data assimilation algorithms proposed to increase the certainty of predictions, there have been few attempts to combine data assimilation with integrated inundation models due to expensive computations and computational instability such as breach of conservation and momentum equations in the updating procedure. In this study, we propose a probabilistic integrated urban inundation modeling scheme using sequential data assimilation. The original integrated urban inundation model consists of a 2D inundation model on the ground surface and a 1D network model of sewer pipes, which are combined by a sub-model to exchange storm water between the ground surface and the sewerage system. In our method, uncertainties of modeling conditions are explicitly expressed by ensembles having different rainfall input, initial conditions, and model parameters. Then, particle filtering(PF), one of sequential data assimilation techniques for non-linear and non-Gaussian models, is applied to sequentially update model states and parameters when new observations are arrived from monitoring systems. Several synthetic experiments are implemented to demonstrate applicability of the proposed method in an urbanized area located in Osaka, Japan. The discussion will be focused on noise specification and updating methods in PF and comparison of accuracy between deterministic and probabilistic inundation modeling methods