Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

User Experiments of a Social, Faceted Multimedia Classification System

Internet document sharing systems such as Flickr store billions of user-contributed images. Many collections on the Web contain large numbers of multimedia objects such as images. While such systems are designed to encourage user contributions and sharing, they are not well-organized collections on any given subject and are not easy to browse for specific subject matters. We have built a system that systematically organizes a large multimedia collection into an evolving faceted classification. This paper discusses the evaluation of such a system through a number of usage studies in a university setting

Development of Inspection Robots for Bridge Cables

This paper presents the bridge cable inspection robot developed in Korea. Two types of the cable inspection robots were developed for cable-suspension bridges and cable-stayed bridge. The design of the robot system and performance of the NDT techniques associated with the cable inspection robot are discussed. A review on recent advances in emerging robot-based inspection technologies for bridge cables and current bridge cable inspection methods is also presented

The Emerging of Hydrovoltaic Materials as a Future Technology: A Case Study for China

Water contains tremendous energy in various forms, but very little of this energy has yet been harvested. Nanostructured materials can generate electricity by water-nanomaterial interaction, a phenomenon referred to as hydrovoltaic effect, which potentially extends the technical capability of water energy harvesting. In this chapter, starting by describing the fundamental principle of hydrovoltaic effect, including water-carbon interactions and fundamental mechanisms of harvesting water energy with nanostructured materials, experimental advances in generating electricity from water flows, waves, natural evaporation, and moisture are then reviewed. We further discuss potential applications of hydrovoltaic technologies, analyze main challenges in improving the energy conversion efficiency and scaling up the output power, and suggest prospects for developments of the emerging technology, especially in China

Investigating causal associations among gut microbiota, metabolites, and liver diseases: a Mendelian randomization study

ObjectiveThere is some evidence for an association between gut microbiota and nonalcoholic fatty liver disease (NAFLD), alcoholic liver disease (ALD), and viral hepatitis, but no studies have explored their causal relationship.MethodsInstrumental variables of the gut microbiota (N = 13266) and gut microbiota-derived metabolites (N = 7824) were acquired, and a Mendelian randomization study was performed to explore their influence on NAFLD (1483 European cases and 17,781 European controls), ALD (2513 European cases and 332,951 European controls), and viral hepatitis risk (1971 European cases and 340,528 European controls). The main method for examining causality is inverse variance weighting (IVW).ResultsIVW results confirmed that Anaerotruncus (p = 0.0249), Intestinimonas (p = 0.0237), Lachnoclostridium (p = 0.0245), Lachnospiraceae NC2004 group (p = 0.0083), Olsenella (p = 0.0163), and Peptococcus (p = 0.0472) were protective factors for NAFLD, and Ruminococcus 1 (p = 0.0120) was detrimental for NAFLD. The higher abundance of three genera, Lachnospira (p = 0.0388), Desulfovibrio (p = 0.0252), and Ruminococcus torques group (p = 0.0364), was correlated with a lower risk of ALD, while Ruminococcaceae UCG 002 level was associated with a higher risk of ALD (p = 0.0371). The Alistipes (p = 0.0069) and Ruminococcaceae NK4A214 group (p = 0.0195) were related to a higher risk of viral hepatitis. Besides, alanine (p = 0.0076) and phenyllactate (p = 0.0100) were found to be negatively correlated with NAFLD, while stachydrine (Op = 0.0244) was found to be positively associated with NAFLD. The phenylacetate (p = 0.0353) and ursodeoxycholate (p = 0.0144) had a protective effect on ALD, while the threonate (p = 0.0370) exerted a detrimental influence on ALD. The IVW estimates of alanine (p = 0.0408) and cholate (p = 0.0293) showed their suggestive harmful effects against viral hepatitis, while threonate (p = 0.0401) displayed its suggestive protective effect against viral hepatitis.ConclusionIn conclusion, our research supported causal links between the gut microbiome and its metabolites and NAFLD, ALD, and viral hepatitis

Psychological distress among women undergoing in vitro fertilization-embryo transfer: A cross-sectional and longitudinal network analysis

BackgroundWomen undergoing in vitro fertilization-embryo transfer (IVF-ET) treatment were generally found to experience varying degrees of psychological distress across the treatment. Existing studies focused on total scores and diagnostic thresholds to characterize the symptoms’ severity, which might hinder scientific progress in understanding and treating psychological distress.AimsWe aimed to investigate (a) how depression and anxiety symptoms are interconnected within a network, and (b) the changes of the network (symptom connections and network centralities) over time, in women undergoing in vitro fertilization-embryo transfer.MethodsA 4-wave longitudinal study was designed with 343 eligible women recruited from the Reproductive Medicine Center of a tertiary hospital in China. The network models were created to explore the relationship and changes between psychopathology symptoms both within and across anxiety and depression, with anxiety measured by the Generalized Anxiety Disorder-7 and depression measured by the Patient Health Questionnaire-9. Symptom network analysis was conducted to evaluate network and network properties, network centrality, and bridge centrality, as well as change trajectory network.ResultsFor the strength centrality, “inability to control worry” and “worrying too much” were the most central symptoms at T1; however, these symptoms decreased. The centrality of “sadness” and “guilt” tended to increase steadily and became dominant symptoms. For bridge centrality indices, several bridge symptoms were identified separately from T1 to T4: “irritability,” “concentration difficulties,” “nervousness,” and “restlessness;” “guilt” exhibited increased bridge symptoms. Furthermore, the change trajectory network indicated that “suicide ideation” became more closely related to guilt but not to worrying too much over time.ConclusionThis study provides novel insights into the changes in central features, connections, and bridge symptoms during IVF-ET treatment and identified several bridge symptoms separately at different stages, which could activate the connection between psychopathology symptoms. The results revealed that sense of guilt was associated with worsening psychopathology symptoms, indicating that future psychological interventions should target guilt-related symptoms as a priority

Similarity Of Climate Control On Base Flow And Perennial Stream Density In The Budyko Framework

Streams are classified into perennial, intermittent, and ephemeral streams based on flow durations. Perennial stream is the basic network, while intermittent or ephemeral stream is the expanded network. Connection between perennial stream and base flow at the mean annual scale exists since one of the hydrologic functions of perennial stream is to deliver runoff even in low flow seasons. The partitioning of precipitation into runoff and evaporation at the mean annual scale, on the first order, is captured by the ratio of potential evaporation to precipitation (Ep/P called climate aridity index) based on the Budyko hypothesis. The primary focus of this thesis is the relationship between base flow and perennial stream density (Dp) in the Budyko framework. In this thesis, perennial stream density is quantified from the high resolution National Hydrography Dataset for 185 watersheds; the climate control (represented by the climate aridity index) on perennial stream density and on base flow is quantified; and the correlation between base flow and perennial stream density is analyzed. Perennial stream density declines monotonically with the climate aridity index, and an inversely proportional function is proposed to model the relationship between Dp and Ep/P. This monotonic trend of perennial stream density reconciles with the Abrahams curve, and the perennial stream density is only a small portion of the total drainage density. The dependences of base flow ratio (Qb/P) and the normalized perennial stream density on the climate aridity index follow a similar complementary Budyko-type curve. The correlation coefficient between iv the ratio of base flow to precipitation and perennial stream density is found to be 0.74. The similarity between the base flow and perennial stream density reveals the co-evolution between water balance and perennial stream network

Statistical Selection And Interpretation Of Imagery Features For Computer Vision-Based Pavement Crack-Detection Systems

This paper aims to explore the statistics of pavement cracks using computer-vision techniques. The knowledge discovered by mining the crack data can be used to avoid subjective crack feature selection for vision-based pavement evaluation systems. Moreover, the statistical evaluation of crack features can be used as fundamental data to justify pavement rehabilitation policies. For this purpose, surface images of flexible pavements in different deterioration stages were analyzed using a novel image-processing technique. Seven imagery features of the detected objects including area, length, width, orientation, intensity, texture roughness, and wheel-path position, which are commonly used in pavement applications, were extracted and analyzed. A comprehensive statistical analysis was performed using filter feature subset selection (FSS) methods to rank crack features based on their significance (relevance and redundancy) for the pavement crack-detection problem. Based on the results, length, intensity, and wheel-path position were identified as the optimal feature-set for the vision-based system. Statistical characteristics of crack features were also analyzed to extract accurate quantitative measures for pavement conditions assessment. The statistical characterization identified longitudinal cracks within the wheel path as the dominant defect of the validation data set. Such information can help management agencies make informed pavement maintenance policies

Crack Recognition And Segmentation Using Morphological Image-Processing Techniques For Flexible Pavements

MorphLink-C is a novel image-processing algorithm to connect crack fragments that are a common problem in crack recognition applications. The algorithm consists of two subprocesses: (a) the grouping of fragments by using a morphological dilation transform and (b) the connection of fragments by using a morphological thinning transform. MorphLink-C can be used with various crack extraction methods to connect crack fragments in crack line paths and for complicated crack shapes, such as single cracks, branched cracks, block cracks, and alligator cracks. MorphLink-C also provides a simple but accurate way to estimate an averaged crack width that is important in measuring cracking severity. The proposed method was validated by using realistic road surface images in different pavement cracking conditions. The results of the statistical hypothesis test showed that the proposed method could improve crack detection accuracy with the proposed crack defragmentation algorithm

Comparison Of Supervised Classifcation Techniques For Vision-Based Pavement Crack Detection

In this study, the application of four classifcation techniques for computer vision-based pavement crack detection systems was investigated. The classifcation methods-artifcial neural network (ANN), decision tree, k-nearest neighbor, and adaptive neuro-fuzzy inference system (ANFIS)-were selected on the basis of the complexity and clarity of their procedures. These methods were evaluated for (a) prediction performance, (b) computation time, (c) stability of results for highly imbalanced data sets, (d) stability of the classifers\u27 performance for pavements in different deterioration stages, and (e) interpretability of results and clarity of the procedure. According to the results, the ANN and ANFIS methods not only provide superior performance but also are more flexible and compatible for the crack detection application. The ANFIS method is called a white-box classifer, and the inferred knowledge from its membership functions can be used to characterize the imagery properties of detected image components