Recognition of Surface Irregularities on Roads: a machine learning approach on 3D models

Roads are composed of various sorts of materials and with the constant use they expose different kinds of cracks or potholes. The aim of the current research is to present a novel automated classification method to be applied on these faults, which can be located on rigid pavement type. In order to collect proper representation of faults, a Kinect device was used, leading to three-dimensional point cloud structures. Images descriptors were used in order to establish the type of pothole and to get information regarding fault dimensions.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Recognition of Surface Irregularities on Roads: a machine learning approach on 3D models

Roads are composed of various sorts of materials and with the constant use they expose different kinds of cracks or potholes. The aim of the current research is to present a novel automated classification method to be applied on these faults, which can be located on rigid pavement type. In order to collect proper representation of faults, a Kinect device was used, leading to three-dimensional point cloud structures. Images descriptors were used in order to establish the type of pothole and to get information regarding fault dimensions.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

4D monitoring of active sinkholes with a Terrestrial Laser Scanner (TLS): A Case study in the evaporite karst of the Ebro Valley, NE Spain

This work explores, for the first time, the application of a Terrestrial Laser Scanner (TLS) and a comparison of point clouds in the 4D monitoring of active sinkholes. The approach is tested in three highly-active sinkholes related to the dissolution of salt-bearing evaporites overlain by unconsolidated alluvium. The sinkholes are located in urbanized areas and have caused severe damage to critical infrastructure (flood-control dike, a major highway). The 3D displacement models derived from the comparison of point clouds with exceptionally high spatial resolution allow complex spatial and temporal subsidence patterns within one of the sinkholes to be resolved. Detected changes in the subsidence activity (e.g., sinkhole expansion, translation of the maximum subsidence zone, development of incipient secondary collapses) are related to potential controlling factors such as floods, water table changes or remedial measures. In contrast, with detailed mapping and high-precision leveling, the displacement models, covering a relatively short time span of around 6 months, do not capture the subtle subsidence (< 0.6-1 cm) that affects the marginal zones of the sinkholes, precluding precise mapping of the edges of the subsidence areas. However, the performance of TLS can be adversely affected by some methodological limitations and local conditions: (1) limited accuracy in large investigation areas that require the acquisition of a high number of scans, increasing the registration error; (2) surface changes unrelated to sinkhole activity (e.g., vegetation, loose material); (3) traffic-related vibrations and wind blast that affect the stability of the scanner

Recognition of Surface Irregularities on Roads: a machine learning approach on 3D models

Roads are composed of various sorts of materials and with the constant use they expose different kinds of cracks or potholes. The aim of the current research is to present a novel automated classification method to be applied on these faults, which can be located on rigid pavement type. In order to collect proper representation of faults, a Kinect device was used, leading to three-dimensional point cloud structures. Images descriptors were used in order to establish the type of pothole and to get information regarding fault dimensions.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

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

Cracking in asphalt materials

This chapter provides a comprehensive review of both laboratory characterization and modelling of bulk material fracture in asphalt mixtures. For the purpose of organization, this chapter is divided into a section on laboratory tests and a section on models. The laboratory characterization section is further subdivided on the basis of predominant loading conditions (monotonic vs. cyclic). The section on constitutive models is subdivided into two sections, the first one containing fracture mechanics based models for crack initiation and propagation that do not include material degradation due to cyclic loading conditions. The second section discusses phenomenological models that have been developed for crack growth through the use of dissipated energy and damage accumulation concepts. These latter models have the capability to simulate degradation of material capacity upon exceeding a threshold number of loading cycles.Peer ReviewedPostprint (author's final draft

Evaluation of geophysical methods to characterize alluvial soils in the arid environment

Non-intrusive geophysical investigations, both seismic and electrical, were performed at several locations on the Las Vegas Springs Preserve in Las Vegas, Nevada, along with intrusive drilling. These investigations were conducted to determine whether it is possible use geophysical methods to detect piping-induced cavities and shallow inclusions such as calcific nodules and horizons known as caliche in dry, desert soil, while at the same time characterizing the mechanical structure of the soil and distribution of soil moisture for engineering purposes. The geophysical methods used were the Spectral-Analysis-of-Surface-Waves (SASW) method, surface-based seismic cavity detection, multi-electrode electrical resistivity, and electromagnetic conductivity. The results of the geophysical measurements across the site were compared to each other, and to the ground truth obtained through intrusive drilling. The seismic and electrical signature of a known air-filled fissure was also established, and was used for comparison to the results obtained throughout the Preserve. The SASW method was successful in characterizing the complex layered geometry of the soil. The electrical resistivity method successfully distinguished between dry soils at shallow depths, and moist and wet soils beneath. The surface-based seismic cavity detection and the electrical resistivity methods were also used successfully for cavity detection, and it is concluded that voids of engineering significance would have been detected if they had been present. The electromagnetic conductivity method was not successful in detecting voids, but proved to be a valuable preliminary reconnaissance tool

Rapid screening approach for cavity detection using surface-based seismic measurements

A rapid screening method for detection of shallow cavities using surface-based seismic waves has been developed and documented. Emphasis is placed on seismic surface waves due to their ease of measurement and dispersive nature in heterogeneous media; The study includes a numerical experiment, field experiments, and an analytical study of data collected for buried voids. Multi-channel and two-channel data acquisition technology are explored. An alternative analysis of the data based on impulse response is investigated. The field experiments were conducted at sites with known natural caves, buried barrels, and earth fissures. Two historic data sets were used. The numerical experiment was performed for a cavity buried in a homogeneous medium; Based on results of field and numerical experiments a new constant-offset method for rapid cavity detection is proposed. Parameters introduced include detection index, normalized receiver spacing, and normalized wavelength. All steps in data reduction are combined into one single automated process. Trends observed in the experimental data match the numerical data well and most of the targets were identified; thus the algorithm was validated. Optimal testing configurations, including source configuration, minimum and maximum receiver spacings and offset are proposed

Context-Enabled Visualization Strategies for Automation Enabled Human-in-the-loop Inspection Systems to Enhance the Situation Awareness of Windstorm Risk Engineers

Insurance loss prevention survey, specifically windstorm risk inspection survey is the process of investigating potential damages associated with a building or structure in the event of an extreme weather condition such as a hurricane or tornado. Traditionally, the risk inspection process is highly subjective and depends on the skills of the engineer performing it. This dissertation investigates the sensemaking process of risk engineers while performing risk inspection with special focus on various factors influencing it. This research then investigates how context-based visualizations strategies enhance the situation awareness and performance of windstorm risk engineers. An initial study investigated the sensemaking process and situation awareness requirements of the windstorm risk engineers. The data frame theory of sensemaking was used as the framework to carry out this study. Ten windstorm risk engineers were interviewed, and the data collected were analyzed following an inductive thematic approach. The themes emerged from the data explained the sensemaking process of risk engineers, the process of making sense of contradicting information, importance of their experience level, internal and external biases influencing the inspection process, difficulty developing mental models, and potential technology interventions. More recently human in the loop systems such as drones have been used to improve the efficiency of windstorm risk inspection. This study provides recommendations to guide the design of such systems to support the sensemaking process and situation awareness of windstorm visual risk inspection. The second study investigated the effect of context-based visualization strategies to enhance the situation awareness of the windstorm risk engineers. More specifically, the study investigated how different types of information contribute towards the three levels of situation awareness. Following a between subjects study design 65 civil/construction engineering students completed this study. A checklist based and predictive display based decision aids were tested and found to be effective in supporting the situation awareness requirements as well as performance of windstorm risk engineers. However, the predictive display only helped with certain tasks like understanding the interaction among different components on the rooftop. For remaining tasks, checklist alone was sufficient. Moreover, the decision aids did not place any additional cognitive demand on the participants. This study helped us understand the advantages and disadvantages of the decision aids tested. The final study evaluated the transfer of training effect of the checklist and predictive display based decision aids. After one week of the previous study, participants completed a follow-up study without any decision aids. The performance and situation awareness of participants in the checklist and predictive display group did not change significantly from first trial to second trial. However, the performance and situation awareness of participants in the control condition improved significantly in the second trial. They attributed this to their exposure to SAGAT questionnaire in the first study. They knew what issues to look for and what tasks need to be completed in the simulation. The confounding effect of SAGAT questionnaires needs to be studied in future research efforts