Adaptive Road Crack Detection System by Pavement Classification

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement

Autonomous concrete crack semantic segmentation using deep fully convolutional encoder-decoder network in concrete structures inspection

Structure health inspection is the way to ensure that structures stay in optimum condition. Traditional inspection work has many disadvantages in dealing with the large workload despite using remote image-capturing devices. This research focuses on image-based concrete crack pattern recognition utilizing a deep convolutional neural network (DCNN) and an encoder–decoder module for semantic segmentation and classification tasks, thereby lightening the inspectors’ workload. To achieve this, a series of contrast experiments have been implemented. The results show that the proposed deep-learning network has competitive semantic segmentation accuracy (91.62%) and over-performs compared with other crack detection studies. This proposed advanced DCNN is split into multiple modules, including atrous convolution (AS), atrous spatial pyramid pooling (ASPP), a modified encoder–decoder module, and depthwise separable convolution (DSC). The advancement is that those modules are well-selected for this task and modified based on their characteristics and functions, exploiting their superiority to achieve robust and accurate detection globally. This application improved the overall performance of detection and can be implemented in industrial practices

Stochastic Simulation of Mudcrack Damage Formation in an Environmental Barrier Coating

The FEAMAC/CARES program, which integrates finite element analysis (FEA) with the MAC/GMC (Micromechanics Analysis Code with Generalized Method of Cells) and the CARES/Life (Ceramics Analysis and Reliability Evaluation of Structures / Life Prediction) programs, was used to simulate the formation of mudcracks during the cooling of a multilayered environmental barrier coating (EBC) deposited on a silicon carbide substrate. FEAMAC/CARES combines the MAC/GMC multiscale micromechanics analysis capability (primarily developed for composite materials) with the CARES/Life probabilistic multiaxial failure criteria (developed for brittle ceramic materials) and Abaqus (Dassault Systmes) FEA. In this report, elastic modulus reduction of randomly damaged finite elements was used to represent discrete cracking events. The use of many small-sized low-aspect-ratio elements enabled the formation of crack boundaries, leading to development of mudcrack-patterned damage. Finite element models of a disk-shaped three-dimensional specimen and a twodimensional model of a through-the-thickness cross section subjected to progressive cooling from 1,300 C to an ambient temperature of 23 C were made. Mudcrack damage in the coating resulted from the buildup of residual tensile stresses between the individual material constituents because of thermal expansion mismatches between coating layers and the substrate. A two-parameter Weibull distribution characterized the coating layer stochastic strength response and allowed the effect of the Weibull modulus on the formation of damage and crack segmentation lengths to be studied. The spontaneous initiation of cracking and crack coalescence resulted in progressively smaller mudcrack cells as cooling progressed, consistent with a fractal-behaved fracture pattern. Other failure modes such as delamination, and possibly spallation, could also be reproduced. The physical basis assumed and the heuristic approach employed, which involves a simple stochastic cellular automaton methodology to approximate the crack growth process, are described. The results ultimately show that a selforganizing mudcrack formation can derive from a Weibull distribution that is used to describe the stochastic strength response of the bulk brittle ceramic material layers of an EBC

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Image-based Condition Assessment for Concrete Bridge Inspection

Abstract Image-based Condition Assessment for Concrete Bridge Inspection Ram Sebak Adhikari, Ph.D. Concordia University, 2014 The following approaches are usually taken for the condition assessment and performance evaluation of civil infrastructure: visual inspection, structural response measurement due to loads, and sensing based inspection of bridge structures. This thesis concentrates on the last alternative using remote sensing for condition assessment of concrete bridge structures. Focusing on defect quantification problems for condition assessment of bridge structures, remote sensing techniques based on digital images provides superior result over conventional visual inspection-based methods. The aim of this thesis is to develop digital image-based condition assessment tools and techniques, which can be integrated with existing bridge management systems (BMSs) in order to enhance the reliability of current inspection practices. The methodology of this research divides the entire task of bridge inspection into two modules. The first module develops quantification models based on the extent and severity of defects, and the second module develops a change detection model defined as change in element condition state over times. For defect quantification, three fundamental concrete defects such as cracks, spalling, and scaling have been considered. To illustrate the proposed methodology, digital images are acquired from laboratory experiments during the testing of reinforced concrete beams in flexure, and from field visits of bridges in Montreal, Quebec using portable digital cameras. This research contributes in the development of crack quantification model based on the corresponding crack skeleton which takes consideration of crack tortuosity for retrieving of crack properties. The output of the crack quantification model is validated by capturing the crack properties using a crack scale. In addition, an automated model for estimating the condition rating and related computational algorithms for bridge inspection are developed using the guidelines of the Ontario Structure Inspection Manual. The developed algorithms for mapping of condition ratings are based on the supervised training of back propagation neural networks. Recognizing the importance of 3D visualization, which can mimic the on-site visual inspection, 3D visualization model is developed using ordinary digital images by manually projecting images on the 3D model of the bridge being inspected. The second module proposes a novel approach for periodic detection of defects in concrete bridges based on a set of dimensionless metrics pertinent to spectral and fractal analyses of the captured images. The fractal analysis of digital images is described by fractal dimension (FD) using Box Counting algorithms. Similarly, the method of spectral analysis requires digital images to be translated from spatial domain to Fourier domain, and then finds one dimensional signatures to quantify change detection. The developed algorithm for change detection demonstrates superior results and eliminates the limitations of traditional approach of change detection based on image subtraction. The developed image-based models can either be applied as standalone condition assessment and rating applications or integrated with existing systems such as PONTIS ( a Bridge Management System in USA) in order to enhance the reliability of visual inspection

Automated Extraction of Road Information from Mobile Laser Scanning Data

Effective planning and management of transportation infrastructure requires adequate geospatial data. Existing geospatial data acquisition techniques based on conventional route surveys are very time consuming, labor intensive, and costly. Mobile laser scanning (MLS) technology enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced point cloud data in the format of three-dimensional (3D) point clouds. Today, more and more commercial MLS systems are available for transportation applications. However, many transportation engineers have neither interest in the 3D point cloud data nor know how to transform such data into their computer-aided model (CAD) formatted geometric road information. Therefore, automated methods and software tools for rapid and accurate extraction of 2D/3D road information from the MLS data are urgently needed. This doctoral dissertation deals with the development and implementation aspects of a novel strategy for the automated extraction of road information from the MLS data. The main features of this strategy include: (1) the extraction of road surfaces from large volumes of MLS point clouds, (2) the generation of 2D geo-referenced feature (GRF) images from the road-surface data, (3) the exploration of point density and intensity of MLS data for road-marking extraction, and (4) the extension of tensor voting (TV) for curvilinear pavement crack extraction. In accordance with this strategy, a RoadModeler prototype with three computerized algorithms was developed. They are: (1) road-surface extraction, (2) road-marking extraction, and (3) pavement-crack extraction. Four main contributions of this development can be summarized as follows. Firstly, a curb-based approach to road surface extraction with assistance of the vehicle’s trajectory is proposed and implemented. The vehicle’s trajectory and the function of curbs that separate road surfaces from sidewalks are used to efficiently separate road-surface points from large volume of MLS data. The accuracy of extracted road surfaces is validated with manually selected reference points. Secondly, the extracted road enables accurate detection of road markings and cracks for transportation-related applications in road traffic safety. To further improve computational efficiency, the extracted 3D road data are converted into 2D image data, termed as a GRF image. The GRF image of the extracted road enables an automated road-marking extraction algorithm and an automated crack detection algorithm, respectively. Thirdly, the automated road-marking extraction algorithm applies a point-density-dependent, multi-thresholding segmentation to the GRF image to overcome unevenly distributed intensity caused by the scanning range, the incidence angle, and the surface characteristics of an illuminated object. The morphological operation is then implemented to deal with the presence of noise and incompleteness of the extracted road markings. Fourthly, the automated crack extraction algorithm applies an iterative tensor voting (ITV) algorithm to the GRF image for crack enhancement. The tensor voting, a perceptual organization method that is capable of extracting curvilinear structures from the noisy and corrupted background, is explored and extended into the field of crack detection. The successful development of three algorithms suggests that the RoadModeler strategy offers a solution to the automated extraction of road information from the MLS data. Recommendations are given for future research and development to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use

Peridynamic and discrete multiphysics for modelling the mechanical and fracture properties of pavement materials

Asphalt pavement experiences different degradation mechanisms under several solicitations. The asphalt mechanical and fracture properties of asphalt mixtures have been investigated using experiment and X-ray CT scan to improve the quality of design. These methods are limited by the number of samples required and high cost. The development of numerical methods provided a powerful tool to investigate the asphalt mixture performance at macro and micro scale, requiring lower number of sample and cost. A key challenge of the numerical method is the reliable modelling of the cracks under different conditions. In this thesis, Peridynamics and Discrete Multiphysics model is used to simulate the mechanical properties and fracture characteristics of pavement materials. The simulations were carried out on the open-source software LAMMPS and visualised on Ovito. Initially, the capability of Peridynamics and Discrete Multiphysics was explored to assess micro-crack formation and propagation in asphalt mixture at low temperatures and under freezing conditions. The results showed the cracks form at the interface and propagate from one void to another along the direction of load. In addition, the water expansion increases the pressure within the voids which adversely has a detrimental effect on the asphalt mixture performance. Experimental studies on three different asphalt mixtures with voids content of 3%, 10%, and 14% were performed at low temperature and freeze-thaw cycle to establish a correlation between the asphalt mixtures’ properties and the voids’ topology. The asphalt mixtures were scanned using Computer Tomography scan to determine the internal structure that evolves during freezing cycles. Semi-circular bending test was used to determine mechanical properties at low temperatures. The results show that asphalt mixture with 3% void content has the lowest and steady degradation rate with the lowest water retention during all cycles. The asphalt mixtures with a 3 high void content have the highest concentration of water in the pores and decay faster during the initial cycles, but slower during the later cycles because there is less water inside the pores which are fully open and do not retain it. A 3D model was used to simulate the asphalts mechanical and fracture properties discussed in the previous chapter at -10 °C. In addition to the previous chapter, asphalt mixtures mechanical and fracture properties at 20 °C were simulated. The results showed that the asphalt mixture performance is reproduced with 23.08% error for the asphalt mixture at -10 °C and 6.9% for the asphalt mixture at 20 °C compared to the experiments. The damage at low and high temperatures such as cracking was reproduced like the real sample. In addition, higher stress occurs in the area where damage was formed

Data comparison schemes for Pattern Recognition in Digital Images using Fractals

Pattern recognition in digital images is a common problem with application in remote sensing, electron microscopy, medical imaging, seismic imaging and astrophysics for example. Although this subject has been researched for over twenty years there is still no general solution which can be compared with the human cognitive system in which a pattern can be recognised subject to arbitrary orientation and scale. The application of Artificial Neural Networks can in principle provide a very general solution providing suitable training schemes are implemented. However, this approach raises some major issues in practice. First, the CPU time required to train an ANN for a grey level or colour image can be very large especially if the object has a complex structure with no clear geometrical features such as those that arise in remote sensing applications. Secondly, both the core and file space memory required to represent large images and their associated data tasks leads to a number of problems in which the use of virtual memory is paramount. The primary goal of this research has been to assess methods of image data compression for pattern recognition using a range of different compression methods. In particular, this research has resulted in the design and implementation of a new algorithm for general pattern recognition based on the use of fractal image compression. This approach has for the first time allowed the pattern recognition problem to be solved in a way that is invariant of rotation and scale. It allows both ANNs and correlation to be used subject to appropriate pre-and post-processing techniques for digital image processing on aspect for which a dedicated programmer's work bench has been developed using X-Designer

3D digital modelling and identification of pavement typical internal defects based on GPR measured data

A three-dimensional ground-penetrating radar (GPR) captures non-destructively internal pavement distress characteristics. However, interpreting radar images and data analysis pose challenges. To improve the accuracy of distress identification, a three-dimensional digital model of internal pavement distress was established. Firstly, initial electromagnetic signal data were pre-processed to effectively eliminate spurious signals and enhance distress characteristic signals. The distress was located, and GPR images of typical distress were extracted and summarised. Next, the 3D dataset was constructed based on the pre-processed electromagnetic echo signals. A 3D digital model of internal pavement distress was generated using the inverse distance weight and ray-casting methods with trilinear interpolation. Finally, relying on the physical project, cores were extracted to validate the distress model. The method effectively reflects the internal pavement distress, and enables realise the interactive images between the pavement entity and the digital model, which can essentially contribute to the digital twin of pavement systems

Monitoring, modelling and quantification of accumulation of damage on masonry structures due to recursive loads

The use of induced seismicity is gaining in popularity, particularly in Northern Europe, as people strive to increase local energy supplies. Τhe local building stock, comprising mainly of low-rise domestic masonry structures without any aseismic design, has been found susceptible to these induced tremors. Induced seismicity is generally characterized by frequent small-to-medium magnitude earthquakes in which structural and non-structural damage have been reported. Since the induced earthquakes are caused by third parties liability issues arise and a damage claim mechanism is activated. Typically, any damage are evaluated by visual inspections. This damage assessment process has been found rather cumbersome since visual inspections are laborious, slow and expensive while the identification of the cause of any light damage is a challenging task rendering essential the development of a more reliable approach. The aim of this PhD study is to gain a better understanding of the monitoring, modelling and quantification of accumulation of damage in masonry structures due to recursive loads. Fraeylemaborg, the most emblematic monument in the Groningen region dating back to the 14 th century, has experienced damage due to the induced seismic activity in the region in recent years. A novel monitoring approach is proposed to detect damage accumulation due to induced seismicity on the monument. Results of the monitoring, in particular the monitoring of the effects of induced seismic activity,, as well as the usefulness and need of various monitoring data for similar cases are discussed. A numerical model is developed and calibrated based on experimental findings and different loading scenarios are compared with the actual damage patterns observed on the structure. Vision-based techniques are developed for the detection of damage accumulation in masonry structures in an attempt to enhance effectiveness of the inspection process. In particular, an artificial intelligence solution is proposed for the automatic detection of cracks on masonry structures. A dataset with photographs from masonry structures is produced containing complex backgrounds and various crack types and sizes. Moreover, different convolutional neural networks are evaluated on their efficacy to automatically detect cracks. Furthermore, computer vision and photogrammetry methods are considered along with novel invisible markers for monitoring cracks. The proposed method shifts the marker reflection and its contrast with the background into the invisible wavelength of light (i.e. to the near-infrared) so that the markers are not easily distinguishable. The method is thus particularly vi suitable for monitoring historical buildings where it is important to avoid any interventions or disruption to the authenticity of the basic fabric of construction.. Further on, the quantification and modelling of damage in masonry structures are attempted by taking into consideration the initiation and propagation of damage due to earthquake excitations. The evaluation of damage in masonry structures due to (induced) earthquakes represents a challenging task. Cumulative damage due to subsequent ground motions is expected to have an effect on the seismic capacity of a structure. Crack patterns obtained from experimental campaigns from the literature are investigated and their correlation with damage propagation is examined. Discontinuous modelling techniques are able to reliably reproduce damage initiation and propagation by accounting for residual cracks even for low intensity loading. Detailed models based on the Distinct Element Method and Finite Element Model analysis are considered to capture and quantify the cumulative damage in micro level in masonry subjected to seismic loads. Finally, an experimental campaign is undertaken to investigate the accumulation of damage in masonry structure under repetitive load. Six wall specimens resembling the configuration of a spandrel element are tested under three-point in-plane bending considering different loading protocols. The walls were prepared adopting materials and practices followed in the Groningen region. Different numerical approaches are researched for their efficacy to reproduce the experimental response and any limitations are highlighted