Pavement Inspection Research Articles

Surface Normal Vector Estimation From Passive Millimeter-Wave Polarimetric Imaging

Surface normal vector is essential for object recognition and three-dimensional reconstruction. This article establishes a passive millimeter-wave polarization observation model of object thermal radiation. Then, a polarization-based method is proposed to acquire the surface normal vector from multipolarization measurements. The simulation results show that our method has high accuracy and robustness. The outdoor imaging experiment was conducted, and the polarization characteristics of several typical objects in the imaging were analyzed. Experimental results show that our method can successfully estimate the surface normal vector. The possible applications of our method include object recognition and pavement inspection.

Application of infrared thermography for debonding detection in asphalt pavements

Conventional testing practices in pavement damage surveillance and maintenance such as coring tend to be slow, destructive in nature and not wholly representative of the entire stretch of the pavement. Newly emerging technologies based on non-destructive testing techniques, do not demand implementing destructive measures or relying on extrapolation of point data and can be implemented after developing them into a proper technique and a set testing protocol. One such emerging technique that uses infrared emissions from any structure to inspect underlying defects is infrared thermography (IRT). In a patch of pavement containing subsurface defects, the heat flow to the surface of the pavement, which itself depends on the incident solar radiation, ambient temperature and other meteorological factors, gets disrupted causing a difference in the thermal signature of the defective and the sound patches. This difference in thermal signature can potentially be detected by a thermal imaging camera. This study aims at exploring the potential of IRT technique to assess the subsurface debonding defect in asphalt pavements. For this purpose, an in situ asphalt pavement test section has been constructed and different interlayer bonding conditions have been artificially induced in it. A novel way for quantitative and qualitative analyses of thermal signatures, using MATLAB for each of these bonding conditions has been carried out. The effect of different debonding agents on the overall thermal behavior of asphalt pavement has also been evaluated. Interchange time duration between day heating and night cooling cycles has been estimated, to determine the suitable time duration for asphalt pavement inspection. The results, advantages, and limitations of the method have been presented.

Deciding on maintenance of small municipal roads based on GIS simplified procedures

This paper describes a survey of a Portuguese municipality road network and the development of a simplified procedure to support road network maintenance. This procedure considers inspection and quality evaluation of pavements. In addition, it allows programming of maintenance actions based on deterministic and probabilistic models to predict the evolution of pavement condition. The potential of this procedure is demonstrated through its use to establish scenarios, which enable the development of maintenance programs for the municipality’s road network.The data was represented in a commercial geographic information system (GIS) and processed through Python scripts. The GIS capabilities can analyse, query and modify the road network database as well as the production of maps and useful graphical information.The methodology was applied to 55.7 km of routes from a small municipality in Portugal, aiming at demonstrating the practical feasibility of the procedure, even when specialized means are not available. This means that after defining the rules for the collection and recording of information, it is not essential to maintain a specialized technical structure to proceed with the process of network analysis and decision-making.Two different techniques provided the prediction of pavement condition. For segments in which traffic of heavy vehicles was significant, the deterministic AASHTO model was applied. For the remaining cases, Markov chains (a probabilistic model) were used.The study made it possible to carry out some analysis on future scenarios of maintenance on the network, providing better decisions for the overall municipality and/or for specific areas of its territory. The obtained scenarios resulted in a prediction of pavement condition over the planning period, as well as a cost estimate for the studied scenarios.

Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery

Asphalt roads are the basic component of a land transportation system, and the quality of asphalt roads will decrease during the use stage because of the aging and deterioration of the road surface. In the end, some road pavement distresses may appear on the road surface, such as the most common potholes and cracks. In order to improve the efficiency of pavement inspection, currently some new forms of remote sensing data without destructive effect on the pavement are widely used to detect the pavement distresses, such as digital images, light detection and ranging, and radar. Multispectral imagery presenting spatial and spectral features of objects has been widely used in remote sensing application. In our study, the multispectral pavement images acquired by unmanned aerial vehicle (UAV) were used to distinguish between the normal pavement and pavement damages (e.g., cracks and potholes) using machine learning algorithms, such as support vector machine, artificial neural network, and random forest. Comparison of the performance between different data types and models was conducted and is discussed in this study, and indicates that a UAV remote sensing system offers a new tool for monitoring asphalt road pavement condition, which can be used as decision support for road maintenance practice.

Development of Smartphone Accelerometer-Based Airfield Friction Assessment Tools

Airfield managers have identified a need for contingency/emergency friction assessment tools that are both cost-effective and commonly found. Presently, airfield managers and pavement inspection teams prefer to measure friction at high speed with continuous friction measuring equipment (CFME) like the Findley Irvine GripTester. This device and the labor required to operate it are cost prohibitive. This paper explores an alternative, more economical measure of a surface’s frictional characteristics, that is, smartphone accelerometers. Accelerometers were standard friction assessment tools before CFMEs became available. Accelerometer-based friction testers measure the peak deceleration of a vehicle during braking. This acceleration, specifically the vehicle’s deceleration, is proportional to the surface’s friction coefficient. This paper covers research conducted to verify that smartphone accelerometers have sufficient accuracy and precision to operate as deceleration-based friction assessment tools. Findings reported here show that smartphone accelerometers, given sufficient conservatism, can function as deceleration-based friction assessment tools. Along with existing models, new regressions are presented that show correlation between smartphone deceleration measurements and high-speed GripTester measurements. The present work found that improved correlation was limited by common problems associated with poor reproducibility in braking tests.

Practical Approach for High-Resolution Airport Pavement Inspection with the Yakumo Multistatic Array Ground-Penetrating Radar System.

It is important to identify the thin cracks within the airport pavement layers. To achieve this goal, a practical interferometric approach using the Yakumo multistatic ground-penetrating radar system was developed to detect the slight variability in wave propagation velocity and/or thickness caused by the thin cracks. In comparison with the conventional common midpoint (CMP) velocity estimation method, the proposed method can provide much higher-resolution estimations of slight deviations in the velocity and thickness from their corresponding reference values in the undamaged asphalt through the comparison of two CMP datasets. These deviations can be obtained analytically instead of graphically extracted from the CMP velocity spectrum. The proposed approach was not only analyzed using the simulated datasets, but also practically demonstrated at both an experimental model site and an actual airport site. In the simulation tests, velocity deviations on the order of a few millimeters per nanosecond were detected, and the experimental results shows good agreement with the ground truth and coring samples. This method provides a novel way to inspect partially damaged pavement when the thin cracks are difficult to detect using the reflected signals.

Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network

Abstract Crack detection is a crucial task in periodic pavement survey. This study establishes and compares the performance of two intelligent approaches for automatic recognition of pavement cracks. The first model relies on edge detection approaches of the Sobel and Canny algorithms. Since the implementation of the two edge detectors require the setting of threshold values, Differential Flower Pollination, as a metaheuristic, is employed to fine-tune the model parameters. The second model is constructed by the implementation of the Convolution Neural Network (CNN) – a deep learning algorithm. CNN has the advantage of performing the feature extraction and the prediction of crack/non-crack condition in an integrated and fully automated manner. Experimental results show that the model based on CNN achieves a good prediction performance of Classification Accuracy Rate (CAR) = 92.08%. This performance is significantly better than the method based on the edge detection algorithms (CAR = 79.99%). Accordingly, the proposed CNN based crack detection model is a promising alternative to support transportation agencies in the task of periodic pavement inspection.

Active solution of homography for pavement crack recovery with four laser lines

An active solution method of the homography, which is derived from four laser lines, is proposed to recover the pavement cracks captured by the camera to the real-dimension cracks in the pavement plane. The measurement system, including a camera and four laser projectors, captures the projection laser points on the 2D reference in different positions. The projection laser points are reconstructed in the camera coordinate system. Then, the laser lines are initialized and optimized by the projection laser points. Moreover, the plane-indicated Plücker matrices of the optimized laser lines are employed to model the laser projection points of the laser lines on the pavement. The image-pavement homography is actively determined by the solutions of the perpendicular feet of the projection laser points. The pavement cracks are recovered by the active solution of homography in the experiments. The recovery accuracy of the active solution method is verified by the 2D dimension-known reference. The test case with the measurement distance of 700 mm and the relative angle of 8° achieves the smallest recovery error of 0.78 mm in the experimental investigations, which indicates the application potentials in the vision-based pavement inspection.

High-Resolution Velocity Analysis Method Using the ℓ-1 Norm Regularized Least-Squares Method for Pavement Inspection

We propose a high-resolution velocity analysis method to estimate the electromagnetic wave propagation velocity in subsurface medium. The estimation is achieved by applying the l-1 norm regularized least-squares method to the conventional common-midpoint (CMP) velocity analysis algorithm. The proposed method can provide not only higher resolution than the conventional velocity analysis method, but can also be applied with a coarse sampling array system, such as our array ground penetrating radar YAKUMO, which returns eight CMP traces within a two meter width. The main purpose of this approach is for precise pavement inspection at shallow depths. We applied this method to both a simulated dataset and real data acquired by YAKUMO at a model airport taxiway to detect the slight velocity changes caused by millimeter-thin cracks filled with air or water within the 15 cm-thick asphalt pavement. In both cases slight velocity changes of about 0.005 m/ns can be detected, and the difference between air- and water-filled cracks can be distinguished. Also, this method is applied to a data acquired at airport taxi-way, the damaged parts are detected successfully and shows good agreement with the corning results. The results indicate that the proposed method is effective for pavement inspection, especially in the presence of thin cracks that cannot be seen directly with the reflected signal.

可視光・近赤外線等による橋梁，トンネル，舗装の検査

可視光・近赤外線等による橋梁，トンネル，舗装の検査

OBJECT-BASED AND SUPERVISED DETECTION OF POTHOLES AND CRACKS FROM THE PAVEMENT IMAGES ACQUIRED BY UAV

Abstract. Roads are the basic element of land transportation system. After construction, the quality of road will decrease because of the aging and deterioration of the road surface. In the end, some distresses will appear on the pavement, such as the most common potholes and cracks. In order to improve the efficiency of pavement inspection, nowadays some new forms of remote sensing data without destructive effect on the pavement are widely used to detect the pavement distresses, such as digital images, LiDAR and Radar. In our study, the digital pavement images acquired by Unmanned Aerial Vehicle (UAV) and four popular supervised learning algorithms (KNN, SVM, ANN, RF) were used to distinguish between the normal pavement and pavement damages (i.e. cracks and potholes). Each of learning algorithms was given a series of different parameters, and the classification accuracy and computational time as two assessment criteria of the algorithm performance were calculated. Finally, four best models for each kind of learning algorithms were selected based on the standard of highest accuracy and minimum running time.

Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection

Automatic inspection of pavement cracking is a critical issue in pavement management systems. In this study, a quadcopter-based digital imaging system is introduced for collecting pavement surface data over a distressed area for visual conditions interpretation. An aerial evaluation was carried out using a quadcopter unmanned aerial vehicle (QUAV), which is a device equipped with a set of automatic systems. Since QUAV flies autonomously and has high maneuverability, it is potentially useful in a variety of conditions particularly the positions dangerous for surveillance and reconnaissance. The main purpose of this work is to design a multi-stage system for QUAV image analysis consisting of image processing, threshold selection, and classification stages. The images are transformed into a new domain; then, an adaptive thresholding is applied to build the pattern of transformed cracks; and finally, the polar support vector machine (the PSVM) is applied for interpretation of crack distress. The PSVM is an automation procedure based on the support vector machine (SVM) classifier defined in the polar coordinate frame. A Mixture of Wavelet modulus and three-dimensional polar Radon transform (3DPRT) are used for feature generation. We show that the PSVM method can be successfully applied to classify the crack and is capable of providing new features about cracking distress, threshold selection and classification. In order to show the applicability and efficiency of the proposed system and method, a test was conducted applying a variety of pavement distresses. The experimental results demonstrate that the applied system provides reliable output. In addition, the comparison of the derived information with the on-site manual quantifications revealed the potentiality of the QUAV and multi-stage system for future practice.

Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review

Pavement condition information is a significant component in Pavement Management Systems. The labeling and quantification of the type, severity, and extent of surface cracking is a challenging area for weighing the asphalt pavements. This paper presents a widespread review on various platform and image processing approaches for asphalt surface interpretation. The main part of this study presents a comprehensive combination of the state of the art in image processing based on crack interpretation related to asphalt pavements. An attempt is made to study the existing methodologies from different points of views accompanied by extensive comparisons on three stages of methods—distress detection, classification, and quantification to facilitate further research studies. This paper presents a survey of the developed pavement inspection systems up to date. Additionally, emerging and evolution technologies considered to automate the processes are discussed.

Hybrid PSO and GA approach for optimizing surveyed asphalt pavement inspection units in massive network

Abstract This study proposes an optimal arrangement of surveyed pavement inspection units (SIUs) for cost reduction, minimization of inspection errors and accuracy improvement of pavement network analysis. Inspection process requires surveying billions of distresses characteristics for different sections of a specific area. A comprehensive database is generally required for such pavement management system (PMS). A major concern with this type of data is lack of powerful methods for an effective analysis in a network level. A number of inspection units are surveyed with various sampling patterns for minimizing the cost and time of inspection. Analysis of large numbers of sections and inspection units is time consuming and needs high computation efforts. To address this issue, this paper focuses on developing efficient methods for decreasing complexity of the system. Accordingly, various combinations of the hybrid genetic algorithm (GA) and particle swarm optimization (PSO) are used for analyzing a typical pavement network. The numerical results confirm the ability of the proposed approach to optimize the arrangement of SIUs in network inspection error (NIE), computation time (CPU Time), number of SIUs (NSIUs), and convergence diagram for network, project and section management levels. The hybrid approaches result in an optimal solution in a short time with high accuracy for each section in a massive network. As a result, the inspection process can be performed with minimal costs.

Optimization of Pavement Inspection Schedule with Traffic Demand Prediction

In order to improve the current pavement inspection systems, many studies have attempted to produce a flexible inspection schedule by minimizing the lifecycle cost of pavement management system. However, these methods do not incorporate the risk of untimely inspection nor adopt the state-of-art intelligent transportation resources, such as traffic data from various infrastructures, flow prediction models with high accuracy, and empirical-mechanistic models for pavement deterioration process. Therefore, this paper proposes a framework to optimize a flexible inspection schedule within a risk boundary defined by pavement state prediction with traffic flow data and a more mechanistic deterioration model. The results validate the outperformance of the optimized inspection over two conventional inspection schemes – 1-year and 2-year regular inspection. Also the optimized inspection is comparably more robust than the regular inspections with different traffic scenarios due to the uncertainty risk taken by regular inspections.

道路舗装の保守点検での画像処理の活用

道路舗装の保守点検での画像処理の活用

Интервью с создателем диагностической лаборатории мониторинга улично-дорожной сети Васильевым Ю.Э.

Интервью с создателем диагностической лаборатории мониторинга улично-дорожной сети Васильевым Ю.Э.

EXPERIMENTAL EVALUATION OF MISALIGNED TIE BAR EFFECTS ON PCC PAVEMENT LONGITUDINAL JOINTS

Jointed Portland cement concrete (PCC) pavements are widely used for roadways construction. Inspection of PCC pavements revealed that it is common for tie bars across pavement longitudinal joints to be misaligned. A misaligned tie bar could inhibit the tie bars ability to provide load transfer across the joint and to prevent excessive joint opening. An experimental research study was performed to determine the effect of misaligned tie bars on the performance of pavement longitudinal joints. The experimental program consisted of testing 35 slab specimens that represented pavement sections with one tie bar placed across a joint. The test matrix included aligned tie bar configuration and four different misalignment configurations. Specimens with aligned tie bar configuration served as control specimens. The misaligned tie bar cases were vertical translation, vertical skew, longitudinal translation, and horizontal translation. For each misalignment configuration, four different misalignment magnitudes were investigated. The experimental results showed that tie bars with vertical translation, vertical skew, and longitudinal translation had negligible effects on the performance of the longitudinal joint. However, tie bars with horizontal skew resulted in reduced joint performance when compared to the aligned tie bar case.

Can technology improve routine visual bridge inspections?

This paper discusses the role of visual inspections in the UK highways bridge inspection regime, and how current practice results in inspection data that are less reliable than they could, and should, be. Evidence that visual inspections in general are not totally reliable is discussed. Similar problems have been overcome in other areas, such as pavement inspection, by using technological aids during the data collection and inspection process. It is hoped that better bridge condition data could be obtained by making use of technology to assist the visual inspection process on bridges. Some of the ways in which images could be recorded to make the inspection process more reliable and reproducible are discussed, and a prototype system that has been developed with the aim of providing a full, high-resolution image record of the visual condition of a bridge, which is suitable for use, is presented.

Life Cycle Cost Analysis on pavement inspection intervals considering maintenance work delay

Inspection strategy has a significant effect on the entire working process of pavement management. Most countries or road agencies casually define their inspection intervals without careful consideration. Moreover, most conventional pavement management analysis models do not treat inspection strategy as an important issue. Thus, with an economic analysis of inspection intervals, this paper aims to give a wider publicity to the importance of inspection strategy. An improved LCCA (Life Cycle Cost Analysis) model has been developed as an analytical tool. This model can take delays into consideration, as well as estimate various LCC contents and decision indicators. Both the hypothesis and the developed LCCA model have been empirically tested with pavement data from Korean National Highways. As a result, we can confirm that 1) the inspection interval affects overall management activities and LCCA results, 2) the suggested method shows an improvement in estimation results which consider the effects of delay in maintenance, and 3) either a 2 year or 3 year interval is the most beneficial interval, from both engineering and economical perspectives. In conclusion, the inspection interval needs to be carefully determined by analysis works. It is potentially a critical index that will improve current management strategy by in-house efforts.