Pavement Inspection Research Articles

Applications of Terrestrial Laser Scanner in Detecting Pavement Surface Defects

An entire roadway system represents a crucial element in the sustainable urban transportation planning process. Pavement surfaces are at continual risk of accumulating serious deteriorations and defects throughout their service life due to traffic loading and environmental impact. Since roadway networks are growing rapidly, relying on visual pavement inspection is not always feasible. Therefore, this paper proposes an effective assessment method for evaluating flexible pavement surface distresses using a terrestrial laser scanner (TLS) and calculating the pavement condition index (PCI). The proposed terrestrial laser scanner method results in road condition assessments becoming faster, safer, and more systematic. It also aims to determine the geometric characteristics of the investigated roads. A major road in Egypt was selected to test the proposed technique and compare it with the traditional visual inspection method. The evaluation was carried out to assess different types of pavement distress, such as cracking, rutting, potholes, and raveling distresses. Every pavement distress was defined in terms of surface area, the width of the crack, and intensity, and the data from TLS were then processed by MAGNET COLLAGE software. A MATLAB program was developed to match the TLS observational data to plane equations. PAVER software was also used to determine the PCI values for each TLS position. The revealed distresses for the investigated road using TLS observations reveal a significant improvement in determining flexible pavement distresses and geometric characteristics.

A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects

Crack recognition is important in periodic pavement inspection and maintenance. The wide application of image recognition technology in daily inspection and maintenance makes the health monitoring of asphalt pavement defects more effective, both intelligently and sustainably. In this study, a mobile automatic system integrating fifth-generation wireless communication technology (5G), cloud computing, and artificial intelligence (AI) was proposed for transportation infrastructure object recognition. The original dataset contained 344 images of pavement defects, including longitudinal cracks, transverse cracks, alligator cracks, and broken road markings. Three lightweight algorithms for automatic pavement crack identification were used and compared, including MobileNetV2, ShuffleNetV2, and Res-Net50 networks, respectively. The results showed that the model based on ShuffieNetV2 achieved the best overall predictive accuracy (ACC = 95.52 percent). A mobile automatic monitoring system based on the cloud platform and Android framework was then established. With the help of 5G technology, the cloud-network-terminal’ interconnection can be achieved to provide fast and stable information transmission between transportation infrastructure and road users. The proposed system provides an engineering reference for the transportation infrastructure inspection and maintenance using the 5G communication technology.

A Faster RCNN Airport Pavement Crack Detection Method Based on Attention Mechanism

Airport pavement inspection is an important link to ensure the safe takeoff and landing of aircraft. At present, the airport pavement safety inspection is still dominated by manual inspection. This method has some problems such as low detection efficiency, strong subjectivity, and unable to fully cover. In this context, Faster RCNN network was used for tunnel crack detection, the backbone network of Faster RCNN was modified, the lightweight network MobileNetV2 was used to improve the network training speed, and CBAM attention mechanism was added to improve the feature extraction ability of the model.

CONCEPT OF MONITORING AND DIAGNOSTICS OF ARTIFICIAL AIRFIELD PAVEMENTS

The paper clarifies the terms related to the inspection and monitoring of airfield pavements for major repairs and reconstruction and their restoration. The paper presents an improved methodology for assessing the condition of the pavement, establishing the residual life and assignment of repairs to the new visual and instrumental inspection. establishing the need and procedure for monitoring and diagnostics of artificial airfield pavements to create a database of the airfield pavement management system.

Pavement Distress Estimation via Signal on Graph Processing

A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way.

Improving FMCW GPR Precision through the CZT Algorithm for Pavement Thickness Measurements

Ground Penetrating Radar (GPR) application in road surface detection has been greatly developed in the past few decades, which enables rapid and economical estimation of pavement thickness and other physical properties in non-destructive testing (NDT) and non-contact testing (NCT). In recent years, with the rapid development of microwave and millimeter-wave solid-state devices and digital signal processors, the cost of Frequency-Modulated Continuous-Wave (FMCW) radar has dropped significantly, with smaller size and lighter weight. Thereafter, FMCW GPR is considered to be applied during pavement inspection. To improve the precision of FMCW GPR for NDT and NCT of pavement thickness, a Chirp Z-transform (CZT) algorithm is introduced to FMCW GPR and investigated in this paper. A FMCW + CZT GPR at 2.5 GHz with a bandwidth of 1 GHz was built, and laboratory and field experiments were carried out. The experimental results demonstrate that the FMCW + CZT GPR radar can obtain the sample thickness with low error and recognize subtle thickness variations. This method realizes the high precision thickness measurement of shallow asphalt pavement by FMCW radar with a narrow bandwidth pulse signal and would provide a promising low-cost measurement solution for GPR.

Full field-of-view pavement stereo reconstruction under dynamic traffic conditions: Incorporating height-adaptive vehicle detection and multi-view occlusion optimization

Three-dimensional (3D) surface information is becoming the data of choice for pavement inspection and maintenance. Pavement digital reconstruction still faces various challenges, including field-of-view (FOV) limits, traffic influences, data acquisition speed and cost. This paper presents a full-FOV pavement stereo reconstruction framework integrating unmanned aerial vehicle (UAV) photography, object detection and multi-view occlusion optimization. A UAV-YOLO vehicle detector embedding depth-wise separable convolution and resolution adjustment unit is developed for noise localization. To improve reconstruction quality and speed, multi-view occlusion optimization is proposed for determining the optimal image series spatial distribution. The results show that the UAV-YOLO detector achieves an overall AP75 of 93.69% with an inference speed of 157 FPS. Through multi-criterion evaluation, the pavement digital models reconstructed under dynamic traffic conditions have satisfactory performance in terms of point cloud noise, density, similarity and accuracy. In addition, the proposed stereo reconstruction workflow saves 30.27% processing time over conventional SfM workflow.

Asphalt pavement characterization by GPR using an air-coupled antenna array

Ground penetrating radar (GPR) has become an effective tool for asphalt pavement inspection. However, a ground-coupled GPR system cannot facilitate a high-speed survey due to the complex road environment and traffic condition. In this paper, an air-coupled antenna array is designed for measurement of common midpoint (CMP) GPR data. From the CMP data, an improved velocity spectrum algorithm considering the refraction on the pavement surface is proposed to simultaneously estimate the thickness and dielectric permittivity of the pavement layer. The results of a laboratory experiment demonstrate that the improved velocity spectrum method can greatly enhance the accuracy of the velocity and thickness estimations, compared with the traditional inversion method. A field measurement conducted on a highway pavement shows that the maximum error of the thickness estimation of the asphalt layer is less than 10 mm (7.1%). It is concluded that the developed CMP technique can be used for quantitative characterization of asphalt pavement.

Expressway Asphalt Pavement Technical Status Cluster Dimensional Reduction Evaluation Method

In the process of asphalt pavement maintenance management, the technical condition of the pavement is accurately predicted, its performance trend is predicted, and the right time is chosen to take targeted maintenance measures that will play a multiplier role, so predicting the technical condition of the pavement is important significance. This paper mainly studies the cluster dimensionality reduction evaluation method of the technical condition of expressway asphalt pavement. This paper summarizes the experience of regular highway inspection data inspection and analysis, and mainly studies the standardized analysis method of highway pavement inspection data, aiming to meet the data requirements at all levels through effective data analysis indicators. The experimental data in this paper show that the driving quality of the two-way pavement is good, the total average SRI is 91.2, and the evaluation is “excellent,” and the left width is slightly better than the right width; the transverse crack penetration of most reconstructed road sections is greater than 0.4, indicating that the transverse crack penetrates the road surface more serious. The experimental results of this paper show that the technical status of asphalt pavement based on cluster analysis has practical significance, and the analysis results show that the treatment effect is ideal.

Efficient pavement distress classification via deep patch soft selective learning and knowledge distillation

Pavement distress classification is a vital step for automatic pavement inspection and maintenance. Recently, patch-based approaches have achieved promising performances and thus extensive attention in this field. However, these methods simply assume that all patches contribute equally to the distress classification, leading to weakly discriminating abilities of models. Moreover, their tedious processes also lead to low efficiency in inference. The authors present a novel patch-based pavement distress classification approach named Deep Patch Soft Selective Learning, which addresses these issues. Similar to other patch-based approaches, Deep Patch Soft Selective Learning partitions the pavement images into patches and aggregates the patch features to accomplish the task. To address the first issue, a succinct Soft Patch Feature Selection Network is introduced to assess the importance of each patch to the distress classification with a score based on its feature. These scores will be considered as patch-wise weights for feature aggregation. In such a manner, the most discriminative patches are selected in a soft way, and thereby benefit the final classification. To address the inference efficiency issue, knowledge distillation is leveraged to transfer the classification knowledge from Deep Patch Soft Selective Learning to the image-based approaches, such as EfficientNet-B3. This distilled model enables incorporating both the advantages of patch-based approaches in classification performance and the advantages of image-based approaches in inference efficiency. Extensive experiments on a large-scale pavement image dataset named CQU-BPDD demonstrate that Deep Patch Soft Selective Learning outperforms the baselines by a large margin of +7.4% in P@R on the detection task and +3.1% in F1 on the recognition task while enjoying 2.1x throughput of baselines.

Evaluation of Small Uncrewed Aircraft Systems Data in Airfield Pavement Crack Detection and Rating

Current practice for airport Pavement Management Program (PMP) inspection relies on visual surveys and manual interpretation of reports and sketches prepared by inspectors in the field to quantify pavement conditions using the Pavement Condition Index method set forth in ASTM D5340. In recent years, several attempts have been made, both by the industry and by airport operators, to use small Uncrewed (Unpersonned/Unmanned) Aircraft Systems (sUAS), or “drones,” to conduct various types of imaging and inspection of airport pavements. As part of a comprehensive study on the use of such sUAS to evaluate airfield pavement conditions, the objectives of this study were to assess the performance of various sUAS platforms and sensors in detecting and rating a subset of crack-based pavement distresses and to evaluate the use of a combination of different sUAS datasets to complement current methods used to support airport PMP. Two airports in Michigan were selected for sUAS data collection, and five sUAS platforms equipped with eight different sensors were flown at these airports at different altitudes to collect red, green, and blue (RGB) optical and thermal data at different resolutions. RGB orthophotos, digital elevation models, and thermal images were visually analyzed to study their usefulness in detecting and rating longitudinal and transverse cracks in flexible/asphalt pavements and longitudinal, transverse, and diagonal cracks, corner breaks, and durability cracks in rigid/concrete pavements. This study demonstrated the capability of using sUAS data in detecting and rating multiple crack-related distresses in both flexible and rigid airfield pavement systems.

Evaluation of Pavement Roughness by the International Roughness Index for Sustainable Pavement Construction in New Taipei City

Good road quality is an indicator of urban progress. In August 1998, the New Taipei City government implemented the “Road Leveling Project” on roads within its jurisdiction to improve the quality of public transportation. This paper aimed to analyze the International Roughness Index (IRI) of 152 asphalt concrete pavement sections in New Taipei City before and after road leveling. An intelligent pavement inspection vehicle that met the requirements of E 950 Class I was used to detect the IRI values of the road sections before and after maintenance. The statistical analysis showed that the implementation of the Road Leveling Project had a significant effect on improving the road roughness of roads under its jurisdiction. Moreover, it became possible to arrange a budget for roads with IRI values above a threshold or to estimate the budget needed to improve roads with an IRI higher than a certain value.

Talking about the Main Points of Field Test and Inspection of Highway Subgrade and Pavement

Talking about the Main Points of Field Test and Inspection of Highway Subgrade and Pavement

An intensity-enhanced method for handling mobile laser scanning point clouds

Currently, mobile laser scanning (MLS) systems can conveniently and rapidly measure the backscattered laser beam properties of the object surfaces in large-scale roadway scenes. Such properties is digitalized as the intensity value stored in the acquired point cloud data, and the intensity as an important information source has been widely used in a variety of applications, including road marking inventory, manhole cover detection, and pavement inspection. However, the collected intensity is often deviated from the object reflectance due to two main factors, i.e. different scanning distances and worn-out surfaces. Therefore, in this paper, we present a new intensity-enhanced method to gradually and efficiently achieve the intensity enhancement in the MLS point clouds. Concretely, to eliminate the intensity inconsistency caused by different scanning distances, the direct relationship between scanning distance and intensity value is modeled to correct the inconsistent intensity. To handle the low contrast between 3D points with different intensities, we proposed to introduce and adapt the dark channel prior for adaptively transforming the intensity information in point cloud scenes. To remove the isolated intensity noises, multiple filters are integrated to achieve the denoising in the regions with different point densities. The evaluations of our proposed method are conducted on four MLS datasets, which are acquired at different road scenarios with different MLS systems. Extensive experiments and discussions demonstrate that the proposed method can exhibit the remarkable performance on enhancing the intensities in MLS point clouds.

Material Permittivity Estimation Using Analytic Peak Ratio of Air-Coupled GPR Signatures

Ground penetrating radar (GPR) is widely applied for civil engineering, such as for building health assessments or pavement inspections. In these applications, information on the material permittivity is important for internal structure inspection and characterization. However, in the case of a large building area, a lightweight, fast and effective permittivity measurement technique is required. In addition, the estimation method for different GPR types and range configurations must be generalized for adaptability and wide applicability. This paper proposes a material permittivity estimation technique for air-coupled GPR that effectively works for mobile and large observation missions. The method employs the peak ratio of analytic representation signals between the antenna direct coupling and the material surface reflection. An interpolation algorithm is applied to the reference characteristic curves generated by the finite-difference time-domain technique to adapt the change in radar range configuration. The proposed method has been tested to estimate the permittivity in three different construction materials and measure two metal rebar depths inside reinforced concrete. The experimental results show that the method works with acceptable accuracy. Therefore, the method is sufficiently promising to be utilized in real applications, especially those that require adaptability and mobility, such as ground and aerial vehicle-based radar systems.

ВИКОРИСТАННЯ ДОСВІДУ БУДІВНИЦТВА МОНОЛІТНОГО ЦЕМЕНТОБЕТОННОГО ПОКРИТТЯ ЗЛІТНО-ПОСАДКОВОЇ СМУГИ АЕРОПОРТУ «ОДЕСА» ПРИ РОЗРОБЦІ НОВОГО ВІТЧИЗНЯНОГО СТАНДАРТУ НА ВЛАШТУВАННЯ АЕРОДРОМІВ

Abstract. Inspection of the monolithic cement-concrete pavement of the «Odessa» airport runway was carried out. Visual inspection of the runway surface, taxiways and apron for defects has been performed. The condition of the runway pavement was assessed as "excellent", destruction category I. In addition, strength tests of the rigid concrete pavement upper layer were carried out using non-destructive testing methods and core testing. The obtained strength characteristics correspond to the design requirements; the concrete has a grade of at least C32/40. Based on the processed data and own scientific developments, proposals were drawn up for a new standard (instead of SNyP 2.05.08-85 "Aérodromy") regarding the requirements for materials, concrete mixtures and concretes based on them for the runways construction in Ukraine. Modern requirements for monolithic cement-concrete airfield pavements require an extension of their service life up to 40 years. To ensure such indicators of serviceability, in addition to high strength indicators, it is imperative to ensure the rigid pavements durability using high-performance modified concretes. The proposals indicate the cement type, aggregates and chemical admixtures that can be used in the manufacture of concrete for airfield pavements. The requirements for the concrete and concrete mixture properties, the process of concrete curing are also presented. Taking into account the modern superplasticizers and the requirements for the workability of concrete mixtures for the construction of monolithic cement-concrete airfield pavements, the maximum W/C should be in the range of 0.3-0.35. To increase the flexural strength and crack resistance, it is desirable to use dispersed concrete reinforcement. To ensure the service life and satisfactory operational condition of the runway pavements, it is necessary to conduct an annual inspection for the timely identification and correction of arising defects.

Data cleaning framework for highway asphalt pavement inspection data based on artificial neural networks

ABSTRACT The acquisition of high-quality asphalt pavement inspection data is a key factor for road maintenance and management throughout the pavement’s life cycle. This paper establishes a data cleaning framework that is based on data mining and deep learning to eliminate abnormal data points and interpolate missing points to improve the accuracy of the original road inspection dataset. The first process in the proposed framework is to analyse the influential factors that are used to evaluate data quality and calculate the abnormality of the data through the confidence interval used in Gaussian fitting. Then, a deep artificial neural network is established to learn the mapping between the inspection data and data abnormality. The abnormal points are removed according to their abnormality level. Next, a second deep artificial neural network predicts the missing point values. Finally, by defining the overall cleanliness index of the dataset as the optimisation target, the steps are repeated until the cleanliness threshold is met. In this study, the proposed framework was applied to rut depth data cleaning for a highway maintenance project in Jiangsu Province, China. The cleanliness of the rut depth data increased by 6–95.75% after one cleaning cycle.

A cloud-edge-terminal-based robotic system for airport runway inspection

PurposeThis paper aims to present a novel robotic system for airport pavement inspection tasks.Design/methodology/approachThe cloud-edge-terminal-based distributed architecture is designed for the proposed robotic system. Then, the following three major parts are designed and deployed, respectively: Terminal: the wheeled-robot-based data collection system. Edge: remote monitoring and data analysis system. Cloud: shared database center of the inspection data and knowledge.FindingsValidation and application results show that the proposed system satisfies the demands of automated airport pavement inspection tasks and saves the cost of manpower and time.Originality/valueThe proposed system provides a novel solution for the full process of airport pavement inspection. Compared with the traditional manual method, the robotic system can guarantee complete coverage and provide high-precision pavement inspection results with less time and labor costs.

DEFECT DETECTION ON ASPHALT PAVEMENT BY DEEPLEARNING

The importance of road infrastructure to the economy of any nation cannot be overemphasized, however, it is not easy to maintain it properly, the increase in maintenance and repair expenditures are issues of concern coupled with the constantly increasing number of roads. Since the inspection of pavements is particularly difficult, an efficient inspection method is required. In this study, a method for detecting damage in asphalt pavements was developed using one of the deep learning techniques, YOLOv3. YOLOv3 is a method for detecting the position and type of an object from an input image, which fits the purpose of this study. The developed method can distinguish between longitudinal crack, transverse crack, alligator crack, and pothole. To confirm the accuracy of the developed method, images of pavements acquired on National Route 4 using were analyzed. From the analysis, it is found that the precision value is 0.7 and the average IoU is 50.39%. From the visualization of the analysis results, it was found that this method based on YOLOv3 was able to detect the damage with good accuracy. This is a significant improvement and can help shape the entire road Inspection procedures.

A CAPSNETS APPROACH TO PAVEMENT CRACK DETECTION USING MOBILE LASER SCANNNING POINT CLOUDS

Abstract. Routine pavement inspection is crucial to keep roads safe and reduce traffic accidents. However, traditional practices in pavement inspection are labour-intensive and time-consuming. Mobile laser scanning (MLS) has proven a rapid way for collecting a large number of highly dense point clouds covering roadway surfaces. Handling a huge amount of unstructured point clouds is still a very challenging task. In this paper, we propose an effective approach for pavement crack detection using MLS point clouds. Road surface points are first converted into intensity images to improve processing efficiency. Then, a Capsule Neural Network (CapsNet) is developed to classify the road points for pavement crack detection. Quantitative evaluation results showed that our method achieved the recall, precision, and F1-score of 95.3%, 81.1%, and 88.2% in the testing scene, respectively, which demonstrated the proposed CapsNet framework can accurately and robustly detect pavement cracks in complex urban road environments.