Relevant bibliographies by topics / Road defect identification

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Road defect identification'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Road defect identification"

1

Hugo, D., P. S. Heyns, R. J. Thompson, and A. T. Visser. "Haul road defect identification using measured truck response." Journal of Terramechanics 45, no. 3 (June 2008): 79–88. http://dx.doi.org/10.1016/j.jterra.2008.07.005.

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2

Frolov, A., and O. Shabratko. "EXAMINATION OF TRUCK TIRES AS TO IDENTIFICATION OF DAMAGES WHICH ARE FORMED AS A RESULT OF OPERATIONAL TIRE WEAR OR PRODUCT DEFECT." Theory and Practice of Forensic Science and Criminalistics 21, no. 1 (December 15, 2020): 441–51. http://dx.doi.org/10.32353/khrife.1.2020_31.

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The sequence of carrying out of researches of tires of a freight vehicle on the basis of expert practice is considered. The research of tires of a truck has been conducted, an example of an expert research of tires is resulted, as a result of which the causes of damage of tires were established. The tire connects the vehicle to the road, perceives its weight, braking effort and dynamic impact that arises due to the roughness of the road surface. It is not uncommon for the vehicle to operate even on roads with a satisfactory road surface and damage to the tires. Damage to the tires can be generated as a result of industrial damage (factory defect) and as a result of operational damage. The most common and serious cause of premature wear and damage to tires is non-compliance with the established air pressure norms and overload tires. Changing the configuration of the profile and increasing the deformation of the bus cause an increase in the voltage in its material. As a result, it increases its premature wear. At increased load (overload) the tension voltage in the places of contact of the tire with the road increases and its specific pressure on the road, from which the tread wears away more quickly. Overvoltage in the material and increase in deformation is accompanied by a general increase in friction and heat formation in the tire. At increased load (overload) the tension voltage in the places of contact of the tire with the road increases and its specific pressure on the road, from which the tread wears away more quickly. Overvoltage in the material and increase in deformation is accompanied by a general increase in friction and heat formation in the tire. In the course of the research it was established that the tires Satoya SD 062-III have operational damage, the detects damage to the tires was formed as a result of their operation, which could contribute to vehicle overload, non compliance with tire pressure and the movement of the vehicle on roads with improper road surface. External inspection the examination of Satoya SD 062-III tires showed no signs of industrial damage (factory defects).
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Sim, Jun-Gi. "Improved image processing of road pavement defect by infrared thermography." Selected Scientific Papers - Journal of Civil Engineering 13, s1 (March 1, 2018): 61–72. http://dx.doi.org/10.1515/sspjce-2018-0006.

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Abstract This paper intends to achieve improved image processing for the clear identification of defects in damaged road pavement structure using infrared thermography non-destructive testing (NDT). To that goal, 4 types of pavement specimen including internal defects were fabricated to exploit the results obtained by heating the specimens by natural light. The results showed that defects located down to a depth of 3 cm could be detected by infrared thermography NDT using the improved image processing method.
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Rawool, Shubham, and Emmanuel G. Fernando. "Methodology for Detection of Defect Locations in Pavement Profile." Transportation Research Record: Journal of the Transportation Research Board 1905, no. 1 (January 2005): 140–47. http://dx.doi.org/10.1177/0361198105190500115.

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Pavement smoothness has become a standard measure of pavement quality. Transportation agencies strive to build and maintain smoother pavements. Road users generally perceive the quality of a pavement on the basis of how well it rides, which is severely affected by the presence of defects (bumps or dips) in the pavement profile. Defects are corrected according to the smoothness specifications prescribed by respective agencies. The effectiveness of any method used to identify defect locations depends on the decrease in roughness obtained on correction of the defects. Following this line of thought, this paper presents a method for the detection of defects based on a comparison of the original profile with a target or a desired profile. The proposed methodology is based on the international roughness index (IRI) gain function for the identification of defect locations to improve smoothness in pavements. This method uses the discrete Fourier transform to help identify defect locations on the basis of deviations of the original profile from the target or the smoothened profile. Areas with defects have a higher deviation from the smoothened profile than areas without defects. This method also estimates the contribution of each defect to roughness. Roughness statistics, such as the IRI and the present serviceability index, are used in the proposed approach to determine the severity of each defect. In addition, the use of a quarter-truck transfer function instead of the IRI gain function is demonstrated to illustrate consideration of dynamic load criteria for the detection of defects. The approach is illustrated through the use of profile data collected for in-service pavement sections.
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Sobol, B. V., A. N. Soloviev, P. V. Vasiliev, and L. A. Podkolzina. "Deep convolution neural network model in problem of crack segmentation on asphalt images." Vestnik of Don State Technical University 19, no. 1 (April 1, 2019): 63–73. http://dx.doi.org/10.23947/1992-5980-2019-19-1-63-73.

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Introduction.Early defect illumination (cracks, chips, etc.) in the high traffic load sections enables to reduce the risk under emergency conditions. Various photographic and video monitoring techniques are used in the pavement managing system. Manual evaluation and analysis of the data obtained may take unacceptably long time. Thus, it is necessary to improve the conditional assessment schemes of the monitor objects through the autovision.Materials and Methods.The authors have proposed a model of a deep convolution neural network for identifying defects on the road pavement images. The model is implemented as an optimized version of the most popular, at this time, fully convolution neural networks (FCNN). The teaching selection design and a two-stage network learning process considering the specifics of the problem being solved are shown. Keras and TensorFlow frameworks were used for the software implementation of the proposed architecture.Research Results.The application of the proposed architecture is effective even under the conditions of a limited amount of the source data. Fine precision is observed. The model can be used in various segmentation tasks. According to the metrics, FCNN shows the following defect identification results: IoU - 0.3488, Dice - 0.7381.Discussion and Conclusions.The results can be used in the monitoring, modeling and forecasting process of the road pavement wear.
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6

Desnerck, Pieter, Pierfrancesco Valerio, Janet M. Lees, and Neil Loudon. "Suggestions for improved reinforced concrete half-joint bridge inspection in England." MATEC Web of Conferences 199 (2018): 06004. http://dx.doi.org/10.1051/matecconf/201819906004.

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Asset management databases play a crucial role in the management of existing infrastructure assets. Highways England (HE) has a long history of using bridge management software to record the current state of bridges and to guide maintenance schemes and interventions. Reinforced concrete half-joints are amongst the most challenging structures to inspect and repair due to their susceptibility to deterioration and construction type. Hence, they require particular attention within asset management programmes. An Interim Management Strategy was developed by HE to identify all the structures on the Highways England road network with half-joint elements. These half-joint structures were then subjected to a special inspection regime. Out of the 428 half-joint structures with inspection data, 252 structures had defects associated with four existing HE defect classes. A review of the inspection database with a focus on half-joints led to an alternative classification of half-joint related defects based on a revised set of Defect Classes, the introduction of Defect Groups and the extraction of Defect Types specifically observed in half-joints. Using this new classification, the most common half-joint Defect Groups were found to be cracking, corrosion, spalling and deterioration mechanisms. In about half of the structures cracking and corrosion tended to be observed together. Correlations were also shown to exist between structural and deterioration, and constructional Defect Classes, emphasising the need for quality control and proper workmanship. Recommendations to address shortcomings in current inspection practice are proposed. Clearer defect definitions and decision-tree guidance for inspectors could enhance the consistency and repeatability of inspection data gathering thereby overcoming some of the limitations of subjective classifications. Acquiring additional information about the observed crack details including zonal information, crack patterns, crack extent, crack orientations and widths combined with local and global pictorial evidence would also be advantageous. This could then provide the basis for the automatic processing and identification of structures with specific half-joint related defects. In this way, asset managers would be better able to allocate limited resources to the most critical structures.
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Khataniar, Himanku. "Identification of Geometrical Defects on ADB Road-A Road Safety Audit." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 1442–48. http://dx.doi.org/10.22214/ijraset.2018.4243.

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8

Liu, Zhen, Wenxiu Wu, Xingyu Gu, Shuwei Li, Lutai Wang, and Tianjie Zhang. "Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance." Remote Sensing 13, no. 6 (March 12, 2021): 1081. http://dx.doi.org/10.3390/rs13061081.

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Improving the detection efficiency and maintenance benefits is one of the greatest challenges in road testing and maintenance. To address this problem, this paper presents a method for combining the you only look once (YOLO) series with 3D ground-penetrating radar (GPR) images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits. First, traditional detection is conducted to survey and summarize the surface conditions of tested roads, which are missing the internal information. Therefore, GPR detection is implemented to acquire the images of concealed defects. Then, the YOLOv5 model with the most even performance of the six selected models is applied to achieve the rapid identification of road defects. Finally, the benefits evaluation of maintenance programs based on these two detection methods is conducted from economic and environmental perspectives. The results demonstrate that the economic scores are improved and the maintenance cost is reduced by $49,398/km based on GPR detection; the energy consumption and carbon emissions are reduced by 792,106 MJ/km (16.94%) and 56,289 kg/km (16.91%), respectively, all of which indicates the effectiveness of 3D GPR in pavement detection and maintenance.
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Hryanina, Ol'ga, Elena Saksonova, and Dmitry Abaev. "Research of transport and operational state of a public highway on the example of Civil-Industrial streets in Kamenka." Construction and Architecture 7, no. 4 (December 19, 2019): 68–73. http://dx.doi.org/10.29039/2308-0191-2019-7-4-68-73.

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The article uses a practical example to consider changes in the transport and operational state of the public highway and road surface, with the possibility of justifying and taking into account these changes in the design, operation and repair. The methodological approach of the road and road surface survey provides for the first stage of visual inspection of the road and identification of areas with visible defects of the existing roadbed and road surface. At the second stage, the asphalt roadbed was opened by pits in the most visible areas of damage in order to take samples of road clothing and subsequent analysis. Defects and causes of deformations of asphalt concrete pavement and existing roadbed were identified. Recommendations for eliminating the causes of deformations and strengthening the coating are given.
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Praticò, Filippo Giammaria, Rosario Fedele, Vitalii Naumov, and Tomas Sauer. "Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach." Algorithms 13, no. 4 (March 31, 2020): 81. http://dx.doi.org/10.3390/a13040081.

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The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.
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Dissertations / Theses on the topic "Road defect identification"

1

Hugo, Daniel. "Haul road defect identification and condition assessment using measured truck response." Diss., University of Pretoria, 2005. http://upetd.up.ac.za/thesis/available/etd-07162008-092104/.

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Book chapters on the topic "Road defect identification"

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Shevtsov, Sergey N., Arkady N. Soloviev, Ivan A. Parinov, Alexander V. Cherpakov, and Valery A. Chebanenko. "Identification of Defects in Cantilever Elastic Rod." In Piezoelectric Actuators and Generators for Energy Harvesting, 89–144. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75629-5_6.

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Shevtsov, Sergey N., Arkady N. Soloviev, Ivan A. Parinov, Alexander V. Cherpakov, and Valery A. Chebanenko. "Set-up for Studying Oscillation Parameters and Identification of Defects in Rod Constructions." In Piezoelectric Actuators and Generators for Energy Harvesting, 145–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75629-5_7.

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Conference papers on the topic "Road defect identification"

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Sobol, B. V., P. V. Vasiliev, A. V. Senichev, and A. I. Novikova A.I. "CONVOLUTIONAL NEURAL NETWORKS APPLICATION IN DEFECT IDENTIFICATION PROBLEMS." In STATE AND DEVELOPMENT PROSPECTS OF AGRIBUSINESS Volume 2. DSTU-Print, 2020. http://dx.doi.org/10.23947/interagro.2020.2.161-163.

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In order to increase an automation of the products non-destructive testing process, a deep convolutional neural network optimized model for identifying defects in road pavement images is proposed. The resulting neural network architecture allows us to solve image segmentation problems of different nature, including in the case of a source data limited set. The study showed a high degree of results repeatability.
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Johnson, Brett, Bereket Tesfaye, Cory Wargacki, Thomas Hennig, and Ernesto Suarez. "Complex Circumferential Stress Corrosion Cracking: Identification, Sizing and Consequences for the Integrity Management Program." In 2018 12th International Pipeline Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/ipc2018-78564.

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Since the late 1980’s Ultrasonic tools have been used for the detection and sizing of crack like indications. ILI service providers developed inspection technologies for liquid and gas lines that are widely used nowadays. In comparison to axial cracking, circumferential cracking is not a prevalent risk to most pipelines and therefore is not as well understood. Nevertheless, pipeline Operators observe from time to time circumferentially oriented defects, often in combination with circumferential welds or local stress/strain accumulations. These are often caused by pipeline movement, which may especially occur in mountain areas. With the introduction of Ultrasonic circumferential crack inspection tools in the late 2000’s the knowledge has steadily increased over time. Extensive data collected from in-ditch NDE validations has provided NDT Global with an increased knowledge of the morphology of single cracking and stress corrosion cracking defects both in the axial and circumferential orientations. Field verifications have shown that not all features have the same morphology. Some of the challenges with circumferential cracking are for features that fall outside of the industry standard specifications. These types of features can exhibit characteristics such as being sloped, skewed or tilted. In 2016 NDT Global was approached by Plains Midstream Canada to complete inspections utilizing the 10″ Ultrasonic Circumferential crack inspection technology. The pipeline system spans 188km within Canada and consists of 2 segments. The pipeline traverses several elevation changes and crosses several creeks and roads. Circumferential cracking was identified during dig campaigns performed for other threats, therefore the need to inspect each pipeline segment with the Ultrasonic circumferential technology was identified. Plains Midstream Canada and NDT Global formed a close collaboration to assess the severity of circumferential crack features in this line. This paper will discuss integrity aspects from an Operator and Vendor perspective. Challenges identified due to the morphology of the circumferential crack like indications and derived analysis rules and interpretation methodologies to optimize characterization and sizing are presented. Finally, potential opportunities to maintain the integrity of similar assets by applying some of the findings and enhance the management and decision making process are suggested.
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