Road Condition Assessment Research Articles

PEMELIHARAAN KONSTRUKSI JALAN DENGAN METODE SDI

The Dawan-Pesinggahan Road section is one of the sections that has the status of a district road in Klungkung Regency. The Dawan Pesinggahan Road section is located in Dawan District, Dawan Village and Pesinggahan Village, which has a length of 3,300 km and an average width of 4.5 m. The Dawan – Pesinggahan Road section has existing conditions in 2021, namely 3,300 km in good condition, 0.9 km in moderate condition and there are conditions of light and heavy damage. In 2022, there will be a lot of road damage which will cause the condition of the road to decline and it is very necessary to assess the condition of the road so that we can find out what measures will be taken to restore road stability on the Dawan-Pesinggahan Road Section. Assessment of road conditions using the SDI method in Klungkung Regency was carried out on the Jl. Dawan – Stopover. The selection of this section as a sample was due to time constraints and the district road section used as a travel center in Klungkung Regency which needs to receive more attention through road management. Apart from that, the section used is a road section that visually has road damage. This research was carried out because there are still damaged roads that need to be treated quickly. This research aims to analyze road conditions, analyze the type of treatment and obtain costs for handling the road section. The data used in the research are primary data and secondary data. Primary data was obtained through a survey of road conditions by measuring road damage, while secondary data was obtained from the community development sector of the Klungkung Regency PUPRPKP Service, namely Klungkung Regency road decrees, Klungkung Regency road maps and basic Klungkung Regency road data. The research stages are, identifying road damage by taking measurements, data recapitulation by processing surveys, data analysis using the SDI method and estimating costs to repair the road. Assessment results for the Jl. Dawan – Pesinggahan Crocodile cracks at 13.54%, random cracks 18.71%, transverse cracks 17.06%, patches 14.66%, holes 12.03% and subsidence 13.24% and the estimated cost required to maintain this road section is IDR. 3,864,775,688.95

Advanced Sensor Technologies in CAVs for Traditional and Smart Road Condition Monitoring: A Review

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, and radar that have been made available on CAVs are able to detect anomalies on roads, including potholes, surface cracks, or roughness. This paper also describes advanced data processing techniques of data detected with sensors, including machine learning algorithms, sensor fusion, and edge computing, which enhance accuracy and reliability in road condition assessment. Together, these technologies support instant road safety and long-term maintenance cost reduction with proactive maintenance strategies. Finally, this article provides a comprehensive review of the state-of-the-art future directions of condition monitoring systems for traditional and smart roads.

Hubungan Penilaian Kerusakan Jalan Metode Bina Marga dan Light Weight Deflectometer (LWD)

The Tegal Siwalan-Banyuanyar Highway in Probolinggo Regency has been damaged at several points and has not received any maintenance for the last 7 years. This has a huge impact on road users because it can interfere with driving comfort and safety, so it is necessary to analyze the road damage that has occurred, including the type of damage and an assessment of road conditions. The Bina Marga method and a light-weight deflectometer (LWD) can be used to assess road deflection in order to assess road condition. The results of this study obtained priority order values for each segment that were in the range 5-11. The greater the value of the Order of Priority obtained, the better the road condition, whereas the smaller the value of the Order of Priority obtained, the worse the road condition. It was also found that the Bina Marga method and the Light Weight Deflectometer (LWD) tool had a poor correlation, so a comparison between the two could not be made for assessing the condition of the road surface layer.

Distribution fitting and ANOVA test to analyze pavement sensing patterns for condition assessments

Purpose The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.Design/methodology/approach Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.Findings Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.Originality/value The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).

Assessment of road pavement conditions using Surface Distress Index (SDI) and Pavement Condition Index (PCI) methods

Road pavement is an important element in transportation infrastructure that affects the comfort and safety of road users. It is important to carry out regular assessments of the condition of the road pavement. This study aims to evaluate the condition of the road pavement using the Surface Distress Index (SDI) and Pavement Condition Index (PCI) methods. It is collecting field data regarding the condition of the road pavement, including the type and extent of existing damage. This data is then analyzed using the formula that has been determined in the SDI and PCI methods. In the research that has been done, the average SDI value for flexible pavement for Jalan Gunung Agung is 18 (Good) and the average PCI value is 63.8 (Good). The technical measures that can be applied to the flexible pavement of Jalan Gunung Agung are P2 (Asphalt), P4 (Filling cracks), P5 (Filling holes), and P6 (Gravelling).

Definition of indicators for the assessment of the structural condition and riding comfort of cycle paths

In recent years, the importance of the bicycle for everyday mobility has increased significantly in Germany. In order to increase the attractiveness of cycle traffic, the provision of safe cycle paths in a good structural condition is necessary in addition to the expansion and new construction of cycling infrastructure. Against this background, the Federal Ministry of Digital and Transport has been funding the research project “Recording and Assessment of the Structural Condition of Urban Cycle Paths” since September 2021. In cooperation with Schniering GmbH and the engineering office Feiler und Hänsel GbR, the University of Applied Sciences Aachen is working on the development of a suitable measurement and assessment procedure. The methods used so far in Germany to record and assess the condition of cycle paths are very much based on the established methods for road condition monitoring and assessment. However, the damage characteristics on cycle paths as well as their effects on road safety aspects, riding comfort and structural value preservation can only be compared with roads to a somewhat limited extent. For this reason, extensive structural assessments of cycle paths were carried out in the research project and a damage catalogue was developed for the recording and assessment of the condition of urban cycle paths. The relevant types of damage are assigned to the following characteristics groups: unevenness, rolling resistance, substance characteristics and vegetation. Based on the results, requirements for the measurement technology and its accuracy could be defined. The conceptual design of a measuring vehicle by the project partner Schniering GmbH will be completed this year and its use tested on various urban cycle paths.

Using an Integrated Multi-criteria Decision-Making Based Approach for Road Condition Assessment

Using an Integrated Multi-criteria Decision-Making Based Approach for Road Condition Assessment

Road Maintenance Management Based on Geographic Information System (GIS)

This research implements GIS in transportation, specifically road maintenance. The system is built by utilizing 2D/3D models from aerial photographs using UAV as a base map. Attribute data such as the type and dimensions of road damage can be obtained by interpreting high-resolution 2D/3D models, which display each road damage, making it easier to measure the dimensions of road damage. The assessment of road conditions is done using the PCI method, which indicates that 51% of the roads fall under the category of people with low incomes to severely damaged category. These roads are prioritized on a map based on their area and cost of maintenance. The projection calculation of the amount of damage is analyzed with one do-nothing scenario, where the roads have not been maintained for ten years. The progression of the damage is observed each year, and the reactive maintenance cost is calculated from 2023 to 2032. The cost and duration are analyzed using three do-something scenarios: optimistic, moderate, and pessimistic. The research results show that the moderate scenario has the lowest cost among the other scenarios and is the most effective scenario, as it produces road conditions with an International Roughness Index (IRI) value of less than 6. This research can assist the government in making informed decisions regarding road maintenance.

Assessment of Road Condition: Traffic Safety Requirement

Road deaths and injuries highly affect the lives of people worldwide. road injuries and deaths happen due to improper driving, unfit motor vehicles, traffic laws disobeying persons and poor road condition. An effort is made here to assess the factors of poor road conditions which can be improved by prioritizing. Multi criteria technique is applied to prioritize the factors. Ann technique is adopted to assess the road condition. The methodology suggested can be used to determine the level of contribution of parameters towards safety hazard. Accordingly, appropriate mitigation measures may be adopted. A further detailed study needs to be conducted on a large scale by carrying out sensitivity analysis to test the stability of the ranking obtained by the suggested methods.

Analisis Perbandingan Nilai Kondisi Jalan Menggunakan Metode SDI dan IRI Dari Alat Survei Roadroid

Good road infrastructure plays an important role in the economic development of a city. Therefore, the government must maintain and improve road conditions yearly to help attract investment and ultimately lead to overall economic growth. Presently, road condition surveys are conducted and assessed using the manual method with the Surface Distress Index (SDI), which is time-consuming and expensive due to the large number of access roads. In order to address this issue, a fast and cost-effective alternative survey approach is required, such as the International Roughness Index (IRI) method using the Roadroid application. Roadroid is an application installed on a smartphone and then attached to a car's windshield to record road conditions while the car is in motion. This study aims to compare the use of the Roadroid application with the manual survey method in terms of time and cost efficiency in identifying road conditions in the city of Palembang, which has 1204 roads. In this study, the Roadroid application results is compared to the road condition assessment results from the SDI methods. The results showed that the time required to obtain road conditions with manual survey and Roadroid application was 37.313 minutes and 4.123 minutes, respectively. The cost required to obtain road conditions was IDR 290.937.000 and IDR 68.006.000 for manual surveys and using the Roadroid application, respectively. Furthermore, the comparison of road condition assessment results between the SDI and IRI methods showed that they have average mean differences of 24.86%, so it can be concluded that the road conditions obtained in the IRI method using Roadroid have high accuracy with the conditions in the SDI method. From the results, it can be concluded that using the Roadroid application to obtain road conditions is more effective and efficient than manual surveys.

Deep Learning–Based Autonomous Road Condition Assessment Leveraging Inexpensive RGB and Depth Sensors and Heterogeneous Data Fusion: Pothole Detection and Quantification

Poor condition of roads is a major factor for traffic accidents and damage to vehicles. A significant portion of car accidents is attributed to severe three-dimensional (3D) pavement distresses such as potholes, ruttings, and ravelings. Insufficient road condition assessment is responsible for the poor condition of roads. To inspect the condition of the pavement surfaces more frequently and efficiently, an inexpensive data acquisition system was developed that consists of a consumer-grade RGB-D sensor and an edge computing device that can be mounted on vehicles and collect data while driving vehicles. The RGB-D sensor is used for collecting two-dimensional (2D) color images and corresponding 3D depth data, and the lightweight edge computing device is used to control the RGB-D sensor and store the collected data. An RGB-D pavement surface data set is generated. Furthermore, encoder-decoder deep convolutional neural networks (DCNNs) consisting of one or two encoders, and one decoder trained on heterogeneous RGB-D pavement surface data are used for pothole segmentation. Comprehensive experiments using different depth encoding techniques and data fusion methods including data- and feature-level fusion were performed to investigate the efficacy of defect detection using DCNNs. Experimental results demonstrate that the feature-level RGB-D data fusion based on the surface normal encoding of depth data outperform other approaches in terms of segmentation accuracy, where the mean intersection over union (IoU) over 10-fold cross-validation of 0.82 is achieved that shows a 7.7% improvement compared with a network trained only on RGB data. In addition, this study explores the efficacy of indirectly using depth information for pothole detection when depth data are not available. Additionally, the semantic segmentation results were utilized to quantify the severity level of the potholes assisting in maintenance decision-making. The result from these comprehensive experiments using an RGB-D pavement surface data set gathered through the proposed data acquisition system is a stepping stone for opportunistic data collection and processing through crowdsourcing and Internet of Things in future smart cities for effective road assessment. Finally, suggestions about the improvement of the proposed system are discussed.

Studi Analisa Kerusakan Jalan Menggunakan Metode Bina Marga dan Pavement Conditian Index (Studi Kasus : Ruas Jalan Napal Peranap)

Assessment of road surface conditions is a form of road maintenance and repair efforts. The two methods used in assessing road conditions are the Bina Marga method and the PCI (Pavement Condition Index) method. This study aims to compare the condition values ​​of the Peranap Napal road sections based on the two methods. The types of damage that can be found on the Napal Peranap road section are 7 types of damage consisting of: crocodile skin cracks (1.66%), elongated cracks (0.16%), edge cracks (0.08%), road joint cracks (0.01%), fillings (0.74%), holes (0.02%) and grain discharge (3.62%) with a total damage area of ​​2644.02 m2 or (6.30%) of 42000 m2 the area of ​​the Peranap Napal road studied. The Bina Marga method obtained a Priority Order (UP) value of 4 (included in the periodic maintenance program), while the PCI method obtained a damage level value of 66.16 (in fair condition with yellow color). The results of these two methods turned out to produce relatively the same assessment, namely the condition of the road section still required maintenance and repair, including: providing additional layers, making and repairing drainage, widening and compacting the shoulders, filling gaps with a mixture of asphalt and sand, and dismantling the pavement layer. and then re-coated with the same material.
 

Assessment of Road Conditions Using Road Lab Pro and Daily Road Voyager

Assessment of Road Conditions Using Road Lab Pro and Daily Road Voyager

ANALISIS KERUSAKAN JALAN DENGAN METODE PCIDAN BINA MARGA JALAN NAGARI KOTO KACIAK KABUPATEN PASAMAN

Roads are land transportation infrastructure that has a very important role in the land transportation sector that supports the continuity of the distribution of goods and services to encourage economic growth in an area. Good road conditions will facilitate community activities, if there is road damage, community activities will be hindered so that accidents can occur. The purpose of this study was to determine the types of damage and the pavement index value for Nagari Koto Kaciak road, Pasaman Regency, so that it could compare the value of the condition of the Nagari Koto Kaciak road, Pasaman Regency based on two predetermined methods. The method used is the PCI (Pavement Condition Index) method and the Bina Marga method. Assessment of road conditions in the PCI method is ranked from 0-100 while the Bina Marga method is based on the order of road priority with a value range of 0-7. Types of damage found on Nagari Koto Kaciak road, Pasaman Regency along a length of 2 km, included holes, crocodile skin cracks, patches, block cracks and side cracks. In the PCI method the average value obtained is 66.31 which is a good road condition. In the Bina Marga method, the priority order value is 6, meaning that the road is under periodic maintenance. After comparing the results of the research on the condition of the Nagari Koto Kaciak road, Pasaman Regency with the two methods, it turns out that the results and values are almost the same, namely the condition of the road is still in good condition but requires maintenance so as not to worsen road conditions.

Comparative analysis of Road Scanning Techniques

A three-dimensional road point cloud is not only useful for civil engineers (road rehabilitation, road condition assessment) but can also be useful for vehicle engineers (autonomous vehicle driving scenario, vehicle dynamics simulation). Currently, there are several scanning techniques can be used to obtain these point clouds, such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) photogrammetry or UAV laser scanning. This paper discusses the investigation of four road surface scanning techniques by comparing their point clouds and the derived products. The comparison was performed for a section of a road with 1136 m length and 4 m width, the TLS survey provided the reference data. Aspects of point cloud evaluation included geometric accuracy, density, and the parameters of plane-fitting. CRG models were created from all studied point clouds to compare the difference between the final products to be used by the automotive industry. The results show that the MLS and the UAV photogrammetry generated the most accurate point cloud, while UAV laser scanning accuracy was the lowest. Similarly, the CRG models comparison showed that there was no significant difference between MLS and TLS models, and the UAV photogrammetry gave a smoother variation relative to the reference surface. Whereas the largest differences were noted for the CRG model derived from the UAV laser scanning models.
 

A Comprehensive Method to Evaluate Ride Comfort of Autonomous Vehicles under Typical Braking Scenarios: Testing, Simulation and Analysis

To highlight the advantages of autonomous vehicles (AVs) in modern traffic, it is necessary to investigate the sensing requirement parameters of the road environment during the vehicle braking process. Based on the texture information obtained using a field measurement, the braking model of an AV was built in Simulink and the ride comfort under typical braking scenarios was analyzed using CarSim/Simulink co-simulation. The results showed that the proposed brake system for the AV displayed a better performance than the traditional ABS when considering pavement adhesion characteristics. The braking pressure should be controlled to within the range of 4 MPa~6 MPa on a dry road, while in wet road conditions, the pressure should be within 3 MPa~4 MPa. When steering braking in dry road conditions, the duration of the “curve balance state” increased by about 57.14% compared with wet road conditions and the recommended curve radius was about 100 m. The slope gradient had a significant effect on the initial braking speed and comfort level. Overall, the ride comfort evaluation method was proposed to provide theoretical guidance for AV braking strategies, which can help to complement existing practices for road condition assessment.

Model development of road performance indicator-related travel time using international roughness index: A case study national road network of Sulawesi

The International Roughness Index (IRI) is widespread in measuring road performance. However, travel time is a more critical benchmark in measuring road performance and the transportation system's performance. Therefore, new studies and models are needed to determine the relationship between IRI and travel time to obtain a segment performance function model influenced by road conditions. The study begins with a traffic condition survey to obtain vehicle travel time data on various road conditions. Assessment of road conditions uses the value of road surface flatness obtained from the results of Hawkeye measurements by carrying out actual travel throughout the national road network of Sulawesi Island. The study results show that the IRI value greatly influences the achievement of travel time as a road performance indicator in Indonesia. The equation of the relationship function of the vehicle travel time in free flow with the value of the flatness of the road surface is y=−6,062x2 +1,068x−1,0873 with R2 0,9314. At the same time, the equation of travel time as a function of road performance is ta = α1 (v/c)β + α1 (v/c) - 6,062x2 + 1,068x - 1,0873.

Detection of Road Condition Defects Using Multiple Sensors and IoT Technology: A Review

The transportation efficiency and driving safety of road networks, which play an essential role in economic prosperity, are impacted significantly by damage and defects on the road surface. In current practice, it can take weeks or even months before related government departments repair such road conditions, mainly due to lack of awareness of any damage. This paper reviews the current status and limitation of a framework for sensors devices and assessment of road surface conditions. The review also incorporates the most relevant machine learning-based methods, challenges, and future trends to underpin large-scale deployment of road defects automation identification. It is expected that the technology can provide both qualitative and quantitative information about the road surface condition and thus enable timely maintenance to improve transportation efficiency and driving safety.

Remaining service life prediction using road structure performance data with pavement condition index (PCI) and Benkelman beam (BB) methods

The design life of a road is the amount of time a road can take from the time the road is used until the road requires major repairs. The inability of a pavement to reach its design life can be caused by several factors. These factors cause the uncertainty of the service life of the road, so that the remaining service life of the pavement becomes difficult to predict. This study will discuss the condition of the road pavement and predict the remaining service life on the Losari - Pejagan road, Demak ring road, and Rembang - Bulu road sections using the PCI (Pavement Condition Index) and Benkelman Beam (BB) methods. Based on the analysis of road condition assessment, the average PCI value for the Losari - Pejagan road section is 45.76 (poor), for the Demak Ring road section is 74.63 (satisfactory), and for the Rembang - Bulu road section is 50.41 (poor). The results of the analysis of the remaining service life using the PCI method found that 67.2% of all segments on the three road had a remaining service life of less than a year and 32.8% of all segments had a remaining service life of more than 1 year. Using the BB method, it was found that the remaining service life of 93.7% segments on the three road are less than a year and 6.7% segments are more than 1 year.

Computer vision for road imaging and pothole detection: a state-of-the-art review of systems and algorithms

Abstract Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of-the-art (SoTA) computer vision techniques, especially deep learning models, developed to tackle these problems. This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition, including camera(s), laser scanners and Microsoft Kinect. It then comprehensively reviews the SoTA computer vision algorithms, including (1) classical 2-D image processing, (2) 3-D point cloud modelling and segmentation and (3) machine/deep learning, developed for road pothole detection. The article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches: classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history; and convolutional neural networks (CNNs) have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic segmentation. We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.