Road Pavement Condition Research Articles

Key pavement condition indicators for assessing highway pavement maintenance quality index

The technical condition of road pavements requires regular monitoring to support accurate maintenance decisions. However, existing pavement maintenance quality index (PQI) assessment methods are often expensive and infrequent, which limits intelligent decision-making. This paper proposes a cost-effective and precise PQI assessment method using data from five highways in Beijing. Key PQI indicators were identified through a hybrid feature selection method, and a CatBoost model optimised by a genetic algorithm (GA-CatBoost) was developed. The model demonstrated superior accuracy, achieving an R2 of 0.938, an MSE of 0.838, and an MAE of 0.576. It achieved 98.46% accuracy compared to traditional methods, confirming its reliability in an on-site application. This approach offers an economical solution for high-frequency PQI assessment, enabling intelligent decision-making in road infrastructure maintenance and supporting the use of lightweight detection equipment.

The use of geodetic measurements in the assessment of the technical condition of road pavement – case study

Abstract Technical infrastructure is exposed to environmental impacts throughout its entire life cycle. For linear objects, such as roads, additional impacts resulting from their continuous use – vehicle traffic – are distinguished. Due to their communication function, these facilities are subject to ongoing condition monitoring. This procedure allows to identify any damage and indicate appropriate protective measures. Observations of the terrain as well as the road surface can be carried out using commonly used geodetic measurements. Cyclic monitoring applies especially to facilities in mining areas, where impacts in the form of continuous and discontinuous deformations significantly contribute to their damage. The research presents the results of selected geodetic measurements made on a deformed road surface affected by underground mining. The aim of the observations was to use both classic and modern measurement techniques to determine, among others, the shape of the surface or identify damage. The applied measurements included commonly used total station measurements and GNSS satellite positioning. Additionally, low-ceiling photogrammetry using an unmanned aerial vehicle and laser scanning were used to illustrate the shape of the road surface. Finally, a comparison was made of selected methods; their advantages and disadvantages allowed us to determine their usefulness in monitoring the condition of the pavement.

Novel approach for forest road maintenance using smartphone sensor data and deep learning methods

ABSTRACT High costs primarily pose challenges to forest management in planning and executing the repair of forest roads. With budget limitations and inadequate oversight, it has become critically essential to monitor the state of these roads. Monitoring the condition of forest roads has become imperative, driven by budget constraints and a lack of effective supervision. While smartphones have proven effective in detecting road defects on public roads, their application on forest roads is hindered by the absence of suitable indices and software infrastructure. Addressing this gap, this research focuses on the development of the Forest Road Pavement Condition Index (FRPCI) to facilitate smartphone-based monitoring. We collected and compared data from 4 kilometers of forest roads, employing two traditional harvesting methods alongside smartphone sensor data. Utilizing deep learning methods, including Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and CNN-LSTM, we processed the collected data. Signal processing using GPS data, coupled with wavelet transformation, demonstrated promising results with an accuracy and recall exceeding 80%. The proposed system functions as a distributed information system, transitioning data from organizational mode to field mode. It measures damage, assesses forest road conditions, and leverages image processing and GPS technologies. This monitoring system technology offers capabilities for preparing, storing, updating, maintaining, and analyzing diverse information. Importantly, adopting this method can significantly reduce operating costs, making forest road monitoring for maintenance purposes more feasible.

Enhancing local road pavement condition prediction using Bayesian-optimized ensemble machine learning and adaptive synthetic sampling technique

ABSTRACT Pavement condition prediction helps road agencies to schedule maintenance, rehabilitation, and reconstruction, and to allocate limited funds and resources to such activities. Compared to state highways, pavement performance prediction for local roads has received relatively little attention in the literature due to perceptions of low importance, low levels of investment in data collection, poor data quality, and high variation within the data elements. Additionally, local road pavement condition data may suffer from dataset imbalance, often leading to unreliable condition predictions. Hence, this paper introduces a methodology to predict local pavement condition using various single estimator and ensemble machine learning (ML) models along with the adaptive synthetic sampling method. The study develops nine (9) Bayesian-optimised ML models: category boosting (CatBoost), adaptive boosting, decision tree, extra trees, gradient boosting, light gradient-boosting machine, k-nearest neighbour, random forest, and artificial neural network. The ensemble ML and CatBoost were found to exhibit the best model performance, with an average testing accuracy of 0.82, specificity of 0.81, sensitivity of 0.63, and F-measure of 0.61. These results underscore the efficacy of ensemble ML models in pavement condition prediction. The proposed approach can be beneficial to local road agencies in their long-term planning, scheduling, and budgeting.

Integrated Strategy in Road Assesment for Tameroddo-Majene City Boundary Road Section Preservation

Tameroddo – Majene City boundary road section is a national road in West Sulawesi, a road connecting South Sulawesi and Central Sulawesi provinces. Its existence is essential for increasing accessability and mobility between regions, for it does not only support economic growth in the West Sulawesi Province, but it also has some positive impacts on socioeconomic development for surrounding areas. Traffic volume, overload, and rainfall are factors that accelerate intensity of road surface irregularities. An analysis of road pavement conditions is, therefore, required to provide existing data for road preservation, to ensure its optimum function in accordance with planned durability. This research is aimed at providing assessment results of the road section conditions, and to compare the results between IRI and PCI measures, as well as to provide an overview or description of the road section conditions. This can, thereafter, be used as an important database for a better planning and implementation. Road assessment had been conducted using an integrated method, by IRI measure performed by using ‘The Hawkeye’ vehicle equipped with high precision laser profilers and accelerometers, DMI, and high-speed cameras along with GPS, incorporating Dinamic Time Warping (DTW) technique, to automatically detect and classifies road irregularities on paved roads. This method, however, followed by manual inspection (or subjective measures). Survey result shows that based on IRI method, ‘fair’ condition reaches 67%, and lightly damaged reaches 33%. While based on PCI method, ‘good’ (equivalent with ‘fair’) condition reaches 66%, and ‘bad’ condition (or equivalent with ‘lightly damaged’) condition reaches 34%. This means that the study found some differences, even though it was only a little. Based on analysis result, amongst total lenght of the road lane had been under assesment (81,8 km), 74,4 km is of ‘fair’ condition, thus it requires a routine maintenance, while the rest 8,4 km is of ‘lightly damaged’ and it requires a regular maintenance.

Characteristics of tire-road wear particles (TRWPs) and road pavement wear particles (RPWPs) generated through a novel tire abrasion simulator based on real road pavement conditions

Microparticles such as tire-road wear particles (TRWPs) and road pavement wear particles (RPWPs) are generated by the friction between tire tread and road surface. TRWPs and RPWPs on roads are dispersed through traffic and transferred to rivers and seas via runoff to accumulate in sediments. However, research on the generation of both TRWP and RPWP has rarely been conducted. In this study, the generation of both TRWP and RPWP was investigated using a novel tire abrasion simulator equipped with paved road and bus tire, and their contributions to the generation of microparticles were examined. Two types of model paved roads, asphalt and concrete pavements (AP and CP, respectively), were applied. TRWPs generated from the simulator exhibited morphologies very similar to those on real roads. The abrasion rate for the CP was 2.8 times higher than that for the AP. The wear particle size distributions peaked at the size ranges of 63–106 μm and 212–500 μm for the AP and CP, respectively. Totals of 84 wt% and 89 wt% of the wear particles were distributed in size ranges of 38–212 μm for the AP and 106–1000 μm for the CP. The tire wear particle (TWP) contents in the total wear particles of 38–500 μm were 21.7 wt% and 30.0 wt% for the AP and CP, respectively, and decreased as the particle size decreased. The weight of RPWP was higher than that of TWP in TRWP. Contributions from road pavement to the generation of wear particles of 38–500 μm were 3.6 and 2.3 times higher than those from tire tread for the AP and CP, respectively, and the contribution increased as the wear particle size decreased.

Studying the Structure of the Road Pavement and Specifying the Repair Method

Studying the structural condition of road pavement is essential for making effective decisions about the type of maintenance or rehabilitation that will be carried out on a pavement section. Reliable quantitative assessment of the volume of necessary repairs will ensure the performance characteristics and durability throughout the entire service life at minimal cost. This article presents detailed results from Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) studies. In addition, an analysis of these results and recommendations for strengthening the road pavement for the Sh-176 “Sighnaghi-Saint Nino Monastery” road to develop rehabilitation and maintenance scenarios. FWD results combined with road pavement layer thickness and traffic load information to determine residual service life and necessary treatment type. Thus, using the nonlinear theory of layered halfspace elasticity, pavement structure analyzed in the same way as other civil engineering structures.

ТЕХНОЛОГІЇ ХОЛОДНОГО РЕСАЙКЛІНГУ ДЛЯ ГЛИБОКОЇ РЕГЕНЕРАЦІЇ НЕЖОРСТКОГО ДОРОЖНЬОГО ОДЯГУ

ntroduction. In the introduction to the article, the briefly performed comparative analysis of the road asphalt concrete cold recycling technologies was adduced and also the actuality of the implementation of widened nomenclature of such technologies in the Ukraine was outlined. Problem statement. The issue of the article concerns processes and materials for technologies of the full-depth reclamation of flexible road pavements using cold recycling trough full depth of asphalt layer/layers including the underlying layers of granular materials as required. Purpose. The aim of the article is to analyze the technological parameters of processes of full-depth reclamation (FDR) and also general requirements for reclaimed materials especially those concerning the estimation of their thermorheological properties. Results. The methods of the reclaimed materials stabilization were examined which, in accordance with the FDR technologies, should be divided in mechanical and those performed with the stabilizing agents. The types of pavement defects were defined under presence of which the application of FDR technologies has been recognized highly effective. The concise analysis of general requirements for reclaimed materials was performed with setting off the materials subjected to special requirements for grading. Also, the technological particularities of processes of the full-depth reclamation were stated which should be attributed to the technical characteristics of road equipment, the road pavement condition, and reclaimed mixtures quantitative proportions as well. According to the analysis of the test methods of reclaimed materials stabilized by bitumen using foamed bitumen, the role of the thermoreological uniformity of cold recycled mixtures for their proper design was shown.

Pengaruh Aksesibilitas Jalan Ende – Nuabosi Terhadap Peningkatan Perekonomian Masyarakat Kecamatan Ende

Nuabosi is one of the villages in the Ende District area, where this village helps improve the economy of Ende District in terms of meeting economic needs, namely in the form of various agricultural and plantation products. However, even though Nuabosi Village is sufficient to meet the economic needs of Ende District, distance and inadequate infrastructure conditions are some of the factors that influence the level of accessibility in Ende District. This research aims to determine the value and level of accessibility in the villages of Ende District. The accessibility measuring parameter data used is travel time, road pavement conditions, supporting facilities, and travel rates or costs. Data analysis uses quantitative descriptive analysis and crosstab analysis methods, using the total accessibility calculation formula. The results of the analysis based on a review of accessibility parameters and crosstabs show that the highest accessibility value for each village is Ria Raja Village with a value of 2.18 and the lowest is Tina Bani Village with a value of 0.37. The average level of accessibility in Ende District as a whole is 0.83, indicating a high level of accessibility, where the existing road conditions can influence the improvement of the economy of the people in Ende District. Crosstab analysis also shows that most of the factors determining accessibility are related to the socio-economic conditions of the community.

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets.

Identification of Road Pavement Conditions in the Tanjung Balai City Road Section

Each region will try to increase its original income and the quality of existing infrastructure in that area. In order to realize this goal, quality infrastructure is needed to support economic development activities and good quality infrastructure. One of the infrastructure needed in this case is the road, where the intended road must have good conditions and quality so that it can function as it should. Where the condition of a good road is a road with good condition is a road with a completely flat pavement surface, no waves and no surface damage. For this reason, it is necessary to identify the condition of the pavement on existing roads in an area, so that the main goal of increasing income and infrastructure can be realized. One of the cities that can be used as a reference for identifying road pavement conditions is the City of Tanjung Balai, where the City of Tanjung Balai is located between 2°58′00″ North Latitude and 99°48′00″ East Longitude. Its area is 60.52 km2. The city of Tanjung Balai also has the longest bridge in the province of North Sumatra along ± 600 m which connects the city of Tanjung Balai with the villages of Sei Kepayang Kiri, Sei Kepayang Tengah to the upstream area in Sei Kepayang Kanan, Asahan district. Identification of road pavement conditions can be done using several methods, one of several methods that can be used is the PCI (Pavement Condition Index) method. Where this method is carried out by measuring the area of road surface damage, determining the level of damage to the road surface by looking at the Deduct Value chart, calculating level of damage occurs on the road surface (density), calculating Total Deduct Value (TDV), calculating Corrected Deduct Value (CDV), calculating PCI values and determining the level of the road, whether it is still feasible or not feasible. From the identification results it was found that on A. Rahman Road, there are several types of damage with varying levels of damage and the deduct value can be determined by looking at the graph, such as holes/potholes (low) damage, shoving with high damage, ravelling with high damage, basin cracks with high damage. Then on SMA 3 Road, there are several types of damage such as high crocodile skin cracks, patch damage with high damage levels, medium damage (medium) damage to basins. Next on Prof. Dr. Ir. Sutami / Education road there are types of holes / potholes (low) damage, grade depression with medium damage, patches with medium (medium) damage levels, ravelling with medium damage levels. And on Husni Thamrin Road, there are several types of damage such as holes/potholes (low) damage, basin cracks with high damage, ravelling with high damage levels. The results of this identification can be used as a reference for maintaining or improving the quality of existing roads in urban areas.

CORRELATION ROAD GEOMETRIC ON ROAD DAMAGE

The implementation of safe road infrastructure can be defined by the minimum number of traffic accidents that occur on these roads. Traffic accidents can caused by several factors, encompassing the geometric attributes of the road, characteristics of vehicles, human elements, and environmental conditions. Previous studies have identified specific geometric features, including superelevation, degree of curvature, road slope, and shoulder type, as influential factors in traffic accidents. Simultaneously, the state of the pavement, characterized by issues such as rutting, potholes, IRI value, and skid resistance, also plays a crucial role in traffic accident. The interplay between road geometric and pavement conditions underscores the importance of effective road management for ensuring safety. Existing research highlights a correlation between road geometric and road damage, with the Pavement Condition Index (PCI) directly correlating with road and shoulder width, and inversely correlating with transverse and longitudinal road slope. This phenomenon is, in part, attributed to heightened shear strain on narrow road shoulders and a reduction in the resilience modulus value of the subgrade on sloped roads.

Road conditions and driving patterns on fuel usage: Lessons from an emerging economy

Given its usage in freight and urban transportation, diesel supply is a major global issue. Diesel dependence and the urgency of the energy transition are normally addressed on the supply side by either expanding oil refineries or evaluating alternative fuels. Here we focus on the demand-side by examining how road pavement conditions and eco-driving practices affect diesel fuel consumption on highways. This can be particularly important in emerging countries, where road pavement is normally not well-managed and conserved. This analysis encompasses six scenarios for Brazil that vary in road conditions, driving cycles, and tire inflation states. Findings show a significant potential for annual diesel savings, reaching 4.52 billion liters, or 16% of fuel efficiency. A financial analysis indicates a positive return by the year 2037. Hence, this study emphasizes the need for a multidimensional approach to reduce fuel consumption and enhance environmental sustainability within the transportation sector of an emerging economy. While infrastructural improvements are crucial, their effectiveness is greatly amplified when paired with efforts in driver education.

Real-Time Pavement Crack Detection Based on Artificial Intelligence

Pavement as a structural element is closely related to the road traffic system because it is essentially a support structure for the movement of vehicles. Modern technology has attained significant improvements in road durability, quality, and safety through the use of new materials and construction technology. All civil engineering infrastructures have a specific lifespan. The road surface can be the worst, and repair is more costly if the pavement is not maintained. The objective of this research is to spot the damages over the inspected road and detect cracks in a short time, useful to achieve more comprehensive monitoring and assessment of the condition of road pavement using a smartphone equipped with You Only Look Once (YOLO) to reduce maintenance costs. The method uses advanced image processing techniques and uses YOLO to detect cracked pavement in a short time. More specifically, it is based on the latest generation of Deep Neural Network (DNN) algorithms, such as YOLO V5. YOLO V5 is used as a detector for cracked pavement and image processing as an automated pavement distress detector. The obtained result for longitudinal cracks shows that the area of cracks is 1.1682 m2, with a threshold value of 0.91, while the result for transversal cracks shows the area of cracks of 1.9627 m2, with a threshold value of 0.85. Otsu’s method works best under conditions such as low noise level, homogeneous lighting, and higher intra-class variance than an inter-class variance. The outcome of this project is improved recognition accuracy compared to the manual recognition currently used.

Forecast of temporal variations in some road condition variables

The article considers issues of forecasting temporal variations in the condition of roads. The road condition is affected by external factors against the background of internal processes, occurring in construction layers. Most mathematical models, forecasting the service life of roads, are developed on the basis of strength characteristics, which, in conditions of limited financing of regional and local significance roads, leads to certain difficulties in their practical application. Therefore, it is necessary to develop a model of variations in the condition of road pavements based on data about their consumer properties, such as evenness, adhesion coefficient and pavement condition. The process of reducing the evenness values, pavement condition, and adhesion coefficient over time can be described using the Weibull distribution. The paper presents mathematical models of temporal variations in the condition of the road pavement, adhesion coefficient and longitudinal evenness. The data of the model and annual road diagnostics, compared for assessing the model effectiveness, demonstrated high convergence. The proposed approach to assessing the variations in the pavement condition, evenness and adhesion coefficient makes it possible to increase the efficiency of managing the road condition.

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).

The Evaluation of Pavement Condition Assessment Methods for Road Assets in Coastal Areas

The Daendels road is a vital provincial road asset that facilitates the distribution of goods and services, enhances tourism access, and promotes socio-economic development in the southern region of Java Island. The deteriorated condition of the Daendels road pavement has the potential to escalate both the likelihood of accidents and vehicle operating costs. In Indonesia, road distress is measured using the Surface Distress Index (SDI), but certain types of distress are not yet incorporated into the calculation. Therefore, this study aims to identify the typical road distress in the coastal region and then to evaluate and compare several visual methods for evaluating the functional condition of road pavements, i.e., the SDI, Pavement Condition Index (PCI), and Pavement Surface Evaluation and Rating (PASER). Pavement conditions for Daendels Road have different analysis results depending on the method used. The average value of PCI is 50.5 (slightly damaged), the SDI is 164 (severely damaged), and the PASER is 4 (slightly damaged). The statistical analyses indicate that both the SDI-PCI and SDI-PASER methods have a very strong relationship. The SDI-PCI method has a higher correlation and coefficient of determination value (R= -0,929, R²= 0,8631) than SDI-PASER (R= -0,807, R²= 0,652). The PCI method is more applicable than the SDI dan PASER as it considers a wider range of pavement distress (19 categories) and more accurately represents the typical distress encountered on the South Coast of Java Island. The pavement condition of Daendels Road is classified as severely damaged with typical distress involving cracking (longitudinal, transversal, alligator, and blocks), patching, and pothole. Hence, a comprehensive plan for road maintenance was suggested, encompassing major rehabilitation using a hot mix asphalt overlay.

Evaluation of Pavement Condition Based on the Value of Surface Distress Index and Pavement Condition Index on the Road Section of Gedong Tataan - Kedondong, Pesawaran

Good road infrastructure conditions can stimulate the economic growth of an area. Therefore, routine road maintenance is carried out by its administrators. One of the road sections that needs improvement is the Gedong Tataan - Kedondong road section. The Gedong Tataan - Kedondong section is a provincial road with a length of 16.67 kilometers. To predict the pavement condition on the road section, an assessment method is required for identification. This method is used as one of the tools to assess pavement damage on a road section, and the methods used are the Surface Distress Index (SDI) and the Pavement Condition Index (PCI). The research results show a PCI (Pavement Condition Index) value of 73%, indicating that the road pavement condition is very good, and routine maintenance is required. Meanwhile, with the SDI (Surface Distress Index) method, a value of 65 is obtained, indicating that the road falls into the fair category, and the appropriate treatment options include patching and overlay. Another implication is to allocate the budget wisely for road maintenance. Information from these two indices helps in determining improvement priorities. Routine maintenance can be a better investment in the long term than letting road conditions deteriorate.

СОЗДАНИЕ СИСТЕМ УПРАВЛЕНИЯ СОСТОЯНИЕМ ДОРОЖНЫХ КОНСТРУКЦИЙ КАК ЭФФЕКТИВНОГО ИНСТРУМЕНТА ПОВЫШЕНИЯ БЕЗОПАСНОСТИ ДВИЖЕНИЯ НА АВТОМОБИЛЬНЫХ ДОРОГАХ

The article discusses the main aspects of managing the condition of road pavements, as a key factor influencing the safety of traffic on roads, and notes the importance of studying the performance and predicting the service life of road structures. The article shows the need to conduct research to quantify the intensity of changes in transport and operational indicators and factors affecting the condition of road structures, traffic safety, and analyzes the main groups of methods and systems used to manage the condition of road pavements. Currently, there is a need for research aimed at assessing the performance of road structures in order to effectively evaluate design decisions, construction quality and ongoing activities to improve traffic safety as part of the maintenance and repair of roads.

Evaluation of Road Pavement Performance and Conditions Simp. Duku (Ketaping) – Pariaman Section

Road section Simp. Duku (Ketaping) - Pariaman is one of the strategic routes in the area of ​​West Sumatra Province. This road section has the potential to be developed because it is a fast lane to the Minangkabau International Airport and the City of Padang as the center of government for the Province of West Sumatra. This study aims to determine the performance of the Simp. Duku (Ketaping) – Pariaman seen from the level of service which refers to MKJI 1997 and the value of road pavement conditions at STA 16+420 to STA 20+500 refers to the PCI (Pavement Condition Index). From the research results for the Performance of Jalan Simp. Duku (Ketaping) - Pariaman is at service level D (0.91 <93) during peak hours (17-00 to 18.00 WIB). Meanwhile, from the analysis conditions of Pavement use the Pavement Condition Index (PCI) method, a PCI value of 53.69 was obtained, which means that the pavement condition was generally poor, dominated by grain ejection damage (63.23%) and crocodile skin crack damage (20.75%). ). For handling of road pavement conditions, after being correlated with the Pavement Condition Index (IKP) from the Ministry of Public Works, the type of handling is Reconstruction / Recycling.