Pavement Condition Index Research Articles

Toward sustainable pavement: Surface Evaluation of Experimental Highway Section Containing Additives

Highway pavements are usually subjected to many types of failures “distresses” since these pavements are exposed to traffic load and weather conditions. Therefore, many agents tried to enhance the asphalt mixes to reduce the possibility and severity of distresses by adding additives to replace small amount of binder (bitumen). SBS in one of the most used additives that been used in many states. In recent years, the SBS has been used in pavement of about 1.2km to be experimental sections within Basra-Baghdad expressway. This paper examines the pavement condition index (PCI) for the experimental section after about six years form been paved and exposed to traffic. So far, only minimal distresses including cracks and potholes were recorded without reducing the initial PCI value of 100. Further work will be followed to examine the mechanical properties of the layers such as the indirect tensile strength and rutting resistance.

Development of Data driven Incheon Pavement Condition Index

Development of Data driven Incheon Pavement Condition Index

Refined evaluation methods for preventive maintenance of project-level asphalt pavement based on confusion-regression model

Accurate evaluation of the performance of airport pavement can help managers make scientific and systematic preventive maintenance (PM) decisions, ensuring pavement safety and extending service life. To this end, firstly, this paper sorted out the pavement performance evaluation index system, based on which a multi-dimensional evaluation system for PM was proposed, including control indexes, macroscopic indexes, and microscopic indexes. Then, the pavement decay model based on the pavement condition index (PCI) was developed, and the regression model between PCI and International roughness index (IRI) and friction coefficient (μ) was established. Finally, by converting the PM decisions for project-level pavement into a binary classification problem in machine learning, the optimal maintenance thresholds and intervals for PM of each index were determined using receiver operating characteristic (ROC) curve and Kolmogorov-Smirnov (K-S) curve. The optimal threshold represents the threshold when the index can separate the binary classification problem (PM decisions) with maximum probability. Based on confusion-regression model, the pavement performance can be evaluated more comprehensively, and the timing of PM can be accurately determined. The model helps airport managers develop accurate PM plans to address future maintenance needs.

Deep Learning based Pavement Crack Detection System

The pavement crack causes the highway service life to shorten, the safety hidden danger to increase. The low efficiency and high cost of manual inspection makes it difficult to detect pavement cracks. This paper proposes a fast and efficient deep learning pavement crack detection system. CRACK2000, an image segmentation dataset with complex interference background and multiple crack types, is constructed based on perspective transformation and image cropping. The scheme corrects the pavement crack images by perspective transformation. The extraction of pavement crack depth features is completed by applying the U-Net network. Finally, the pavement condition index PCI (pavement condition index) is calculated by quantifying the different types of crack information based on the segmentation results. The experimental results show that the Precision, Recall, F1-score and AUC of the U-Net network are 76.67%, 72.32%, 74.43% and 99.46% respectively. The AUC values reflect that the method is more capable of filtering out complex background interference from cracked images. The automatic pavement crack detection system designed in this paper can accurately locate and classify the location and category of pavement cracks, and perform quantitative pavement evaluation to obtain the pavement deterioration of the road section and the corresponding repair recommendations, enhancing the practicality of pavement crack detection.

Proposed severity ranking for ASTM E3303-21 protocol to quantify asphalt pavement cracking from automated 3D surveys

Cracking is one of the essential indicators to evaluate pavement surface conditions; however, it is challenging to rate pavement cracking automatically from 3D digital images. Recently, a two-level automated crack rating system was proposed for pavement management in Singapore where Level 1 provides detailed crack information including cracking extent, types, and severity. Level 2 is a macro-indicator ranging from 0 to 5 based on crack extent over a 10-m length pavement section, with 0 being excellent condition and 5 being very bad condition. On the other hand, the new ASTM E3303-21 standard has introduced the Pavement Surface Cracking Metric which is a dimensionless measure equivalent to crack density and the Pavement Surface Cracking Index which provides ratings of pavement cracking ranging from 0 to 100, with 0 being the worst possible condition and 100 being the best possible condition. This study was conducted to compare and potentially bridge the gaps between the two mentioned cracking rating methods. Cracking data were collected from the Singapore road network using the Laser Crack Measurement System-2 (LCMS-2). Based on the study findings, three severity ranks (low, medium, and high) were proposed to facilitate the inclusion of the ASTM E3303-21 cracking protocol into Pavement Condition Index calculations.

KAJIAN FINANSIAL PENGADAAN PERALATAN PENGUKUR KETIDAKRATAAN JALAN LASER PROFILEMETER KELAS I HAWKEYE 2000 DI LINGKUNGAN BALAI PELAKSANAAN JALAN NASIONAL BENGKULU

Balai Pelaksanaan Jalan Nasional (BPJN) Bengkulu was born based on PUPR Regulation Number. 26 of2020. The Function is the implementation of feasibility studies, surveys, investigation, and evaluation ofplanning for the technical field of road and bridge including road safety. The Important type of survey arelongitudinal profile survey International Roughness Index (IRI), and Pavement Condition Index (PCI). TheMethod is to examined secondary data obtained from literature review and analyzed financial data (NetPresent Value, Benefit Cost Ratio, and Payback Period. The author builds four scenarios (pessimistic,medium, optimistic, and do nothing). The Benefit Cost Ratio for the scenario 1 (pessimist) is 0.33, thescenario 2 (medium) is 1.54, the scenario 3 (optimistic) is 2.82, and the scenario 4 (do-nothing) is N/A.Value of Payback Period for the scenario 2 (medium) is 5.62 years, and the scenario 3 (optimistic) is 2.79years. Based on the analysis, the decision to purchase a Hawkeye 2000 digital laser profilemeter withinBPJN Bengkulu is financially feasible as long as the hawkeye used not only for Internal, but as a source ofPNBP from client like private sector or provincial government, and distric or city governments in BengkuluProvince.

The Pavement Condition Index Functional Evaluation of Runway Pavement Used Pavement Condition Index (PCI) Method (Case Study : Fatmawati Soekarno Airport Provinsi Bengkulu)

Maintenance of air transportation infrastructure, in the pavement system section at Fatmawati Airport, Bengkulu Province and other airports in Indonesia, must had high performance, safety, security, reliability and sustainability. Routine inspection activities at major airports in Indonesia usually use the Pavement Condition Index (PCI) method as a parameter. This journal examines handling costs, handling periods and the minimum based on the reduction in PCI values due to existing traffic loads by building several types of handling scenarios. In addition, it also evaluates the structural performance of the runway pavement. The results of the PCI value analysis show that it is decreasing every year. This decline prediction used forecasting from previously obtained data. For 2016 the PCI value obtained was 89 (satisfactory), 2017 was 83 (satisfactory), and 2018 was 73 (sufficient). Based on the PCI value of 3 years and the growth in aircraft traffic, the impairment can be calculated by modeling the equation. The PCI prediction value in 2019 is 66 (fair), the PCI value in 2020 is 60 (fair), and the PCI value in 2021 is 54 (poor). It can be concluded that the increasing growth of aircraft traffic with routine maintenance will make the PCI value on the runway pavement decrease every year.

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.
 

ANALYSIS OF ROAD DAMAGE USING THE PCI (Pavement Condition Index) METHOD AND ALTERNATIVE HANDLING (Case Study : Jl. Raya Dumaja - Jl. Raya Tanah Merah, Kab. Bangkalan STA 32+000 - 35+000)

The highway is an essential facility for the community to achieve its goal, so the community needs a safe and convenient road, and it is expected to improve the industry and the economy of the community. However, over time, the condition of the highway will decrease according to the age of the road, and it will be an obstacle for their trip. This research is conducted to see the condition of the damaged surface flexible stiffening in Jl. Raya Dumaja - Jl. Raya Tanah Merah, Kab. Bangkalan. The method used in this study is the PCI (Pavement Condition Index) method to evaluate road stiffening by type, level, and level of damage that occurs, as well as a reference and maintenance effort. The result of this research based on the PCI method is JL. Raya Dumaja -Jl. Raya Tanah Merah STA, 32 + 000 - 35 + 000, gets a total score of 53.68 medium (Fair) with a PCI percentage of the highest road conditions is 45% in good road conditions (Good), 38.3% in medium road conditions (Fair), 15% in bad road conditions (Poor) and very bad (Very Poor), 1.67% with the alternative handling is the treatment of abilities.

Prediction of asphalt pavement performance based on DEPSO-BP neural network

With the application of machine learning rapidly gaining popularity in computer science and other fields, neural network techniques have successfully simulated the performance of in-service pavements as they are efficient in predicting and solving nonlinear relationships and in dealing with uncertain large-area pavement problems. In this paper, we address the problem of the optimal timing of preventive maintenance of asphalt pavements to accurately predict the condition index (pavement condition index, PCI) of highway asphalt pavements and develop a highly accurate, long-period, multifactor prediction model with the suitability of preventive maintenance at its core. The prediction model is called differential evolution particle swarm optimization back propagation (DEPSO-BP) neural network, and the input dimension of the prediction model is determined by gray correlation analysis (GCA), and DEPSO is used to improve the search efficiency of BP neural network and the asphalt pavement usage performance with parameter continuity prediction model. Finally, the Qinglan Highway (G22) PCI of Gansu Province, China, is selected for example validation, and the prediction results are compared with those of the four models. The results show that the multifactor prediction model based on DEPSO-BP neural network has good generalization ability. This model is important for improving the economic efficiency of road maintenance, and can be used in the long-cycle process to provide model reference and scientific basis for the subsequent road maintenance budget application and decision-making scheme.

Impact of Pavement Defects on Traffic Operational Performance

The present research investigates the influence of pavement distresses on traffic operational performance in terms of speed and capacity. The pavement distresses are expressed by the pavement condition index (PCI). Three roads of multi-lane highways located on the road network of Kafrelsheikh governorate, Egypt, were chosen as a case study. Road geometry, pavement condition characteristics, and traffic volume were collected manually from 23 sections for field data surveys. For speed measurements, spot speed data were gathered using the stopwatch method under the free-flow conditions, and the percentile speeds (V50% and V85%) have been calculated for different vehicles classes. Traffic capacity was determined via extrapolation of the quadratic function. Regression analysis models were created to express the relationships of both the percentile speeds and traffic capacity as dependent variables, and three independent variables (PCI, lane, and shoulder widths). These empirical mathematical relations help in developing road design and maintenance works on multi-lane highways. The main conclusion of the study demonstrated that traffic operational performance was more significantly affected by pavement distresses than road geometry. Moreover, the results indicated that pavement distresses have a significant impact on light vehicles more than heavy ones.

Development of Pavement Condition Index for Local Street of Pune

The project titled "Development of Pavement Condition Index for Local Streets of Pune" aims to assess the condition of local streets in Pune and develop a comprehensive and reliable index to measure pavement condition. The study involves field surveys to collect data on pavement distress and distress severity, followed by data analysis and the development of a new pavement condition index (PCI) specific to the local streets in Pune. The proposed index is expected to provide a standardized and objective approach for evaluating pavement conditions and prioritizing maintenance and repair activities. The study is expected to contribute to the development of more efficient and effective pavement management strategies for local streets in Pune, ultimately leading to improved road safety, reduced maintenance costs, and increased user satisfaction. Key Word: Pavment Condition index (PCI).

Data-Driven Web-Based Patching Management Tool Using Multi-Sensor Pavement Structure Measurements

Automating pavement maintenance suggestions systems is challenging, especially for actionable recommendations such as patching location, depth, and priority. It is a common practice among state agencies to manually inspect road segments of interest and decide maintenance requirements based on the pavement condition index (PCI). However, standalone PCI only evaluates the pavement surface condition which, coupled with the variability in human perception of pavement distress, limits the accuracy and quality of current pavement maintenance practices. In this case, there is a need for multi-sensor data integrated with standardized pavement distress condition ratings. This study explores estimating the appropriate pavement patching strategy (i.e., patching location, depth, and quantity) by integrating pavement structural and surface condition assessment with pavement ratings of distress. In particular, it combines pavement structural condition parameters and falling weight deflectometer deflections with surface condition parameters, international roughness index, and cracking density, for a better representation of overall pavement distress conditions. Then, a pavement-specific, threshold-based patching suggestion algorithm is designed to suggest pavement maintenance operations. The thresholds were determined based on a reliability concept and were verified with the structural number ratio. The threshold values were then used in the patching suggestion algorithm to create patching suggestion tables. A web-based Patching Management Tool (PMT) was designed as an interactive tool to visualize these patching suggestion maps and analyze the pavement distress data using geographical maps and graphs. The PMT was validated with road surface and right-of-way images obtained from three-dimensional laser sensors, and it could successfully capture localized distresses in existing pavements.

Optimal pavement maintenance and management strategies for flood affected low volume rural roads

Due to the increase in urbanization and industrial development in India, there has been substantial traffic on the existing roads. This impact had increased public demand for good quality roads with better maintenance and high serviceability. Further, catastrophic events like flooding can cause damage to pavements, especially in those with granular base layers and thin asphalt mix surface layers. The degree of damage relies upon the vulnerability of the pavement to the entrance of surface water, the measure of water immersion, and the drainage quality of the pavement. The extensive literature review found limited studies on the post-flood maintenance strategy for low-volume roads by considering flood resilience. In the present scenario of changing climatic conditions, it is necessary to conduct a study on the post-flood maintenance strategy of low-volume rural roads in India. This study focuses on an approach to developing post-flood maintenance and rehabilitation strategies for flood-affected low-volume roads. Ten road stretches adversely affected by the flood were selected to fulfil the aim of the study. The post-flood maintenance strategy was determined based on the Pavement Condition Index (PCI) and deflection-based pavement performance with flood resiliency of pavement materials and budget constraints. The extensive data analysis revealed that the pavement with a cement-treated base or subbase has a longer service life and less Net Present Value (NPV) than all other alternatives. NPV is 30 to 45 for rehabilitation and 52 to 55 for reconstruction, lower than other alternatives considered in the study.

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.

ANALISIS KERUSAKAN JALAN RAYA WEWEWA UTARA DENGAN MENGGUNAKAN METODE PAVEMENT CONDITION INDEX (PCI)

The highway is a vital piece of land transportation infrastructure in supporting the economic life of a region, because the road has a supporting role in the continuity of the distribution of services and goods. In addition to traffic activity factors, there are also several factors that reduce the quality of the road surface. Based on these problems, it is necessary to maintain the structure of the North Wewewa highway, so that the distribution process of services and goods that pass through the North Wewewa highway is not disturbed, and the economic growth of the community becomes smoother. Therefore, to be able to carry out proper maintenance, it is necessary to analyze the damage to the road in order to know the degree of damage and repairs that need to be done. In this case, the Pavement Condition Index (PCI) method is used because the condition of the North Wewewa highway has different characteristics in determining the type, level, and level of damage, as well as handling road damage

Machine-Learning-Based Framework for Prediction of the Long-Term Field Performance of Asphalt Concrete Overlays in a Hot and Humid Climate

Pavement performance prediction models are used by state agencies to determine pavement maintenance and rehabilitation strategies. However, most performance prediction models are based on a limited number of parameters and a maximum prediction period of five years. With the ever-increasing amount of available pavement performance data, machine-learning techniques have become a promising alternative to traditional performance prediction models. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (up to 11 years) of asphalt concrete (AC) overlays on asphalt pavements based on key project conditions. The pavement condition index (PCI) was used as the pavement performance indicator. Two machine-learning algorithms, namely, random forest (RF) and CatBoost, were examined. A total of 892 log-miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development Pavement Management System database. Based on the collected data, six models were trained (for each algorithm) and validated to predict the PCI of AC overlays for up to 11 years. The results indicated that the RF algorithm yielded higher accuracy than the CatBoost algorithm. Therefore, the RF-based models were considered in the proposed decision-making framework.

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.

Influence of Traffic Characteristics on Pavement Performance of Parking Lots

This research aims to identify and interpret the main traffic characteristics affecting the pavement condition index (PCI) of asphalt within parking areas. The study also examines the effect of severity and type of asphalt pavement distress on parking decisions. Previous literature examines the impact of the environment on asphalt pavement deterioration. This paper will focus on the shape of traffic operations within a parking area. The methodology includes applying a multilevel mixed regression model to reveal the hidden hierarchical relationship between traffic activities and the different types of pavement distresses and then discovering their effect on asphalt PCI. The resulting models showed that parking lots are aging faster than the common traveling roads due to the increased impact of axial loads on their surfaces at low and static speeds and some distinctive traffic characteristics. Moreover, some types of pavement distress, such as alligator cracks, potholes and depressions, appear more widely than others on the parking lot surface and significantly influence the parking decision. The conclusion of the research may significantly impact pavement design and maintenance management for parking lots.

Impact of Road Pavement Condition on Vehicular Free Flow Speed, Vibration and In-Vehicle Noise

Road infrastructure in good condition is a key requirement for efficient transportation systems which leads to economic prosperity and improved quality of life. However, road surface conditions deteriorate over time according to traffic loads and environmental factors. Poor road conditions lead to congestion, accidents, lost productivity, and driver fatigue. This work considers the relationship between road pavement condition and vehicle speed, vibration, and in-vehicle noise. A 7 km section of the Grand Trunk Road, Peshawar, Pakistan divided into 280 segments (140 for each lane), of length 50 m was observed and the Pavement Condition Index (PCI) of each segment was determined based on the recurrent distress type and density according to ASTM D6433-011 guidelines. The number of very good, satisfactory, fair, poor, and very poor conditions are 51, 52, 81, 48, and 42, respectively. The mobile app BotlnckDectr was employed to measure vehicle speed, RPM, noise, vibration, GPS location, and time. Statistical analysis was employed to determine the relationship between PCI and vehicle speed, vibration, and in-vehicle noise. The results obtained indicate that noise and vibration increase by 3.3% and more than 30%, respectively, as the pavement condition changes from good to very poor, and vehicle speed decreases by 8.8%.