Pavement Condition Index Research Articles

Utility Cut Impact Assessment and Fee Development Using Pavement Management System

The purpose of this study is to estimate any damage caused by utility cuts to pavements and develop a fee schedule to recover the costs associated with such damage using the City of Pacifica, CA, U.S.’s pavement management system (PMS). To accomplish this, the study looked at the functional deterioration of pavements resulting from utility cuts. The City of Pacifica’s PMS containing a vast dataset of pavement distress spanning 15 years, comprising thousands of data points encompassing various pavement ages and conditions, was analyzed to assess the impact of utility cuts. The findings from this study include: 1) Pavements with cuts of any size deteriorate more than pavements without cuts across all age groups with one exception for older residentials with small cuts; 2) On average, pavement condition index drops by 30% if the cut area is greater than 10% of the section area; and 3) Utility cuts inflict greater damage on newer pavements (<10 years old) compared with older ones (≥10 years old). Consequently, new pavements experience an average percent reduction of approximately 33% in their remaining service life, while older pavements suffer a reduction of about 17%. The statistical analyses indicate that utility cuts caused significant damage, and supported a tiered utility cut fee structure based on functional classification, age of pavement, and size of the cut. The resulting fee schedule is based on 2021 material and construction cost estimates; consequently, implementation including an annual adjustment for inflation is recommended. Example fee calculations are provided.

EVALUATION OF THE AIRPORT PAVEMENT CONDITION INDEX IN THE AIRCRAFT LATERAL WANDER AREA

EVALUATION OF THE AIRPORT PAVEMENT CONDITION INDEX IN THE AIRCRAFT LATERAL WANDER AREA

Functional assessment of pavements on a university campus

With the constant increase in traffic volume at the Darcy Ribeiro Campus of the University of Brasília (UnB), attention to the preservation of road infrastructure is necessary. The implementation of an Urban Pavement Management System (UPMS) emerges as a strategy. This study aims to evaluate the functional condition of the pavements at the UnB University Campus by observing distresses on their surfaces, using the Fixed Value Matrix for 5 Distress method by Zanchetta (2017). A total of 161 road segments were evaluated, and both Objective and Subjective Pavement Condition Index (PCI) values were obtained, along with the recommended intervention for each segment. The most common types of defects found in the pavements were Raveling and Fatigue Cracks. The most suggested interventions were Preventive Maintenance (23.60%) and Corrective Maintenance (22.98%). Statistical analysis led to the conclusion that there were no significant differences between the Objective and Subjective PCI data. Thematic maps were generated for the Objective PCI, Subjective PCI, and maintenance activities in order to make georeferenced spatial information available.

Predicting pavement surface conditions through artificial neural networks

In this study, six distinct types of pavement distress were evaluated using the pavement condition index (PCI) rating: ravelling, rutting, cracking (including alligator, transverse and longitudinal cracks), shoving, patching and potholes. The severity levels of these distress types are utilised for classification, and these levels are subsequently converted into PCI values. The proposed methodology incorporates a thorough analysis, considering various variables such as average daily traffic, land-use classification, number of lanes, road width and length, pavement age, road alignment, vehicle type and weather conditions in Berzhite, Tirana, Albania. To predict PCI values, an artificial neural network model was employed due to its versatility in handling diverse inputs. Key performance metrics, including R2, mean squared error and mean absolute error, are used for model assessment. Notably, model 2, featuring two hidden layers, exhibits superior performance over model 1. However, due to size constraints in testing and validation datasets in this study, the accuracy of the model is somewhat limited. Future directions for research involve expanding the dataset to enhance model accuracy and incorporating advanced features to capture a more complex comprehension of pavement conditions.

Enhancing pavement crack segmentation via semantic diffusion synthesis model for strategic road assessment

Computer-aided deep learning has significantly advanced road crack segmentation. However, supervised models face challenges due to limited annotated images. There is also a lack of emphasis on deriving pavement condition indices from predicted masks. This article introduces a novel semantic diffusion synthesis model that creates synthetic crack images from segmentation masks. The model is optimized in terms of architectural complexity, noise schedules, and condition scaling. The optimal architecture outperforms state-of-the-art semantic synthesis models across multiple benchmark datasets, demonstrating superior image quality assessment metrics. The synthetic frames augment these datasets, resulting in segmentation models with significantly improved efficiency. This approach enhances results without extensive data collection or annotation, addressing a key challenge in engineering. Finally, a refined pavement condition index has been developed for automated end-to-end defect detection systems, promoting more effective maintenance planning.

Cost-Effective Pavement Condition Survey for Municipal Road Networks

One of the most challenging issues for municipalities in the U.S. is to secure federal funding, state funding, or both, for local roadway improvement. Existing frameworks such as manual data collection, light detection and ranging have proven to be expensive and cumbersome. In this paper, a low-cost pavement management framework is proposed using artificial intelligence (AI). AI has solidified itself across industries as a revolutionary advancement that can automate many tasks that were performed by humans. AI has the potential to make roadway assessment easier and more cost-effective than ever, but this application has been hindered by dataset quality and quantity. Roadway datasets are often imbalanced, containing many more images of certain deformations than others. This decreases the performance of AI models. In this paper, different methods of pavement dataset labeling are tested to gain an understanding of which is best for pavement distress detection using a classification neural network. An AI-friendly pavement condition index is designed to give a clear indicator of the current pavement condition and provide a metric by which to rank the roads based on the need to repair them. The best-performing AI model is incorporated into the low-cost pavement management framework.

Machine learning-based technology for asphalt concrete pavement performance decision-making in hot and humid climates

Highway state agencies incur significant budget savings through optimal allocation of pavement Maintenance, Rehabilitation, and Reconstruction (MR&R) activities. These activities require robust prediction models that can handle large-scale, real-world data and can forecast pavement performance in the long run. Unfortunately, the traditional performance prediction models have been questionable in terms of efficiency and accuracy, are based on a limited number of explanatory variables, and are designated to predict short-term (up to five years) pavement conditions. Therefore, the goal of this study was to propose a machine learning-based technology that can predict the field performance by up to 11 years of Asphalt Concrete (AC) overlays placed on asphalt pavements in Southern states in the US based on key project conditions. The proposed technology resulted from assessing the prediction accuracy of machine learning algorithms, including Decision-Tree (DT), eXtreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and ensemble-learning method, in forecasting the Pavement Condition Index (PCI) as the pavement performance indicator. For each algorithm, six models were developed sequentially based on historical pavement condition data collected from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. The six models learned from 892 log miles of randomly placed AC overlay sections in Louisiana. The output of these models was the future PCI of AC overlays at a biannual rate from one to 11 years. The findings showed that XGBoost and ensemble learning showed similar performance during model training and were further evaluated using the testing dataset. During model testing, the ensemble learning method yielded higher prediction accuracy than other algorithms, with R2 values decreasing from 0.77 at age 1 to 0.67 at age 11, Root Mean Square Error (RMSE) values increasing from ±1.65 to ±4.74, and Mean Absolute Error (MAE) increasing from 1.24 to 4.59. The resulting models can be used by state agencies to replace traditional prediction techniques.

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.

Characteristics of life-cycle carbon dioxide emissions of arterial highway maintenance and the influencing factors

With the focus of highway development transitioning from construction to maintenance, a comprehensive understanding of the characteristics and influencing factors of carbon dioxide (CO2) emissions from highway maintenance activities is crucial for formulating effective strategies to promote the low-carbon development of road infrastructure. However, the quantitative relationships between CO2 emissions from highway maintenance schemes and factors such as pavement deterioration, traffic volume, and road grade remain unclear owing to a lack of comprehensive, multi-category, and real data. Using real maintenance data from 340 arterial highway segments in China, this study conducts the life cycle assessment (LCA) to estimate CO2 emissions from maintenance activities and examines the primary emission sources among various structural layers and materials. Furthermore, multiple linear regression (MLR) analysis is conducted to investigate the impact of traffic volume, road grade, and pavement deterioration on CO2 emissions from maintenance projects, and factors influencing the early-stage degradation of pavement performance. The results demonstrate that average CO2 emissions from heavy rehabilitation projects are 6.97 times higher than those from medium rehabilitation projects. Emissions from heavy rehabilitation projects exhibit a significantly negative linear relationship with the riding quality index (RQI) before maintenance (p < 0.05), and emissions from medium rehabilitation projects show a significant negative linear relationship with the pavement condition index (PCI) before maintenance (p < 0.05). Emissions from heavy and medium rehabilitation projects are significantly positively correlated with heavy vehicle traffic volume before maintenance (p < 0.05). Moreover, the early-stage degradation of PCI after heavy rehabilitation and RQI after medium rehabilitation exhibit significantly negative linear relationships with their respective indicators before maintenance (p < 0.05). The early-stage degradation of RQI after heavy rehabilitation is significantly positively correlated with CO2 emissions from the base course and cushion layers (p < 0.05). The findings emphasize that timely maintenance and reduction of CO2 emissions from asphalt mixing equipment are essential for mitigating emissions from road maintenance. This study offers valuable insights for advancing the low-carbon development of highways in temperate regions.

Airfield pavement performance prediction using Clustered Markov Chain Models

This study pairs unsupervised learning methods of clustering and classification with discrete-time Markov Chain models for airport pavement performance prediction, with the goal of allowing the clustering algorithm to take the place of typical pre-analysis grouping of pavement sections. The influences of cluster method, number of clusters, input years, interval time span, and training data on prediction results are analysed. Results indicate that clustering using the deterioration profiles of Pavement Condition Index (PCI) before applying the Markov Chain is sufficient to reduce the error by half in predicted pavement conditions for most cases. The initial pavement degradation is a reliable source for clustering and future condition prediction using the appropriately matched Markov Chain. This approach minimises the data required to predict pavement condition and proves the early deterioration rate of a pavement section can be used to predict its overall lifespan.

Bibliometric Analysis Of Flexural Pavement Road Damage On Airport-Port Main Corridor Roads Using The Pavement Condition Index (PCI) Method

Bibliometric analysis of the use of PCI methods on roads, main corridors, airports and ports is a network of research from around the world on related topics. The main objective of this study is to see trends that continue to develop in various points of view. By capturing various studies from 2000 to 2023 using Crossref sources, 1000 studies with related topics were produced. This research accompaniment uses the help of Publish or Perish, which is then processed in the VOSviewer application with the Research Information Systems (RIS) format. There is a reasonably broad relationship in research keywords produced with the dominance of rigid pavement keywords (299 studies), pavement condition index (112 studies), road damage (79 studies), rehabilitation (63 studies), and other keywords with different number variations. The results of the conclusion data regarding the development of research on pavement condition index continue to increase continuously, with the highest number in 2022 (86 studies).

Assessing Multifaceted Effects of Speed Humps and Bumps: Travel Time, Safety, and Environmental Considerations

This study focuses on investigating the significant impacts of speed breakers on various parameters, including travel time delays, vehicle speeds, fuel consumption, pavement maintenance costs, and vehicular exhaust emissions. Field data was collected and analyzed to assess the effects of different types of traffic calming measures on these parameters. The findings provide valuable insights into the implications of speed breakers on road safety, environmental pollution, and overall road infrastructure management. The results reveal that the implementation of speed humps, speed bumps, and triple bumps effectively slows down vehicles, as evidenced by considerable reductions in the 85th percentile speeds. The reduction percentages were 41.65% for speed humps, 73.52% for speed bumps, and 86.27% for triple bumps. This indicates the effectiveness of these traffic calming measures in improving road safety by reducing vehicle speeds. However, the presence of speed breakers also leads to increased travel time delays. On average, traversing stretches with speed humps, speed bumps, and triple bumps resulted in delays of 9.31, 16.42, and 29.51 seconds, respectively. While the individual delay times may appear relatively short, the cumulative effect of multiple speed obstacles along a road needs to be considered. Another significant impact observed is the increased fuel consumption associated with speed breakers. The study found that for every 100 km of travel, motorcycles and passenger cars consumed approximately 12.07 km and 27.37 km of additional fuel, respectively, when the density of speed breakers was 1.33/km. This translates to a fuel consumption increase of 13.73% for motorcycles and 37.74% for passenger cars. Furthermore, the presence of speed humps was found to contribute to pavement deterioration, as indicated by decreased Pavement Condition Index (PCI) values. The study also revealed that sections with speed humps incurred significantly higher maintenance costs compared to sections without speed humps. The increase in maintenance cost ranged from 100 to 264% across different road sections, with higher traffic volumes leading to greater cost escalation. Additionally, the study confirms that lower vehicle speeds, particularly between 0-15 km/hr, are associated with higher emissions of pollutants, including carbon monoxide (CO) and other pollutants. This highlights the environmental implications of speed breakers and their contribution to urban air pollution. Doi: 10.28991/CEJ-2024-010-07-07 Full Text: PDF

Assessment of Relationship between Asphalt Concrete Pavement Performance Indicators: Pavement Condition Index and RoadRoid based International Roughness Index

Pavement condition assessment is one of the important tasks to be carried out to prioritize maintenance needs of the road. The Pavement Condition Index and International Roughness Index are some of the most widely used and comprehensive indices for the assessment of pavement condition. The maintenance planning is generally carried out in terms of SDI in Nepal which is only a subjective measure so PCI can be used as a more objective indicator than SDI. The PCI is more representative of the overall condition of the pavement surface by considering all forms of distresses. Whereas, IRI, which is a measure of perceived road roughness is mainly concerned with the ride quality and is based on the distresses occurring on the wheel path. The research aimed to develop the correlation between the two indices to look for usability of either of the indices whenever required. For the determination of PCI, visual inspection survey was carried out based on which the distress type, density and severity were quantified. The IRI data was collected with the help of RoadRoid application after calibration and validation using Laser Profiler. The data matching in respective sections was carried out and relationships between the two indices are determined in terms of linear, logarithmic and polynomial models of different degree. The coefficient of determination for polynomial relationship model was found to be the highest with coefficient of determination of 0.7858. All the models presented with negative correlation between the indices indicating the increase in IRI would cause the decrease in PCI value and vice versa.

SMART quality control analysis of pavement condition data for pavement management applications

Assuring the accuracy and reliability of pavement condition data is crucial for effective decision-making in pavement management. Despite existing data collection protocols, concerns persist regarding data quality. This paper introduces SMART, a systematic statistical method designed to analyze the quality of pavement condition data from field surveys for pavement management applications. SMART employs a structured procedure that expands traditional descriptive statistics by applying interrater reliability statistics combined with bootstrapping methods and Modified Blant Altman diagrams to evaluate data quality. A comparative analysis of interrater statistics, including Cohen’s Kappa (CK), Interclass Correlation (IC), Krippendorff’s Alpha (KA), Percent Agreement (PA), and Weighted Cohen’s Kappa (WCK), is conducted in the research study. As a result, the adoption of KA and Modified Bland-Altman diagrams for data analysis is recommended. KA demonstrates versatility across diverse data types, accommodating nominal, ordinal, interval, and ratio-level data, while Modified Bland-Altman diagrams facilitate data dispersion analysis to visualize possible bias trends for the condition ratings. A case study is presented to demonstrate the applicability of SMART to analyzing Pavement Condition Index (PCI) data provided by the Metropolitan Transportation Commission (MTC) in California. This methodological approach aims to enhance pavement management decisions by ensuring the reliability of condition field survey data through the implementation of robust analytical quality control procedures.

Automated Multi-Type Pavement Distress Segmentation and Quantification Using Transformer Networks for Pavement Condition Index Prediction

Automated Multi-Type Pavement Distress Segmentation and Quantification Using Transformer Networks for Pavement Condition Index Prediction

EVALUATION OF THE LEVEL OF ROAD DAMAGE IN JELIDRO II-KUWUKAN-SAMBIKEREP ROADS USING THE PAVEMENT CONDITION INDEX (PCI) AND BINA MARGA METHOD

The road is a facility for land transportation that serves regional development. The deterioration of road quality can pose constraints on economic and social activities and endanger road users. Small damages can escalate if not promptly addressed, leading to losses. This reasearch aims to determine the type and level of damage on Jelidro II – Kuwukan - Sambikerep Roads STA 0+000 to STA 1+600. There are two methods for obtaining road condition values in this research, the methods are Pavement Condition Index method and the Bina Marga method. On the section of Jelidro II – Kuwukan - Sambikerep Roads, 8 types of damage were found, those are crocodile skin cracks, grade depression, edge cracks, potholes, patches, longitudinal cracks, slip cracks, and ravelling. The results of calculating the road condition value using the PCI method is 63,6 where the road is included in the good category. Calculations using the Bina Marga method get a priority order of 9. The two methods show that the road has the same type of treatment, that is a routine maintenance program with repair methods that can be carried out, namely surface layers such as asphalt resurfacing and patching holes. Keywords: Road Damage; PCI method; Bina Marga method.

Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements

Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, terminal runways, and taxiways and developed prediction models related to structural thickness and air traffic. Performance jump (PJ) and deterioration rate reduction (DRR) were used to measure maintenance benefits. Based on 112 composite pavement sections in the Long-term Pavement Performance Program, this study analyzed the influences of five typical preventive maintenance technologies on PJ, DRR, and PCI deterioration rates. The logarithmic regression relationship between PJ and PCI was obtained. For sections treated with crack sealing and crack filling, the DRR was nearly 0. For sections treated with fog seal, thin HMA overlay, and hot-mix recycled AC, the DRR was 0.2, 0.7, and 0.8, respectively. To solve the multi-objective maintenance problem, this study proposed a decision-making optimization method based on dynamic programming, and the solution algorithm was optimized, which was applied in a five-year maintenance plan. Considering different PCI deterioration tendencies of airport regions, as well as PJ, DRR, and costs of maintenance technologies, the preventive maintenance decision-making optimization method meets performance and financial requirements sufficiently.

Analysis of Road Damage Level Using the Pavement Condition Index (PCI) Method on the Surabaya-Gresik Toll Road, East Java

The Surabaya–Gresik toll road is a crucial infrastructure connecting Surabaya City with Gresik Regency. In industrial areas such as Gresik Regency, this toll road is indeed needed to expedite the flow of industrial goods distribution to Surabaya City. The toll road experiences frequent damage, such as alligator cracking, as it is traversed daily by heavy traffic. If the road, as a connecting infrastructure, is damaged, it will impede the flow of vehicles passing through it. Therefore, road maintenance is necessary to ensure optimal service for passing vehicles. In this research, an analysis of road damage on the Surabaya – Gresik toll road seeks solutions for addressing these issues utilizing the method of Pavement Condition Index (PCI). The method involves several stages, including a survey of road conditions, determination of severity levels, assessment of damage, and determination of pavement condition. The survey was conducted in 100-meter segments from KM 10 to KM 14, revealing various types of damage on the toll road, including alligator cracking, bleeding, corrugation, shoving, potholes, and more. Thereafter, an analysis of the severity level and damage assessment was carried out, resulting in an average PCI value of 68, categorized as a "Good" condition. Based on these findings, it is recommended to address this issue through routine maintenance, including repairing minor damage, patching potholes, sweeping, repairing pavement edge damage, sidewalk maintenance, side channel and complementary building drainage, road equipment, and roadside maintenance.

Impact Assessment and Damage Fee Development for Pavement Utility Cuts Using Functional and Structural Field Data

The objectives of this study were: 1) to provide transparency of a recent pavement utility cut impact study which informed the adoption of a pavement utility cut fee in Davis, California; and 2) to present an additional methodology for assessing pavement damage using field data referenced to pavement condition. A total of 24 test sites were selected to represent all functional classifications across the Pavement Condition Index (PCI) spectrum. Functional and structural field data were collected and analyzed. Of the 24 test sites, 67% exhibited both functional and structural damage with 96% exhibiting at least one form of damage. The average PCI reduction resulting from utility cuts was 5.1 points and the corresponding percent reduction in pavement service life was 6%. Of the test sites, 63% exhibited damage 2 ft from the edge of the patch, thus indicating that the zone of influence extends past the current restoration area. The statistical analyses indicated that the pavement utility cuts caused significant damage and supported a tiered utility cut fee structure based on functional classification and PCI. The resulting fee schedule is based on 2022 material and construction cost estimates; consequently, implementation including an annual adjustment for inflation is recommended. Since the negative impact of utility cuts is amplified when there are multiple or large cuts on the same street, and treatments are typically carried out across entire management sections, a full-recovery fee at a threshold of 10% of the management section area is appropriate. Example fee calculations were provided.

Presentation of artificial neural network models based on optimum theories for predicting accident severity on rural roads in Iran

This study focuses on predicting the number and severity of rural accidents using nine accident-related variables by the multi-layer perceptron (MLP) approach as a type of artificial neural network modeling method. The models were developed using the input parameters of shoulder width, roadway width, roadside hazard, access density, passing zone ratio, speed limit, pavement condition index, shoulder rumble strips, and centerline rumble strips. MLP models were created based on accident data that occurred from 2019 to 2020 on the Tehran–Qom and Tehran-Saveh rural roads in Iran. In order to achieve the highest accuracy, twenty MLP models have been built with various structures. The MLP model, consisting of a multilayer feedforward network with hidden sigmoid and softmax output, has been used in this study. In this regard, an attempt was made to present an optimum theory to select the best model. The most accurate model was chosen based on the R-value and root mean square error (RMSE), mean absolute error (MAE), f, SD, and R2. Results indicated that the R-value obtained from the optimum model was 0.912, representing the accurate performance of the selected model. In addition, access density, roadside hazard, and roadway width were identified as the most significant variables.