Pavement Management Research Articles

AI-Enhanced Prediction of Pavement Crack Propagation: A Study Using Traffic Load, Environmental and Material Data

This study develops an AI-based predictive model for forecasting pavement crack propagation by integrating traffic load, environmental conditions, and material property data. Traditional pavement management systems often struggle to accurately predict crack growth due to the complex interactions between these influencing factors. By leveraging data from various sources, including sensor-based traffic metrics, meteorological data, and material composition tests, this study identifies significant variables contributing to crack initiation and progression. The proposed model utilizes a blend of machine learning algorithms, including Random Forest and neural networks, with a cross-validation approach to ensure robustness. Results indicate that the model achieves high prediction accuracy, with an RMSE of 1.2 mm/year and an R-squared value close to 0.93. The findings support the use of AI-enhanced models as reliable tools for road infrastructure planning and maintenance, promising reductions in maintenance costs and improved pavement durability.

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.

State-of-the-art review on the integration of BIM with pavement management systems

State-of-the-art review on the integration of BIM with pavement management systems

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.

A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide.

Enhanced understanding on permanent deformation behaviour of subgrade compacted clay under long-term cyclic loading

The long-term performance of pavement structures is heavily reliant on the sustained load-carrying capacity of the subgrade soil. Under repetitive traffic loads, permanent deformation (PD) gradually accumulates in the subgrade due to plastic yielding and soil particle rearrangement, which can compromise the serviceability and durability of overlying pavement layers. This study aimed to enhance the understanding of compacted clay response under long-term cyclic loads through a systematic repeated load triaxial (RLT) testing approach. The proposed approach considered depth-dependent static and dynamic stresses exerted on compacted clay beneath pavement structures and traffic loads. A series of RLT tests were conducted to investigate the impact of key factors, including soil properties (moisture content and compaction degree), stress conditions (confining pressure and deviator stress), and load characteristics (load duration and rest period), on the PD behaviour of compacted clay subgrade. Stress-strain hysteresis loops and damping ratios were analyzed to enhance the fundamental understanding of subgrade PD evolution. The results showed that higher moisture content and lower compaction degree significantly increased PD, with the PD response transitioning from plastic shakedown to plastic creep. Greater deviator stress also exacerbated PD accumulation. Variations in loading duration and rest period influenced the PD behaviour, demonstrating the importance of accurately simulating the stress history experienced by subgrade soil elements under traffic loading. The findings provide valuable insights to optimize subgrade design and implement performance-based management of pavements.

Data Cleaning Framework for Pavement Maintenance and Rehabilitation Decision-Making in Pavement Management System Based on Artificial Neural Networks

Data Cleaning Framework for Pavement Maintenance and Rehabilitation Decision-Making in Pavement Management System Based on Artificial Neural Networks

Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces

Accurate assessment of road pavement friction is crucial for maintaining road safety. This study explores the prediction of the friction coefficient (µ) using 3D texture parameters of pavement surfaces. Measurements were conducted on 17 different rural road sections using the Traction Watcher One (TWO) for friction coefficients and a newly developed Static Road Scanner (SRS) for surface texture. Multiple linear regression models were created, incorporating texture parameters such as the valley material portion (Smr2,MIC), arithmetic mean peak curvature (Spc,MAC), and dale void volume (Vvv,PS). The results demonstrate a strong correlation between texture characteristics and friction, with R2 values up to 0.80 and an RMSE as low as 0.076, validating the model’s accuracy. This approach highlights the potential of using non-contact texture measurements for reliable prediction of friction, offering a significant advancement in pavement management and safety.

Gradient boosting decision trees to study laboratory and field performance in pavement management

Gradient boosting decision trees to study laboratory and field performance in pavement management

Using an Airport Pavement Management System to Optimize the Influence of Maintenance Alternatives on Operating Conditions

Using an Airport Pavement Management System to Optimize the Influence of Maintenance Alternatives on Operating Conditions

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.

An environmentally-friendly optimization framework for road pricing and pavement management under public-private-partnership

Vehicular emissions and the unsustainable development of transportation systems have received increasing attention in recent years. On one hand, transportation emissions increase as car ownership increases. On the other hand, construction of the new transportation infrastructure and maintenance of the existing transportation facilities are needed as travel demand increases. Thus there is a growing market for Public-Private Partnership (PPP), which aims to overcome existing deficiencies of traditional approaches while stimulating the sustainable development of transportation systems. This paper proposes an environmentally-friendly multi-objective optimization framework to derive dynamic toll pricing strategies and pavement maintenance and rehabilitation (M&R) activities for road networks containing PPP projects. A case study of a road network containing toll and toll-free highways in Florida, United States is used to verify the performance of the proposed approach. The results show that the public and private sectors could coordinate their actions to guarantee profits for the private sector while alleviating negative externalities for society.

UPDAPS-Flood: a mechanistic-empirical flexible pavement analysis tool to evaluate the effect of flooding events on flexible pavement performance

ABSTRACT Flooding can cause considerable damage to flexible pavements and negatively affects their performance over time. This study aims to evaluate the flooding effects on the flexible pavement performance and the potential resiliency of the pavement network through a mechanistic-empirical (ME) pavement analysis approach. The Unified Pavement Distress Analysis and Prediction System (UPDAPS) programme, which includes ME analysis models, was used to develop the UPDAPS-Flood programme. A total of 7,655 flexible pavement sections were extracted from the Highway Pavement Management System (HPMS) database in three United States regions. Finally, the resiliency of the pavement network to flood events was quantified using the loss of pavement service life based on the international roughness index (IRI). The results showed that flooding has the highest impact on rutting performance, followed by IRI and fatigue cracking. It has been found that a proper drainage system can reduce the flooding impact by a factor of two. The predicted pavement performance was found to be very sensitive to the coefficients of the rutting model, which brings the need for further studies to re-calibrate the rutting prediction model at higher moisture content levels.

Image-based severity analysis of Asphalt pavement bleeding using a metaheuristic-boosted fuzzy classifier

Pavement management systems play a vital role in maintaining transportation infrastructures by evaluating pavement distress to perform maintenance tasks efficiently. Severity analysis is an important step in this process. With an increasing focus on automating the pavement distress inspection, challenges persist, including limited attention to severity analysis of texture-based distresses and lack of applying fuzzy systems in this analysis despite the linguistic and qualitative description of severity levels. Accordingly, this paper presents a methodology leveraging computational intelligence frameworks such as fuzzy logic and metaheuristic optimization to develop a reliable system for severity analysis, particularly focusing on asphalt pavement bleeding. Employing GLSM and statistical feature extraction in conjunction with a fuzzy classifier, optimized with metaheuristic-based algorithms like GA, HBA, GWA, ARA, and SSA, the proposed boosted fuzzy classifier achieves an impressive accuracy of 93% and notable improvements in performance metrics, underscoring its superiority over classic fuzzy classifiers.

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.

Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

Local High Reliability Calibration of Faulting Model Using Pavement Management Data

Local High Reliability Calibration of Faulting Model Using Pavement Management Data

Pilot Study to Incorporate Network-Level Structural Condition in Agency Pavement Management Practices

Pilot Study to Incorporate Network-Level Structural Condition in Agency Pavement Management Practices

Equity in Transportation Asset Management: A Proposed Framework

Transportation asset management has historically overlooked equity considerations. However, recently, there has been a significant increase in concerns about this issue, leading to a range of research and practices aimed at achieving more equitable outcomes. Yet, addressing equity is challenging and time-consuming, given its complexity and multifaceted nature. Several factors can significantly impact the outcome of an analysis, including the definition of equity, the evaluation and quantification of its impacts, and the community classification. As a result, there can be a wide range of interpretations of what constitutes equity. Therefore, there is no single correct or incorrect approach for equity evaluation, and different perspectives, impacts, and analysis methods could be considered for this purpose. This study reviews previous research on how transportation agencies are integrating equity into transportation asset management, particularly pavement management systems. The primary objective is to investigate important equity factors for pavement management and propose a prototype framework that integrates economic, environmental, and social equity considerations into the decision-making process for pavement maintenance, rehabilitation, and reconstruction projects. The proposed framework consists of two main steps: (1) defining objectives based on the three equity dimensions, and (2) analyzing key factors for identifying underserved areas through a case study approach. The case study analyzed pavement condition and sociodemographic data for California’s Bay Area. Statistical analysis and a machine learning method revealed that areas with higher poverty rates and worse air quality tend to have poorer pavement conditions, highlighting the need to consider these factors when defining underserved areas in Bay Area and promoting equity in pavement management decision-making. The proposed framework incorporates an optimization problem to simultaneously minimize disparities in pavement conditions between underserved and other areas, reduce greenhouse gas emissions from construction and traffic disruptions, and maximize overall network pavement condition subject to budget constraints. By incorporating all three equity aspects into a quantitative decision-support framework with specific objectives, this study proposes a novel approach for transportation agencies to promote sustainable and equitable asset management practices.

LiDAR-Based Automatic Pavement Distress Detection and Management Using Deep Learning and BIM

LiDAR-Based Automatic Pavement Distress Detection and Management Using Deep Learning and BIM