Utilizing spatial artificial intelligence to develop pavement performance indices: a case study

Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of each observed distress, the pavement condition surveys were conducted by actually walking through all the sections. Using these data, PCI was calculated utilizing Micro PAVER software. Dynatest Road Surface Profiler (RSP) was used to collect IRI data of all the sections. Using the SPSS software, linear and nonlinear regressions have been used for developing two models between PCI and IRI based on the collected data. These models have the coefficients of determination (R2) equal to 0.715 and 0.722 for linear and quadratic models. Finally, the results indicate the linear and quadratic models are acceptable to predict PCI from IRI directly.

A pavement management system (PMS) is a tool that supports decision-making specialists to maintain the pavement at an optimal service level. The absence of a PMS could lead to inadequate decision-making, a disorganized road inventory, poor selection in road maintenance techniques and inefficient technical support to justify the execution of maintenance activities. For these reasons, this research proposes the application of a PMS, with the objective of improving and standardizing the processes for evaluating the condition of roads that are under the jurisdiction of low-budget government organizations, guaranteeing the selection of the most appropriate type of maintenance. As a case of study, a 1.1 km local road located in one of the districts of Lima was evaluated, which was divided into 11 sections. With this evaluation, it was obtained that 2,605.43 m2 of the total road is in poor condition, due to the presence of functional failures. For the identification of failures, the pavement condition index (PCI), the international roughness index (IRI) and the present serviceability index (PSI) were used to determine the condition, roughness and serviceability of the pavement. In conclusion, it was identified that the proposed PMS is adequately adapted to the way of working and available budget of a local administration, since if it is applied it would only spend 20.55% of the fund that would be destined to a total reconstruction of road.

Road damages might affect pavement condition which leads to reducing the remaining service life of the pavement. Two methods widely known to measure the pavement condition are among others: the Pavement Condition Index (PCI) and the International Roughness Index (IRI). Both intended to measure the functional condition of the pavement. This study aims to show the relationship between PCI and IRI, hence the road roughness can be predicted from on-foot survey measurements. It will start by collecting the distress and its severity parameter as well as the roughness of 6 road sections using Hawkeye mobile car. The car is a complete modular system to measure roughness (using profilometer), capture images and measure the severity of the road distresses and else. The results show that the pavement condition of the 6 sections fall into the category of poor and above. While 44% of the segments are fair. The roughness conditions of all road sections are good or acceptable. The correlation analysis shows that the PCI cannot necessarily explain the IRI, even though both are used to express the functional condition of road pavements, because what Hawkeye measures for the two indices is different.

Roughness and condition prediction models for airfield pavements using digital image processing

ABSTRACTThe performance of asphalt pavement is highly affected by climate factors, such as temperature and precipitation. Different temperature and moisture conditions change the paving material properties, which consequently influence pavement performance. Considerable research has been conducted to study the effect of climate factors on pavement performance; however, there are no focused studies that have investigated the performance of asphalt pavements in cold regions. In this research, the effect of different design factors on asphalt pavement performance in cold regions is investigated utilising data extracted from the Long-Term Pavement Performance database. Only control sections with no historical maintenance or rehabilitation records were considered. The International Roughness Index (IRI) was adopted as the pavement performance measure. The IRI value is expected to increase gradually over time due to pavement deterioration. Initial screening of the data showed that most of the pavement sections experienced remarkable IRI increase followed by an unexpected decrease. An in-depth investigation revealed that frost heave was the main reason of this unexpected IRI trend as it was correlated in most of the cases to sudden monthly freezing index increases, especially for sections with no subsurface drainage and less layers thicknesses. An Artificial Neural Network with a Forward Sequential Feature Selection algorithm and regression analysis were employed to model pavement performance in cold regions and determine the most significant design factors prevailing in cold climate conditions. Moreover, a sensitivity analysis was performed to investigate the interrelation between the considered features and the IRI. As a result, several climate-related factors were found to have a significant impact on the performance of asphalt pavements in cold regions such as average temperature, freezing index, freeze/thaw, wind velocity and relative humidity.

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.

Pavement management systems (PMSs) have a primary role in determining pavement condition monitoring and maintenance strategies. Moreover, many researchers have focused on pavement condition evaluation tools, starting with data collection, followed by processing, analyzing, and ultimately reaching practical conclusions regarding pavement condition. The analysis step is considered an essential part of the pavement condition evaluation process, as it focuses on the tools used to find the most accurate results. On the other hand, prediction models are important tools used in pavement condition evaluation to determine the current and future performance of the road pavement. Therefore, pavement condition prediction has an effective and significant role in identifying the appropriate maintenance techniques and treatment processes. Moreover, pavement performance indices are commonly used as key indicators to describe the condition of pavement surfaces and the level of pavement degradation. This paper systematically summarizes the existing performance prediction models conducted to predict the condition of asphalt pavement degradation using pavement condition indexes (PCI) and the international roughness index (IRI). These performance indices are commonly used in pavement monitoring to accurately evaluate the health status of pavement. The paper also identifies and summarizes the most influencing parameters in road pavement condition prediction models and presents the strength and weaknesses of each prediction model. The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems.

The first step in the road handling effort is to survey to get an accurate road condition value to take correct action in implementing maintenance. As pavement performance is known to be probabilistic, various levels of uncertainty must be assumed. Modern pavement management methods are ineffective without an effective model to predict pavement performance. Discrete-time Markov chains are the most widely used probabilistic models, and examples from different countries worldwide can be found in pavement management systems. This research aims to predict the value of road conditions during maintenance and compare road assessments with real conditions during road maintenance using the IRI, SDI, and PCI methods using the Markov process. The analysis method used is to collect secondary data from related departments and carry out direct data collection in the field to obtain condition values based on IRI, SDI, and PCI to forecast by making a pavement condition prediction model based on the Markov process and then assessing road conditions by comparing the three index values with the slightest deviation value. The analysis showed that the average value of road conditions with the IRI indicator is 4.45, which is moderate, and the most negligible difference between the probability distribution of pavement condition prediction modeling and the actual survey results was the IRI (International Roughness Index) method. This model is closest to the actual conditions during implementation, with a difference value of 5.7%.

In Nigeria, literature on the integration of traffic of pavement condition and traffic characteristics in predicting road traffic accident frequency on 2-lane highways are scanty, hence this article to fill the gap. A comparison of road traffic accident frequency prediction models on IIesha-Akure-Owo road based on the data observed between 2012 and 2014 is presented. Negative Binomial (NB), Ordered Logistic (OL) and Zero Inflated Negative Binomial (ZINB) models were used to model the frequency of road traffic accident occurrence using road traffic accident data from the Federal Road Safety Commission (FRSC) and pavement conditions parameters from pavement evaluation unit of the Federal Ministry of Works, Kaduna. The explanatory variables were: annual average daily traffic (aadt), shoulder factor (sf), rut depth (rd), pavement condition index (pci), and international roughness index (iri). The explanatory variables that were statistically significant for the three models are aadt, sf and iri with the estimated coefficients having the expected signs. The number of road traffic accident on the road increases with the traffic volume and the international roughness index while it decreases with shoulder factor. The systematic variation explained by the models amounts to 87.7, 78.1 and 74.4% for NB, ZINB and OL respectively. The research findings suggest the accident prediction models that should be integrated into pavement rehabilitation.
 
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The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model’s applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications.

The quality of roads directly impacts the services they provide. Different indices are used to determine the pavement condition. Based on these indices, the pavement management system provides an estimation of the future costs of roadways and the ways of optimizing them. One of the indices used in the specification of the pavement condition is the Pavement Condition Index (PCI). To determine the value of this index, considering the amount and the severity of each sample's distress, the deduct values should first be calculated based on the experimental charts. In this study, the researchers try to present a model based on the Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the deduct value used to calculate the PCI.

This review paper investigates methodologies for assessing pavement distress and roughness in transportation infrastructure.The adopted approach, synthesized from various literature sources, includes road inventory surveys, traffic volume counts, and pavement condition surveys.Distress analysis encompasses the identification and quantification of issues such as cracks, potholes, and surface deformations, while the paper establishes a crucial relationship between the International Roughness Index (IRI) and Pavement Condition Index (PCI).The calculation of PCI involves distress type determination, deduct value computation, and corrective measures, while IRI is evaluated using the MERLIN instrument.The conclusion underscores the importance of proactive maintenance, innovative technologies, and sustainable materials for ensuring a safer and more efficient transportation infrastructure.

Accurate road roughness prediction is essential for sustainable transportation planning and cost-effective maintenance strategies.This study develops a systematic algorithm to optimize Artificial Neural Networks (ANN) for predicting International Roughness Index (IRI) values using Equivalent Standard Axle (ESA) and road age as primary inputs.The methodology employs comprehensive parameter space exploration across four optimization stages, evaluating various ANN configurations to identify the most effective architecture.Rigorous statistical validation through Analysis of Variance (ANOVA) and cross-validation ensures model reliability.Data quality assessment with outlier detection using the Interquartile Range method was implemented, retaining 94.3% of original observations.The optimized 6-30-25-20-1 ANN configuration, employing logsig and purelin transfer functions, achieved strong performance metrics, including R = 0.9554, R 2 = 0.9020, MSE = 0.0153, RMSE = 0.1236, and MAPE = 0.0285.Statistical validation confirmed significant model improvements with an F-statistic of 24.367 and a cross-validation mean of 0.892.The RMSE accuracy of 0.1236 m/km enables reliable pavement condition classification within established IRI thresholds, supporting timely maintenance decisions.This streamlined approach addresses critical infrastructure management challenges by enabling cost-effective maintenance planning with minimal data requirements, particularly valuable for developing countries with limited pavement monitoring infrastructure.The model's computational efficiency facilitates network-wide deployment for long-term planning and strategic resource allocation.Road agencies can apply this model for maintenance budget prioritization, networklevel condition assessment, and multi-year intervention scheduling, particularly in resource-constrained environments where comprehensive pavement monitoring systems are unavailable.This study establishes a structured approach to optimize ANN for IRI prediction, enhance the effectiveness of Pavement Management Systems (PMS), and support sustainable transportation infrastructure through improved maintenance scheduling.

Natural disasters can cause severe damage to infrastructure such as the road network. Currently, a Pavement Management System (PMS) does not incorporate flooding in a Life Cycle Analysis (LCA). A few Road Deterioration (RD) models have addressed flooding, but they have limitations. As a result, there are not any comprehensive RD models that can incorporate flooding in pavement performances. In addition, no optimum life-cycle road maintenance strategy is available. No study investigated the pavement performances because of a loss in Modulus of Resilience (Mr) at granular and subgrade layers during extreme moisture intrusion. The derivation of pre- and post-flood road maintenance strategies and a flood risk assessment should also be incorporated in a PMS.As a case study, this research has considered the January 2011 flood of Queensland, Australia, and has used 34,000 km road database of the Queensland’s main roads authority. The major objectives of this study are to derive: i) network and project level roughness and rutting-based RD models with flooding, ii) pavement performances due to Mr loss at granular and subgrade layers, iii) flood-resilient pavements, iv) optimum road maintenance strategies at without flood, pre- and post-flood scenarios, and v) pavements’ flood risks. The current scope covered the pavements that are affected after a flood, but are not washed away completely and need rehabilitation for structural strengthening.This research has used a probabilistic approach for deriving the RD models, which are valid at both network and project levels. Moreover, the proposed RD models can estimate road deterioration after a flood at different probabilities of flooding.The actual roughness and rutting vs. time data are assessed for the representative road groups or site specific roads to get the transition probability matrices for with and without flooding conditions, which are used in a Monte Carlo simulation. The new RD models show significant pavement deterioration at different probabilities of flooding events. The results are found valid with actual data for about 2 to 3 years after the January 2011 flood. A t-test also supports this match. A pavement’s performance due to Mr losses at granular layers is checked using the two renowned roughness models, which results are found close match with the actual after flood data and RD models.All these results are used estimating pavements flood resilience from three techniques, i.e., i) using the RD modelling results and an indicator of Change in International Roughness Index (∆IRI) in year 1 over the probability of flooding (∆IRI/Pr); ii) with the indicator of ∆IRI in year 1 over Mr loss (∆IRI/MrL); and iii) using the flood-risk consequences. Expectedly, a flood-resilient pavement performed better in the life-cycle.The study has derived optimum pavement maintenance strategies at without flood, pre- and post-flood conditions for the Queensland roads authority as a case-study. About $17.8bn is needed in the next 20 years at normal condition to maintain its flexible and composite roads at 4.0 IRI. The post-flood strategy framework uses the new RD models for predicting after flood deterioration and the Highway Development and Management model (HDM-4) for getting optimum solutions. The unconstrained budget solution requires $49.7bn to keep the network at an excellent condition, while the constrained one provided a reasonable solution with about $26.1bn in life-cycle. The pre-flood maintenance strategy considered an effective approach by upgrading a pavement’s structural strength now with a thin overlay, and then evaluating pavement life-cycle performance if a flood comes in different years for predicting after-flood deterioration using the RD models and selecting cost effective treatments utilising the HDM-4. The total pre-flood strategy cost varied within the range of $37bn to $38bn. Comparing to a post-flood strategy, the pre-flood strategy can maintain the network better and provides positive economic benefits.Finally, the current study aimed to evaluate pavement performances before and after a flood. The roughness vs. time data were used to get a flood and the time gap between two floods was considered as likelihood. Distribution changes of roughness data before and after a flood have been used to calculate flood consequences, and then risk results. The flood consequence and risk results were validated with actual data for two road groups.A road authority can use the RD models for an after flood road deterioration prediction to select appropriate post-flood treatments. Moreover, pavements flood-resilience, life-cycle performances and approaches for the pre- and post-flood strategies may be used for efficiently managing roads with flooding. Any road authority may plan converting flood prone roads into resilience ones for better life-cycle performances. The pavement’s flood resilience has addressed one of the major challenges of climate change and could be used for an innovative pavement design. The research findings are useful for sound strategic planning and sustainable road asset management, as it addresses the impact of flooding events in LCA. All these help in improving a PMS incorporating flooding.

Repeated and periodic assessments of pavement conditions at intersection sites to improve the serviceability of intersection, is an essential component of the transportation system. Further, continuous maintenance of deterioration and defects that appear on the surface layers of pavement at intersection sites according to pavement condition indices (PCI), is a vital process in pavement management. This research aims at conducting spatial analysis for pavement conditions at intersection sites. The application of the developed measure is demonstrated for three signalized intersections in Hilla city, Iraq, by using distress definition. Point density Estimation (PDE) as spatial analyses and interpolation tool (IDW) in ArcGIS software is used to estimate the position intensity. PDE for Pavement condition applications enables the visualization and extraction of distress density in a selected zone or a network of road, which gives the makers of decision an advanced vision to the problem within a location. In this paper, PDE is used to generate potential distress hotspots map based on distress definition data. The result of this research shows the suitability of the heat map for the maintenance entitlement of a particular intersection. It gives a clear indication of the deterioration in the pavement layers depending on the severity of the colours shown in the heat map. For instance, a section of high score of PCI may include a very deteriorating units of low score of PCI due to the presence of many defects. Hence, it can be concluded that, it is doubtful that the PCI section represents the reality of all units. As a sample section (D) of the AL Thawra intersection where there were damaged units, PCI reached 55, 56, 59, while the PCI of section (D) was 74. The developed heat map demonstrates deterioration condition thoroughly for all units and provides advanced vision to pavement condition at the studied sites.