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

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.

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.

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.

Pavement performance assessment and prediction are crucial for efficient infrastructure management and strategic planning of maintenance activities. Conventional techniques are insufficient and lack the efficiency and flexibility required for modern transportation networks. This study proposes a groundbreaking integrated approach that merges machine learning (ML) classification techniques with Geographical Information Systems (GIS) to evaluate road conditions using the Pavement Condition Index (PCI) and the International Roughness Index (IRI). Given that IRI data collection is more straightforward and cost-effective than gathering pavement distress data, this study aims to classify the IRI of flexible pavements to estimate PCI models using advanced ML algorithms (Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), Decision Trees (DT), and Random Forest (RF)) and accurately determine pavement conditions. This research gathered 1042 data points using a smartphone application, TotalPave, to measure the IRI values for the (Nizwa–Muscat) and (Muscat–Nizwa) routes in the Sultanate of Oman. It meticulously applied feature selection techniques to identify the pavement parameters significantly impacting pavement performance. The research then spatially visualized and analyzed the results to determine the critical pavement sections. Among the ML models, RF demonstrated outstanding performance with an accuracy rate of 99.9% and an F1-score of 99. SVM has the lowest accuracy of 85.8% and an F1-score of 40.3. A comprehensive assessment comprising a confusion matrix, uncertainty analysis, box and whisker plot, and noise sensitivity provides in-depth insights into the reliability and consistency of predictions. The ML and GIS methods revolutionized the way transportation agencies interpret and implement their findings. The proposed framework is not merely a tool, but a transformative solution that facilitates the formulation of proactive maintenance strategies and optimizes resource utilization by providing a scalable and intelligent decision-support tool designed specifically for pavement management systems (PMS).

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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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.

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.

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.

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.

Road maintenance is important to maintain the safety and comfort of road users. Surveys are needed to determine the condition of cross-country roads. Road maintenance management along with budget costs based on the results of the correlation between the IRI method values (International Roughness Index) Software-based Roadlab Pro against the value of road surface damage by the PCI method (Pavement Condition Index) and Bina Marga can be done for the purpose of simplifying the survey process in evaluating road conditions and how to properly handle damage to the Pondok-Pulau Sangkar road section in Kerinci Regency. IRI value based on Roadlab Pro can be used to assess road conditions based on the PCI method because it has values that influence each other based on the results of correlation regression analysis with a coefficient value of 88.7%. Determination of the road maintenance budget is made based on 3 proposed scenarios which include maintenance according to needs or when damage occurs, maintenance every year, and periodic maintenance in the form of improvements in the first year, routine maintenance in the second year, and periodic maintenance from the third to the fifth year.

Swelling is one of the distresses that affect the pavement condition index (PCI) of airport runways. In northwest Queensland, swelling was observed in three different runways where sealed flexible pavement runways were deformed by the heaving of the expansive clay subgrades due to water intrusion. In the study, the trends of moisture content (MC) and corresponding swelling of these expansive soils were observed at different times. Subsequently, the PCI was calculated for the swelling of these runways using PAVER software. A correlation has been developed between swelling of different severity levels and the corresponding PCI. In addition, the relationship between MC and PCI has been established to demonstrate the influence of MC on the swell behavior, and the influence of seasonal MC variations on PCI was also examined. Finally, the study generated empirical equations to predict the swelling effects on the PCI and subsequent functional performance of the runway pavements constructed on expansive soils. A good correlation has been achieved between the swell parameter and the PCI. The correlation coefficient of determination R2 is computed to be 0.9981, 0.999, and 0.9987 for low, medium, and high severity of the swelling, respectively. The equations fitted with the experimental data well, which demonstrates the reliability of the empirical equations. From the result of swelling against MC and calculated PCI, it is evident that adverse effects are inevitable when water intrusion occurs in swelling soil subgrade. This high plasticity soil can deteriorate pavement performance and cause early pavement damage if allowed to swell.

In order to fight against over-speeding, the government and people in Egypt resorted to the installation of speed humps without adequate studies. On this basis, speed humps became widespread in most of the Egyptian roads in a semi-random way. It has been observed that the pavement condition of the roads has deteriorated in the vicinity of these humps. For impact assessment of speed humps on the pavement condition index (PCI), three roads link Kafr El-Sheikh city with three major towns in Kafr El-Sheikh governorate were selected to conduct PCI survey. The results indicated that the presence of speed humps contributes greatly to reduce the pavement condition index (PCI) values. It should be mentioned that the average reduction in PCI values ranged between 15% and 22% due to the presence of speed humps. Also, the most frequently observed distress on the pavement sections including humps considered for the selected roads was Raveling and Rutting. Accordingly, to avoid the pavement defects at the locations where the speed humps are installed, it is proposed to study the structural section of the road in the vicinity of the speed hump to resist the braking force and speed reduction effect.

The pavement condition index (PCI) is an inexpensive and simple method to evaluate pavement distresses. The main purpose of this study is to identify and evaluate pavement distress of Jalalabad highway using pavement condition index (PCI) to find a practical solution of road distress by suggesting different alternatives of treatment. The evaluation of pavement condition index (PCI) is carried out by visual survey pursuing ASTM D 6433. The highway is divided into A and B two sections, each section is divided into sample units, and the intensity of distress is evaluated in each sample unite. The results show that section A is in good condition with 70 PCI value and section B is in good condition with 59 PCI value. Based on the PCI values of the road sections, the necessary maintenance or rehabilitation procedure is suggested.

Letjen S. Parman road is one of the urban roads with a length of 4.2 km located in West Jakarta area and is a primary arterial road with three two-way lanes divided, where the median is a toll road in the city of Jakarta. This study aims to evaluate road pavement conditions functionally and compare the value of road pavement conditions based on two methods, namely Pavement Condition Index (PCI) and Surface Distress Index (SDI) which are used as the basis for knowing the type of road maintenance handling. This study uses direct observation method in the field by conducting a visual survey of the condition of pavement, where the length of this road is divided into 42 segments with one segment is 100 meters. From the results of the road damage research Letjen. S. Parman using pavement condition index (PCI) and Surface Distress Index (SDI) method there are no significant differences in results. For pavement condition index (PCI) method get a percentage result of 90% with perfect value and 10% with excellent value while with Surface Distress Index (SDI) method get a percentage result of 96.5% with good category and 3.5% with medium category.

To achieve these requirements need to assess the pavement surface condition (Functional Performance). The pavement surface damage that frequently happens has an effect on the safety and comfort of road users. Pavement condition assessment is the basic step to select a suitable type of maintenance and rehabilitation program. This study aims to estimate the functional condition of the Mohammed Al-Qasim Expressway pavement surface by the Pavement Condition Index (PCI) assessment with data collected throughout 2017. The result showed that the AC pavement surface condition assessment is equal to 70 based on PCI value, and rated as fair, and the transverse tining PCC pavement surface condition is rated as satisfactory and PCI value equals 77.