Pavement Performance Indicators Research Articles

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.

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.

Revolutionising Egyptian pavement design: a comprehensive E* database and advanced modeling approach for contextually informed performance predictions

ABSTRACT This research assesses the dynamic modulus (E*) as a pivotal rheological attribute for characterizing viscoelastic behaviour in various indigenous asphalt mixtures across diverse scenarios. An exhaustive investigation involving 32 laboratory-designed asphalt mixes and two field mixes explores the influence of traffic loading patterns, air voids, binder sources, and climatic conditions on E*. Utilizing Superpave gyratory compaction, 68 E*-specimens are compacted and subjected to E*-testing, leading to master curve development. Evaluation of NCHRP 1-37A and NCHRP 1-40D Witczak models demonstrates their fair to excellent accuracy in predicting E* for Egyptian mixes. The NCHRP 1-40D model stands out with an R² of 0.94, showcasing superior accuracy and minimal bias. This study integrates AASHTOW are Pavement ME Design (PMED) to analyse pavement sections, revealing the impact of design factors, characterized by measured E*, on predicted pavement performance, encompassing asphalt concrete rutting, alligator cracking, longitudinal cracking, and terminal international roughness index. The investigation highlights the marginal impact of substituting predicted E* for measured E* on AASHTOWare pavement performance indicators for Egyptian mixes under default binder characteristic values. This research contributes valuable insights into the interplay of E* and pavement performance, facilitating wellinformed decisions in pavement design and analysis across diverse conditions in the Egyptian context.

Research and comparison of pavement performance prediction based on neural networks and fusion transformer architecture

<abstract> <p>The decision-making process for pavement maintenance from a scientific perspective is based on accurate predictions of pavement performance. To improve the rationality of pavement performance indicators, comprehensive consideration of various influencing factors is necessary. To this end, four typical pavement performance indicators (i.e., Rutting Depth, International Roughness Index, Longitudinal Cracking, and Alligator Cracking) were predicted using the Long Term Pavement Performance (LTPP) database. Two types of data, i.e., local input variables and global input variables, were selected, and S-ANN and L-ANN models were constructed using a fully connected neural network. A comparative analysis of the predictive outcomes reveals the superior optimization of the L-ANN model. Subsequently, by incorporating structures such as self-attention mechanism, a novel predictive approach based on the Transformer architecture was proposed. The objective is to devise a more accurate predictive methodology for pavement performance indices, with the goal of guiding pavement maintenance and management efforts. Experimental results indicate that, through comparative analysis of three quantitative evaluation metrics (root mean square error, mean absolute error, coefficient of determination), along with visual scatter plots, the predictive model employing the fused Transformer architecture demonstrates higher robustness and accuracy within the domain of pavement performance prediction when compared to the L-ANN model. This outcome substantiates the efficacy and superiority of the model in terms of predictive performance, establishing it as a reliable tool for accurately reflecting the evolution of asphalt pavement performance. Furthermore, it furnishes a theoretical reference for determining optimal preventive maintenance timing for pavements.</p> </abstract>

Estimating local pavement performance and remaining service interval using neural networks-based models and automation tool

This study introduces an integrated approach to enhance county pavement management, emphasising operational efficiency in determining the Remaining Service Interval (RSI) for rigid and flexible pavements. It establishes a robust methodology for systematically processing raw county road data through dynamic segmentation and summarisation to create a structured pavement database. It also incorporates innovative approaches and input configurations in employing Artificial Neural Networks (ANNs) to predict current and future county pavement performance indicators, including International Roughness Index (IRI), rutting, transverse, and longitudinal cracks, even with limited data. Evaluation of the ANN models on independent county road databases exhibited high prediction accuracies (0.86 < R 2 < 0.99), varying with specific performance indicators. The study results in an automation tool for expediting road performance estimation over multiple years. This tool seamlessly integrates the ANN models, empowering county engineers to make data-driven decisions and optimise resource allocation for effective pavement management, achieving significant cost savings.

Optimal pre-maintenance timing decision for asphalt pavement based on TOPSIS and combination weighting method

With the continuous growth of highway quantity in China, the post-maintenance of highways has become a focus of highway management, attracting widespread attention in the industry. Selecting an appropriate maintenance timing is of significant importance for highway maintenance. Based on the TOPSIS theory and the deterioration law of asphalt pavement performance indicators, this study takes cost, effectiveness, energy consumption, and carbon emissions as research indicators. Combined with the entropy weighting method and the analytic hierarchy process, the study establishes a decision model for determining the optimal preventive maintenance timing for asphalt pavement. This model determines the best timing for preventive maintenance of asphalt pavement through the values of relative closeness. Finally, by analyzing a practical maintenance case of a highway in Jiangsu Province, it is found that when the Pavement Condition Index (PCI) equals 90, it is the optimal timing for preventive maintenance of asphalt pavement, which verifies the rationality of the decision model and provides a scientific reference for selecting maintenance timing.

Use of recurrent neural networks considering maintenance to predict urban road performance in Beijing, China.

A correct understanding of the pavement performance change law forms the premise of the scientific formulation of maintenance decisions. This paper aims to develop a predictive model taking into account the costs of different types of maintenance works that reflects the continuous true usage performance of the pavement. The model proposed in this study was trained on a dataset containing five-year maintenance workdata on urban roads in Beijing with pavement performance indicators for the corresponding years. The same roads were matched and combined to obtain a set of sequences of pavement performance changes with the features of the current year; with the recurrent-neural-network-based long short-term memory (LSTM) network and gate recurrent unit (GRU) network, the prediction accuracy of highway pavement performance on the test set was significantly increased. The prediction result indicates that the generalization ability of the improved recurrent neural network model is satisfactory, with the R2 achieving 0.936, and of the two models the GRU model is more efficient, with an accuracy thatreaches almost the same level as LSTMbut with the training convergence time reduced to 25 s. This study demonstrates that data generated by the work of maintenance units can be used effectivelyin the prediction of pavement performance. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

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

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

International Roughness Index prediction for flexible pavements using novel machine learning techniques

International Roughness Index (IRI) is an important pavement performance indicator that is widely used to reflect existing pavement condition and ride quality. Due to the importance of this significant index, the current research aims to develop a precise IRI prediction model using the Gaussian Process Regression (GPR) and Locally Weighted Polynomials (LWP). The long-term pavement performance (LTPP) datasets of pavement age, initial IRI, alligator, longitudinal and transverse cracks, standard deviation of rutting, and subgrade plasticity index variables are employed in predicting IRI. These datasets are collected from 126 different flexible pavement sections of the LTPP specific pavement studies (SPS-1) located in different climatic zones in the US. The total number of IRI measurements in the collected database is 925 which covers a wide range of IRI values. Multiple linear regression (MLR) is firstly applied to classify the input variables. Then the MLR model is compared with four machine learning techniques which are GPR, LWP, Particle Swarm Optimization-Adaptive Network based Fuzzy Inference System (PSO-ANFIS) and PSO-Artificial Neural Networks (PSO-ANN). The developed models’ performance is validated using different statistical indices, including the coefficient of determination (R2). The results demonstrate that the GPR (R2=0.93) and LWP (R2=0.90) outperformed the PSO-ANFIS (R2=0.65) and PSO-ANN (R2=0.52) in predicting IRI. Thus, the GPR model is found to be more accurate for IRI modeling compared to the hybrid investigated models.

Impacts of pavement performance indicators on traffic oriented parameters and road safety

With the passage of time, due to vehicle movement and environmental conditions, the road pavement deteriorates. The accident rate increases if there is no proper maintenance of road deterioration. This study evaluates the safety performance due to the change in pavement performance indicators on the selected roads of Bahawalpur, Punjab Pakistan. Three roads different pavement conditions were selected, based upon their physical inspection, of 5 Km length (each road). The pavement performance indicators used in this study were International Roughness Index (IRI), rutting and cracking. The accident data of last five years (Jan 2015 to April 2019) was used in this study. The pavement performance indicators were measured on site and accident data was taken from emergency service (Rescue 1122). The relationships between pavement performance indicators and accident data were examined. No linear relationship is found between pavement performance indicators and traffic safety, however a parabolic relation is best fitted between these variables. The parabolic models are developed by keeping the pavement performance indicators on independent axis and accident data on dependent axis which can be use by road designers for assessing the value of IRI, rutting and cracking for safe traffic flow. Some values are also suggested which can be used as evaluation criteria that whether the road needs rehabilitation work or not. The suggested values/range for IRI is 1.75-2 m/Km, for rutting 9 – 10 mm and for cracking-Pavement Condition Rating (PCR) 75-80.

Experimental investigation of nano materials applicability in Hot Mix Asphalt (HMA)

• The efficacy of using Nano materials in Hot Mix Asphalt (HMA) was examined. • The effect of CNT and NC, loading frequency and temperature on dynamic modulus and resilient modulus were investigated. • Nano materials (CNT and NC) led to higher stiffness improved Dynamic stability rutting resistance and moisture resistance of HMA. • The optimized percentage of CNT and 1.5% CNT with NC in HMA was determined to be 1.5% and 6%, respectively. • MEP and regression techniques was developed in predicting the dynamic modulus of the nano material modified HMA. Nanotechnology for asphalt modification and incorporating nanomaterials into Hot Mix Asphalt (HMA) to deal with road failure have gained popularity recently. Researchers have reported the improved performance of asphalt modified by nanomaterials but most of the study is limited to binder modification using nanomaterials. The utilization of Nanomaterials in HMA can significantly improve the performance indicators in the form of extension in pavement service life and decrease pavement distresses. The Nanomaterials reinforcement of HMA can enhance pavement performance indicators, including low and high-temperature dynamic modulus and stability and enhancement in rut resistance and stiffness modulus. Nano Materials modified is a suitable solution to improve the mechanical properties of HMA. The current study objectives focus on the experimental investigation of Nano Materials such as Carbon Nano Tubes (CNT) and Nano Clay (NC) applicability in HMA. CNT and NC percentages used in HMA to the weight of bitumen are 0.5%, 1.5%, 2.5% and 2%, 4%, 6%, 8%. Conventional and Nanomaterial modified asphalt mixes were subjected to performance tests such as Simple Performance Tester (SPT), Dynamic Stability and Rutting, Tensile Strength Ratio (TSR), and Resilient Modulus ( M R ) using UTM-25. The present study further aimed to predicting dynamic modulus of nanomaterial modified HMA through Artificial intelligence (AI) modeling using Multi Expression Programming (MEPX) and Regression modelling with validation. Results have indicated that the addition of CNT and NC has improved HMA properties such as enhancement in dynamic modulus and dynamic stability and improves stiffness, rut resistance, and moisture resistance. It has been observed that 1.5% CNT with 6% NC content by weight of bitumen in asphalt mixtures outperformed other CNT and NC percentages. Moreover, Dynamic stability and Rut resistance have been improved by 820% and 64.7%, respectively, while resilient modulus increased by 309% and moisture susceptibility has been reduced by 22.26%. Overall, the asphalt mixtures with 1.5% CNT with 6% NC content has the best results.

Warm Mix Asphalt Demonstration Projects in Louisiana: Case Study of Five to Eight Years of Field Performance

Warm mix asphalt (WMA) technology has gained popularity in recent years because of its ability to enhance mixture compaction at relatively low temperatures and to improve environmental sustainability. The role of WMA in sustainable pavement technology means that there is a need for comprehensive research in relation to its field performance. The objective of this study was to evaluate the field performance of WMA pavement sections that have been in service for five to eight years. Four rehabilitation projects were evaluated throughout the State of Louisiana. The study included field and laboratory experiments. In the field experiment, WMA test sections were evaluated along with companion conventional hot mix asphalt (HMA) mixture sections on each project. Pavement performance indicators (rutting, roughness, and cracking) from the Louisiana Pavement Management System (PMS) were analyzed. The laboratory experiment consisted of evaluation of mixtures from each project to determine mechanical performance indicators such as loaded wheel tracking (LWT) rut depth, flow number, semi-circular bend energy release rate (SCB Jc), and dissipated creep strain energy (DCSE). An analysis was conducted to rank the capability of laboratory mechanical performance properties to predict field performance. Generally, WMA test sections were found to exhibit similar or better rutting and cracking performance compared with their companion control HMA sections. In addition, the LWT rut depth was found to be a better indicator of field rutting performance than the flow number parameter. It is worth noting that the SCB Jc parameter was found to correctly rank more field cracking indicators than the DCSE parameter and therefore it may be a better indicator of field cracking performance.

Life-cycle cost analysis of rehabilitation strategies for asphalt pavements based on probabilistic models

Optimal applications of pavement rehabilitation are critical for highway agencies to allocate limited budgets. This study applied life-cycle cost (LCC) analysis, combined with probabilistic pavement performance models based on survival analysis, to evaluate the cost-effectiveness of different overlay strategies. Survival models were developed using the international roughness index (IRI) as the pavement performance indicator and considering the impact of terminal IRI values, discount rates, precipitation, and traffic volumes. The results showed that there was a significant difference in survival probabilities between thin (2 in.) and thick (5 in.) overlays, while the pavement service life was not significantly related to the overlay material type (virgin or recycled) or whether it was milled. A sequence of thin overlays was more cost-effective than their thick counterparts when traffic was not considered. However, for pavements in wet areas with annual traffic exceeding 500,000 equivalent single axle loads, thick overlays were more cost-effective.

Determination of ride comfort thresholds based on international roughness index for asphalt concrete pavements

ABSTRACT The study is aimed to mathematically model the relationship between the amount of whole-body vibration exposed in a passenger car type vehicle and the ride speed of the vehicle in the same section and the International Roughness Index (IRI), which is used as a pavement performance indicator. Vibration measurements were analysed according to the evaluation method determined in the ISO 2631 standard, and frequency-weighted root-mean-square acceleration (aw) values were obtained in the vertical direction. Mathematical relationships between real measurement data performed in 1114 road sections were investigated using regression analysis, artificial neural networks (ANN) and Adaptive Neural Fuzzy Inference System (ANFIS) modelling techniques. The models' performance levels were determined using comparison metrics, and it was determined that ANFIS was the model with the best goodness of fit, with the regression coefficient 0.955, mean absolute error (MAE) 0.013, and root mean squared error (RMSE) 0.020. The threshold values affecting the ride comfort of IRI and the ride comfort values corresponding to the IRI limit values recommended by the Federal Highway Administration (FHWA) were determined through the ANFIS mathematical model. Then, a sensitivity analysis was conducted to determine the effects of a certain increase in the IRI value on discomfort.

Machine Learning Approach to Predict International Roughness Index Using Long-Term Pavement Performance Data

On-time pavement maintenance and rehabilitation are required to maintain or improve pavement roughness, which is an indicator of pavement performance and road safety. Better prediction of maintenance time can help in budget planning and allocation for highways as well as a better and safer driving experience for drivers. In this research, the International Roughness Index (IRI) for asphalt concrete pavement is predicted based on the 12,637 observations in the Long-Term Pavement Performance (LTPP) data set for 1,390 roads and highways in 50 states of the US and the District of Columbia from 1989 to 2018. To identify the research gaps and to better understand the state-of-the-art research in IRI prediction, a systematic literature review (SLR) has been performed to develop a comprehensive view of machine learning techniques used for IRI prediction. We used a machine learning algorithm that can handle missing data in the LTPP data set. Extreme gradient boosting (XGBoost) was used to predict the IRI. Also, the support vector regression (SVR) and random forest (RF) models were used to compare the results. Our results show that XGBoost provides a better model fit in terms of mean absolute error and coefficient of determination. Moreover, our results show that No.-200-passing, hydraulic conductivity, and equivalent single-axle loads in thousands (KESAL) are the most important factors in predicting the IRI.

Pavement Surface Condition Index Prediction Based on Random Forest Algorithm

With the rise of three-dimensional road detection technology, more and more roads have begun to use three-dimensional road detection vehicles to detect road conditions. The pavement surface condition index (PCI), an important indicator of pavement performance, was used as the research target, and the PCI used the actual pavement data collected by ARAN9000 three-dimensional, multifunctional road detection vehicle. First, the data mining technology is used to consider the factors such as pavement distress, environment, and pavement structure, and to process and analyze the data related to the pavement surface condition index of a certain highway in Ontario, Canada, such as data cleaning and feature screening. Then, a machine learning prediction model of a pavement surface condition index was constructed, and the complex correlation coefficients (R2) of the multiple linear regression model, the neural network model, and the random forest model were 0.562, 0.711, and 0.895. Compared with the neural network model, the accuracy of the random forest model in predicting the pavement surface condition index was improved by 0.184, the error was reduced by 1.599, and the training speed was improved by 33 s. Finally, the random forest model with high precision is selected for optimization. Due to the large number of input variables, it is impossible to determine which line of data is the outlier through simple statistical analysis, so after establishing and predicting the model, the outlier is determined and deleted through the fitting effect between the predicted value and the real value. Then the modified data was used to retrain the model to achieve the optimal training of the current model. The results show that the prediction efficiency and accuracy of the improved random forest model are higher, and the R2 reaches 0.898. The proposed pavement condition index prediction model is accurate and effective, and the prediction results can assist the maintenance department to make more scientific and reasonable maintenance decisions.

Mechanical properties of fiber-reinforced asphalt concrete: Finite element simulation and experimental study

Abstract Polymer fiber reinforcement can elevate the pavement performance indicators of asphalt concrete, including low temperature crack resistance, high temperature stability, and water stability. Fiber-reinforced asphalt concrete (FRAC) is a suitable solution to improve the road performance of asphalt concrete. However, the mechanism of the fiber parameters in asphalt concrete has still not been introduced. Herein we studied the influence of fiber geometry characteristics on improving the mechanical properties of asphalt concrete through numerical simulation. The finite element method (FEM) was used to establish an elastoplastic mesoscopic model for indirect tensile test of the asphalt mixtures via ABAQUS. The best fiber length, volume ratio, diameter, and modulus were confirmed based on the simulated results. Furthermore, the indirect tensile tests were undertaken to prove the influence of polyacrylonitrile (PAN) and polyvinyl alcohol (PVA) fiber parameters on the FRAC’s splitting stability. This study fills the gap between theoretical simulations and laboratory experiments, and it is a promising solution to improve engineered FRAC road performance.

Analysis of overweight vehicles on asphalt pavement performance using accelerated failure time models

ABSTRACT Vehicle overweighting has been one of the key driving forces of deteriorated pavements. The overweighting characteristics that dominate the pavement performance deterioration have been inadequately investigated. This study filled such a gap by analyzing the survival time of pavement under different overweighting scenarios. The survival model was based on the accelerated failure time (AFT) formulation. For improved predictivity, an implementation of the AFT using the gradient boosting tree method was employed. All data concerned were collected from the Long-Term Pavement Performance (LTPP) program. Fatigue cracking and rutting were selected as the pavement performance indicators. Each indicator had three levels of failure thresholds to investigate the impact of overweighting on short-term, mid-term, and long-term pavement performance. The predictive performance of AFT models showed that long-term pavement performance was more dependent on overweighting than short-term pavement performance. Compared with the volume and weight of overweight vehicles, the overweighting percentage described the overweighting situation the best. Fatigue cracking was more sensitive to the overweighting percentage than rutting. Without overweighting, the average pavement fatigue life and rutting life would be extended by about 43% and 29%, respectively.

Policy implications of standalone timing versus holistic timing of infrastructure interventions: Findings based on pavement surface roughness

Abstract Transportation agencies routinely make repair decisions in numerous contexts which include the timing of different repair treatments over an extended period. The timing policy may be characterized either by a standalone manner that maximizes the outcome or utility of individual treatments at a given time, or by a holistic manner that maximizes the utility of all treatments together within a given horizon period. This study begins with a hypothesis that the effect of the sum of treatment prescriptions may be different from the sum of the effect of individual treatment prescriptions. The paper discusses separate practical applications where each of these two situations is pertinent. The study is based on the use of surface roughness as the indicator of pavement performance, and therefore, the framework includes the development of treatment-specific treatment performance jump and performance loss models in terms of roughness. The framework also includes identification of the optimal roughness thresholds for each pavement treatment as a standalone activity, followed by a determination of the thresholds for each treatment from a holistic standpoint. The latter involves the establishment of an optimal repair schedule over the pavement life cycle and recording the resulting roughness thresholds at which each treatment is recommended for application within the optimal schedule. Then the paper presents a case study to demonstrate application of the framework, and to compare the optimal-schedule thresholds versus the stand-alone thresholds. The case study involved fourteen treatment types, two surface material types (rigid and flexible) and three road functional classes. The results suggest that in this context of highway management, the optimal triggers associated with individual standalone repair treatments are not necessarily the same (and yield inferior outcomes) compared to the application thresholds of those treatments when they are considered within an optimized life-cycle schedule. The results can provide guidance to agencies as they examine or update their current roughness thresholds for each standard treatment and their long-term condition-based or time-based repair schedules. The paper’s framework and results can also help agencies assess the consequences of deviations from a repair timing policy (delayed or hastened applications) due to lack of funds and other causes.

Using e-bikes and private cars in dynamic road pavement monitoring

The high cost and time involved with traditional road pavement monitoring approaches as well as the high costs of pavement monitoring equipment necessitate the need for introducing a cheap, accurate and straightforward road pavement monitoring method. Pavement monitoring systems are considered as an important preventive step to maintain the quality of road pavements. Road pavement conditions should be accurately monitored to measure the severity of road pavement degradations. This paper presents a new technique for road pavement monitoring. The proposed road pavement monitoring technique applies road pavement vibration measurements from an e-bike and a private sedan car equipped with vibration data and video recordings. In addition, a pavement performance indicator named Present Serviceability Rating (PSR) is used to detect the level of pavement degradation and overall pavement condition depending on visual inspections. The results from the proposed vibration monitoring technique and the PSR are compared to identify the accuracy of the proposed technique to measure the road pavement condition. The results of the case study road show that the e-bike and the sedan car are appropriate and accurate as test vehicles in pavement monitoring. They also show that travel speed and the number of monitoring iterations have a significant impact on the accuracy of pavement vibration data and consequently, on the accuracy of the road pavement monitoring of both test vehicles.