Pavement Structural Condition Research Articles

Impact of Pavement Stiffness on Performance of Traffic Speed Deflection Measurements

Traffic speed deflection devices (TSDDs) have attracted much attention as innovative tools for assessing the structural capacity of highway networks. However, the sensor and operational limitations of these devices in assessing accurately and precisely the pavement structural condition have not been thoroughly established. In this study, the performance of the traffic speed deflectometer (TSD) measurements was evaluated using data from six instrumented pavement sections. The tested sections included different pavement types such as flexible, composite, and rigid pavements with varying overall level of stiffness. Based on the comparison between TSD measurements and the data obtained from field-installed instrumentation, TSD could measure the deflection velocity profiles reliably at flexible pavement sections. The level of noise superimposed on the signal of TSD deflection velocities was highly dependent on the pavement type and stiffness.

Intelligent Assessment of Pavement Structural Conditions: A Novel FeMViT Classification Network for GPR Images

Intelligent Assessment of Pavement Structural Conditions: A Novel FeMViT Classification Network for GPR Images

Identification and Ranking of Road Safety Hazardous Locations: Case Study of Kotre–Aabhukhaireni Section of Prithvi Highway

In Nepal, due to rapid urbanization, there are many emerging towns and settlements near highways, which have raised issues of increased traffic density resulting in frequent road crashes like in Kotre- Aabhukhaireni section of Prithvi Highway. Higher crash rates depict lower safety conditions. To improve safety conditions and reduce the loss of life and property, hazardous conditions along the highways need to be identified and ranked for treatment purpose. This study is based on a methodological framework for identifying hazardous locations based on a Field Survey (condition rating) and Analytical Hierarchy Process (AHP) for ranking road safety hazardous locations of Kotre- Aabhukhaireni road Section of Prithvi Highway by weighing the safety parameters of the road section and calculating the Safety Hazardous Index (SHI). Field condition rating with a focus on pavement surface conditions, structural conditions, shoulder and drainage conditions, pedestrian infrastructure and vehicle restraint system, street lighting, traffic signs, island and road marking conditions was conducted, and risk factors were identified by comparing these variables against norms.
 The study identified 15 different locations as safety hazardous locations. The results showed Yampa, Baradi, and Chirrkeni as the most hazardous ones with an SHI index of 0.852, 0.773 and 0.712 respectively, among the study section considered. In contrast, Dhaaptar was considered as the least hazardous one with SHI value of 0.210.

Temporal Convolutional Network-Based Axle Load Estimation from Pavement Vibration Data

Measuring the axle loads of vehicles with more accuracy is a crucial step in weight enforcement and pavement condition assessment. This paper proposed a vibration-based method, which has an extended sensing range, high temporal sampling rate, and dense spatial sampling rate, to estimate axle loads in concrete pavement using distributed optical vibration sensing (DOVS) technology. Temporal convolutional networks (TCN), which consist of non-causal convolutional layers and a concatenate layer, were proposed and trained by over 6000 samples of vibration data and ground truth of axle loads. Moreover, the TCN could learn the complex inverse mapping between pavement structure inputs and outputs. The performance of the proposed method was calibrated in two field tests with various conditions. The results demonstrate that the proposed method obtained estimated axle loads within 11.5% error, under diverse circumstances that consisted of different pavement types and loads moving at speeds ranging from 0~35 m/s. The proposed method demonstrates significant promise in the field of axle load reconstruction and estimation. Its error, closely approaching the 10% threshold specified by LTPP, underscores its efficacy. Additionally, the method aligns with the standards set by Cost-323, with an error level-up to category C. This indicates its capability to provide valuable support in the assessment and decision-making processes related to pavement structure conditions.

Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions

Timely maintenance of road pavements is crucial to ensure optimal performance. The accurate prediction of trends in pavement defects enables more efficient allocation of funds, leading to a safer, higher-quality road network. This article systematically reviews machine learning (ML) models for predicting the international roughness index (IRI), specifically focusing on flexible pavements, offering a comprehensive synthesis of the state-of-the-art. The study’s objective was to assess the effectiveness of various ML techniques in predicting IRI for flexible pavements. Among the evaluated ML models, tree ensembles and boosted trees are identified as the most effective, particularly in managing data related to traffic, pavement structure, and climatic conditions, which are essential for training these models. Our analysis reveals that traffic data are present in 89% of the studies, while pavement structure and climatic factors are featured in 78%. However, maintenance and rehabilitation history appears less frequently, included in 33% of the studies. This research underscores the need for high-quality, standardized datasets, and highlights the importance of model interpretability and computational efficiency. Addressing data consistency, model interpretability, and replicability across studies are crucial for leveraging ML’s full potential in fine-tuning IRI predictions. Future research directions include developing more interpretable, computationally efficient, and less complex models to maximize the impact of this research field in road infrastructure management.

Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar

The inspection and monitoring of structural conditions are crucial for the maintenance of semi-rigid base pavement. To achieve the inverse calculation of material parameters and obtain the mechanical response of asphalt pavement, a method of modulus correction by reducing the error between tested and simulated strains was first developed. The relationship between the temperature at various depths within the pavement structure and atmospheric temperature was effectively demonstrated using a dual sinusoidal regression model. Subsequently, pavement monitoring data illustrated that as loading weight and temperature increased and loading speed decreased, the three-way strain of the asphalt layer increased. Thus, the relationship model between loading conditions and three-way strain was established with a good fitting degree (R2 > 0.95). The corrected modulus was obtained by approximating the error between simulated and measured strains. Then, the finite element analysis was performed to calculate key mechanical index values under various working conditions and predict the fatigue life of asphalt and base layers. Finally, ground-penetrating radar (GPR) detection was performed, and the internal pavement condition index was defined for quantitative assessment of structure conditions. The results show that there is a good correlation between the internal pavement condition index (IPCI) and remaining life of pavement structure. Therefore, our works solve the problems of the parameter reliability of pavement structures and quantitative assessment for structural conditions, which could support the performance prediction and maintenance analysis on asphalt pavement with a semi-rigid base.

Traffic Speed Deflectometer for Network-Level Pavement Management in Tennessee

The traffic speed deflectometer (TSD) overcomes the limitations of the falling weight deflectometer (FWD) with respect to traffic interruption and test inefficiency and is considered the preferred method for network-level pavement structural evaluation. However, some challenges exist when applying the TSD to routine survey work. This study evaluated the long-term repeatability of TSDs and compared TSD measurements with FWD measurements to examine device-related errors in TSD measurements. In addition, a framework for incorporating TSD measurements into the pavement management system (PMS) was developed by introducing deflection bowl parameters (DBPs). All data analyses were based on TSD data collected in Tennessee. From the current study, the repeatability of the TSD has not been fully validated because of device-related errors. Since TSD measurements and FWD measurements are broadly similar, periodic validation of TSDs by FWDs is recommended for quality control. TSDs provide valuable information about pavement structural conditions that are not reliably or adequately reflected in the current PMS (which focuses mainly on pavement functional conditions). DBPs are a simple and fast method for making initial estimates of pavement structural conditions without the need for layer thickness. In the proposed DBP system, the surface curvature index (SCI_12) and D60 (TSD deflection at 60 in. before the load center) represent the structural strength of the asphalt layer and subgrade, respectively. The combination of pavement functional conditions and TSD-measured structural conditions (PMS+DBPs) may help to make more accurate predictions of pavement performance and to make more appropriate decisions on maintenance and rehabilitation strategies.

Expansion method and application of initial deflection data for asphalt pavement based on the unascertained number theory: a case study

Deflection is an essential parameter for pavement structural condition assessment. For the total pavement section length, sample size for deflection data is relatively small, which makes it challenging to characterize the pavement structural properties. To solve the problem, an expansion method for the initial deflection data based on unascertained number theory was proposed in this study. And the decay evaluation of the pavement structure strength during service was conducted based on pavement deflection samples before and after expansion. Results showed that after expansion, deflection sample size increases exponentially and the deflection range was broadened significantly. And the decay analysis of the pavement structure strength ratios at two – and three-years demonstrated that the expanded deflection sample can lead to a more reasonable evaluation of the initial pavement structural performance. The study can help to understand the decay laws of the pavement structure performance to make pavement maintenance plans more rationally.

Analysis of the Damage Level of the Road Pavement Surface with the Surface Distress Index (SDI) Method as a Basis for Road Maintenance

Pavement is the main structure of road construction where the pavement management system is required to determine the condition of the road pavement structure. Factors that can affect road pavements are weather, traffic, construction implementation, pavement design, and maintenance. Each layer of pavement has a design life and with increasing pavement design life, the road condition will gradually decrease until it reaches a level that must be repaired. The research was carried out by observing the condition of the damaged road pavement, using the Surface Distress Index (SDI) method, based on the SDM 03/RCS guideline regarding the 2011 road condition survey. In this study, a Surface Distress Index (SDI) value of 5.25 was obtained for Jalan Bilo-Tambun Tolitoli Regency. From the value of road conditions based on the SDI criteria, it was found that the road conditions were in good condition and the recommendation for handling the road was to receive routine maintenance

Data-Driven Web-Based Patching Management Tool Using Multi-Sensor Pavement Structure Measurements

Automating pavement maintenance suggestions systems is challenging, especially for actionable recommendations such as patching location, depth, and priority. It is a common practice among state agencies to manually inspect road segments of interest and decide maintenance requirements based on the pavement condition index (PCI). However, standalone PCI only evaluates the pavement surface condition which, coupled with the variability in human perception of pavement distress, limits the accuracy and quality of current pavement maintenance practices. In this case, there is a need for multi-sensor data integrated with standardized pavement distress condition ratings. This study explores estimating the appropriate pavement patching strategy (i.e., patching location, depth, and quantity) by integrating pavement structural and surface condition assessment with pavement ratings of distress. In particular, it combines pavement structural condition parameters and falling weight deflectometer deflections with surface condition parameters, international roughness index, and cracking density, for a better representation of overall pavement distress conditions. Then, a pavement-specific, threshold-based patching suggestion algorithm is designed to suggest pavement maintenance operations. The thresholds were determined based on a reliability concept and were verified with the structural number ratio. The threshold values were then used in the patching suggestion algorithm to create patching suggestion tables. A web-based Patching Management Tool (PMT) was designed as an interactive tool to visualize these patching suggestion maps and analyze the pavement distress data using geographical maps and graphs. The PMT was validated with road surface and right-of-way images obtained from three-dimensional laser sensors, and it could successfully capture localized distresses in existing pavements.

Implementation of ensemble Artificial Neural Network and MEMS wireless sensors for In-Situ asphalt mixture dynamic modulus prediction

Dynamic modulus is an essential mechanical property for pavement structural condition assessment yet obtaining the in-situ dynamic modulus of asphalt mixture efficiently and accurately has never been easy. With the development of sensing technology, a type of Micro-Electromechanical Sensor (MEMS), SmartRock, has been applied in pavement monitoring because of its convenience and high efficiency; but interpreting the collected data as useful engineering information becomes a new challenge. This paper aims to present a data processing algorithm for sensing data in pavement engineering and develop a dynamic modulus predictive model based on the ensemble artificial neural network (ANN) model using SmartRock sensing data. Five asphalt mixtures were tested by the uniaxial dynamic modulus test, and one of the 5 mixes was also used in the third-scale model mobile load simulator (MMLS3) test. SmartRock sensors were installed in the specimens to collect the mechanical response during the tests. The empirical mode decomposition (EMD) method was used for data preprocessing. Dynamic modulus test data, in combination with a small portion of early-stage MMLS3 sensing data, were used as the training dataset, while the remaining MMLS3 data were used as the testing dataset. The results show that the ensemble ANN model is feasible and robust in predicting the dynamic modulus of the asphalt mixture in the MMLS3 test. The parametric analysis shows that the ensemble ANN model has reliable stability. Future studies are recommended to expand the database with more material variety and loading conditions for model verification.

Light Weight Deflectometer Evaluation of Low-Volume Road Structural Deterioration under Rapidly Increased Traffic Patterns

When evaluating low-volume road (LVR) condition, there is not a tremendous emphasis on structural capacity, since deterioration is typically caused by environmental factors. However, in cases where LVRs experience rapidly increased traffic loading, structural condition becomes important. The research objective was to determine whether the light weight deflectometer (LWD) can be used as a structural evaluation tool for LVRs. This study explored three major factors that affect measurement of flexible pavement structural capacity: load-induced deterioration (e.g., rutting and cracking), soil moisture, and asphalt temperature. Four full-scale flexible pavement test items were constructed with varying base-course materials and layer thickness typical of LVR. Instrumentation was installed during construction to monitor moisture content, temperature, and ambient weather conditions. Accelerated traffic was applied with a 4-axle military truck. A total of 10,000 cumulative vehicle passes were completed. Falling weight deflectometer (FWD) and LWD testing equipment were utilized to measure pavement structural condition at selected traffic intervals. The LWD was shown applicable for LVRs experiencing accelerated traffic. The LWD is portable and is more efficient for LVRs when high rut depths are permitted. The LWD tracked trends of the FWD; therefore, the LWD, based on these experiments, can be recommended for use in lieu of FWD to assess structural condition in some LVR conditions.

Structural Behavior Prediction Model for Asphalt Pavements: A Deep Neural Network Approach

Abstract Structural behavior of pavements is assessed using various destructive and nondestructive tests, albeit they are found to be cost-intensive. There is a need to develop cost-effective structural condition evaluation methods that are scientifically sound so appropriate maintenance interventions can be performed at the right time. The objective of this research study was to develop a Deep Neural Network (DNN)–based approach to predict pavement structural condition using functional, traffic, and climatic characteristics. A DNN was developed to calculate the deflection bowl parameters along with peak surface deflections from roughness, traffic, pavement age, pavement temperature, and climatic conditions. Over 26,000 data points covering various geographic locations were used to establish a global model (R2 = 82 % for the test data) to evaluate the structural integrity of asphalt pavement layers. It is envisioned that this study would assist roadway agencies in assessing the overall condition of asphalt pavements synergizing functional and structural characteristics.

DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation

Cracks are important indicators of pavement structural and operational conditions. Early pavement crack detection and treatments can help extend pavement service life, reduce fuel consumption, and improve safety and ride quality. Pavement distress surveys have traditionally been performed manually by visually inspecting the roads, which is labor-intensive and time-consuming. Therefore, computer-vision-based automated crack detection has great practical significance in pavement maintenance and traffic safety. Traditional image processing techniques are sensitive to noise in images and are thus likely to miss detecting some cracks due to the crack texture variety, complex lighting conditions, and various similar but irrelevant objects on the road. This paper adopts and enhances DeepLabv3+, a popular deep learning framework for semantic image segmentation, for road pavement crack detection. We propose a multi-scale attention module in the decoder of DeepLabv3+ to generate an attention mask and dynamically assign weights between high-level and low-level feature maps. Compared with fixed weights across different features, the dynamic weights strategy can assign more reasonable weights to different feature maps. Ablation experiments show that the attention mask can effectively help the model better combine multi-scale features and generate more accurate pavement crack segmentation results. The proposed method achieves state-of-the-art results on three benchmarks, including Crack500, DeepCrack, and FMA (Fitchburg Municipal Airport) datasets. We further test it on pavement crack images captured by smartphones, and the results show that it provides a viable approach to road pavement crack segmentation in practice with excellent performance.

Predicting Pavement Structural Condition Using Machine Learning Methods

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.

Determination Asphalt Layer using Ground Penetrating Radar (GPR)

Ground Penetrating Radar (GPR) is one of a new instrument in the field survey that has emerge along with the advancement of the world technology. The non-destructive technique (NDT) that was introduced along with the GPR instrument is able to be used in assessing the condition of pavement structure and condition. This data can be used to assess the underground features and thus detect any anomalies and buried features such as cable pipe. The current technique used to assess the pavement structure is very destructive and cost a lot of time to be conducted. This paper is aim to evaluate the performance of GPR in assessing the pavement layer with regards to JKR standard specification. The GPR will be equipped with two different types of frequencies which are 250 MHz and 700 MHz to determine the most suitable frequencies for pavement assessment. The obtain result will then be compared with the specification from JKR in order to evaluate whether the GPR is suitable or not. Based on the result, 700 MHz or higher frequencies are more suitable for pavement layer assessment as it can produce detailed and higher resolution of radargram. The comparison results also determined that the GPR can be used for pavement layer assessment as the measured thickness is within the JKR specification.

Impact of the Great Flood of 2016 on the Asphaltic Concrete Road Infrastructure in Louisiana

Extreme weather events, including inundation, are the major causes of weather-related disruption to the transportation sector. Considerable research has been conducted in recent years to evaluate the impacts of inundation on pavement functional and structural conditions. Yet, limited studies quantified how inundation may affect the long-term pavement performance and pavement life-cycle costs. As such, the main objective of this study was to quantify the impact of the 2016 inundation event in Louisiana, U.S., on the long-term pavement performance (in relation to reduction in the pavement service life [PSL]) and on pavement life-cycle costs. To achieve this objective, a total of 10 inundated pavement sections were evaluated. Pavement conditions, fatigue, and rutting indices for these sections were analyzed over a monitoring period of up to 15 years (11 years before and 4 years after inundation). Results indicated that the 2016 inundation event reduced the PSL of the inundated sections to an extent that depended on their pre-flooding pavement condition, traffic level, and asphalt concrete thickness. This reduction was attributed to the accelerated fatigue, rutting damage, or both, that resulted from prolonged water submergence and heavy traffic loading during debris hauling that took place after the inundation event. A logistic regression model with adequate accuracy was developed to predict whether an inundated pavement would experience reduction in its PSL based on the project conditions. The study findings were incorporated into a simple tool to assist transportation agencies make effective decisions for the maintenance and rehabilitation of inundated pavements.

Using mix design information for modelling of fundamental viscoelasticity of asphalt mixtures

Fundamental linear viscoelastic (LVE) characteristics of asphalt material is a critical component of pavement performance analysis and modelling. This study presents a practical and innovative approach to predict the complex modulus (E*) if terms of dynamic modulus (|E*|) and phase angle (δ) for asphalt mixtures by means of predicting the shape parameters of master curves using the advanced statistical analysis methods. Twenty distinct mixtures were evaluated and included in this study. The complex modulus test was performed on the mixtures and the |E*| and δ master curves were constructed. The statistical stepwise regression (SSR) model associated with F-test was employed to determine the significant factors and further develop the models for prediction of the shape parameters of these master curves. The prediction models developed only requires the general mixture design information that are readily available during any preliminary mixture design procedure, reducing the needs and efforts of preparing the samples and performing lab tests. The developed models show the good effectiveness and capability to predict the |E*| and δ values at any given frequency and temperature while incorporating the time–temperature superposition principle (TTSP). The developed models show the ability to be used for balanced mix design (BMD) as a predesign tool for evaluating mixtures’ cracking and rutting performance, and be incorporated into the advanced pavement performance simulation and prediction programs for predicting mixtures’ field performance in context of pavement structure, traffic and climatic conditions.

Road subsurface distress recognition method using multiattribute feature fusion with ground penetrating radar

ABSTRACT Ground penetrating radar (GPR) has been a widely used nondestructive testing method for pavement structure conditions. Random scattering occurs when GPR waves propagate, leading to complex reflected signals with a large amount of clutter from medium anomalies. The original GPR amplitude profile cannot fully display the physical parameters and geometric dimensions of the subsurface medium anomalies, which could lead to misjudgment and omission of the subsurface distress. To overcome these challenges, we obtain the instantaneous frequency, instantaneous phase, and instantaneous amplitude characteristics of the signal by the Hilbert time–frequency transform of GPR data. Then, a fusion framework was proposed to combine the original profiles with the abovementioned three features based on a two-dimensional wavelet transform. In addition, the information entropy (IE) and Laplacian operator gradient (LG) values are finally utilized to evaluate the effectiveness of the proposed fusion method. The results showed that instantaneous attributes of GPR can reinforce the information that might not be revealed in standard amplitude-based data visualization. The proposed method strengthens the contrast of the reflection and absorption features of the signal at the anomalies, which can be used in the automatic interpretation of GPR data to improve its discrimination ability in the future.

A Characterization Method for Pavement Structural Condition Assessment Based on the Distribution Parameter of the Vehicle Vibration Signal

A pavement structural survey plays a vital role in road maintenance and management. This study was intended to explore the feasibility of a non-stop pavement structure assessment method by analyzing the vibration data from a vehicle sensor. In this study, three falling weight deflectometer (FWD) tests and four vehicle vibration tests were conducted on five pavement structures. The FWD test results show that the continuously reinforced composite pavement has a higher structural stiffness than the semi-rigid base asphalt pavement. According to the statistical distribution of vehicle acceleration, a distribution parameter, the peak probability density (PPD), was proposed. The correlation coefficient (−0.722) of the center deflection (D1) and PPD indicates a strong correlation between the two variables. Therefore, PPD is strongly correlated with pavement structural stiffness. This study proposed a novel characterization method for pavement structural conditions based on the distribution parameter of the vehicle vibration signal.