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Under slippery conditions, the thickness of accumulated water is affected by uneven runways and changed by the fluctuation of the road surface, and the safety of aircraft take-off and landing is also affected. Therefore, the international flatness index (IRI) was introduced to build a tire-water-pavement finite element model based on the IRI. Through simulation, the influence of different IRI indexes on the critical hydroplaning speed of aircraft was analysed. Combined with IRI index, the reduction range of apparent friction coefficient of pavement with different tire speed and accumulated water thickness was compared, and the influence of IRI on pavement friction performance was analysed. According to the experimental results of apparent friction coefficient of an airport, the simulation results were modified to explore the influence of the IRI on aircraft braking effect. The results show that when the thickness of accumulated water in the pavement levelling section is constant, the critical water skiing speed of aircraft is reduced by the increase of IRI, and it is reduced by about 30% compared with the flat pavement when IRI = 5. When the speed of the aircraft tires is constant, the apparent friction coefficient of the pavement is decreased by the decrease of the level of the pavement smoothness. When IRI = 5, the apparent friction coefficient of the pavement is reduced by as much as 98% compared with the smooth pavement, which seriously affects the safety of the aircraft operation. When the road surface smoothness grade is “ good”, under the same thickness of accumulated water in the pavement levelling section, the apparent friction coefficient between the aircraft tire and the road surface is decreased by the increase of the IRI. Considering the influence of the IRI index and the apparent friction coefficient corrected by the test data on the braking effect of the aircraft, it is found that the braking effect of the aircraft will be degraded with the gradual increase of the flatness index and the thickness of accumulated water in the pavement levelling section. Under the conditions of a domestic airport pavement with 1. 5 < IRI≤2 and the thickness of accumulated water in the pavement levelling section≥7. 66 mm, the braking effect of the aircraft is reduced by a maximum of 2 grades, that is, from “ good to medium” to “medium to poor”, at this time the aircraft directional control and deceleration capabilities are decreased. When the flatness of the water surface is evaluated as “medium” or “ poor”, the degradation of the aircraft braking effect is increased suddenly, and the pavement surface conditions cannot be guaranteed the safety of aircraft operation. And when the thickness of accumulated water in the pavement levelling section is 7. 66 ~ 13 mm, the braking effect of the aircraft is greatly affected by the change of the IRI, and the degradation is changed obviously, so special attention should be paid during the maintenance process. [ABSTRACT FROM AUTHOR]

The as-built roughness, or smoothness obtained during pavement construction, plays an important role in road engineering since it serves as an indicator for both the level of service provided to users and the overall standard of construction quality. Being able to predict as-built roughness is therefore important for supporting pavement design and management decision making. An as-built IRI prediction model for asphalt overlays based on profile transformation was proposed in a previous study. The model, used as basis for this work, was developed for the case of wheeled pavers without automatic screed levelling. This study presents further development of the base prediction model, including the use of an automatic screed control system through a long-distance averaging mobile reference. Formulation of linear systems that constitute the model are presented for the case of a wheeled paver using contactless acoustic sensors set-up over a floating levelling beam attached to the paver. To calibrate the model, longitudinal profile data from the Long-Term Pavement Performance SPS-5 experiment was used, obtaining a mean error of 0.17 m/km for the predicted IRI. The results obtained demonstrate the potential of the proposed approach as a modelling alternative. [ABSTRACT FROM AUTHOR]

The road network is a major asset for every developing nation. Data collection, analysis, and arriving at economical maintenance work plans are said to be major goals for every road agency. Successful implementation of Pavement Management Systems can result in ease of decision-making about investments incurred for the maintenance of road networks. Advanced pavement maintenance strategies ensure the rational utilisation of limited funds scientifically. Based on existing pavement conditions, a systematic procedure is required to analyse the impacts of different maintenance strategies based on the predicted future pavement conditions. The present study attempts to develop a prioritised work programme for an identified non-core road network in India using the Highway Development and Management tool. Pavement distress data for non-core roads with a total length of 1743.9 km were collected using a Network Survey Vehicle and stored in a customised central database. Six maintenance strategies were formulated and predicted the International Roughness Index values, and they were compared with those obtained from the conventional maintenance strategy adopted by the state agency. Furthermore, an economic analysis was carried out, and the best strategy was proposed based on optimised costs. The predicted International Roughness Index (IRI) from the proposed strategy was found to be 37% lower than the predicted IRI using a conventionally adopted maintenance strategy. Such road asset management studies should be popularised by the state public works department for non-core roads to optimise the resources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Minimising opposing forces is a matter of interest to most cyclists. These forces arise from passage through air (‘drag’) and interaction with the road surface (‘resistance’). Recent work recognises that resistance forces arise not only from the deformation of the tyre (‘rolling resistance’) but also from irregularities in the road surface (‘roughness resistance’), which lead to power dissipation in the body of the rider through vibration. The latter effect may also have an adverse impact on human health. In this work we offer a quantitative theory of roughness resistance and vibration that links these effects to a surface characterisation in terms of the International Roughness Index (IRI). We show that the roughness resistance and the Vibration Dose Value (or VDV, the usual vibration dosage metric) can be expressed in terms of elementary formulae. The roughness resistance depends only on the vertical stiffness of the bicycle and the roughness index. Surprisingly, other apparently relevant parameters, such as physiological characteristics of the bicycle rider and other features of the bicycle, do not enter. For roads of moderate roughness, roughness resistance is larger than rolling resistance. For very rough roads, roughness resistance is larger than aerodynamic drag. So only on roads of high quality (in most jurisdictions, accounting for less than 10% of the total) can roughness resistance be ignored. Roughness resistance can be mitigated by reducing the vertical stiffness of the bicycle. In common with other recent reports, we find that almost any cycling activity will breach public health guidelines relating to Vibration Dose Value. [ABSTRACT FROM AUTHOR]

Probabilistic pavement models, with Markov chains as the most widely used type, are considered to capture an accurate representation of the in situ pavement performance. Homogeneous Markov chain models present the same transition probability matrix (TPM) for all the transitions of the period and require data from multiple duty cycles of one or two years. The aim of this paper is to explore the feasibility of developing homogeneous Markov chain models with variations of the duty cycle (in increments of either one or two years). Without considering maintenance and rehabilitation works, this research found that TPMs for a one-year duty cycle can be calculated from the two-year duty cycle, without a noticeable effect on accuracy using International Roughness Index (IRI) values from the Spanish State Road Network. However, for developing coherent TPMs, two primary assumptions were made: (1) heavy vehicle traffic volumes determine the traffic category (TC), and (2) only roads from the same climatic region were modelled. The satisfactory results verified the validity of the methodology and overcame the disadvantages of homogeneous Markov models. Furthermore, the results suggest that pavement sections are adequately designed in Spain for each TC because of the similar deterioration patterns. [ABSTRACT FROM AUTHOR]

Developing an accurate pavement prediction model plays a dominant role in pavement M&R optimization. Despite employing different robust machine learning techniques to predict pavement conditions, these methods have some weaknesses in synchronising with exact optimization algorithms. The main contribution of this study is to propose a novel method for optimizing the pavement M&R plan with high accuracy. Contrary to conventional approaches, a robust prediction algorithm, Random Forest Regression (RFR), is applied to predict the pavement International Roughness Index (IRI). In addition, Multiple Linear Regression (MLR) is employed to assess the performance of the proposed technique in terms of IRI prediction accuracy. Whale Optimization Algorithm (WOA), as a powerful metaheuristic optimization algorithm, is utilised to obtain the optimal solution to the pavement M&R optimization problem. RFR is run as an internal part of the WOA in the introduced method. Furthermore, Genetic Algorithm (GA) is used to examine the performance of the proposed approach in finding the optimal solution. The RFR results conclude a more accurate prediction of IRI than MLR based on all machine learning performance indicators. Furthermore, the newly developed hybrid model significantly outperforms GA in finding the optimal and cost-effective solution to the M&R optimization problem. [ABSTRACT FROM AUTHOR]

The study proposes a Kalman Filter Family-schemed algorithm to identify the vehicle’s parameters and evaluate the road roughness simultaneously, based on the response of an adapted monitoring vehicle. The vehicle is simplified as a three-dimensional multi-body system consisting of the unsprung mass of wheels and the sprung mass of the car body, connected by springs and dampers that represent the suspension system. Accelerometers and a gyroscope measure the accelerations or angular velocity of different positions of the vehicle during vibrations. The Kalman filter (KF) with unknown inputs enables the prediction of the car body’s accelerations from those of wheels. On the other hand, the Augmented KF can compute the accelerations of wheels based on the car body’s accelerations. Thus, an inner-systemic objective function incorporating the vehicle’s parameter is built between the predicted and measured accelerations and solved in an optimized manner with the Genetic Algorithm, so that an individual complicated impact test or bump test is avoided. The numerical examples carry out field tests on a 900m long standardized test road under two scenarios of with and without a bump. The road roughness estimated by these two KF methods from different measurements is used to calculate the International Roughness Index (IRI) and further the Riding Quality Index, which are compared with those directly provided by a standardized laser IRI profilometer. Accordance in between confirms the accuracy of the proposed algorithm under different scenarios. The analysis underlines the measurements from unsprung wheels that contact directly with the road enable a more accurate estimation of road roughness than those from the sprung car body. In addition, a vehicle speed lower than 40km/h can provide a better estimation of the road profiles. [ABSTRACT FROM AUTHOR]

Studies have been initiated to investigate the potential impact of connected and automated vehicles (CAVs) on transportation infrastructure. However, most existing research only focuses on the wandering patterns of CAVs. To bridge this gap, an apple-to-apple comparison is first performed to systematically reveal the behavioural differences between the human-driven vehicle (HDV) and CAV trajectory patterns for the first time, with the data collected from the camera-based next generation simulation dataset and autonomous driving co-simulation platform, CARLA and SUMO, respectively. A gradient boosting-based ensemble learning model for pavement performance (i.e. international roughness index) prediction is then developed with the input features including three driving pattern features, namely, lateral wandering deviation, longitudinal car-following distance and driving speed, plus 20 other context variables. A total of 1707 observations is extracted from the long-term pavement performance database for model training purposes. The result indicates that the trained model can accurately predict pavement deterioration and that CAV deteriorates pavement faster than HDV by 8.1% on average. According to the sensitivity analysis, CAV deployment will create a greater impact on the younger pavements, and the rate of pavement deterioration is found to be stable under light traffic, whereas it will increase under congested traffic. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'. [ABSTRACT FROM AUTHOR]

When choosing the pavement maintenance scheme, the actual pavement condition cannot be truthfully reflected by traditional fixed indexes and weights, making accurately evaluating the pavement condition particularly important. This study aims to analyze the relationship between international roughness index (IRI) and pavement condition index (PCI), and propose the IRI-based pavement condition rating to improve the correctness of the maintenance decision. Based on the inspection data and maintenance data of large and medium-sized repair and maintenance projects in Shaanxi Province, representative road sections in northern Shaanxi, The Central Shaanxi Plain, and southern Shaanxi were selected to establish a simple model to examine the relationship between PCI and IRI. The model development data were selected from 312 different road sections, including 1665 data points. The model validation data were selected from 140 road sections, including 333 data points. The road sections selected for validation data and those for development data had the same data range. Results demonstrate that, the S-function could best express the relationship between PCI and IRI. The function established by data fitting had a high coefficient of determination (R2=0.959), and the deviation of the predicted IRI value was low. The model validation of different datasets all yielded relatively more accurate predictions (R2=0.973). Finally, the IRI-based pavement condition rating was proposed, and the pavement condition was divided into five grades, namely, excellent, good, fair, poor, and very poor, by using IRI detection data. This proposed rating provides double verification of the pavement condition with PCI rating and improves the correctness of the maintenance decision. [ABSTRACT FROM AUTHOR]

Roadway infrastructure management focuses on quality of the road surfaces which influences the pavement longevity and riding quality. The road surface quality can be expressed in many ways from which the International Roughness Index has been recognized widely around the developed countries. This paper summarizes the derivation of International Roughness calculation and proposes a new numerical method for its computation. Compared to original Sayers's method, it does not use iterative approximation, which makes it much faster for non-uniformly sampled road data. This is useful, for example, for profiles generated from LIDAR point clouds. The method can be used for arbitrary polynomial model of segments between elevation samples. Except the Fortran code listed in the original paper, the code for the original algorithm has not been publicly available and most researchers relied on the ProVAL software with several limitations, including uniform sampling, the lack of automation, and little control over the influence of resampling methods and the initialization of the quarter-car simulation procedure. We provide Matlab codes for both the original method and the algorithm newly proposed in this paper. [ABSTRACT FROM AUTHOR]

Stone pavements are present in many cities and their historical and cultural importance is well recognized. However, there are no standard monitoring methods for this type of pavement that allow road managers to define appropriate maintenance strategies. In this study, a novel method is proposed in order to monitor the road surface conditions of stone pavements in a quick and easy way. Field tests were carried out in an Italian historic center using accelerometer sensors mounted on both a car and a bicycle. A post-processing phase of that data defined the comfort perception of the road users in terms of the awz index, as described in the ISO 2631 standard. The results derived from the dynamic surveys were also compared with the corresponding values of typical pavement indicators such as the International Roughness Index (IRI) and the Pavement Condition Index (PCI), measured only on a limited portion of the urban road network. The network's implementation in a Geographic Information System (GIS) represents the surveys' results in a graphical database. The specifications of the adopted method require that the network is divided into homogeneous sections, useful for measurement campaign planning, and adopted for the GIS' outputs representation. The comparisons between IRI-awz (R2 = 0.74) and PCI-awz (R2 = 0.96) confirmed that the proposed method can be used reliably to assess the stone pavement conditions on the whole urban road network. [ABSTRACT FROM AUTHOR]

One of the key objectives of a pavement management system (PMS) is to find the optimal maintenance strategy, which minimises the sum of a road agency and user costs by maximising the net benefit to society. The PMS helps road agencies to better apply a selection of the right pavement treatments. The objective of the present paper is to show how the HDM-4 (Highway Development and Management 4) approach allows to develop a policy road leading to the optimal and robust selection of maintenance strategies under budgetary constraints, which minimises the sum of agency costs and road user costs in present value or maximises the net benefit to society over an analysis period. The findings could help road agencies to better apply the right pavement treatments. The present study is based on the exploitation of road network data, obtained from pavement structural condition survey and performed by the National Studies and Road Research Center. The study applies a long-term simulation (20 years) of maintenance budget needs under different budget scenarios. [ABSTRACT FROM AUTHOR]

The IRI International Regularity Index is a performance indicator that evaluates the functional condition of a pavement structure. Its value is a key input for the management of road assets, allowing to establish the opportune moment for carrying out interventions on the pavement. In addition, it is used to receive road surfaces, assess vehicle operating costs, evaluate the profitability of road projects and establish the cash flow in the financial administration of the project. The IRI data obtained from measurements carried out in the field, feed the deterministic deterioration model that allows future estimations of the indicator and the development of pavement maintenance programs. This research proposes to evaluate in a probabilistic way the model of the IRI International Regularity Index of the HDM-4 program, by assigning probability density functions to the input variables from real data taken in the field. To achieve this objective, a Montecarlo-type simulation model was developed, where roads must be classified by their geographical location, structural capacity of the pavement and traffic intensity expressed in Number of Equivalent Axes. The research results provide the IRI characterized by probability density functions, allowing its estimation from an expected reliability value. [ABSTRACT FROM AUTHOR]

Measuring pavement roughness is one of the most important activities needed in any Pavement Management System; moreover, pavement roughness plays an important role for determining riding comfort on the road network. Today's smartphones have very small sensors with high ability to monitor movements and vibrations in all directions and collect thousands of data points in a short period. This increases the potential of using it as a tool for pavement condition estimation. This method will help to save a reasonable amount of money compared to high-cost roughness measurement devices. Also, due to the limited resources of transportation agencies in developing countries, this method could help to expand the pavement roughness collection survey. This study presents a cost-effective pavement roughness evaluation procedure in terms of the International Roughness Index (IRI) using the smartphone for street pavement in urban areas. Different signal processing techniques were used to filter the collected acceleration data, including Butterworth bandpass filter, moving average filter, and baseline correction filter. The effect of vehicle speed, smartphone mounting position, acceleration collection sampling rate, and integration method were studied to determine the best factors that could be used to estimate IRI accurately. It was found that the proposed method could estimate IRI with very good accuracy (r² = 0.72) at a sampling rate of 200 samples/s and with vent mount type and using the double integration method. Also, the results proved that the increase of vehicle speed increased the root mean square and variance of vertical acceleration values and it could be minimized by applying the speed normalization technique. [ABSTRACT FROM AUTHOR]

Pavement roughness monitoring has been a concern in road asset management. Nevertheless, the adoption of laser profilometers for measuring pavement surface roughness poses challenges due to its high cost and inconvenience. The International Roughness Index (IRI) has been widely deployed to evaluate pavement surface roughness. Hence, there is a necessity to create a cost-efficient and simplified method for estimating IRI to assist pavement maintenance management. This study has developed a cost-effective portable roughness measurement system primarily leveraging a low-cost three-axis accelerometer and a Global Positioning System (GPS) sensor. These components are integrated with an Arduino board and installed within a vehicle to monitor road conditions effectively. Before training the model with collected data, preprocessing techniques like Kalman fusion and Fourier transform were applied to reduce errors and expand input variables. Subsequently, a Random Forest Regression (RFR) model is established to predict the IRI using optimized pavement response data. The results shows that the proposed RFR model surpasses other reference models in terms of pavement roughness estimation accuracy, exhibiting a coefficient of determination (R 2) of 0.967 and a mean square error (MSE) of 0.036. Its low cost and user-friendly nature hold promising implications for supporting decision-making in pavement maintenance within road asset management. • Develop a portable roughness measurement device based on a quarter-car model. • Integrate pavement response data with feature analysis using advanced algorithms. • Construct a machine learning model with optimized features for predicting roughness. [ABSTRACT FROM AUTHOR]

Road roughness is considered a primary indicator of pavement condition and serviceability, and the performance of paved roads is linked to road roughness. The focus of this study is to develop a relationship between two important roughness indicators, namely the international roughness index (IRI) and slope variance (SV), based on actual road roughness data to achieve a suitable correlation between these two indices using artificial neural networks (ANNa) and gene expression programming (GEP) techniques. Different study areas were selected to develop the prediction model. The first study area is the Desert Highway in Jordan, while the three remaining study areas are located in the US. A total of 533 data sets were used in this study to develop a model to predict the IRI from the SV. The GeneXproTools 5 software package was used to build the GEP model, while MATLAB 2019 was employed to develop the ANN model. The results showed that the GEP and ANN models outperformed all other previous models. The GEP-Based model showed a better performance and more precise results than the ANN model according to the coefficient of determination (R²). [ABSTRACT FROM AUTHOR]

Road pavements continuously deteriorate mainly due to the combined influence of traffic load and environmental conditions. The pavement ability to satisfy the road user demands along its designed time of service expresses its performance level. The prediction of performance deterioration curves is a basic part of any Pavement Management System (PMS). Road roughness is an important parameter in determining the performance of pavements. The International Roughness Index (IRI) is the universal standard for measuring the pavement roughness and it has become the most widely employed pavement index. This paper presents the development of a model for predicting the deterioration of the IRI and calibrating its parameters based on the pavement structural factors such as structural number, asphalt layer thickness, subgrade strength, and environmental conditions. The approach adopted for the IRI deterioration model development was based on the combination of the empirical-mechanistic approach and the regressive empirical approach. The predicted results were compared with the measurements of road sections located in various climate zones, which are embedded in the PMS of Netivei Israel (NETI), National Company for Transport Infrastructures. The comparison shows a very good correlation, and most of the predicted results are within the measurement and interpretation error range. [ABSTRACT FROM AUTHOR]

International Roughness Index (IRI) is a key parameter in pavement performance evaluation. This study investigates developing a reliable prediction model that can be used to estimate IRI of rigid pavements using innovative machine learning techniques. Optimally Pruned Extreme Learning Machine (OP-ELM) and Wavelet analysis are integrated to improve the OP-ELM results and design a novel hybrid Wavelet-OPELM (WOPELM) model for the IRI prediction. The proposed model is compared statistically to the OP-ELM and conventional feed-forward Artificial Neural Network (ANN) as well as regression model with respect to their efficiency to predict IRI of jointed plain concrete pavement (JPCP) sections in USA. The relevant data was collected from the Long-Term Pavement Performance (LTPP) database. Eight input variables, initial IRI, pavement age, transverse cracks, percent joints spalled, flexible and rigid patching areas, total joint faulting, freeze index, and percent subgrade passing No. 200 U.S. sieve, are assessed and used to predict the IRI. The results show that the initial IRI, total joint faulting, and freezing index are the most significant parameters for IRI prediction. The WOPELM is found to be a robust and more accurate modelling technique compared to OP-ELM, ANN, and regression for IRI prediction with only a 7% prediction error. [ABSTRACT FROM AUTHOR]

The use of concrete overlays has long been recognised as a cost-effective pavement maintenance and rehabilitation strategy. However, the long-term performance of various types of concrete overlays has not been fully investigated since there has not been enough performance data available to conduct such an evaluation. Concrete overlays have been regularly constructed on Iowa roadways since the late 1970s and many older projects are still in use. Performance-related data for in-service concrete overlays have been acquired from the available resources to evaluate long-term performance of concrete overlays in Iowa. The information collected includes Pavement Condition Index (PCI), International Roughness Index (IRI), overlay type, construction year, overlay thickness, joint spacing, traffic, and other construction and design-related data. Based on an evaluation of PCI and IRI changes during service life, it is observed that concrete overlays can provide at least 20 years of service life. In terms of PCI ratings, 89% of concrete overlays investigated have PCI values greater than 60% as of the time of the analysis. Similarly, 93% of concrete overlays have IRI values lower than 2.7 m/km (170 in/mile). The effects of overlays type and design features on long-term performance of Iowa concrete overlays are also discussed. [ABSTRACT FROM AUTHOR]

Nowadays, probabilistic prediction models commonly represented by Markov Chain Process (MCP) attract more attention in pavement management systems. Additionally, roughness condition indices provide a broad judgment. One element that contributes the most to the roughness progression is the initial roughness of pavements which has not been considered in the prediction models developed by MCP. On the other hand, the prediction results of MCP, which address the whole pavement network as an average, are inconvenient to be deployed in decision-making programmes. This paper utilised MCP to forecast pavement roughness regarding its initial value. International Roughness Index (IRI), extracted from the long-term pavement performance (LTPP) database, was selected to be analyzed. Based on M&R history, four major families, including 1770 pavement sections in total, were introduced. The prediction process of MCP was modified led to the direct prediction of IRI values instead of the deteriorated portion of the pavement sections. A framework, which had derived from the concept of Master Curves, is proposed to pave the way for the optimisation programmes to be applied to the MCP results. The Root Mean Squared Errors (RMSE) of the composed master curves were significantly low; the average RMSE was 0.00675. [ABSTRACT FROM AUTHOR]

Quantification of the pavement serviceability is most often based on the international roughness index (IRI). However, in urban environments it can become challenging to drive at 80 km/h as vehicle operating speeds are much lower, among other limitations associated with the use of IRI as an indicator of ride quality in the urban context. This study was conducted to formulate an alternative pavement serviceability evaluation criteria for low-speed roads (30–60 km/h) in urban settings. Deterministic and probabilistic models were developed to predict the PSI based on the whole-body vibration (WBV) concepts, and thereafter, the results were compared with the ISO 2631 standard that addresses human exposure to multiple mechanical shocks. The ISO 2631 standard uses a WBV-based frequency-weighted root-mean-square acceleration parameter (aw) for evaluating discomfort in a multi-axis environment. Based on the estimated models, an awz criterion of 0.98 m/s2 at a probability acceptance of 85%, for a vehicle operating speed of 50 km/h, was proposed for urban roads in this study. Overall, the study demonstrated that for accurate estimation of ride quality and comfort (i.e. PSI) of low-speed urban roads, the evaluation criteria should be based on low vehicle speeds that are more representative of urban field conditions. [ABSTRACT FROM AUTHOR]

International Roughness Index (IRI) and Pavement Condition Index (PCI) are among other pavement condition indices used to assess pavement surface condition. The literature suggests that most of the pavement indices are related as a result of which several models have been developed to predict one index from the other. This study uses the Long-Term Pavement Performance (LTPP) database to develop a simplified regression model that links PCI with IRI. Measured pavement distresses from 1448 LTPP sections from the Specific Pavement Studies (SPS) and General Pavement Studies (GPS) representing 12744 data points were utilised for the PCI estimation. A total of 1208 sections with 10868 data points were used for model development while 240 sections with 1876 data points were used for the model validation. A sigmoid function is found to best express the relationship between PCI and IRI with a coefficient of determination (R2) of 0.995. The bias in the predicted IRI values is significantly very low. The model validation using a different dataset also yielded highly accurate predictions (R2 = 0.992). Finally, a pavement condition rating based on IRI is proposed. This system yields rating equivalent to the widely used PCI rating method which is based on the pavement condition. [ABSTRACT FROM AUTHOR]

We develop a framework to address the shortcomings of current smartphone-based approaches for road roughness sensing and monitoring through combining vehicle dynamics, random vibration theory and a two-layer inverse analysis. The proposed approach uses in-cabin recordings of the vehicle's vertical acceleration measured by a smartphone positioned inside the car for the estimation of road roughness. The mechanistic road roughness–vehicle interaction model at the core of the proposed framework links the frequency spectrum of the vehicle's vertical acceleration to the road roughness power spectral density and lends itself to the quantitative characterization of roughness-induced energy dissipation. We demonstrate that the measure of roughness provided by the stochastic model of car dynamics interacting with a rough road is fully compatible, in a statistical sense, with the spatial but deterministic definition of road roughness, and validate the identification strategy that originates from it against laser measurements of road roughness. The critical crowdsourcing features of the proposed framework, such as the marginal impact of phone position and transferability, are examined and its utility to meld with big data analytics to identify the class of vehicles travelling on a roadway network is demonstrated. [ABSTRACT FROM AUTHOR]

• Introduced improved Extreme Gradient Boosting Tree for predicting rigid pavements performance. • Identified all variables related to rigid pavements performance. • Faulting is the most important factor affecting rigid pavement performance. The International Roughness Index (IRI) is a crucial indicator for evaluating pavement performance. However, existing prediction models for IRI in rigid pavements primarily rely on linear regression or simple machine learning techniques, which necessitates improvements in both training efficiency and interpretability. In addressing this issue, a proposed IRI prediction model combines the Extreme Gradient Boosting (XGBoost) method with advanced hyperparameter optimization utilizing the Tree-structured Parzen Estimator (TPE) technique. Furthermore, the model incorporates the Shapley Additive Explanations (SHAP) interpretability framework. To train and validate the model, 146 historical observation records were extracted from the Long-Term Pavement Performance database. The Boruta algorithm was employed to select the final input variables. Results demonstrated that after TPE hyperparameter optimization, the XGBoost model achieved an R2 value of 0.896, and an RMSE value of 0.187. Surpassing performance compared to other ensemble learning models. The Boruta algorithm successfully identified all significant variables, thereby improving the model's accuracy on the testing set. Additionally, the SHAP framework confirmed the primary factors influencing IRI and provided explanations for the model's results at both global and local levels. These findings highlight the ability of the proposed model to accurately predict IRI, providing precise guidance for pavement maintenance while reducing the workload associated with data collection and management for pavement engineers. [ABSTRACT FROM AUTHOR]

With cold in-place recycled (CIR) pavements becoming an increasingly popular treatment for pavement life extension, it is important for local agencies to have data that justifies the design parameters that influence the effectiveness of such treatments. Studies have shown that the effectiveness of CIR depends on its milling depth, and in order for agencies to better decide the pavement design, depth of CIR to be milled and placed, the authors have developed a procedure to back-calculate the structural layer coefficient of CIR layers based on performance data, in order to evaluate their structural capacity. This paper attempts to establish a relation between the structural capacity of the CIR layer and the international roughness index (IRI) of the pavement over time, using the results of previous research determining a definite differential between the IRI of pavements with different CIR thicknesses. [ABSTRACT FROM AUTHOR]

This article focused on quantifying environmental impact of asphalt pavement preservation at construction and use stage using life-cycle assessment (LCA) approach. Preservation treatments considered in the analysis include thin overlay, chip seal, and crack seal that are typically used by highway agencies. At construction stage, there are significant differences in emissions caused by various preservation treatments mainly due to different raw material components and manufacturing processes. At use stage, fuel consumptions vary significantly depending on tire rolling resistance that is affected by pavement surface characteristics. The Carbon Dioxide (CO2) emissions at the use stage were predicted using the Motor Vehicle Emission Simulator (MOVES) and pavement roughness models obtained from Long-Term Pavement Performance (LTPP) program. The results show that pavement preservation brings significant environmental benefit in reduction of CO2 emission due to the improved pavement surface condition despite the emission generated at construction stage. Thin overlay produces the highest life-cycle reduction in CO2 emission due to the large International Roughness Index (IRI) jump after treatment; while crack seal has the lowest reduction of CO2 emission. The optimum application timing of preservation treatment was determined for chip seal and thin overlay assuming one-time treatment applied before reaching the IRI threshold. [ABSTRACT FROM AUTHOR]

Pavement data collection is the most expensive and time consuming component of Pavement Management System (PMS). Thus, possible methods of minimizing the need of such data might be critical in reducing pavement condition monitoring costs. Also the ability to relate pavement performance prediction models (frequently roughness based) to hot mix asphalt field performance models (distress based) provides valuable conclusions and input in pavement design, performance assessment, maintenance and rehabilitation strategies. Objective of this study was to examine whether specific distresses can influence roadway profile so as to be able to relate the two. The influence of pavement distresses on road profile has been investigated over the years. However, past studies provided conflicting conclusions. Thus, in this study an alternative approach was considered due to the availability of high quality and detailed distress data collected with the Laser Crack Measurement System (LCMS) of the Automatic Road Analyzer, ARAN. As it was expected, specific distresses have higher impact in longitudinal roughness since they are present on the roadway surface at regular intervals (i.e., specific frequencies). For this reason, instead of using summary indexes (i.e., International Roughness Index (IRI), Pavement Condition Index (PCI)), the Power Spectral Density (PSD) of the roadway profile at specific frequency bandwidths was considered along with distresses. The analysis indicated that a specific subset of distresses is affecting roughness at definite wavelength frequencies. Alligator cracking and rutting standard deviation provided the best correlation. IRI was correlated better with distress (e.g. rutting standard deviation) at lower profile frequencies. At high frequency domain (i.e., below 0.8 m wavelengths) better correlation between IRI and high severity cracking was observed through the PSD. Considering multiple frequencies in the regression models between roughness and distresses, the goodness of fit has not necessarily improved. However, the role of different bandwidths was evident. In addition to the specific results, the methodology presented in this study can be used elsewhere to assess potential relations between pavement roughness and distress components. [ABSTRACT FROM AUTHOR]

Data collection of the pavement condition is the main core of the pavement management systems. The automated data collection method has some advantages over the traditional one such as safety, consistency, speed, and repeatability. However, one of the main disadvantages of the automated data collection is the high cost of required devices. It is necessary to present an innovative cost-effective automated data collection system with adequate accuracy and precision. Pavement roughness is one of the most important parameters showing the pavement condition and directly affecting the ride quality. The main aim of this research is to develop a new automated cost-effective data collection device for measuring roughness. An extra wheel equipped with accelerometers and a geographical positioning system are utilized for evaluating pavement roughness. Longitudinal profiles are employed to calculate the International Roughness Index through the application of a linear model with a high coefficient of determination of 0.87. The root-mean-square error and the average error percentage are sufficiently low, equal 10% and 20%, respectively. • Development of an inexpensive roughness monitoring system via a vehicle fifth wheel • Measurement of the International Roughness Index (IRI) using the developed system • Indication of the system dynamic parameters using soft computing methods: PSO and GA • Application of DSP to filtrate acceleration signals to obtain longitudinal profiles [ABSTRACT FROM AUTHOR]

Pavement management systems employ pavement performance models to plan the most effective allocation of public funds. The most used models are the deterministic and the probabilistic or stochastic ones. Among stochastic models, the Markov Chains are receiving considerable research attention. In this paper, transition probability matrices (TPMs) are presented for Portland Cement Concrete (PCC) pavement road network in the Republic of Moldova with available International Roughness Index (IRI) data collected in spring and in autumn from 2013 to 2015. Although one-year cycle time is usually employed, half-year cycle time was defined to handle a bigger amount of transitions. The aim of this paper is to prove that TPMs for rigid pavements can be developed using IRI values from a short period, with a half-year step time, for a road network where the time since last major rehabilitation varies in each section and where maintenance and rehabilitation activities are carried out simultaneously. In order to consider all these aspects, some assumptions must be made. Roughness seasonal variations can arise in PCC pavements due to the effect of curl and warp. Hence, a threshold of 0.40 m/km was defined as the maximum improvement on a section after half-year step for not being considered as maintenance work or spurious data. When the IRI value improves more than 4 m/km after one step, a rehabilitation activity is considered. That rehabilitation is not used in the matrix calculation, but the subsequent deterioration contributes. Results demonstrate that rigid road sections can jump two condition states in one transition, instead of only remaining in the same condition state or shifting to the next one. Obtained matrices give an overview of deterioration tendencies of rigid pavements, and they fit better to real data if a large proportion of no rehabilitated or maintained sections are compared to predicted values. [ABSTRACT FROM AUTHOR]

In northern regions, the frost heave of the subgrade soils due to formation of ice lens is the main mechanism involved in the high degradation rate of the flexible pavement. This paper presents developments of flexible pavement damage models, developed through a multiple linear regression analysis, associated long-term roughness performance to frost heave and degradation mechanisms. Actually, there is no deterioration model that establishes a link between frost heave and flexible pavement. At a design stage, those models would be essential to evaluate the benefits or consequences to have a frost heave lower, equal or higher than the allowable threshold values established by the MTMDET according to the roads functional classification. The result presented illustrate that a significant increase in long-term IRI deterioration rate, usually caused by a more variable subgrade soil, is likely to contribute to the rehabilitation of the pavements up to four years before the end of the pavement service life. This project will allow the administration and the builders to adapt the construction of road infrastructures in cold regions in order to achieve the objectives established to maintain the safety of the users. [ABSTRACT FROM AUTHOR]

The increasing pressure on air transportation infrastructures and the unpredictability of the weather conditions are factors that accelerate the deterioration of airport pavements, making them more susceptible to unacceptable/dangerous degradation levels. A crucial tool such as a pavement management system is essential to assist managers in the decision-making process of the most adequate preventive and cost-effective solutions to perform maintenance and rehabilitation at the right time and in the right location. These actions needed more than an general structural behaviour analysis to be set, once is many times a functional distress that drives to a sooner maintenance intervention than the one indicated by lack of structural resistance of the pavement. A study on the evolution trends for two parameters that characterise the pavement condition at a functional level (and indirectly also, in part, at a structural level) is presented herein. These parameters are the pavement condition index (PCI) and the international roughness index (IRI). Data from two Portuguese international airports were analysed between 2011 and 2013. The adjusted coefficients of determination for the regression models ranged between 50 and 90% for the PCI and were lower than 15% for the IRI. These results indicate that the regression models obtained for PCI can be used to signalise the need of action on the pavement, whereas it was not possible to identify a useful trend with IRI. [ABSTRACT FROM AUTHOR]

Nine asphalt concrete overlays were prepared using three different mix designs: Type C, 12.5 mm Superpave, and Coarse Matrix High Binder Hot-Mix (CMHB-C) mixtures and three different coarse aggregate: siliceous gravel, quartzite and sandstone. Pavement overlays were placed on field sections constructed along IH-20 in Harrison County, Texas. Field sections included each of the nine different surface mixture types. The base course was the same for all surface mixtures and was designed with 90% limestone and 10% local field sand. For all mixtures including the base course, PG 76-22 binder was used. Three pavement condition surveys were conducted on the outside lanes of eastbound and westbound field sections; immediately after, after two years, and after three years of the construction of asphalt concrete pavement system. IRI values were estimated from the left and right wheel paths. Each data-set was analysed separately to compare the pavement smoothness of different asphalt concrete overlays. Section 3 (Superpave quartzite mix) in the westbound lanes and Section 4 (CMHB-C gravel mix) in the eastbound lanes showed the best ride qualities. A paired t-test was conducted for each section in order to assess changes in IRI values with time under real traffic conditions. Test results and statistical analyses indicated that except for the IH-20 westbound left lanes of field Section 3 (i.e. Superpave quartz mix) the IRI values of the asphalt concrete overlays were time stable over the research period of three years. [ABSTRACT FROM AUTHOR]

Concrete overlays provide cost-effective maintenance and rehabilitation strategies for pavement systems. A database has been developed in Iowa that records the historical performance of overlays based on records of International Roughness Index (IRI) and Pavement Condition Index (PCI) over a 20-year period. Based on these data concrete overlay service life has been modeled for various joint spacings. The data demonstrate t hat durability and service life can be improved. A review of PCI data indicates that improving construction quality to eliminate premature failure has the potential to add at least 10 years to the service life of PCC overlays. Even though concrete overlay technology is not a new concept, most of its design procedures still follow empirical methods, therefore this study applied AASHTOWare Pavement ME Design (Version 2.3.1) software to identify the effects of desig n para ms on concrete overlay service life. The theoretical insights provided by Pavement ME Design were compared with historical performance data and used to provide recommendations with respect to optimized joint spacing in overlay pavement structures. Comparison of the historical performa nce-related data with Pavement ME Design software results indicates that the Pavement ME Design software is conservative in predicting concrete overlay service life. [ABSTRACT FROM AUTHOR]

Research studies often propose additives to improve the mechanical properties that extend pavement service life by reducing the rate of permanent deformation of conventional asphalt mixtures. However, questions remain regarding the consistency and efficacy of additives compared to the conventional mixtures, particularly under different conditions of loading and climate inherent to various countries and locations. To shed further light, this paper presents a laboratory and field evaluation of Marshall and Superpave Gyratory-compacted crumb rubber-modified and traditional asphalt concrete mixtures. A laboratory-based study was conducted to ascertain the resilient modulus, indirect tensile strength, and permanent deformation of crumb rubber-modified asphalt mixtures. The Marshall stability and resilient modulus of mixtures prepared with crumb rubber-modified mixtures were increased on average by 30 and 43%, respectively, as compared to the control mixtures, whereas the permanent deformation of the crumb rubber-modified mixtures was improved on average by 12% in contrast to the control mixtures. Field investigations on short-term effectiveness revealed crumb rubber-modified asphalt mixture treatment exhibited on average 36.16% drop in International Roughness Index. In contrast, the control mixture pavement showed 24.20% drop in International Roughness Index. The results indicate and quantify the extent to which crumb rubber improves the performance of the conventional mixtures, in terms of the performance criteria specified. [ABSTRACT FROM AUTHOR]

This paper evaluates the lab and in situ mechanical properties of non-selected mixed recycled aggregates from construction and demolition waste (CDW) used as base and subbase unbound materials. Excavation materials are mixed with CDW to produce recycled mixed aggregates with soil, as well as a finer material referred to as mixed recycled soil. The research was divided into two different stages: a laboratory study characterizing the properties of recycling aggregates and a road test track evaluating the long-term performance of these materials under real traffic and weather conditions. During construction, several density, plate load, and falling weight deflectometer tests were performed to determine the bearing capacity of all layers. A laser profiler was also used to obtained the international roughness index. After the road was opened to traffic, a follow up of deflections and surface roughness was performed during the following seven years. Two different moduli calculation methods were used: back calculation and forward calculation. Both methods shown acceptable values for these recycled materials. Low quality recycled mixed aggregates can be used as substitutes for natural aggregates as unbound layers. The mechanical performance and surface roughness values obtained from the experimental road shown an acceptable behaviour. [ABSTRACT FROM AUTHOR]

Due to imperfect surface profiles, heavy vehicles moving at high speed on flexible pavement structures oscillate in the vertical axis. This phenomenon induces dynamic loads, which oscillate at lower and higher values than the average load associated with static load considered with most pavement analysis and design applications. Higher loads applied to flexible pavements are likely to significantly reduce pavement service life. A new multibody dynamic truck model was used to study heavy vehicle wheel load for various pavement profiles of varying international roughness index (IRI). The modelled heavy vehicle wheel load response were used to calculate the dynamic load coefficient, and a relationship withIRIwas proposed. On the basis of this relationship, the evolving pavement surface profile, and thus evolvingIRI, was used to determine the evolution of dynamic loading with pavement life. A comparison of pavement service life for the classical static loading and for dynamic loading was made for three highway flexible pavement structures. When dynamic loads are considered, it was found that the pavement service life reduction may be reduced of about 29 and 20% for bottom-up fatigue cracking and structural rutting failure criteria. [ABSTRACT FROM AUTHOR]

• Organizing a panel data matrix from Pavement Management System in Guizhou Province. • Forming the method of establishing International Roughness Index prediction model. • Random-effects model of semi-rigid asphalt pavement in subtropical monsoon climate. • Considering the combined effects among age, climate, pavement structure and loads. • High accuracy and good robustness to the disturbances of region and climate. Pavement performance prediction is the crucial basis for decision-making of Pavement Management Systems (PMS). Pavement roughness is not only related to safety and comfort but also a vital index to evaluate highway performance. Therefore, it is of great significance to develop a prediction model for pavement roughness. The International Roughness Index (IRI) is the most commonly used index to represent pavement roughness in the worldwide. Many deterministic models based on time series data have been developed, but there are still few researches based on panel data to study IRI. This paper aims to investigate the rich information of panel data and develop a prediction model of IRI for semi-rigid asphalt pavements in subtropical monsoon climate. With the data from the PMS of Guizhou Province, the data matrix was built from 2017 to 2020. For the independent variables, the internal and external factors affecting IRI were considered, including climate, road age, traffic, and pavement structure. The random-effects model was employed for panel data analysis and the obtained model yielded a coefficient of determination (R2) value of 0.8858. This model can be applied to the highways which are designed, constructed, and qualified under the same standard and are located in the subtropical monsoon climate region. The predicted values in 2021 were compared with the measured values of highways in Guizhou Province. The average error is 12.7%. It shows that the model has better prediction accuracy and generalization performance with good robustness to the interference of geographic region and climate. [ABSTRACT FROM AUTHOR]

This study is aimed quantify dissipated power in a damper of automobile suspension to predict energy harvesting potential of a passenger car more accurately. Field measurements of power dissipation in a regenerative damper are still rare. The novelty is in using the broad database of real road profiles, a 9 degrees-of-freedom full-car model with real parameters, and a tyre-enveloping contact model. Results were presented as a function of road surface type, velocity and road roughness characterised by International Roughness Index. Results were calculated for 1600 test sections of a total length about 253.5 km. Root mean square of a dissipated power was calculated from 19 to 46 W for all four suspension dampers and velocity 60 km/h and from 24 to 58 W for velocity 90 km/h. Results were compared for a full-car model with a tyre-enveloping road contact, full-car and quarter-car models with a tyre–road point contact. Mean difference among three models in calculated power was a few per cent. [ABSTRACT FROM AUTHOR]

Prediction of pavement condition is one of the most important issues in pavement management systems. In this paper, capabilities of artificial neural networks (ANNs) and group method of data handling (GMDH) methods in predicting flexible pavement conditions were analysed in three levels: in 1 year, in 2 years (short term) and in the pavement life cycle (long term). For this purpose, three effective groups on pavement condition including traffic conditions, environmental changes and pavement structures were studied and nine effective variables were selected as input variables. International roughness index (IRI) was also used as the indicator of pavement performance. Various ANN structures and GMDH models in partial description configurations of 1st, 2nd, 3rd and 4th polynomials were formed and examined. Results indicate that while ANN models predict future condition of pavement with high accuracy in the short and long terms, GMDH models do not have accepted accuracy. [ABSTRACT FROM AUTHOR]

In this paper, for a quarter-car model, root-mean square response variables are calculated for roads classified by the international standard organization, based on the straight-line spectrum models. Covariance expressions are derived for vehicle response variables assuming that the road spectrum is that of a Motor Industry Research Association (MIRA) spectrum and compared with the existing results in the literature. The international standard and the MIRA spectra are then compared with the models delivered by high resolution frequencydomain subspace identification algorithms. The IRI statistic of the quarter-car response is calculated for the road models built by the curve fitting and the identification methods from the measured road spectrum assuming that the road excitation is a zero-mean Gaussian process. [ABSTRACT FROM AUTHOR]

Paper summarised the proposals of fourteen new alternative indexes intended for road roughness evaluation of longitudinal road profile, which have been introduced in the last decade approximately. The main advantages of proposed indexes are: (1) effort to reflect the most of ride quality aspects such as ride comfort, ride safety and dynamic load of road or cargo; (2) using thein situmeasurements of vibration response or subjective perception of vibration; (3) effort to reflect the vehicle responses such as roll and pitch vibration, or longitudinal and lateral vibration; (4) effort to detect the critical parts of road in a spatial domain; and (5) effort to reflect a vehicle velocity. The main limitations of proposed indexes are: (1) many proposals are only alternative to the international roughness index (IRI) or are based on the IRI procedure; (2) a simple one-dimensional vehicle model is often used with the constant parameters and velocity; and (3) orientation to one vibration response category as ride comfort. [ABSTRACT FROM AUTHOR]

The capability of a construction contractor greatly affects the quality of public construction. Many countries are developing assessment methods for construction contractors and establishing assessment systems for contractor performance. This study investigates one innovation of the performance records system (PRS) for public construction contractors, established in Taiwan in 2010. The PRS provides performance records pertaining to the project management contractor, the supervision contractor, and the construction contractor who have taken part in public construction in the last 5 years. Moreover, to improve the quality of public construction, Taiwan has begun implementing the policy of the lowest tender with different qualities (LTDQ) since 2006. The LTDQ uses the performance records in the PRS as the essential criteria for awarding a contract. This study uses the smooth roads project (SRP) promoted in Taipei City between 2009 and 2013 as an interesting and representative case study to explore the LTDQ policy. The international roughness index (IRI) is used to represent the road evenness, thus rendering the construction capability of the contractor. A comparison between the road evenness after implementation of the LTDQ based on the PRS (2011-2013) and before the implementation (2009-2010) reveals that the overall evenness of road sections is significantly better and the overall deterioration amount of road evenness is lower. These findings verify the capability of the LTDQ based on the PRS to select construction contractors as well as its effectiveness in improving the quality of public construction, which are worthy of reference to the global construction industry. [ABSTRACT FROM AUTHOR]

The objectives of this study are to simulate the Thornthwaite moisture index (TMI) for zones within the Atlantic provinces of Canada (APC) during three 30-year periods in the 21st century and to estimate the interactive effect of TMI and simulated freight traffic loads on the deterioration of pavement structure during the same period. Regional Highways 1, 2, 7, 15, 16, 102, and 104 connecting the APC are considered as the case study. Integration of spatial input-output and transportation models simulates freight movements on the selected regional highways during the period of 2012-2100. TMI is estimated using downscaled average monthly precipitation and temperature at 34 stations within the APC. Simulated traffic loads and TMI are applied to mechanistic modeling of roughness progression on the pavement structure. The findings of this study show that an increase in TMI can cause 11-68% increase of roughness progress rate on pavement structure. [ABSTRACT FROM AUTHOR]

The Louisiana Department of Transportation and Development (LA DOTD) has initiated a major effort to minimize the bridge end bump problem associated with the differential settlement. As a result, a new design for the approach slab was proposed, which requires increasing the slab flexural rigidity (EI), and using reinforced soil foundation (RSF) to support the slab and traffic loads at the roadway pavement/approach slab joint (R/S joint). The Bayou Courtableau Bridge was selected as a demonstration project to monitor, evaluate, validate, and verify the new bridge approach slab design method. The west approach slab was designed using the proposed design method with slab thickness of 406 mm, while the east approach slab was designed using the traditional design method with slab thickness of 305 mm. The pavement end side of the west approach slab was supported by a 1.2-m wide strip footing with the soil underneath it was reinforced by six layers of geogrid placed at a vertical spacing of 305 mm. Two static load tests were conducted on both the west and east approach slabs at two different times after construction. The test results indicated that the east approach slab (with traditional design) kept losing its contacts from the underneath embankment soil starting from the bridge side towards the pavement side after about a year and half. Meanwhile, the west approach slab (with new design) lost most of its supports from the underneath embankment soil, but with less observed faulting at the R/S joint and improved rideability [i.e., lower International Roughness Index (IRI) values]. The field monitoring program at Bayou Courtableau Bridge demonstrated much better performance of the new approach slab design system (west approach slab) compared to traditional design (east approach slab). [ABSTRACT FROM AUTHOR]

In order to analyze the dynamic load caused by uneven airport pavement, the relationship between international roughness index (IRI) and power spectral density (PSD) of airport pavement is studied. The dynamic load characteristic based on aircraft-pavement interaction is analyzed by building a quarter aircraft model，and a probability-based analysis model of dynamic load coefficient under aerodynamic correction is established. Finally, taking a certain aircraft type for example, the influence factors of dynamic load coefficient are discussed. The results indicate that the dynamic load coefficient increases at first and then decreases with the increase of velocity. The grade of airport pavement roughness is the most critical factor influencing the dynamic effect of aircraft on pavement. The relationship between air craft weight and lift impact factor (LIF) is nonlinear, so the change of dynamic load coefficient with the increase of the aircraft weight is nonlinear. When the aircraft is taxiing at a high-speed, the discrete degree of dynamic load coefficient changing with aircraft weight is greater, and as the aircraft weight increases, the dynamic effect of aircraft on pavement increases. Dynamic load coefficient approximatively linearly changes with the increase of the stiffness of aircraft wheel tire, damping and stiffness of fuselage buffer system. The damping of fuselage buffer system increases, then the dynamic load coefficient decreases. The stiffness of aircraft wheel tire and fuselage buffer system increases, then the dynamic load coefficient increases. [ABSTRACT FROM AUTHOR]

Local calibration is an important step before a transportation agency adopts the American Association of State Highway and Transportation Officials' (AASHTO) mechanistic-empirical pavement design guide (MEPDG). This paper presents the challenges of and findings from the local calibration of flexible pavements in provincial highways under the jurisdiction of the Ministry of Transportation of Ontario (MTO). A calibration database was developed that involved a hierarchical framework of the input parameters required for AASHTOWare Pavement ME (the MEPDG software) and the historical field performance data based on the MTO's second-generation pavement management system. A regression analysis is carried out for preliminary calibration of rutting and international roughness index (IRI) models by comparing the predicted distress to observed distress. The analysis suggested that whereas the MEPDG provided fairly unbiased prediction of the IRI value, it often over-predicted the total rutting. Calibrated predicted IRI and rut depth are found for Ontario's local conditions from MEPDG distress prediction models. A further clustering analysis based on Functional Class and geographical zone for the rutting and IRI, respectively, improved the precision of the locally calibrated models. [ABSTRACT FROM AUTHOR]

The Mechanistic–Empirical Pavement Design Guide (MEPDG) is the software of the American Association of State Highway and Transportation Officials (AASHTO). It uses mechanistic–empirical–numerical models to analyze input data on traffic, climate, materials and structures, also estimating damage accumulation over roadway service life. This study focuses on its application in Lambayeque (Peru): with MEPDG, different sections of flexible pavement were modeled and prediction values for the International Roughness Index (IRI) were obtained. These values were contrasted with empirical data from evaluated sections. Using Equivalent Simple Axis Load (ESAL) traffic data as input, the model gave values largely in agreement with field measurements. Hence, a regional calibration coefficient for IRI prediction can be derived. The model accommodates the Level 3 load spectrum and ESALs, providing compatibility and good precision. [ABSTRACT FROM AUTHOR]

This paper presents the modelization of road infrastructure of logistics corridors in Colombia, using dynamic system to assess the impact of investment policies carried out in the paved network. There is considered five categories for assessing the quality of the paved network defined by the Instituto Nacional de Vías y Transporte - INVIAS. The model contains the parameters to evaluate the wear of roads depending on traffic and average investments made for the rehabilitation and maintenance of roads. [ABSTRACT FROM AUTHOR]

In damage analysis of flexible pavement sections, the AASHTO 2002 design guide, also called as MEPDG (Mechanistic-Empirical Pavement Design Guide), adopted a mechanistic-empirical approach. In the MEPDG, the pavement performance is computed using different input parameters that characterize pavement materials, design features and condition. However, these input parameter values are expected to differ to varying degrees and, therefore, the predicted performance may also vary to some degree depending on the input parameter values. The current study evaluated the influence of four input parameters, namely, reliability level, climate, traffic characteristics and modulus values on the performance of selected pavement sections using MEPDG software. Knowledge gained from the sensitivity analysis of different pavement sections using MEPDG is expected to be useful to pavement designers and others using MEPDG for future pavement design. Specifically, this paper focuses on the sensitivity study of flexible pavement sections at four different locations namely, Chicago in Illinois, Grand Forks in North Dakota, Oklahoma City in Oklahoma, and Houston in Texas, for addressing sensitivity towards climatic conditions. For addressing effect of reliability and traffic three levels of reliability (80%, 90%, 95%) and traffic (low, medium, high) were used, respectively, for designing flexible pavement sections. Additionally, sensitivity towards modulus of subgrade soil was evaluated by designing pavement sections containing 6% lime, 15% class C fly ash (CFA), and 15% cement kiln dust (CKD). The performance of each pavement section was monitored for 240 months (20 years) using MEPDG software by generating plots for rutting, alligator cracking, and International Roughness Index (IRI). It was found that rut predicted by MEPDG is sensitive towards climate, modulus values of chemically stabilized layer and reliability level. IRI values showed sensitiveness toward only reliability and traffic level. Alligator cracking showed sensitiveness toward climate with unexpected trend, modulus of chemically stabilized layer, reliability and traffic level. [ABSTRACT FROM AUTHOR]