In-service Road Research Articles

Machine Learning Approach to Rapidly Evaluate Curling of Concrete Pavement

This paper focuses on the methodology for evaluating the degree of total curling in concrete pavement using machine learning. Deflection induced by falling weight deflectometer (FWD) testing is known as a direct correlation to total curling including built-in and daily curling. However, deflection measurement in the in-service road is also affected by others, such as environmental conditions, pavement geometry, subgrade stiffness, and mixture design. Thus, it is challenging to determine the level of curling from FWD data due to the complexity of influencing parameters. To navigate this complexity, prominent machine learning models are exploited to identify a non-linear relationship between curling and FWD deflections. A finite-element simulation of FWD is conducted to generate a vast data set, and a robust regression model is trained to estimate the total effective temperature difference (TETD) to quantify the effects of curling. Since input parameters for testing pavements can be measurable in the field, curling from TETD can be readily obtained using the proposed methodology. Comparative simulations highlight that the proposed models, with an MAE less than 0.5 °C significantly outperform the multiple regression performance, which registers an MAE of 3.45 °C in TETD, particularly in offering cost-effective and noise-tolerant prediction.

Time-domain expression and mechanical response of the viscoelastic Poisson's ratio in asphalt pavement

Poisson's ratio (PR) is a fundamental mechanical parameter for materials. Unlike the constant Elastic Poisson's Ratio (EPR), the Viscoelastic Poisson's Ratio (VEPR) is significantly influenced by external factors such as environmental temperature, stress level, and loading frequency. Using the VEPR to characterize the stress state of asphalt pavements provides a more accurate reflection of their actual service conditions. Moreover, the VEPR plays a more critical role than the EPR in predicting fatigue life, analyzing temperature sensitivity, and optimizing pavement structure design. In this study, the Elastic-Viscoelastic Correspondence Principle (EVCP) and the Zener mechanical model were combined to derive a time-domain expression for the VEPR of asphalt pavement. Fiber Bragg Grating (FBG) sensors were embedded within various structural layers of an in-service road to collect mechanical response and temperature data under traffic loads. Based on the measured temperature data, Finite Element Models (FEMs) were developed for both the VEPR and EPR conditions. The mechanical responses were validated using these FEMs, and the results were compared with the strain data collected by the FBG sensors. The results indicated that both VEPR and EPR increase most rapidly between 20°C and 35°C, with VEPR resulting in higher transverse and longitudinal strains and stresses than EPR, while vertical strain remained constant. Furthermore, the VEPR was better suited to actual driving conditions, with a peak error of only 3.97 % and an average error of 10.04 %.

Experimental application of the Italian guidelines for the risk classification and management and for the safety evaluation of existing bridges

After some recent collapses of Italian bridges, specific guidelines on risk classification and management, safety assessment and monitoring of existing bridges were issued in Italy in 2020. The application of the first three levels of analysis of these guidelines to a large stock of bridges of two highways located in the Sicilian region (southern of Italy) is presented in this paper. The examined levels of analysis comprise census, visual inspection and calculation of the so-called class of attention of bridges.Based on the results of these analyses, an experimental database on more than 300 in-service road bridges has been collected and catalogued by means of a customized web application. The results are described and critically analysed, highlighting both abilities and limitations of the examined levels of analysis of the Italian Bridges Guidelines. Finally, a prioritization method is proposed to classify groups of bridges in view of more refined levels of analysis and interventions.

Health State Assessment of Road Tunnel Based on Improved Extension Cloud Model

A scientifically accurate assessment of tunnel health is the prerequisite for the safe maintenance and sustainability of the in-service road tunnel. The changing trend of tunnel health is not considered in existing research. Most evaluation indicators are static and the ambiguities or randomness at the boundary of the health level intervals is neglected in most evaluation methods. In this paper, the evaluation index system combined with dynamic, and static is set to solve these problems. The changing trend of the health state of tunnels is analyzed through the cubic b-spline function. The weights of evaluation indicators are calculated based on the AHP-improved entropy method. The health evaluation method is proposed based on combing the extension theory and the cloud model improved by the cloud entropy optimization algorithm. Finally, the evaluation results of the proposed method are compared with the detection data of the Beilongmen Tunnel of Zhangzhuo Expressway. The results demonstrate that 80% of the evaluation results in the sample tunnel data are consistent with the standard results, and the remaining 20% show a lower level of health than the standard results. This reflects the evaluation results are impacted by the trend of tunnel health status changes. The health state can be more accurately evaluated by dynamic indicators. The improved extension cloud model is feasible and applicable in the health assessment of tunnels. This work provides innovative ideas for the evaluation of the health state of tunnels and theoretical support for the formulation of reasonable maintenance measures.

Permanent Deformation Monitoring and Remaining Life Prediction of Asphalt Pavement Combining Full-Scale Accelerated Pavement Testing and FEM

Permanent deformation (rutting) is one of the typical failure modes of asphalt pavement with a semirigid base in high-temperature areas. To address issues of pavement structure deformation and remaining life prediction, a method of combining accelerated pavement testing and finite element (FE) simulation was proposed. A modified Burgers model considering creep parameter damage was obtained using strain monitoring data and laboratory test values, which was performed in the FE model to analyze rutting development. The results showed that high temperature and heavy axle loads greatly influenced rutting development: the tested road was loaded 360,000 times at high temperature, contributing 50% to accumulated rutting; rutting increased by 90.7% under the condition of 50% overload under high-temperature seasons from the simulated results. Then, the ratio of the bulge area to the depression area (KR) was proposed to define the rutting deformation mechanism. The KR analysis results illustrated that rutting development was a transition process from compaction rutting to flow rutting. Finally, the relationship between the action times of axle load in the tested and in-service roads was established based on the rutting equivalence principle. When the action times of axle load reach approximately 25 million times (insufficient remaining life) for the investigated Hu-Ning highway, necessary maintenance measures should be taken to treat rutting deformation.

Research on the 3D Fine Modeling Method of In-Service Road

To achieve the digitization of all traffic infrastructure elements and enable three-dimensional digital representation of physical facilities, a multilevel road three-dimensional reverse modeling method is proposed based on road point cloud data obtained by a vehicle laser scanning system. First, based on the distribution characteristics of each target structure in the road scene and the modeling requirements, a levels of detail (LOD) modeling specification is designed, and the required feature data format for each level is defined. Next, the three-dimensional characteristic parameters needed for modeling are extracted from the vehicle point cloud data. Finally, the continuous quadrilateral algorithm is used to reconstruct the road model, the topological structure relationship algorithm is used to reconstruct the intersection model, and the instantiation lofting technology is used to reconstruct the rod-shaped target model, all based on the three-dimensional modeling platform. This approach allows for the rapid reverse reconstruction of three-dimensional road models with different LOD levels. The point cloud data of two sections of urban roads with different slopes and one section of expressways were modeled and compared with the original vehicle-mounted laser point cloud data. The data volume of different level models decreases with decreasing model fineness, and the LOD1 level model has the highest similarity with point cloud data, at approximately 92.17%. The similarity of LOD2 and LOD3 decreases in turn, at 82.91% and 75.25%, respectively. For flat or undulating roads, the overall accuracy of the nearest point distance between different levels of road models and point cloud data is better than 10 cm, significantly higher than that of traditional manual modeling. The results demonstrate that vehicle mobile laser scanning technology provides new modeling data for the rapid realization of three-dimensional reverse reconstruction of large-scale traffic infrastructure. Automatic modeling technology can effectively improve modeling efficiency, reduce data redundancy, and ensure model quality.

LiDAR Perception and Evaluation Method for Road Traffic Marking Retroreflection

To solve the problem of low efficiency in retroreflection maintenance of road traffic markings, in this study a vehicle-mounted LiDAR-based perception and evaluation method of retroreflection of markings is proposed. First, this method establishes a calibration prediction model for LiDAR based on a regression decision tree. Then, a marking maintenance evaluation model is constructed in combination with the decision threshold proposed by China’s national standard, and the accuracy of the maintenance evaluation model is analyzed using F1-score, recall, and precision. In this study, four marking lines were used as the calibration data source, and a dataset of an independent 1,300 m road section was used to verify the established models. The results show that the coefficient of the retroreflected luminance ( RL) and the reflection intensity of the markings are positively correlated. During the construction of the calibration prediction model, the multiple linear regression functions, the second-order polynomial functions, and the decision tree are compared, and the result indicates that decision tree has the best fit to the data with the coefficient of determination for the established calibration prediction model better than 0.95. The agreement between the maintenance decision obtained from the maintenance evaluation model and the traditional method is more than 85%. The time cost is reduced by at least 90%. The proposed calibration prediction model can accurately predict the RL, and can quickly collect the RL values of the in-service road traffic markings. The proposed maintenance evaluation model is highly efficient and can replace the traditional evaluation method for markings.

Performance Evaluation of Traffic Speed Deflectometer Based on Virtual Standard Test Road

It is challenging to find actual standard test roads (ASTRs) to evaluate the measuring performance of the Traffic Speed Deflectometer (TSD), leading to misjudgment of its evaluation results. To address this issue, a method to construct virtual standard test roads (VSTRs) using in-service roads is proposed. Firstly, the principle of TSD based on the Laser-Doppler effect to measure pavement deflection is introduced, and the shortcomings of traditional evaluation methods are reviewed and summarized. Secondly, an adaptive construction method for VSTRs is proposed based on the K-means clustering algorithm. It layers the Falling Weight Deflectometer (FWD) measurements on in-service roads and reconstructs them as virtual roads with stable deflections but different ranges. Thirdly, two new indexes, the coefficient of variation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CV_{k}$ </tex-math></inline-formula> and the influence coefficient of speed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ICS_{k}$ </tex-math></inline-formula> , are proposed to replace the traditional indexes, such as repeatability and correlation coefficient. Finally, the feasibility of evaluating TSD performance based on VSTRs and the new indexes is verified by experiments. Results show that the newly constructed VSTRs solve the problem that the ASTRs are challenging to obtain, reducing the experimental requirements for evaluating TSD, and the new indexes achieve an efficient and scientific evaluation of TSD.

A Multidimensional Analysis of Factors Impacting Mobility of Open-Access Multilane Highways

Open-access multilane highways have a significant share in the highway network of Pakistan and other developing countries in Asia. These highways have high access density, design inconsistency, and other operational characteristics that differentiate them from partially controlled-access multilane highways. This study identified significant factors affecting the mobility of open-access highways based on road users’ perceptions as well as field observations. An interview-based questionnaire survey from local respondents and an in-service road survey formed the database for the present study. Questionnaire survey results showed that heavy traffic was the most critical mobility influencing factor on open-access multilane highways, followed by road width and condition, whereas the result of multilinear regression revealed that the most significant variable was access density, followed by the flow and pedestrian crossings. However, it was concluded that controlling access density, preventing pedestrian crossings, and improving pavement condition will improve the mobility of open-access multilane highways.

Pressure Relief Joint for Preventing Concrete Pavement Blowup

Blowup refers to concrete pavement distress resulting from compressive failure at joints or cracks when excessive concrete expansion occurs, compressive forces are generated, and the horizontal movement of the pavement is constrained. Blowup occurs because of many reasons including heatwaves caused by abnormal climatic conditions, improper installation spacing between expansion joints, pavement expansion due to an alkali-aggregate reaction, concrete pavement aging, and contraction joint failure. In the summer of 2018, the Korean peninsula experienced the worst heatwave ever recorded, and hence, the possibility of blowup increased. Consequently, the Korea Expressway Corporation established a draft policy for installing pressure relief joints (PRJs) to prevent blowup. In this draft, three installation classes were specified for installing PRJs on in-service roads, and the installation priority for each class was set such that the limited budget could be used efficiently. In addition, the draft presented specific guidelines for installing each class of PRJs and suggested PRJ cross sections that could ensure structural stability. However, the draft lacks systematic research on the causes of blowup and the development of countermeasures and does not provide an engineering basis for the suggested technological solutions to prevent blowup. Therefore, this study aimed to reexamine and complement the suitability of the existing PRJ installation standards through field studies, laboratory experiments, and analytical techniques.

Fatigue process analysis of aged asphalt concrete from two-point bending test using acoustic emission and curve fitting techniques

This paper presents a new method for analyzing experimental data stemming from the Two-Point Bending (2 PB) fatigue test conducted on French standardized trapezoidal specimens, in accordance with the European standard EN 12697-24. These tests were performed on an asphalt mix specimen sawn from an in-service road pavement, adopting a loading frequency of 25 Hz and a target temperature of 10 °C. The Acoustic Emission (AE) technique was implemented to monitor the damage and failure processes during fatigue. The fatigue life, defined as the critical number of loading cycles inducing specimen failure, is investigated herein according to several methods, including a normalized criterion, models proposed in the literature, experimental fitting, and Acoustic Emission activity analysis. First, based on the AE analysis, new damage and failure criteria are proposed (Damage Initiation and Failure Propagation Thresholds). In a second step, the mechanical behavior is fitted, in terms of strain, force, stiffness modulus, and phase angle, using four functions, namely: a polynomial function, a continuous Fourier series, a sum of sine functions, and a Gaussian function. After the fitting process, the first derivative is calculated and the singular points of the curves are detected and discussed. Lastly, a phenomenological analysis is carried out in order to understand the damage and failure process stages.

Effects of speeds and weights of travelling vehicles on the road surface temperature

It is important to predict the fatigue conditions of in-service roads for monitoring the state of internal pavement layers, planning the routine maintenance services and for ensuring the safety of travelling vehicles. Among various factors, the frictional heat generated by the tires of moving vehicles is considered to be an important factor causing such a distress in in-service roads. Because of this frictional heat, the temperature of wheel tracks is slightly warmer than other parts of the pavement, thereby, causing a non-uniform distribution of road surface temperature (RST) that may be responsible for developing cracks and subsequent increase in permeability of existing roads. In this study, the effect of travelling vehicles on the RST was investigated by using an infrared (IR) camera in an in-service road. In order to know the actual distribution of temperature, the RSTs were compared between the wheel track of each lane and in-between lanes. Then, the effect of speeds and weights of travelling vehicles on the RST was investigated. A bridge weigh-in-motion (BWIM) system using MEMS accelerometers was utilized to identify the speed and weight of a travelling vehicle. A strong correlation was observed between the rise of RST of wheel tracks due to the friction with the speeds and weights of travelling vehicles. The rate of increase in surface temperature was observed to be higher for the higher axle vehicles. Additionally, an influence of ambient air temperature was observed on the relationship between the rise of RST with the speeds and weights of travelling vehicles.

The comparison between the method of Bina Marga and the pavement condition index (PCI) in road damage condition evaluation (case study: Prof. Ali Hasyimi Street, Banda Aceh)

Monitoring in-service road surface conditions is a part of road pavement maintenance. The most effective maintenance type will save costs and time. Road damage usually occurs on roads that have reached the design age, but some roads have been damaged before reaching the design age. To assess road damage, several methods can be used. The most widely used method in Indonesia is the Bina Marga method and the PCI (Pavement Condition Index) method. This study aims to compare both methods, in terms of accuracy and workability, in monitoring the surface condition of Prof. Ali Hasyimi street, which is located in the city of Banda Aceh. The data required for these two methods is the primary data, which are geometric, visual road damage, and traffic volume. Based on this study’s results, the Bina Marga method is easier and faster to use, while the PCI method is more detailed to utilize. In terms of accuracy, both methods relatively have the same accuracy.

Quantitative evaluation of asphalt binder extraction from hot mix asphalt pavement using ashing and centrifuge methods

Asphalt binder requires more investigation to be accurately and precisely extracted since it is an effective procedure for quality assurance quality control (QA/QC) and subsequent binder characterization. In this research, the authors presented a hands-on experience with binder extraction to deliver recommendations concerning the sensitive steps that may affect the outcomes (extracted binder content, Pbe%). Two mineral matter determination methods (ashing and centrifuge) were addressed based on the centrifuge extraction method. Field cores were investigated by comparing the Pbe% to the actual binder content, Pba%. Analysis of variance (ANOVA) and Tukey Post-Hoc statistical analyses, in addition to linear least square regression analysis, were used to show the significance of difference according to 38 variant cores randomly obtained from the field segments (in-service roads) via the first two weeks from their construction dates. Such cores involved reclaimed asphalt pavement (RAP), reclaimed asphalt shingles (RAS), and a wide range of additives. The two extraction methods were compared, concluding that the centrifuge method was highly recommended based on a quantitative evaluation, which delivered the same average Pba% based on the 38 cores. Furthermore, the centrifuge method provided much saving in the experimental time (almost half the time required for the ashing method). It was found that the ashing outcomes were equal to the centrifuge outcomes when disregarding the ammonium carbonate addition. Thus, it could be recommended to reassess the ammonium carbonate addition as it might excessively compensate for minerals that have not been lost by the ignition oven.

Road marking retroreflectivity study via a visual algorithm

The retroreflectivity (Rl) of road markings is important and should be inspected and maintained throughout their service life. The specifications are provided by European nations, the United States, and many other countries. Although acceptance tests ensure the good Rl quality of newly placed road markings, the RL values of all in-service road markings are rather difficult to inspect by using currently available devices. This study, therefore, aims to determine the relationship between Rl and corresponding image brightness of yellow road markings to evaluate their visibility by analyzing recorded images captured at night. An integrated algorithm was developed to analyze recorded images continuously for identifying road marking brightness 30 m away from a vehicle. Field experiments on three types of road marking materials were performed and repeated at four separate locations. The findings provide a promising direction for using the image brightness of road markings to predict their field Rl. However, limitations of this study are discussed and suggestions for future direction are presented.

Prediction Model for Asphalt Pavement Temperature in High-Temperature Season in Beijing

Asphalt pavement temperatures greatly influence on the bearing capacity and performance, especially in high-temperature season. The variation rules of pavement temperatures under the high-temperature range affect the design and maintenance management of the asphalt pavement, as well as the accurate prediction for pavement temperatures. However, asphalt pavement temperature is greatly affected by various strongly correlated environmental factors and cannot be measured directly or predicted effectively. In this project, temperature sensors were embedded in the pavement of in-service road to collect temperature data by continuous record measurement, and regression model was conducted by the partial least squares method through comprehensive analysis on the pavement temperature data and synchronously environmental data from local weather station measured in July 2013, July 2014, and July 2015. The quantitative relationships in high-temperature season between environmental factors and pavement temperature were determined, and a model was established to predict the temperature of asphalt pavement based on environmental data. The model was verified by the recorded data from July 1, 2016, to July 31, 2016, and the results indicated that the pavement temperature can be predicted accurately and reliably by the proposed model.

Remaining fatigue life assessment of in-service road bridge decks based upon artificial neural networks

By using a multi-scale simulation together with the pseudo-cracking method, the remaining fatigue life of real RC bridge decks is estimated based upon their site-inspected surface crack patterns of a wide variety, and it is confirmed that the crack location and its orientation are primary factors on the remaining life together with crack width. For quick diagnosis for the remaining fatigue life at the site, an artificial neural network (ANN) to correlate the fatigue life with observed cracks is built up based upon large numbers of assessed fatigue life related to wide range of crack patterns and their widths. Artificially created crack patterns associated with the firm coupling of shear and flexure are also included in training dataset to cope with indeterminate crack events which may arise in future and to assure a robust and reliable artificial neural network model. It is recognized by conducting the cross-validations that the training data-set for ANN shall include crack patterns rooted in mechanically possible modes of failure. Otherwise, the risk of wrong assessment due to overlearning will arise.

Comparative studies of lightweight deflectometer and Benkelman beam deflectometer in low volume roads

Abstract A comparative subgrade moduli study is carried out by static and dynamic deflection methods using lightweight deflectometer and conventional Benkelman beam deflectometer on low volume road. Field and laboratory tests are performed at 40 test locations on in-service road of 2 km stretch that contains three common types of cohesive soils (CH, CI, and CL). Pavement static and dynamic responses are estimated to ascertain static, backcalculated, and composite moduli of subgrade. The backcalculated and composite moduli of subgrade is validated at given moisture content using repeated triaxial test. Static moduli values are on lower side as compared with dynamic moduli values whereas the composite, and laboratory moduli of subgrade are approximately consistent with 2% to 7% variation, respectively. Correlation analyses between static and dynamic moduli of different types of subgrade soils depict good correlation of determination (R2) varies between 0.75 and 0.91. Subsequently, validation of static moduli with California bearing ratio (CBR) related subgrade moduli shows moderate correlation of 0.67 to 0.74 whereas dynamic moduli shows good correlation of 0.74 to 0.93 for different types of soils, respectively. Therefore, the comparative analysis shows that lightweight deflectometer provides reliable subgrade moduli values, and it can be used as a quick subgrade strength evaluating tool for low volume roads.

International Roughness Index specifications around the world

The International Roughness Index (IRI) is an indicator used worldwide for the characterisation of longitudinal road roughness. This study summarised IRI limit values for new, reconstructed, or rehabilitated roads; for in-service (existing) roads; and road classification schemes used around the world. An overview of practices in 35 US states and 29 non-US states was provided. Limit values are a function of road surface type, road functional category, road speed limit, road construction type, or average annual daily traffic (AADT). IRI specifications are defined for a broad range of evaluation lengths from several metres to the entire length of a section. Large differences in IRI limit values were observed for the same segment length among various countries. The IRI-based methodology used in US states was compared with that used in non-US countries. Non-US countries used more often specifications as a function of road functional category and AADT, and are based on percentile of IRI observations. US states used more often pay adjustment and specifications as a function of road construction type and road speed limit.

THE EFFECT OF INCORPORATING RECLAIMED ASPHALT PAVEMENT ON THE PERFORMANCE OF HOT MIX ASPHALT MIXTURES

In pavement industries, incorporating appropriate amount of reclaimed asphalt pavement (RAP) in the fresh mixtures is one of the approaches to attain sustainable principle in construction. Usage RAP materials have been practiced since 1970s, however, pavements made with RAP will reach the end of service life and need to be recycled again. Only a few studies done on the second recycle of RAP (R2AP). Therefore, this paper aimed to investigate the effect of incorporating RAP and R2AP in the asphalt mixture. RAP was collected from in-service road which was exposed to the environment and traffic for seven years. While, the second cycle of RAP (R2AP) was obtained through the laboratory aging process. 20, 40, and 60 % of RAP and R2AP were mixed with fresh dense graded aggregates to form Asphaltic Concrete with 14 mm nominal maximum aggregate size (AC 14). Resilient modulus test was performed to evaluate the performance on rutting resistance. Tensile strength was also evaluated at 25 °C as an indicator for fatigue resistance. 60 % of RAP and 40 % of R2AP are observed to the best optimum amount to be added in the fresh mixture in order to improve both fatigue and rutting resistance.