Predicting pavement condition index based on the utilization of machine learning techniques:A case study

Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time.The pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's performance.This research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement sections.The authors collected field data spans for 2018 and 2021.Eight pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and delamination.This study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.

PotholeEye^(+): Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance

We propose a mobile system,called PotholeEye+,for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video.PotholeEye+pre-processes the images,extracts features,and classifies the distress into a variety of types,while the road manager is driving.Every day for a year,we have tested PotholeEye+on real highway involving real settings,a camera,a mini computer,a GPS receiver,and so on.Consequently,PotholeEye+detected the pavement distress with accuracy of 92%,precision of 87%and recall 74%averagely during driving at an average speed of 110 km/h on a real highway.

Structural Design of Pavement Overlays Based on Functional Parameters

This paper reports a practical pavement overlay design method based on PCI (Pavement Condition Index). Current pavement investigation method (JTJ 073 96) is compared to the ASTM D 5340, which is the standard test method for airport pavement condition evaluation initially developed for US Air Force. The deficiency in the calculation of PCI based on field data in JTJ 073 is discussed. The proposed design method is compared to AASHTO overlay design method with good agreement. The paper concludes with an example illustrating how the existing pavement structural capacity is related to pavement distress survey results. The presented design method can be used in the design for overlay rehabilitation of pavements of highways, urban streets and airports.

Review:Asphalt Pavement Rutting Distress and Affects on Traffics Safety

Highway is an essential facility that led to both economic success and quality of life.Maintenance is necessary to ensure that highway will able to continue to carry out its functions.The rutting of asphalt pavement structures during their exploitation is considered to be one of the main problems in the entire world.This kind of pavement distress makes a negative impact to the exploitation characteristics of the asphalt pavement to the residual life of pavement structure,also to the safety and quality of the traffic.The main purpose of this review is to define the effects of rutting on roads safety.

Damage Mechanism of Ultra-thin Asphalt Overlay(UTAO) based on Discrete Element Method

Aiming to analyze the damage mechanism of UTAO from the perspective of meso-mechanical mechanism using discrete element method(DEM),we conducted study of diseases problems of UTAO in several provinces in China,and found that aggregate spalling was one of the main disease types of UTAO.A discrete element model of UTAO pavement structure was constructed to explore the meso-mechanical mechanism of UTAO damage under the influence of layer thickness,gradation,and bonding modulus.The experimental results show that,as the thickness of UTAO decreasing,the maximum value and the mean value of the contact force between all aggregate particles gradually increase,which leads to aggregates more prone to spalling.Compared with OGFC-5 UTAO,AC-5 UTAO presents smaller maximum and average values of all contact forces,and the loading pressure in AC-5 UTAO is fully diffused in the lateral direction.In addition,the increment of pavement modulus strengthens the overall force of aggregate particles inside UTAO,resulting in aggregate particles peeling off more easily.The increase of bonding modulus changes the position where the maximum value of the tangential force appears,whereas has no effect on the normal force.

A review on pavement distress and structural defects detection and quantification technologies using imaging approaches

Pavement distress detection(PDD)plays a vital role in planning timely pavement maintenance that improves pavement service life.In order to promote the development of PDD technologies and find out the insufficiencies in PDD field,this paper reviews the technical development history and characteristics of various PDD technologies,which contributes to the current state of research on PDD.First,processes of PDD are briefly introduced.The PDD technologies based on radar ranging,2D image,laser ranging and 3 D structured light are illustrated.The newest 3D PDD technology based on interference fringe,which has better accuracy,is in progress.The principles and implementation processes of these methods are discussed.Finally,the shortcomings of these technologies in the field of PDD are concluded.Recommendations for future development are provided.The research results show that various PDD technologies have been continuously improved,developed,over the past decade,and have achieved a series of results.However,the measurements from existing PDD technologies can not be metrological traced to acquire the true dimensions of pavement distresses.The lack of metrological traceability technology in the PDD field needs to be further solved.In order to achieve more accurate and efficient PDD,the metrological traceability technology of PDD systems has become the future development direction in this field.

Automated classification and detection of multiple pavement distress images based on deep learning

To achieve automatic,fast,efficient and high-precision pavement distress classification and detection,road surface distress image classification and detection models based on deep learning are trained.First,a pavement distress image dataset is built,including 9017pictures with distress,and 9620 pictures without distress.These pictures were captured from 4 asphalt highways of 3 provinces in China.In each pavement distress image,there exists one or more types of distress,including alligator crack,longitudinal crack,block crack,transverse crack,pothole and patch.The distresses are labeled by a rectangle bounding box on the pictures.Then ResNet networks and VGG networks are used respectively as binary classification models for distressed and non-distressed imagines classification,and as multi-label classification models for six types of distress classification.Training techniques,such as data augmentation,batch normalization,dropout,momentum,weight decay,transfer learning,and discriminative learning rate are used in training the model.Among the 4 CNNs considered in this study,namely ResNet 34 and 50,and VGG 16 and 19,for the binary classification,ResNet 50 has the highest Accuracy of 96.243%,Precision of 95.183%,and ResNet 34 has the highest Recall of 97.824%,and F2 score of 97.052%.For multi-label classification,ResNet 50 has the best performance,with the highest Accuracy of 90.257%,higher than 90%required by the Chinese standard(JTG H20-2018)for road distresses detection,F2 score-82.231%,and Precision-76.509%,and ResNet34 has the highest Recall of 87.32%.To locate and quantify the distress areas in the images,the single shot multibox detector(SSD)model is developed,in which the ResNet 50 is used as the base network to extract features.When the intersection over union(IoU)is set to 0,0.25,0.50,0.75,the mean average precision(mAP)of the model are found to be 74.881%,50.511%,28.432%,3.969%,respectively.

Prediction of performance and evaluation of flexible pavement rehabilitation strategies

Five test sections with different additives and strategies were established to rehabilitate a State-maintained highway more effectively in Rhode Island （RI）： control, calcium chloride, asphalt emulsion, Portland cement and geogrid. Resilient moduli of subgrade soils and subbase materials before and after full depth rehabilitation were employed as input pa- rameters to predict the performance of pavement structures using AASHTOWare Pave- ment ME Design （Pavement ME） software in terms of rutting, cracking and roughness. It was attempted to use Level i input （which includes traffic full spectrum data, climate data and structural layer properties） for Pavement ME. Traffic data was obtained from a Weigh- in-Motion （WIM} instrument and Providence station was used for collecting climatic data. Volumetric properties, dynamic modulus and creep compliance were used as input pa- rameters for 19 mm （0.75 in.} warm mix asphalt （WMA） base and 12.S mm （0.5 in.） WMA surface layer. The results indicated that all test sections observed AC top-down （longitu- dinal） cracking except Portland cement section which passed for all criteria. The order in terms of performance （best to worst） for all test sections by Pavement ME was Portland cement, calcium chloride, control, geogrid, and asphalt emulsion. It was also observed that all test sections passed for both bottom up and top down fatigue cracking by increasing thickness of either of the two top asphalt layers. Test sections with five different base/ subbase materials were evaluated in last two years through visual condition survey and measurements of deflection and roughness to confirm the prediction, but there was no serious distress and roughness. Thus these experiments allowed selecting the best reha- bilitation/reconstruction techniques for the particular and/or similar highway, and a framework was formulated to select an optimal technique and/or strategy for future rehabilitation/reconstruction projects. Finally, guidelines for long-term evaluation were developed to verify short-term prediction and performance.

Influence of computation algorithm on the accuracy of rut depth measurement

Rutting is one of the dominant pavement distresses, hence, the accuracy of rut depth measurements can have a substantial impact on the maintenance and rehabilitation （M 8： R） strategies and funding allocation. Different computation algorithms such as straight- edge method and wire line method, which are based on the same raw data, may lead to rut depth estimation which are not always consistent. Therefore, there is an urgent need to assess the impact of algorithm types on the accuracy of rut depth computation. In this paper, a 1B-point-based laser sensor detection technology, commonly accepted in China for rut depth measurements, was used to obtain a database of 85,000 field transverse profiles having three representative rutting shapes with small, medium and high severity rut levels. Based on the reconstruction of real transverse profiles, the consequences from two different algorithms were compared. Results showed that there is a combined effect of rut depth and profile shape on the rut depth computation accuracy. As expected, the dif- ference between the results obtained with the two computation methods increases with deeper rutting sections： when the distress is above 15 mm （severe level）, the average dif- ference between the two computation methods is above 1.5 mm, normally, the wire line method provides larger results. The computation suggests that the rutting shapes have a minimal influence on the results. An in-depth analysis showed that the upheaval outside of the wheel path is a dominant shape factor which results in higher computation differences.