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.

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.

Effect of speed hump characteristics on pavement condition

Speed humps are the most common type of traffic calming devices due to their low cost and easy installation. However, in many Egyptian roads, considerable number of these humps is randomly placed without proper engineering studies and justifications. Deteri- oration of pavement condition is observed near these humps. This paper presents a case study applied to collect and analyze visual inspection data for the reason of evaluating the impact of speed humps on pavement condition on intercity rural roads. The paper used 52 speed humps located in an intercity two-lane, two-way road that connects two cities, Tahta and Gerga, in Upper Egypt. The total length of this road is about 34 km. Pavement condition index （PCI）, in road sections, near speed humps in the two directions of travel were calculated from the visual inspection measurements. The characteristics of each speed hump （width, height, and distance from preceding hump） were measured. Using statistical analyses, the correlations between the pavement conditions and hump char- acteristics were examined. Regression analysis models were developed to represent the relationships between pavement conditions and hump characteristics. Generally, the re- sults proved that the pavement conditions are greatly influenced by the presence of speed humps and hump characteristics.

Improvement of the Algorithm for Computing Adjusted Structure Number for Determination of Backlog Maintenance

The pavement strength is very important for the evaluation of backlog maintenance. The current trend in many developing countries used pavement conditions index-PCI in estimating maintenance costs. The PCI can only justify periodic and routine recurrent maintenance. The condition strength is rarely determined in a flexible pavement creating an opportunity for back long maintenance. This paper reports the study conducted to develop and improve the algorithm for estimating the adjusted structure number to estimate the remaining thickness of the flexible pavement. The analysis of eight ways of computing structure numbers from FWD data ware analyzed and found that the improvement of the HDM 3 - 4 models can influence the usefulness of data collected from road asset management in Tanzania. The algorithm for estimating structural numbers from CBR was improved to compute adjusted structural numbers finally used to estimate the remaining life of the flexible pavement. The analysis of the network of about 6900 km including ST and AM was found that 64.72% were very good, 12% were Good, 10% were fair and 7.84% were poor and 5.4% were very poor.

Modeling the dynamic performance of transportation infrastructure using panel data model in state-space specifications

In this study,different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed,and the panel data models(PDMs)are highlighted for longitudinal data sets.The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation.The models could simultaneously capture the heterogeneity and update forecast through inspections.PDMs are applied to tackle the cross-section heterogeneity of longitudinal data,and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data.To illustrate the methodology,three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China.Estimation results obtained by ordinary least square(OLS)estimator and system generalized method of moments(SGMM)are compared for two dynamic instances.The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time.There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time.In addition,SGMM does not obtain higher prediction accuracy than OLS in this case.Finally,it is recommended to specify the inspection intervals as several constants with integer multiples.