International Roughness Index (IRI) is a pavement performance indicator which reflects not only the pavement condition but also the ride quality and comfort level of road users. The aim of this pap...

International Roughness Index prediction model for flexible pavements

Investigation of waste oils as rejuvenators of aged bitumen for sustainable pavement

A comprehensive review of warm mix asphalt mixtures-laboratory to field

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...

A simplified pavement condition index regression model for pavement evaluation

One of the most destructive environmental issues around the world is the disposal of construction and demolition (C&D) wastes such as recycled concrete aggregate (RCA), crushed rock (CR), w...

Effect of Crushed Glass on Behavior of Crushed Recycled Pavement Materials Together with Crumb Rubber for Making a Clean Green Base and Subbase

The hot-mix asphalt (HMA) dynamic modulus (E*) is a fundamental mechanistic property that defines the strain response of asphalt concrete mixtures as a function of loading rate and temperat...

Comparing Artificial Neural Networks with Regression Models for Hot-Mix Asphalt Dynamic Modulus Prediction

nternational Roughness Index (IRI) is an important parameter that indicates the ride quality and pavement condition. In this study, an Artificial Neural Network (ANN) model was developed to predict the IRI for Jointed Plain Concrete Pavement (JPCP) sections. The inputs for this model are: initial IRI value, pavement age, transverse cracking, percent joints spalled, flexible and rigid patching areas, total joint faulting, freezing index, and percent subgrade passing No. 200 U.S. sieve. This data was obtained from the Long Term Pavement Performance (LTPP) Program. It is the same data and inputs used for the development of the Mechanistic-Empirical pavement Design Guide (MEPDG) IRI model for JPCP. The data includes a total of 184 IRI measurements. The results of this study shows that using the same input variables, the ANN model yielded a higher prediction accuracy (coeficint of determination: R2 = 0.828, and ratio of standard error of estimate (predicted) to standard deviation of the measured IRI values: Se/Sy =0.414) compared to the MEPDG model (R2 = 0.584, Se/Sy =0.643). In addition, the bias in the predicted IRI values using the ANN model was significantly lower compared to the MEPDG regression model.

International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application

Accurate estimation of the layer moduli for an existing pavement system is a key element for making an effective decision on pavement rehabilitation. Backcalculation is one of the most wide...

Simplified Closed-Form Procedure for Network-Level Determination of Pavement Layer Moduli from Falling Weight Deflectometer Data

Egypt is ranked among the countries with the highest rates of road accidents. According to the American Chamber of Commerce more than 96% of Egypt's goods are transported by trucks and due to their large volume and excessive weight, the severity and number of truck accident fatalities are much higher than other vehicles in Egypt. The present study aims at identifying truck driver's behavior and its influence on crash involvement. Due to the shortage in recording accident data and the inaccurate road accident audit, data was collected from several governorates in Egypt through questionnaire. Questionnaire forms were filled out through personal interviews with truck drivers. The total number of respondents was 643. The final analysis was made on the 615 questionnaires with complete answers. The data was analyzed and logistic regression was applied to accident related data to examine the contributing factors affecting accident occurrence of truck drivers. Results showed that fatigue in terms of driving hours (continuous and total) and lack of sleep, drug use during driving, and driver obesity are the most influencing factors on the occurrence of truck accidents in Egypt. The findings of this research highlight the important role human factors have on the risk of crash involvement amongst Egypt's truck drivers and the need to improve their work conditions.

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Ragaa T. Abd El-Hakim

Papers

International Roughness Index prediction model for flexible pavements

Comparing Artificial Neural Networks with Regression Models for Hot-Mix Asphalt Dynamic Modulus Prediction

International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application

Simplified Closed-Form Procedure for Network-Level Determination of Pavement Layer Moduli from Falling Weight Deflectometer Data

Factors Affecting Accidents Risks among Truck Drivers In Egypt