Abstract: Pavement distress is a common problem for an opening road network and this distress is caused due to overloading of vehicles, poor maintenance, rapid traffic growth and improper design and implementation. This research study focused in Adama to Awash-Arba road section. The main objective of this research work was to evaluate the pavement distress using pavementconditionindex for the road section from Adama to Awash Arba. The result of the research show that the PCI value range from 8 to 97.1 and this shows that all section of the road have all types of pavementcondition rating (Good, Satisfactory, Fair, Very Poor, Poor, Serious and Failed) in which 12.12% good, 9.09% satisfactory, 18.18% fair, 21.21% poor, 18.18% very poor, 18.18% serious and 3.03% was failed. Based on the pavementcondition rating, seven soil samples was collected for the failed and serious road section using manual hand auger. Samples were air- dried before taken to laboratory test determination of subgrade soil. According to the pavementcondition survey the road section from Adama to Awash Arba required maintenance and based on this, possible maintenance option had been recommended for pavement distress with respect to level of severity on the pavementcondition of the study area in order to sustain the design life of the Pavement.
Abstract: Construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavementconditionindex (PCI), structural conditionindex (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.
The conventional method used for calculating pavementconditionindex (PCI) has two major drawbacks: safety problems during pavement inspection, and human error. This paper proposes a method for removing these problems. The proposed method uses surface deflection data in falling weight Deflectometer test to estimate PCI. The data used in this study were derived from 236 pavement segments taken from Tehran-Qom freeway in Iran. The data set was analyzed using multi layers perceptron (MLP) and radial basis function (RBF) neural networks. These neural networks were optimized by levenberg-marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic (RBF-GA) algorithms. After initial modeling with four neural networks mentioned, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of analysis have been verified by the four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SD). The best reported results belonged to CMIS, including APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.
________________________________________________________________________________________________________ Abstract— the purpose of this study is to conduct a physical survey for distresses analysis of 50 lanes of urban flexible roads and to conduct a comparative study on pavementcondition rating methods (PCRM) using IRC method, and Highway preservation system. 50 lanes of urban flexible pavements from all over Pune city are selected as a case study. The lanes are visually surveyed to detect the types, severity and extent of the distresses based on IRC and WDOT guidelines (Distress identification manuals). Firstly the lane wise data base is created. Secondly the severity and extent is determined based on IRC and WDOT (DIM). And thirdly the lane wise Pavementconditionindex (PCI) values are calculated by the methods of IRC, and HPS. To differentiate between both the methods the correlation analysis has been carried out.
The Architecture of back propagation three layer artificial neural network model for pavementcondition forecasting modelling is designed as shown in figure 2.Two input variables, first present pavement age and second present pavementconditionindex (PCI) has been considered. Asan output of the model, future PCI has been taken. BPA first phase namely forward pass calculates the network output by propagating the input data through the network. The network output is then compared with the desired output to calculate the error using a backward pass; during the backward pass connection weights are modified to reduce the target error. Sigmoidal transfer function was used as a neuron transfer function between input layer to hidden layer and hidden layer to output layer. Network training represents acquiring the knowledge of forecasting the PCI value.MATLAB software package was used for training and testing the ANN model. Training was stopped when the mean absolute error, root mean squared error and mean absolute relative error reached a previously specified minimum value (0.001).
Pavement maintenance has attracted growing attention of pavement engineers in recent years. Evaluation of pavement conditions is the most important factor for the effective and economical maintenance of the pavement network that can lead to the promotion of service life . The condition of an in-service pavement is assessable in two categories including functional and structural. Both functional and structural conditions play an important role in pavement management at the network- level. In most Pavement Management Systems (PMSs), non-structural indices such as PavementConditionIndex (PCI) are used as pavement indicators to select treatments [3,5] while ignoring the structural conditions of pavement . It has recently been proven that there is a statistical relationship between functional and structural conditions . Hence in recent years, various agencies around the world have attempted to use indices of structural capacity in PMS and decision-making processes .
To identify effective management and maintenance, Pavement management system (PMS) involves systematic activities to this, based on Pavementconditionindex (PCI). To calculate PCI value for “Al Shahid Mohammed Ali Al Hassani” road branch in Al-Muthanna governorate, Al- Rumaitha city, used PAVER 5.2.3 program. The length of the selected road is 2 km and two lanes in each direction. After sampling process, visual survey is managed for studying type, level of severity and quantity of distress in the sample units at the selected road. Further, collection road for selected road are inventoried and estimated using PAVER 5.2.3 to compute the PCI. As the PCI of the inspected pavement was “85” that means the pavement needs preventive maintenance. Each type of distresses has been studied to identify failure causes. The treatments of each type of distresses have been suggested as a countermeasure. These treatments include pothole patching, crack filling and isolated overlay.
The objective of the proposed guidelines for a roadway management system (RMS) is to describe a framework for a modular and user-friendly RMS that will assist local government agencies of all sizes in coordinating and planning routine and preventive maintenance, rehabilitation, and reconstruction. These guidelines include a step-by-step procedure to establish a customized RMS for local government agencies. The resulting RMS, based upon the proposed guidelines, will be a systematic methodology that can assist local government agencies to evaluate the current pavementcondition, identify problems on the pavements, select the best repair and maintenance strategies with the minimum cost, and generate a schedule and priority program for these actions at both project and network levels at both the present time and the future. The terms and definitions used in the inventory program, the referencing and the defining methods for the roadway network, and the understanding between the project and the network level are established, such that the data collection process can be initiated to gather information from concerned pavements within the roadway network. A step-by-step procedure is described for obtaining the pavementcondition as represented by the pavementconditionindex (PCI) value for different low-volume flexible and built-up pavement types as well as different maintenance strategies. In the proposed guidelines, the PCI value forms the basis for
surface inspection to detect cracks and anomalies. Hyperspectral imaging, HSI, has been used previously to classify road conditions from satellite images     . The research was intended to classify road conditions in general and the spatial resolution can not detect road cracks or defects. Only few papers considered the detection of pavement cracks based on hyperspectral data    . In such case HSC were fitted on drones of low altitude flights to have higher spatial resolutions to enable observing cracks. The previous studies considered using descriptors of the spectrum such as the VIS2 (intensity difference between 830nm and 490nm-showing metal oxide content) and Short Wave Infra Red, SWIR (Intensity difference between 2120nm and 2340 showing hydrocarbon content). The metrics measure the rise and decay of spectral response curve at the wavelength regions for metal oxides and hydrocarbon which usually characterizes road conditions. These metrics have also been linked  to the PavementConditionIndex, PCI,(A standard metric by ASTM D6433 and D5340, used to indicate the condition of road pavement and ranges 0-100) and is usually computed using visual surveys .
To describe the pavementcondition, an index know as PCI (PavementConditionIndex) is used of which value varied between 0 (for an unusable pavement) and 100 (for a proper pavement). PCI is both calculated from a field visual study and also accurately by considering the type and the severity of the deteriorations by measuring their amount. A schematic image of this technology is apparent in fig. (2), to determine PCI, a segment of pavement from all the pavement classification is selected. Due to deterioration state related to its general condition, the PCI number will be obtained that is saved in Micro Paver software database.
3.4. Sweater Condition Environnementales conditions affect pavementcondition. Moisture in particular adversely affects pavement performance and causes the pavement to deteriorate faster. Moisture not only affects pavement ingredients but can also Effect by causing physical damage. In colder climates freezing may possibly lead to deterioration through cracks. Freeze-thaw cycle damages pavement integrity. An increase in the moisture content in pavement materials may lead to faster pavement deterioration. The prediction model, therefore, has to consider and account for adverse weather conditions. Since climatic diversity within Tehran is non-existent therefore this factor was considered a constant and not modeled.
When a road is inspected, the pavement engineers and technicians working with local authorities review the database by searching the road photos. A wide range of information can be created or extracted from the geo-tagged image graphic by clicking on the image. Then, the technical staff will accordingly verify or update the severity of the existing distresses that have not been repaired, remove previous distresses that have been repaired, or add new distresses that have been identified.
The system was developed to be installed on public vehicles and to be used while the car is travelling at different speeds. As the vehicle travels, two cameras will take snap shots of the pavement and process the images onboard and the resultant file data will be stamped by the time, location and speed and transmitted by wireless system to a central location for data collection and further statistical processing. The collected data at the control center are used to feed a live map of the city road which evolves slowly over weeks and months to provide the road manager with a visual as well as numerical assessment of the road conditions throughout the city. The measurement system is a compact system to be mounted on the back of public vehicles. A picture of the measurement system and the measurement setup is shown in Figure 1. It consists of:
Age hardening enhancement the binder viscosity the degree of hardening relies on atmospheric condition, time and the thickness of bitumen layers. The hardening procedure wills development rapidly with superior temperatures pavement and larger porosity of the asphalt mixture. Extra age hardening can influence in fragile binder with mainly reduction flow ability. This hardening has both advantage and disadvantage action. On the disadvantage, the asphalt altered to brittle material with low flexibility. This will increment the hazard erode and cracking of pavement While on the affirmative side it lead to an enhancement in the modulus of stiffness or increment the capability to spreading loads of the bituminous pavement and gives better resistance against deformation In the few thickness of asphalt pavements age hardening is not acceptable. It will reduction flexibility of the pavement under the action of traffic loads and precocious cracking will be occur.
Road transport is the dominant mode of transport in ,QGLD EHFDXVH RI DGYDQWDJHV OLNH ÀH[LELOLW\ GRRUWR door service and easy accessibility to rural habitations [Thabassum, 2013]. Road network in India has expanded from 0.4 million km in 1951 to about 3.32 million NLORPHWHUV SUHVHQWO\ D VHYHQIROG LQFUHDVH EXW WUDI¿F has increased 120 times. This leads to the deterioration of surface of the pavements and a need to rehabilitate them before further damage could occur [Jundhare et al. 2012]. Pavement is the actual travel surface especially PDGH GXUDEOH DQG VHUYLFHDEOH WR ZLWKVWDQG WKH WUDI¿F load commuting upon it. Today a lot is known about how to build roads, but not so much is known on how to keep roads in a good condition, and very little is known about how to determine the structural condition of a road in some not too complicated and slow manner [Andren, 2006]. Pavement evaluation should be done to know the nature, severity and extent of the road deterioration. The key determinants for the performance of any road are analyzed through unevenness index and structural GHÀHFWLRQ>.XPDUHWDO@1RQGHVWUXFWLYHWHVWLQJ is a collective term for evaluations conducted on an existing pavement structure. Non-destructive testing methods can assess either functional or structural condition. In the present study, the four lane divided carriageway of National Highway (NH) No: 9 from Km. 240.000 to Km. 270.000 (Nandigama – Ibrahimpatnam VHFWLRQLVVHOHFWHGDVORFDWLRQWRFRQGXFWGHÀHFWLRQ and roughness surveys to evaluate its structural and functional condition and to establish a model between these two. The location: 2 selected to validate the model is from Km. 55.000 to Km. 70.000 (Naidupet – Sullurpet section of 4 lane divided carriageway) of NH-5 in the State of Andhra Pradesh (India). Study locations: 1 and 2 are shown in Figures 1 and 2.
The children conditionindex questionnaire is aimed at measuring health and wellbeing in healthy preschool and school children. The Karlskoga-Degerfors report in 4 - 5-year-old healthy children found that ten percent did not feel well. According to this report, play at school and at home usually predicts health and wellbeing . The children with uro-genital and bowel malformations were largely satisfied with their play (mean 79.4 before an 84.2 after intervention). Values were improved in all domains (NS) except environment; they were less satis- fied with the traffic and that they often hurt themselves when in school.
In general, the distresses in most of the selected pavement sites increase with age as expected. However, some of distress quantities in some of selected pavement sites appear to decrease with time (see Figure 4) or increase then drop back down (see Figure 6). Similar behaviors have been also observed by recent studies of Wisconsin DOT (Kang, 2007) and Washington DOT (Li, 2009). This behavior may be related to some repair activities performed on these pavements to alleviate serious distresses which are not clearly identified in the Iowa DOT PIMS. Thus, it is recommended that the Iowa DOT PMIS should be updated to provide detailed information related to repair activities for distresses such as the type and time of repair as well as the distress measurements before and after repair.
One of the most valuable, extensive, and important resources in the US and Europe is their roads. Assess- ment and monitoring is crucial to maintaining a safe and effective road system. There are multiple surface and sub- surface indicators of distress and defects which are observed using traditional, geotechnical engineering methods. While effective, many of these methods can be time consuming, laborious, destructive, costly, and provide information for only limited areas. The use of remote sensing techniques offers new potential for pavement managers to assess large areas, often in little time. Although remote sensing tech- niques can never entirely replace traditional geotechnical methods, they do provide an opportunity to reduce the num- ber or size of areas requiring site visits or manual methods. Employing remote sensing methods to evaluate pavement and transportation networks during and after natural or man- made disasters can also provide comprehensive information for emergency managers.
causes and pavementcondition have positive coefficients implying that a unit increase in these characteristics results in an increase in the chance of grievous injury. A unit decrease in road curve radius, vehicle responsible and day/night leads to an increase in the chance of grievous injury. The odds ratios of causes and pavementcondition are greater than 1 implying that a grievous injury has a higher chance of occurrence as the values of these characteristics increase. The odds ratios for road curve radius, vehicle responsible and day/night are less than 1 indicating that the chance of occurrence of non-injury increases as the values of these characteristics increases.
and street lighting condition (B=-0.282, IR R=0.755) is 24.5 percent lower as compared with driving during dark roadway conditions. This result corresponds with the results of (Edwards, 1998; Oňa et al., 2011; Wu et al., 2014). As compared with daylight condition, a 113 percent increase in multivehicle crash driver fatality was reported during dark roadway conditions (Wu et al., 2014). Pavement surface condition was also a determinant factor for the occurrences of severe injury and fatality on the selected highways. The results of this study suggested that the likelihood of occurrences of severe injury and fatal crashes on wet pavement surface (B=-2.858, IRR=0.057) on the selected highways was 94.3 percent less than the crashes observed on dry pavement surfaces. This was possibly attributed to the short duration of wet- weather condition per year, reduction in operating speed and traffic volume due to rainfall and bad weather conditions (Keay and Simmonds, 2005; Gunaratne et al., 2012) and, drivers taking extra preventive actions to avoid accidents due to rainfall and slippery road surface conditions (Edwards, 1998; Nassar et al., 1994; Jung et al., 2011). Using the principal factors identified in Table 11, for the aggregate severe injury and fatal crashes per 100 million VMT (CR) can be represented by the following negative binomial regression, Eq. (4):