Pavement Condition Index (PCI)

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Evaluation of Pavement Distress for Asphalt Pavement Using Pavement Condition Index: Case Study from Adama to Awash Arba

Evaluation of Pavement Distress for Asphalt Pavement Using Pavement Condition Index: Case Study from Adama to Awash Arba

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 pavement condition index 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 pavement condition 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 pavement condition 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 pavement condition 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 pavement condition of the study area in order to sustain the design life of the Pavement.
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Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Abstract: Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi- layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., Levenberg-Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS model outperforms other models with the promising results of APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.
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Comparison of Pavement Performance Models for Urban Road Management System

Comparison of Pavement Performance Models for Urban Road Management System

Usually pavement condition is characterised by a number of pavement distresses and other factors. The pavement condition index (PCI) is most commonly used. Shah, Jain, Tiwari, & Jain (2013) conducted a study, in which the combined OPCI (Overall PCI) was evaluated by considering the effects of four main pavement performance indicators such as distresses, roughness, structural capacity and skid resistance for selected urban road sections of Noida city. They concluded that pavement structural strength is one of the crucial factors, influencing pavement performance, thus should be considered when deciding the maintenance and repair strategy for an urban road network. The Ohio Department of Transportation (2006) uses a Pavement Condition Rating (PCR) in a form of scale, which has a range from 0 to 100, where 100 represents an ideal pavement with no notable distress, while 0 represents a pavement with all distress of the highest severity and extent. Baladi, Dawson, Musunuru, Prohaska, & Thomas (2017) describe a balanced pavement condition rating system based on the remaining structural period (RSP) and remaining functional period (RFP). RFP can be considered a pavement rating for the users, whereas RSP is an agency rating. RFP is calculated for a section of the road based on 3 PPIs: IRI, rut depth and skid resistance. RSP is calculated for 6 PPIs, including different types of cracking, as well as rut depth. Any road section in question is assigned the lowest calculated RFP and RSP.
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A Comparative Study on Pavement Condition Rating Methods for Flexible Roads

A Comparative Study on Pavement Condition Rating Methods for Flexible Roads

Pavement condition index is analyzing and evaluating of the pavement condition for each segment in length. PCI can be determined by manual visualization and that has to be conducted periodically and the numerical rating starting from 1 to 100 is awarded to each road section. The typical rating is: 90 to 100 for excellent; 80 to 89 for very good; 70 to 79 for good; 60 to 69 for fair; and lower than 60 for poor. There are so many standards developed by different organizations such as the method of U.S Army corps of engineering, the Highway preservation system useful in Canada, the Indian Road Congress (IRC) method. Each method is based on different criteria. The main use of PCI is to determine the fund required to maintenance and rehabilitation and to determine the priority of the segment to be repaired first [1].
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Usage Surface Deflection Data for Performance Prediction in Flexible Pavement

Usage Surface Deflection Data for Performance Prediction in Flexible Pavement

The conventional method used for calculating pavement condition index (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.
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Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

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), pavement condition index (PCI), structural condition index (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.
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Study about level of railway road damage 
		railways with sustainable PCI method

Study about level of railway road damage railways with sustainable PCI method

Assessment of road surface conditions is one the important steps to determine the type of revaluation program that needs to be done. This study aims to obtain the results of pavement value evaluation and follow-up plan to be taken. The method used is Method pavement condition index (PCI). The data used are observation data on location, the sequence of data processing starting from determining Damage Level, Hardening Condition Conditioning, and Classification of Pavement Quality, PCI value index consists of 0 to 100. Evaluation of road condition of Mayor HE Sukma Rd with PCI method resulted in 64% PCI value included in good category, but in samples there were 3.33% blow up damage, 13.33% corner break, logitudinal, tranverse, and diagonal cracks 23,33%, durability cracking 3,33%, patching large and utility cut 13,33%, scalling 3,33%, spalling tranverse and longitudinal joint equal to 13,33%. The follow-up plan that can be taken on the basis of the highest density of cracks is logitudinal, tranverse, and diagonal with cracking improvements exceeding 3 mm needs to be cleaned and filled with asphalt to prevent water infiltration into cracked cracks, if the cracks exceed 5 mm return the plate locally.
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EVALUATING PAVEMENT CONDITION FOR SELECTED SEGMENTS OF ROAD NETWORK IN AL-MUTHANNA CITY.

EVALUATING PAVEMENT CONDITION FOR SELECTED SEGMENTS OF ROAD NETWORK IN AL-MUTHANNA CITY.

To identify effective management and maintenance, Pavement management system (PMS) involves systematic activities to this, based on Pavement condition index (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.
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Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

surface inspection to detect cracks and anomalies. Hyperspectral imaging, HSI, has been used previously to classify road conditions from satellite images [4] [5] [6] [7] [8]. 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 [1] [9] [10] . 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 [11] to the Pavement Condition Index, 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 [5].
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Pavement Condition Forecasting Through Artificial Neural Network Modelling

Pavement Condition Forecasting Through Artificial Neural Network Modelling

The Architecture of back propagation three layer artificial neural network model for pavement condition forecasting modelling is designed as shown in figure 2.Two input variables, first present pavement age and second present pavement condition index (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).
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Index Terms Algebraic inverse, Free plate, Pavement,

Index Terms Algebraic inverse, Free plate, Pavement,

For many years, the numerical inverse procedure has been extensively employed for the determination of the layers moduli and thicknesses of a layered structure. However, this technique is complex time consuming and requires experienced person and engineering judgment. Ideally, the inverse should be obtained by means of a true mathematical inverse. The frequency equation for the system is expanded as a polynomial, and is solved for the unknown parameters of the system. A direct symbolic solution for the determination of the thickness of the free plate model, using mathematical software, has been developed. It is used to calculate the thickness of the uniform free plate corresponding with pairs of the values of phase velocity and frequency as data. The wave propagation in elastic plates is analogous to propagation in layered spaces and therefore this system can be used to model the surface layer of a single layered pavement structure. To assess the effectiveness and reliability of the proposed technique, the application of the method to a published set of data obtained in the field is presented and discussed. The comparison between the experimental results and the developed algebraic solutions show very good agreement, indicating that the proposed method can be used as an economic and effective technique to determine the thickness of the pavement surface layer.
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Behavior of Asphalt Pavement Structure Under Influenced Of Alteration Climatic Condition

Behavior of Asphalt Pavement Structure Under Influenced Of Alteration Climatic Condition

And it has a significantly effecting on manufacturing of pavement and renovation and protection. Presently, bygone weather condition utilized to scheme built and preservation and repair processes. However, changes in atmospheric conditions mean that operation and application presently applied may not be proper and suitable for the futurity atmospheric state and hence for the whole service life load pavement. In recent time highways and road project are mainly planed and built for up to 40 years for nominal service life, with the anticipation that duration renewal and replacing layers of asphalt pavement will happen approximately as average each 13 years. Confirming that road construction and highway and repair perform recently is must be appropriate for the subsequent weather condition is to prohibit precocious deterioration. Besides, that the consequence of climate on the asphalt also related to on more other reasons, like as the properties of the pavement (constituent substance, texture and condition), water seepage, characteristic of layers, profile and contour of the ground and transportation movement. Commonly, the result of these risks are damage of the surface, fundamental substrate and composition of the asphalt and, sometime, in the occurrence of severe climatic, disastrous defeat and collapse. The threats are further interconnect, for instance high traffic movement enhancement rutting at high hot weather. Hot dry summer subsequently with wet cold winter increase the rate of peril and detrimental in many high way project on clay soil by
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Volume 3, Issue 2, February 2014 Page 165

Volume 3, Issue 2, February 2014 Page 165

Ultimate objective of any maintenance management systems is to optimize the resources (mainly money) required to minimize the maintenance cost. Here in this study, maintenance treatment and its corresponding cost are correlated to quantify condition state of pavement by using non liner regression analysis. The research is useful to identify the deterioration process and its severity with respect to time which will guide to decide the cost effective maintenance policy and the right time of doing it well before pavement needs rehabilitation.
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An analysis of drought events for central plains of Iran through an employment of NOAA-AVHRR data

An analysis of drought events for central plains of Iran through an employment of NOAA-AVHRR data

• Best conditions were prevalent in May 2002 and 2003. Nevertheless, drought condition (in 2000. 2001) exerted the worst thermal stress. • Based on validated analysis such vegetation indices as NDVI and VCI are in high correlation with Z square of average temperature and relative humidity. However, TCI and VHI do not exhibit any correlation with climate indices. Despite the promising results found in this study, there is need to improve the quality of satellite data for operational applications. Satellite data is largely affected by various sources of error such as satellite changes, orbital drift, and sensor degradation, atmospheric perturbation due to aerosols and clouds, and surface non-uniformity change in equator crossing time during the satellite life span. It can, however, be concluded that when used together with climate stations data, AVHRR data from NOAA polar orbiting satellites can provide valuable information regarding the start of drought, development, and impacts on regional scale as well as on such arid zone scales as Kashan area.
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Validity of a PCI Bleeding Risk Score in patient subsets stratified for body mass index

Validity of a PCI Bleeding Risk Score in patient subsets stratified for body mass index

This is a real-world, large-scale retrospective analysis utilis- ing American College of Cardiology (ACC) data from the Ascension Health System (AHS). The AHS includes a group of 39 community hospitals across the USA. A central repository, independent of the NCDR-CathPCI database, was prospectively initiated across the health system in 2007 with mandatory reporting of 84 standardised data points de fi ned by the ACC. Data were entered prospectively by trained personnel at the time of the heart catheterisation for consecutive patients from all AH hospitals performing catheterisation in this healthcare system. This data entry was collected and entered into the hospital registry inde- pendent of national reporting by hospitals to the NCDR and, unlike the NCDR, does not include university hospital or tertiary centre data. Cath laboratory technicians and nursing staff entered the data immediately following each procedure. The registry represents procedures and devices as used in routine clinical practice per operator discretion. The database is routinely audited for accuracy and com- pleteness. The data from the most recent 3-year period from 1 June 2009 through 30 June 2012 for index PCI pro- cedures was selected (n=5114). Preprocedure creatinine values were used for the glomerular fi ltration rate (GFR) calculation. Patients missing preprocedure creatinine (n=254) had postprocedure creatinine imputed into the calculation. An additional 167 patients had missing pre- creatinine and postcreatinine and were excluded from the analysis (3.2%). Patients with complete BRS information were included in this study (n=4693).
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High triglyceride–glucose index is associated with poor prognosis in patients with acute ST-elevation myocardial infarction after percutaneous coronary intervention

High triglyceride–glucose index is associated with poor prognosis in patients with acute ST-elevation myocardial infarction after percutaneous coronary intervention

IR: insulin resistance; TyG index: triglyceride–glucose index; STEMI: acute ST- elevation myocardial infarction; PCI: percutaneous coronary intervention; ACS: percutaneous coronary intervention; CVD: cardiovascular disease; MACCE: major adverse cardiac and cerebrovascular events; HOMA-IR: homeostasis model assessment of insulin resistance; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; AMI: acute myocardial infarc- tion; IQR: interquartile range; hs-CRP: hypersensitive C-reactive protein; FPG: fasting plasma glucose; TC: total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SCr: serum creatinine concentration; eGFR: estimated glomerular filtration rate; LVEF: left ventricular ejection fraction.
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Prediction of The Pavement Condition For Urban Roadway A Tehran Case Study (RESEARCH NOTE)

Prediction of The Pavement Condition For Urban Roadway A Tehran Case Study (RESEARCH NOTE)

3.4. Sweater Condition Environnementales conditions affect pavement condition. 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.
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Utilization of Public Transit Vehicles to Provide Continous Condition Survey of Asphalt Pavement

Utilization of Public Transit Vehicles to Provide Continous Condition Survey of Asphalt Pavement

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:
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An approach to produce a GIS database for road surface monitoring

An approach to produce a GIS database for road surface monitoring

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.

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Сomparative Characteristics of Indices to Assess the Quality of Mussel Production by an Example of Cultivated Mytilus galloprovincialis (Сrimea, the Вlack Sea)

Сomparative Characteristics of Indices to Assess the Quality of Mussel Production by an Example of Cultivated Mytilus galloprovincialis (Сrimea, the Вlack Sea)

The GSI index is most often used to assess the reproductive characteristics of mollusks (Zardi et. al., 2007; Toro, Thompson, & Innes, 2002), and some authors suggest using it to assess the quality of cultured bivalve production (Kholodov et al., 2017). We consider the using of the GSI to assess the quality of mussel production is inconvenient because it is rather difficult to separate gonads from the soft tissues and the technique requires a lot of samples to accurately calculate GSI. However, if this index is used to evaluate the time of harvesting of mussel it is necessary to take into account that M. galloprovincialis reach the commercial quality at GSI>8.5.
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