Forecasting Overall Pavement Condition with Neural Networks: Application on Florida Highway Network

2003 ◽  
Vol 1853 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jidong Yang ◽  
Jian John Lu ◽  
Manjriker Gunaratne ◽  
Qiaojun Xiang
Keyword(s):  
Neural Networks ◽  
Surface Condition ◽  
Composite Index ◽  
Critical Issue ◽  
Pavement Management ◽  
Pavement Condition ◽  
Pavement Surface ◽  
Condition Rating ◽  
Highway Network ◽  
Ann Models

Timely identification of undesirable crack, ride, and rut conditions is a critical issue in pavement management systems at the network level. The overall pavement surface condition is determined by these individual pavement surface conditions. A research project was carried out to implement an overall methodology for pavement condition prediction that uses artificial neural networks (ANNs). In the research, three ANN models were developed to predict the three key indices—crack rating, ride rating, and rut rating—used by the Florida Department of Transportation (FDOT) for pavement evaluation. The ANN models for each index were trained and tested by using the FDOT pavement condition database. In addition to the three key indices, FDOT uses a composite index called pavement condition rating (PCR), which is the minimum of the three key indices, to summarize overall pavement surface condition for pavement management. PCR is forecast with a combination of the three ANN models. Results of the research suggest that the ANN models are more accurate than the traditional regression models. These ANN models can be expected to have a significant effect on FDOT's pavement management system.

Cohen’s Weighted Kappa Statistic in Quality Control–Quality Assurance Procedures: Application to Network-Level Contract Pavement Surface Condition Surveys in British Columbia, Canada

2003 ◽  
Vol 1860 (1) ◽  
pp. 103-108 ◽  
Author(s):  
Shawn Landers ◽  
Wael Bekheet ◽  
Lynne Falls
Keyword(s):  
Quality Assurance ◽  
Surface Condition ◽  
Composite Index ◽  
Kappa Statistic ◽  
Weighted Kappa ◽  
Weighted Kappa Statistic ◽  
Testing Procedures ◽  
Pavement Surface ◽  
Surface Distress ◽  
Level Of Agreement

Like many provincial and municipal agencies, the British Columbia Ministry of Transportation (BCMoT) contracts out the collection of pavement surface condition data. Because BCMoT is committed to contracts with multiple private contractors, quality assurance (QA) plays a critical role in ensuring that the data are collected accurately and repeatably from year to year. Comprehensive QA testing procedures for surface distress data have been developed and implemented since the data collection has been based on visual ratings with event boards. Control sites that are manually surveyed are used to evaluate whether the contractor is correctly applying the BCMoT pavement surface distress rating system. To date, the QA testing has been based on a composite-index–based criterion for assessing the level of agreement and supplemented with the detailed severity and density rating data. However, the use of a composite index presents some limitations related to the model formulation and weightings assigned to particular distress types. Although the detailed ratings are useful as a diagnostic tool to pinpoint discrepancies, in the disaggregated format, they are not conducive as acceptance criteria for QA testing. Not widely used in the field of engineering, Cohen’s weighted kappa statistic has been applied since the 1960s in other areas to assess the level of agreement beyond chance among raters. The statistic was therefore identified as a possible solution for improving the ministry’s QA surface distress testing process by providing an overall measure of the level of agreement between the detailed manual benchmark survey and the contractor severity and density ratings. The application is described of Cohen’s weighted kappa statistic for visual surface distress survey QA testing using the BCMoT survey and testing procedures as a case study.

scholarly journals Pengaruh Kecepatan Kendaraan Terhadap Tingkat Kerusakan Jalan Menggunakan Metode PCI (Pavement Condition Index)

2020 ◽  
Vol 17 (2) ◽  
pp. 161-171
Author(s):  
Eko Prayitno
Keyword(s):  
Surface Condition ◽  
Condition Index ◽  
Traffic Load ◽  
Pavement Condition ◽  
The Road ◽  
Pavement Surface ◽  
Maintenance Program ◽  
Periodic Maintenance ◽  
Pavement Condition Index ◽  
Fair Poor

The pavement and pavement structure are structure consisting of one or several layers of processed materials, whose the function is to support the weight of the traffic load without causing significant damage to the construction. Pavement Condition Index (Pavement Condition Index) is the level of pavement surface condition and its size in terms of the power function that refer to the conditions and damage on the pavement surface that occurs. The Pavement Conditions Index or PCI is a numerical index that has values ​​ranging from 0 to 100 with criteria excellent, very good, good, fair, poor, very poor, and failed. The field study of this research is the road section starting from STA 310 + 000 up to 320 + 000. The assessment of road conditions according to the Pavement Condition Index (PCI), where the data was collected through field surveys. The types of damage on the ivory tip of the STA 310 + 000 - 320 + 000 are patches, crocodile cracks, holes, edge cracks, loose grains, waves and elongated cracks. Obtained pavement condition index (PCI) average is 34.6 with an assessment of the condition of road damage is bad (poor). Based on the PCI value the road is included in the periodic maintenance program.

scholarly journals Elaboration of thematic maps as a tool to support urban pavement management

2019 ◽  
Author(s):  
Cassio V. Carletti Negri ◽  
Paulo Cesar Lima Segantine
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Surface Condition ◽  
Pavement Management ◽  
Financial Resources ◽  
Urban Mobility ◽  
Considerable Influence ◽  
Thematic Maps ◽  
Pavement Condition ◽  
The City

Considering the fact that the pavement condition of municipal roads has considerable influence on urban mobility, appropriate management of this structure is necessary and requires a significant amount of financial resources and labour. The visualization of the pavement condition on thematic maps can optimize decision making and resource allocation. Thus, this work has as its main objective to elaborate thematic maps of the pavement condition and to evaluate the utility of these representations for allocation of investments intended to the maintenance of these structures. For that, thematic maps were created in QuantumGIS (QGIS) software, using the Value of the Surface Condition (VCS) of some sections evaluated in the city of Ribeirão Preto/SP. The results indicate that the visualization of this information through thematic representations, created in Geographic Information Systems (GIS), allow the pavement management to become more efficient, optimizing resource allocation and economizing in pavement valuation services.

scholarly journals Integrating Pavement Sensing Data for Pavement Condition Evaluation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3104
Author(s):  
Konstantinos Gkyrtis ◽  
Andreas Loizos ◽  
Christina Plati
Keyword(s):  
Surface Condition ◽  
Pavement Management ◽  
Transport Infrastructure ◽  
Pavement Condition ◽  
Laser Sensors ◽  
Condition Evaluation ◽  
Pavement Roughness ◽  
Inspection Data ◽  
Ground Penetrating ◽  
Falling Weight

Highway pavements are usually monitored in terms of their surface performance assessment, since the major cause that triggers maintenance is reduced pavement serviceability due to surface distresses, excessive pavement unevenness and/or texture loss. A common way to detect pavement surface condition is by the use of vehicle-mounted laser sensors that can rapidly scan huge roadway networks at traffic speeds without the need for traffic interventions. However, excessive roughness might sometimes indicate structural issues within one or more pavement layers or even issues within the pavement foundation support. The stand-alone use of laser profilers cannot provide the related agencies with information on what leads to roughness issues. Contrariwise, the integration of multiple non-destructive data leads to a more representative assessment of pavement condition and enables a more rational pavement management and decision-making. This research deals with an integration approach that primarily combines pavement sensing profile and deflectometric data and further evaluates indications of increased pavement roughness. In particular, data including Falling Weight Deflectometer (FWD) and Road Surface Profiler (RSP) measurements are used in conjunction with additional geophysical inspection data from Ground Penetrating Radar (GPR). Based on pavement response modelling, a promising potential is shown that could proactively assist the related agencies in the framework of transport infrastructure health monitoring.

Statistical Analysis of Automated versus Manual Pavement Condition Surveys

2005 ◽  
Vol 1940 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Jason M. McQueen ◽  
David H. Timm
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Analysis ◽  
Condition Index ◽  
Pavement Management ◽  
Severity Level ◽  
Pavement Condition ◽  
Condition Rating ◽  
Pavement Condition Index ◽  
Level 3

The Alabama Department of Transportation (ALDOT) has used a vendor to perform automated pavement condition surveys for the Alabama pavement network since 1997. In 2002, ALDOT established a quality assurance (QA) program to check the accuracy of the automated pavement condition data. The QA program revealed significant discrepancies between manual and automatically collected data. ALDOT uses a composite pavement condition index called pavement condition rating (PCR) in its pavement management system. The equation for PCR was developed in 1985 for use with manual pavement condition surveys; however, ALDOT continues to use it with data from automated condition surveys. Since the PCR equation was developed for manual surveys, the discrepancies between the manual and automated data led ALDOT to question the continuity between its manual and automated pavement condition survey programs. A regression analysis was completed to look for any systematic error or general trends in the error between automated and manual data. Also, Monte Carlo simulation was used to determine which distress parameters most influence the PCR and whether they require more accuracy. The regression analysis showed the following general trends: automated data overreport outside wheelpath rut depth, under-report alligator severity Level 1 cracking, and overreport alligator severity Level 3 cracking. Through Monte Carlo simulation, it was determined that all severity levels of transverse cracking, block cracking, and alligator cracking data require greater accuracy.

Network Pavement Evaluation with Falling-Weight Deflectometer and Ground-Penetrating Radar

2003 ◽  
Vol 1860 (1) ◽  
pp. 90-99 ◽  
Author(s):  
A. Samy Noureldin ◽  
Karen Zhu ◽  
Shuo Li ◽  
Dwayne Harris
Keyword(s):  
Data Collection ◽  
Ground Penetrating Radar ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Pavement Condition ◽  
Condition Rating ◽  
Structural Capacity ◽  
Pavement Evaluation ◽  
Ground Penetrating ◽  
Falling Weight

Nondestructive testing has become an integral part of pavement evaluation and rehabilitation strategies in recent years. Pavement evaluation employing the falling-weight deflectometer (FWD) and ground-penetrating radar (GPR) can provide valuable information about pavement performance characteristics and be a very useful tool for project prioritization purposes and estimation of a construction budget at the network level. Traditional obstacles to the use of the FWD and GPR in pavement evaluation at the network level used to be expenses involved in data collection, limited resources, and lack of simplified analysis procedures. Indiana experience in pavement evaluation with the FWD and the GPR at the network level is presented. A network-level FWD and GPR testing program was implemented as a part of a study to overcome those traditional obstacles. Periodic generation of necessary data will be useful in determining how best to quantify structural capacity and estimate annual construction budgets. Three FWD tests per mile on 2,200 lane-mi of the network is recommended annually for network-level pavement evaluation. The information collected will allow the equivalent of 100% coverage of the whole network in 5 years. GPR data are recommended to be collected once every 5 years (if another thickness inventory is needed) after the successful network thickness inventory conducted in this study. GPR data collection is also recommended at the project level and for special projects. Both FWD and GPR data are recommended to be used as part of the pavement management system, together with automated collection of data such as international roughness index, pavement condition rating, rut depth, pavement quality index, and skid resistance.

Adequate data collection frequency and gauging length for surface condition rating in Alberta

2001 ◽  
Vol 28 (5) ◽  
pp. 871-874
Author(s):  
Ashraf El-Assaly ◽  
Simaan AbouRizk ◽  
Jane Stoeck
Keyword(s):  
Data Collection ◽  
Surface Condition ◽  
Pavement Condition ◽  
Condition Rating ◽  
Deterioration Rate ◽  
Pavement Evaluation ◽  
Pavement Deterioration ◽  
Transportation Departments ◽  
Data Collection Procedure ◽  
Adequate Data

In order to ensure safe and comfortable riding, increase network serviceability, and slow pavement deterioration rate, two different strategies are being used simultaneously by transportation departments: routine maintenance and major rehabilitation. Within Alberta Infrastructure, the decision between these alternatives is made based on the pavement condition. Data are collected frequently for the purpose of pavement evaluation. Experienced engineers and technicians normally decide the most appropriate data collection procedure and gauging length associated with it.Key words: pavement, data collection, evaluation, gauging length, condition rating.

scholarly journals On the Applicability of Laser Scanning for Evaluation of the Pavement Serviceability Parameters

2021 ◽  
Vol 906 (1) ◽  
pp. 012138
Author(s):  
Veronika Valaskova ◽  
Jozef Vlcek ◽  
Alicja Kowalska-Koczwara
Keyword(s):  
Laser Scanning ◽  
Surface Condition ◽  
The State ◽  
Pavement Management ◽  
International Roughness Index ◽  
Pavement Management System ◽  
Operational Conditions ◽  
Pavement Surface ◽  
Roughness Surface ◽  
Complex Knowledge

Abstract Pavement performance is influenced by man factors such as climate and environmental conditions, traffic and operational conditions and type of pavement. These factors cause a pavement deterioration what leads to the restriction of the pavement serviceability or pavement efficiency. The pavement serviceability is the ability of the pavement to fulfil the service function represented by the actual values of variable parameters such as pavement surface roughness, surface evenness, pavement surface condition. The state of the pavement is assessed using different performance indicators when International Roughness Index (IRI) is most used. This approach allows to classify the state of the pavement in the pavement management system as a most used indexing, generalizes the pavement surface to the response of the testing car tire and the pavement. Laser scanning presented in this paper is able to bring the knowledge about the real pavement surface considering the accuracy of the method and equipment. Realized laser scanning proved the applicability of this method for the measurement of the pavement surface. Because of the complex knowledge of the pavement surface morphology, we can evaluate the pavement serviceability in terms of roughness, surface evenness or even pavement surface condition (rutting or cracks).

Sign in / Sign up

Export Citation Format

Share Document