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.

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.

Application of Network-Level Pavement Management System Technology to a Unique Pavement Deterioration Problem

1996 ◽  
Vol 1524 (1) ◽  
pp. 67-75
Author(s):  
K. Helali ◽  
T.J. Kazmierowski ◽  
A. Bradbury ◽  
M. A. Karan
Keyword(s):  
Management System ◽  
Steel Slag ◽  
Cost Effective ◽  
Pavement Management ◽  
Pavement Management System ◽  
Unique Problem ◽  
Pavement Condition ◽  
Greater Toronto Area ◽  
Condition Evaluation ◽  
Rehabilitation Needs

A study is described that was conducted in response to the premature deterioration of dense friction course/open friction course (DFC/OFC) hot mix surfaces with steel slag aggregates in the greater Toronto area. The deterioration manifested itself in the form of severe raveling and early formation of map cracking. A network-level pavement management system (PMS) was applied to this unique problem. A pavement condition evaluation was conducted, and a steel slag DFC/OFC-specific deterioration model was developed. The application of the PMS has been efficient. It facilitated estimating the rehabilitation needs, prioritizing the rehabilitation strategies, and demonstrating the most cost-effective budget.

Protocol for FWD Data Collection at Network-Level Pavement Management in Iran

2018 ◽  
Vol 2672 (40) ◽  
pp. 68-77 ◽  
Author(s):  
Narges Matini ◽  
Nader Tabatabaee ◽  
Mojtaba Abbasghorbani
Keyword(s):  
Management System ◽  
Condition Index ◽  
Structural Condition ◽  
Pavement Management ◽  
Pavement Management System ◽  
Potential Safety ◽  
Time Required ◽  
Falling Weight Deflectometers ◽  
Ground Penetrating ◽  
Falling Weight

The objective of this study was to develop an approach for incorporating techniques used to interpret and evaluate deflection data for network-level pavement management system applications. A national pavement management system is being developed in Iran and the use of falling weight deflectometers (FWDs) at the network level was deemed necessary to compensate for the lack of vital construction history data in the pavement inventory. Because FWD measurements disrupt traffic flow and are a potential safety hazard, it is imperative to increase the interval between FWD testing points as much as possible to allow scanning of the entire 51,000 km network of freeways, highways, and major roads in a reasonable time span with the least traffic disruption. A project-level dataset at 0.2 km intervals in different environments and diverse traffic categories was selected for analysis. In addition, data from continuous ground-penetrating radar was collected concurrently and compared with a limited number of cores. The overall analysis included evaluation of interval variation, segmentation, the structural condition index (SCI), and layer moduli calculated using the AASHTO and ELMOD methods. The analysis was done to determine the optimum interval between test points. Analysis showed that the collection intervals could be increased from 0.2 to 0.6 km. Subsequently, the applicability and time efficiency of the network-level intervals were verified by calculating overlay thickness and time required.

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 A new method to determine maintenance and repair activities at network-level pavement management using falling weight deflectometer

2017 ◽  
Vol 23 (3) ◽  
pp. 338-346 ◽  
Author(s):  
Amir KAVUSSI ◽  
Mojtaba ABBASGHORBANI ◽  
Fereidoon MOGHADAS NEJAD ◽  
Armin BAMDAD ZIKSARI
Keyword(s):  
Resilient Modulus ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Maintenance And Repair ◽  
Structural Evaluation ◽  
Data Collection And Analysis ◽  
Field Data Collection ◽  
Falling Weight

Pavement condition assessment at network level requires structural evaluation that can be achieved using Falling Weight Deflectometer (FWD). Upon analysing FWD data, appropriate maintenance and repair methods (preser­vation, rehabilitation or reconstruction) could be assigned to various pavement sections. In this study, Structural Condi­tion Index (SCI), defined as the ratio of Effective Structural Number (SNeff) to Required Structural Number (SNreq), was used to determine if a pavement requires preservation or rehabilitation works (i.e. preservation SCI > 1, rehabilitation SCI < 1). In addition to FWD deflection data, SCI calculation requires pavement layer thicknesses that is obtained using GPR with elaborated and time consuming works. In order to reduce field data collection and analysis time at network-level pavement management, SCI values were calculated without having knowledge of pavement layer thicknesses. Two regression models were developed based on several thousand FWD deflection data to calculate SNeff of pavements and resilient modulus (MR) of their subgrades. Subgrades MR values together with traffic data were then used to calculate SNreq. Statistical analysis of deflection data indicated that Area under Pavement Profile (AUPP) and the deflection at distance of 60 cm from load center (D60) parameters showed to have strong correlation with SNeff and MR respectively. The determination coefficients of the two developed models were greater than those of previous models reported in the literature. The significant result of this study was to calculate SNeff and MR using the same deflection data. Finally, imple­mentation of the developed method was described in determining appropriate Maintenance and Repair (M&R) method at network level pavement management system.

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.

Application of Traffic Speed Deflectometer for Network-Level Pavement Management

2018 ◽  
Vol 2672 (40) ◽  
pp. 348-359 ◽  
Author(s):  
Shivesh Shrestha ◽  
Samer W. Katicha ◽  
Gerardo W. Flintsch ◽  
Senthilmurugan Thyagarajan
Keyword(s):  
Surface Condition ◽  
Structural Condition ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Pavement Management System ◽  
State Highway ◽  
Structural Evaluation ◽  
Traffic Speed ◽  
State Highway Agencies ◽  
Falling Weight

In this paper, the traffic speed deflectometer (TSD), a device used for network level structural evaluation, is assessed. TSD testing was performed in nine states on a total of 5,928 miles (some repeated) during three time periods: November 2013, May to July 2014, and June to September 2015. This paper presents (1) the results of repeatability and comparison of the TSD with the falling weight deflectometer (FWD), (2) the results of the comparison of TSD measurements with typical pavement management system (PMS) data, and (3) an approach that can be implemented by State Highway Agencies (SHAs) to incorporate indices derived from TSD data into their PMS decision-making process. The results show that repeated TSD measurements follow similar trends and the TSD measurements and FWD measurements on the same pavement sections follow similar trends as well. Comparing TSD measurements with PMS surface condition data confirmed that the TSD provided valuable information about the structural condition of the tested pavement sections that cannot be derived from the already available pavement surface condition as part of an agency’s PMS. An example of how TSD information can be used to refine the triggered maintenance treatment category as part of a network-level PMS analysis is presented for a roughly 75-mile section of I-81 south in Virginia.

Nondestructive data analysis for pavement profile evaluation

2021 ◽  
Author(s):  
Christina Plati ◽  
Konstantinos Gkyrtis ◽  
Andreas Loizos
Keyword(s):  
Structural Integrity ◽  
Traditional Approach ◽  
Surface Condition ◽  
Cost Effective ◽  
Destructive Testing ◽  
Sensing Data ◽  
Pavement Surface ◽  
Structural Problems ◽  
Pavement Roughness ◽  
Ground Penetrating

&lt;p&gt;Highway pavements serve the need for safe transportation of human being and freights, so their condition deserves continuous monitoring and assessment. However, pavements are most often monitored in terms of their surface performance evaluation. Either with or without surface distresses, excessive pavement unevenness and/or texture loss may lead to a reduced road users&amp;#8217; satisfaction. Most often, the pavement surface condition is sensed through laser profilers that operate at traffic speeds. Once detected through the stand-alone use of laser profilers, pavement roughness along a pavement surface may be of major concern for the related agencies, since the root causes of roughness issues are in most cases unknown.&lt;/p&gt;&lt;p&gt;Excessive unevenness might sometimes be interrelated with structural issues within one or more pavement layers or even issues within the pavement foundation support. Traditionally, coring and boreholes are considered suitable to detect the condition of pavement surface layers and pavement substructure respectively. However, these processes are destructive and time-consuming. On the contrary, Non-Destructive Testing&lt;/p&gt;&lt;p&gt;(NDT) can be alternatively used to rapidly evaluate potential structural problems at areas with roughness issues and identify areas for further investigation. A popular method to assess the pavement structural integrity is the use of nondestructive deflectometric tests, including the Falling Weight Deflectometer (FWD). This kind of testing outperforms the traditional approach; thus it is both desirable and practical.&lt;/p&gt;&lt;p&gt;On these grounds, related research is pursued towards integrating pavement profile and deflectometric data in order to further evaluate indications of increased pavement roughness. In particular, Long Term Pavement Performance (LTPP) data including deflectometric and pavement profile data is used. Additional sensing data through geophysical inspections with the Ground Penetrating Radar (GRP) is used to assist the overall pavement assessment. The study demonstrates the power of pavement sensing data in order to provide the related agencies with cost-effective and reliable evaluation methods and approaches.&lt;/p&gt;

scholarly journals Identification of Pavement Distress Types and Pavement Condition Evaluation Based on Network Level Inspection for Jazan City Road Network

2014 ◽  
Vol 10 (2) ◽  
pp. 44 ◽  
Author(s):  
M Mubaraki
Keyword(s):  
Road Network ◽  
Safety Evaluation ◽  
Condition Index ◽  
Pavement Management ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Transverse Cracking ◽  
Condition Rating ◽  
Condition Evaluation ◽  
Surface Distress

 The first step in establishing a pavement management system (PMS) is road network identification. An important feature of a PMS is the ability to determine the current condition of a road network and predict its future condition. Pavement condition evaluation may involve structure, roughness, surface distress, and safety evaluation. In this study, a pavement distress condition rating procedure was used to achieve the objectives of this study. The main objectives of this study were to identify the common types of distress that exist on the Jazan road network (JRN), either on main roads or secondary roads, and to evaluate the pavement condition based on network level inspection. The study was conducted by collecting pavement distress types from 227 sample units on main roads and 500 sample units from secondary roads. Data were examined through analysis of common types of distress identified in both main and secondary roads. Through these data, pavement condition index (PCI) for each sample unit was then calculated. Through these calculations, average PCIs for the main and secondary roads were determined. Results indicated that the most common pavement distress types on main roads were patching and utility cut patching, longitudinal and transverse cracking, polished aggregate, weathering and raveling, and alligator cracking. The most common pavement distress types on secondary roads were weathering and raveling, patching and utility cut patching, longitudinal and transverse cracking, potholes, and alligator cracking. The results also indicated that 65% of Jazan's main road network has an average pavement condition rating of very good while only 30% of Jazan's secondary roads network has an average pavement condition. 

scholarly journals The Relation between Structural Distresses in the Flexible Pavements

2020 ◽  
Vol 9 (3) ◽  
pp. 1194-1196
Keyword(s):  
Correlation Coefficient ◽  
Management System ◽  
Pearson Correlation ◽  
Flexible Pavements ◽  
Pavement Management ◽  
Pearson Correlation Coefficient ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Condition Evaluation ◽  
The Relationship

The pavement management system deals with maintenance, repair, and rehabilitation of pavements. Pavement Condition evaluation is one of the critical steps in the pavement management system. It required distresses identification and measurement on the pavement surface. Structural distresses are an essential component in the pavement condition evaluation. To take decisions precisely, it is necessary to develop the relationship between various distresses. In this study, the relationship between structural distresses in flexible pavements has been analyzed. The primary structural distresses as longitudinal cracking, transverse cracking, Fatigue cracking, Block cracking, and deflection has been considered for the study. The correlation between all the distresses has been analysis. For correlation analysis, the Pearson Correlation Coefficient is used. The values of the Pearson correlation coefficient indicate that there is a strong positive relationship betweenthe distresses. Furthermore, to develop a prediction model, the regression analysis has been done. The values of the coefficient of regression indicate that the relationship is linear and positive between the structural distresses.

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