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.

Evaluation and Validation of a Model for Predicting Pavement Structural Number with Rolling Wheel Deflectometer Data

2015 ◽  
Vol 2525 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Mostafa A. Elseifi ◽  
Kevin Gaspard ◽  
Paul W. Wilke ◽  
Zhongjie Zhang ◽  
Ahmed Hegab
Keyword(s):  
Pavement Management ◽  
Average Error ◽  
Falling Weight Deflectometer ◽  
Department Of Transportation ◽  
Data Set ◽  
The Road ◽  
Average Prediction Error ◽  
Structural Capacity ◽  
Rolling Wheel ◽  
Falling Weight

Because of costs and the slow test process, the use of structural capacity in pavement management activities at the network level has been limited. The rolling wheel deflectometer (RWD) was introduced to support existing nondestructive testing techniques by providing a screening tool for structurally deficient pavements at the network level. A model was developed to estimate structural number (SN) from RWD data obtained in a Louisiana study. The objective for this study was to evaluate the use of the Louisiana model to predict structural capacity in Pennsylvania and to compare the results with those of existing methods. RWD testing was conducted on 288 mi of the road network in Pennsylvania, and falling weight deflectometer (FWD) testing and coring were conducted on selected sites. The prediction from a model used to estimate SN from RWD deflection data was compared statistically with the prediction obtained from FWD testing and from roadway management system records used by the Pennsylvania Department of Transportation to calculate SN. The results of this analysis validated the use of the model to estimate the pavement SN according to RWD deflection data. In general, the predicted SN was in agreement with the SN calculated from the FWD. The original model with the fitted coefficients developed for Louisiana showed an average prediction error of 27%. However, after the model was refitted to the data set from Pennsylvania, the average error dropped to 19%. Results indicated that the model developed for SN prediction from the RWD provided an adequate prediction of SN for conditions different from those for which it was developed in Louisiana.

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.

scholarly journals Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

2020 ◽  
Author(s):  
Nader Karballaeezadeh ◽  
Hosein Ghasemzadeh Tehrani ◽  
Danial Mohammadzadeh S. ◽  
Shahaboddin Shamshirband
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ground Penetrating Radar ◽  
Flexible Pavement ◽  
Falling Weight Deflectometer ◽  
Machine Learning Methods ◽  
Back Calculation ◽  
Ground Penetrating ◽  
Falling Weight ◽  
Surface Deflections

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

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.

Estimated remaining fatigue life of flexible pavements based on the normalized comprehensive area ratio deflection parameter

2020 ◽  
Vol 47 (5) ◽  
pp. 546-555
Author(s):  
Karthikeyan Loganathan ◽  
Mayzan M. Isied ◽  
Ana Maria Coca ◽  
Mena I. Souliman ◽  
Stefan Romanoschi ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Area Ratio ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Structural Capacity ◽  
Pavement Management Systems ◽  
Pavement Structures ◽  
Traffic Disturbance ◽  
Falling Weight

A lot of pavement deflection data are available that may be utilized as a tool to evaluate the structural capacity of pavement structures at network and project levels. Falling weight deflectometer (FWD) is one of the most widely utilized devices in pavement deflection testing. Under FWD testing, deflections generated at several lateral locations as a result of surface loading application are recorded. One of the major downsides of the static FWD testing is the traffic disturbance due to the required lane closures during testing. As an effort to reduce the amount of the required FWD testing on the network level, this study aims to run an advanced computer simulation analysis to mimic the FWD deflection bowl obtained from the field. The entire simulated FWD deflection bowl was utilized in the development of the new comprehensive pavement deflection bowl area parameters. The tensile strain at the bottom of the asphalt layer was successfully related to the developed normalized comprehensive area ratio parameter ([Formula: see text]) and to the number of load repetitions to fatigue failure. The newly developed parameter was evaluated utilizing data for 35 long term pavement performance sections in Texas. The newly developed [Formula: see text] can be easily implemented and utilized as a tool in any pavement management systems.

Falling Weight Deflectometer Correlation

2005 ◽  
Vol 1905 (1) ◽  
pp. 90-96 ◽  
Author(s):  
Sameh Zaghloul ◽  
Zubair Ahmed ◽  
D. J. Swan ◽  
Andris A. Jumikis ◽  
Nick Vitillo
Keyword(s):  
New Jersey ◽  
Prediction Models ◽  
Pavement Management ◽  
Atlantic City ◽  
Falling Weight Deflectometer ◽  
Remaining Service Life ◽  
Structural Capacity ◽  
Pavement Management Systems ◽  
Rehabilitation Needs ◽  
Falling Weight

The falling weight deflectometer (FWD) is commonly used to perform project-and network-level structural evaluations. Some highway agencies perform network-level FWD testing as a part of their pavement management systems to assess in situ structural capacity, remaining service life, and current rehabilitation needs. Through prediction models, future condition and needs are also estimated. In contrast, project-level FWD testing is typically performed as part of the rehabilitation design process. Calibrated FWD equipment provides repeatable data for a pavement section (i.e., data obtained with the same unit, at the same location, and under similar conditions). However, different FWD devices manufactured by the same or different manufacturers do not necessarily provide similar deflection basins when they test the same section, even if they are calibrated. This paper summarizes the results of a study performed for the New Jersey Department of Transportation to assess the differences among the FWD devices available in New Jersey and to correlate the results obtained with the different devices. Two rounds of testing were performed on flexible and rigid pavement sections located in the FAA William J. Hughes Technical Center in Atlantic City. The first testing cycle was performed in November 2002, and the second was performed in May 2004. The analysis results indicate that significant differences in repeatability and reproducibility may exist between different FWD devices.

scholarly journals Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

2020 ◽  
Author(s):  
Nader Karballaeezadeh ◽  
Hosein Ghasemzadeh Tehrani ◽  
Danial Mohammadzadeh S. ◽  
Shahaboddin Shamshirband
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ground Penetrating Radar ◽  
Flexible Pavement ◽  
Falling Weight Deflectometer ◽  
Machine Learning Methods ◽  
Back Calculation ◽  
Ground Penetrating ◽  
Falling Weight ◽  
Surface Deflections

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes &ldquo;structural number&rdquo; as output and &ldquo;surface deflections and surface temperature&rdquo; as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

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.

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