Integration of field and laboratory testing to determine the causes of a premature pavement failure

2006 ◽  
Vol 33 (11) ◽  
pp. 1345-1358 ◽  
Author(s):  
Dar Hao Chen ◽  
Tung-Tsan Chen ◽  
Tom Scullion ◽  
John Bilyeu
Keyword(s):  
Ground Penetrating Radar ◽  
Laboratory Testing ◽  
Permanent Deformation ◽  
Base Material ◽  
Falling Weight Deflectometer ◽  
Type B ◽  
Premature Failure ◽  
Class 1 ◽  
Ground Penetrating ◽  
Falling Weight

The main objective of the forensic study was to identify the cause(s) of the pavement failure on a temporary detour of an interstate highway in Austin, Texas. Ground penetrating radar (GPR), falling weight deflectometer (FWD), coring, trenching, and comprehensive laboratory tests were performed. It was found that the main cause of the premature failure was attributed to material and construction practices. The base material used on this project did not meet the Triaxial class 1 requirement; it tested as a class 2.3 material. The base material was found to be highly moisture susceptible; it did not meet the Texas Department of Transportation's (TxDOT's) compressive strength requirements when subjected to capillary soaking. In addition, the repetitive triaxial test results revealed that the stiffness and load-carrying capability and resistance to permanent deformation became inadequate when the base material was exposed to moisture. It is believed that moisture entered this pavement primarily through poorly compacted AC layers and longitudinal joints. Cores taken in March 2004 from the original type B and C layers confirmed that the majority of cores have air voids exceeding 9%. The lower type B layer was also badly segregated and debonded from the upper type C layer at some locations. GPR results also indicated that the joints in the pavement were excessively porous. Further tests on the recovered binder for the type B layer indicated that the binder was prematurely aged, most probably from overheating during production.Key words: pavement failure, forensic, ground penetrating radar (GPR), falling weight deflectometer (FWD), laboratory testing.

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.

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.

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 3D MODELLING THE INVISIBLE USING GROUND PENETRATING RADAR

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 33-37 ◽  
Author(s):  
P. Agrafiotis ◽  
K. Lampropoulos ◽  
A. Georgopoulos ◽  
A. Moropoulou
Keyword(s):  
Data Acquisition ◽  
Ground Penetrating Radar ◽  
3D Model ◽  
Base Material ◽  
Interdisciplinary Team ◽  
3D Modelling ◽  
Important Task ◽  
Structural Behavior ◽  
Ground Penetrating ◽  
The Church

An interdisciplinary team from the National Technical University of Athens is performing the restoration of the Holy Aedicule, which covers the Tomb of Christ within the Church of the Holy Sepulchre in Jerusalem. The first important task was to geometrically document the monument for the production of the necessary base material on which the structural and material prospection studies would be based. One task of this action was to assess the structural behavior of this edifice in order to support subsequent works. It was imperative that the internal composition of the construction be documented as reliably as possible. To this end several data acquisition techniques were employed, among them ground penetrating radar. Interpretation of these measurements revealed the position of the rock, remnants of the initial cave of the burial of Christ. This paper reports on the methodology employed to construct the 3D model of the rock and introduce it into the 3D model of the whole building, thus enhancing the information about the structure. The conversion of the radargrams to horizontal sections of the rock is explained and the construction of the 3D model and its insertion into the 3D model of the Holy Aedicule is described.

scholarly journals Case analysis of integrated maintenance technology for multi-dimensional rapid detection and trenchless reinforcement

2021 ◽  
Vol 261 ◽  
pp. 02087
Author(s):  
Yunwu He ◽  
Tao Liu ◽  
Tao Wang ◽  
Xiayi Liang ◽  
Hanxin Wei ◽  
...  
Keyword(s):  
Rapid Detection ◽  
Service Level ◽  
Development Stage ◽  
Road Surface ◽  
Falling Weight Deflectometer ◽  
The Road ◽  
Treatment Techniques ◽  
Grouting Reinforcement ◽  
Ground Penetrating ◽  
Falling Weight

China’s highway construction has moved from “construction-oriented” to the development stage of “equal attention to construction and maintenance”. The infrastructure represented by urban expressways, under heavy and complex traffic loads, is prone to a variety of diseases that take place in the middle and lower layers of pavement such as “frost boils” and “voids”, which cannot be repaired ideally with the help of conventional detection and treatment techniques. In order to solve the above problems, this paper adopts nondestructive testing plans such as ground penetrating radar and falling weight deflectometer to conduct multidimensional rapid detection of the road surface to obtain the image information and mechanical data of the road structure. Based on the improved calculation method, the pavement disease area, depth and type can be effectively judged. Combined with the observation of water level, the polymer grouting reinforcement plan is designed to eliminate the problems in the middle and lower layers of the road surface. It can effectively reduce the incidence of diseases in the upper layer of the sidewalk, and significantly improve the efficiency and service level of the sidewalk.

Feasibility of Backcalculation of Nonlinear Flexible Pavement Layer Parameters from Nondestructive Testing

2003 ◽  
Vol 1860 (1) ◽  
pp. 16-25 ◽  
Author(s):  
Amitis Meshkani ◽  
Imad N. Abdallah ◽  
Soheil Nazarian
Keyword(s):  
Nondestructive Testing ◽  
Laboratory Testing ◽  
Nonlinear Behavior ◽  
Falling Weight Deflectometer ◽  
Nonlinear Parameters ◽  
Pavement Materials ◽  
Linear Elastic ◽  
Texas Department ◽  
Pavement Structures ◽  
Falling Weight

Nondestructive testing (NDT) methods are typically used to measure the variations in the modulus of different pavement layers. The falling-weight deflectometer (FWD) and the seismic pavement analyzer (SPA) are two of the NDT devices used for this purpose by the Texas Department of Transportation. Since the loads applied by the FWD to the pavement are similar to those exerted by traffic, the FWD moduli are used in pavement design and analysis without adjusting them for the nonlinear behavior of the materials. Seismic moduli are similar to linear elastic moduli since they correspond to small external loads. A constitutive model that considers nonlinear behavior of pavement materials is essential in order to convert seismic moduli to those appropriate for the state of stress applied by a truck. Nonlinear parameters needed for these models can normally be obtained from laboratory testing. A study was carried out to determine whether these nonlinear parameters can be estimated from the FWD deflection basin alone or from integration of the seismic and FWD data. FWD deflection alone in most cases does not seem to contain enough information to reliably provide the nonlinear parameters of the layers. Combining the seismic and deflection data would allow the estimation of some of the nonlinear parameters for weaker pavement structures. In the authors’ experience, the most reliable way to estimate the nonlinear parameters of base and subgrade is still laboratory testing.

Integration of Traffic Speed Deflectometer and Ground-Penetrating Radar for Network-Level Roadway Structure Evaluation

2017 ◽  
Vol 2639 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Kenneth Maser ◽  
Pete Schmalzer ◽  
William Shaw ◽  
Adam Carmichael
Keyword(s):  
Ground Penetrating Radar ◽  
Layer Structure ◽  
Project Planning ◽  
Preliminary Review ◽  
Remaining Life ◽  
Wide Range ◽  
Traffic Speed ◽  
Ground Penetrating ◽  
Falling Weight ◽  
Structure Evaluation

The project has focused on the East Idaho Loop Corridor (EILC), representing 518 mi of primary roadways covering a wide range of geographic conditions. The Idaho Transportation Department (TD) has pursued this effort to support future project planning and design efforts and advance the management of its assets into a more efficient, best-first set of priorities. The EILC was surveyed with a traffic speed deflectometer (TSD) and with ground-penetrating radar (GPR). After preliminary review of the TSD data, segments were selected for falling weight deflectometer (FWD) testing to confirm patterns observed in the TSD data and to adapt FWD analysis methods to the TSD data. Ninety-nine borings were taken to confirm the pavement layer structure and verify thickness calculations. The Idaho TD data were analyzed at a 10-m interval with the GPR layer thickness data to determine subgrade modulus, pavement modulus, and structural number. These values were then used to estimate overlay requirements and, given traffic projections, to calculate the remaining life as a continuous function of roadway position. The data were incorporated into a spatial geodatabase that provides the Idaho TD with a convenient means to visualize and evaluate the overall condition of the network down to the detail of its individual segments. The segments were subdivided into homogenous subsections on the basis of remaining life, and these subsegments were used for identifying and programming rehabilitation projects. The level of condition detail available at the subsegment level allows for pavement design and for scoping restoration projects.

Sign in / Sign up

Export Citation Format

Share Document