Deflection Basin Research Articles

COMPARISON OF ROLLER COMPACTED CONCRETE PAVEMENT RESPONSES TO STATIC AND DYNAMIC LOADS

The objective of the study was to compare Roller Compacted Concrete (RCC) pavements responses to static and dynamic loads. Two sets of non-destructive load testing were performed on RCC pavements. The first set used static loading and dynamic loading was utilized for the second. Static loading was provided by front wheels of a Letro-Porter (front loader) and a Heavy Weight Deflectometer (HWD) was used for dynamic loading. A back-calculation procedure for estimating concrete pavement parameters is also presented. The Roller Compacted Concrete pavements were located at Moran Terminal, Boston, Massachusetts. The pavement consisted of a RCC layer of 38 cm (15 in.), a 23-cm (9-in.) gravel subbase and a compacted subgrade. The RCC layer was constructed in three lifts from bottom to top of 14, 14, and 10 cm (5.5, 5.5 and 4 in.). Four slabs were selected for testing. Strains and deflections were measured for static loading tests. A Benkelman beam was used for measuring the static load induced deflections. Deflection basins were determined during HWD tests. Load transfer efficiency at cracks and construction joints was also measured for both tests. The back-calculation procedure has been demonstrated to be satisfactory in estimating in situ pavement material properties. Using estimated RCC elastic moduli and estimated moduli of subgrade reaction, the stresses in the slabs caused by the static loads were computed by the finite element computer program JSLAB and compared to field measured stresses. It was found that the computed stresses were always greater than the measured values. A factor of safety of about 4% can be expected when HWD deflection data are used in place of static vehicle test data in rigid pavement analysis and design.

Structural condition assessment of asphalt pavement semi-Rigid bases using FWD deflection basin parameters

Assessing structural conditions of semi-rigid bases in asphalt pavements is essential for effective base treatment in reconstruction and expansion (R&E) projects. Although various falling weight deflectometer (FWD) parameters have been developed for this purpose, field-validated parameters are still missing. This study identifies the appropriate FWD deflection basin parameter (DBP) through sensitivity analysis, correlation examination, and on-site milling and inspection. Findings indicate D20-D60 as the most suitable DBP, with 23 µm and 40 µm thresholds for warning and severe conditions, respectively. The correlation between D20-D60 and cracking rate suggests that high cracking rates suggest base weakness, whereas low rates may not guarantee structural integrity due to maintenance masking effects. On-site milling further confirmed this finding, demonstrating the alignment between D20-D60 assessments and field observations. The outcome of this study is anticipated to provide a practical DBP for evaluating semi-rigid base conditions and to inform base treatment strategies in R&E projects.

Fatigue cracking prediction of cobblestone interlayer pavement using non-destructive testing and mechanistic-empirical analyses

Several non-destructive testing (NTD) methods have been used to measure surface deflection, which makes to determine the elastic moduli of pavement layers through the back-calculation process and assess the structural capacity of asphalt pavements. In this study was evaluated the back-calculated moduli of the cobblestone interlayer pavements and the load capacity of this type of pavement related to the fatigue cracking criterion based on a mechanistic-empirical analysis. The employed methodology included the performance of on-site trials using non-destructive testing with the Falling Weight Deflectometer (FWD) devices on 84 test points in granular and cobblestone interlayer pavements, determination of deflection basin parameters (DBP), back-calculation layers’ moduli, and estimate of the fatigue cracking performance of the pavements by mechanistic-empirical analyses in MeDiNa software. The pavements with a cobblestone base layer displayed greater deflection measurements on the load application point compared to those measured on pavements with a granular base layer, indicating that conventional pavement displayed more stiffness. Cobblestone interlayer pavement displayed greater amounts of cracked area compared to granular base layer pavements showing lower load capacity based on the fatigue criterion. The DBP-based method by FWD test was able to identify the structural differences between the layers of pavements evaluated and identify the cracking evolution.

Impacts of Backcalculation Software Selection on Airport Flexible Pavements Using the Aircraft Classification Rating/Pavement Classification Rating Method

From November 2024, the aircraft classification rating/pavement classification rating (ACR/PCR) method will be accepted by civil aviation authorities to notify the bearing capacity of airport pavements. Structural characteristics are necessary to calculate the parameters relating to this method, such as the modulus of elasticity which, in the case of existing pavements, can be obtained by backcalculation. However, the results of this procedure may vary depending on the type of method and algorithms used to minimize adjustment errors between the deflection basins adopted in the software. Given the above, this article aims to evaluate whether the choice of backcalculation software affects the resistance classification and admissibility of aircraft on airport pavements using the ACR/PCR method based on the backcalculated elastic moduli. The elastic modulus values of the materials from two runways were backcalculated using BAKFAA, BackCAP, BackMedina, and ELMOD software, and then used to calculate the PCR. The results of the backcalculation showed differences in the elastic moduli obtained by the software, mainly in the subgrade. It was found that the backcalculation software used can limit the type of aircraft and the runway resistance. Therefore, the choice of software for backcalculation of deflection basins can influence the results of the ACR/PCR method because of divergences in determining the resistance of the pavement’s bearing capacity and the admissibility of aircraft operations in the analyzed structures.

Comparative Evaluation of Falling Weight Deflectometer, Traffic Speed Deflectometer and Rapid Pavement Tester in Deflection Measurement

This study intends to compare deflections obtained from traffic speed deflection devices (TSDDs) with the deflections of the falling weight deflectometer (FWD). For this purpose, deflections were measured on five sections of three roads in the Norwegian road network using traffic speed deflectometer (TSD) and rapid pavement tester (Raptor). Deflections were also measured using FWD at three different temperatures and the curves of FWD deflections versus temperature (FWD temperature-dependent deflection curves) were obtained. These curves were used to correct the effect of temperature difference. It was shown that both TSD and Raptor have the potential to detect structural deficiencies; however, TSD had better consistency with FWD with regard to deflection values and deflection basin parameters. A refinement was then made to make the Raptor data more consistent with FWD data. Calculating bearing capacity before and after refinement revealed that refining Raptor data can substantially increase the consistency between Raptor and FWD.

Adaptation of an alternative neural network instead of ANN for backcalculating pavement layer moduli

The structural condition of the pavement is often evaluated using deflection data from the non-destructive test such as the Falling Weight Deflectometer (FWD). Young's moduli calculated from surface deflections are used to characterize pavement layers. One of the most popular techniques for analyzing FWD data to determine pavement layer moduli is backcalculation. Artificial Neural Network (ANN) has been explored or used in studies to backcalculate layer moduli from FWD data, although its applications in this area are relatively recent. However, ANN suffers from limitations in terms of convergence accuracy and generalization capability. This research aims to develop an alternative neural network instead of ANN for backcalculating pavement layer moduli. Based on FWD data, Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) networks were used to overcome the shortcomings of the traditional ANN. The LSTM, RNN, and ANN networks were developed and trained with the same training properties. The results revealed that LSTM attained high convergence accuracy and fastest convergence speed than ANN and RNN. LSTM network produced reasonable and accurate layer moduli values with determination coefficient (R2) of 0.9298 when compared to the measured values, while R2 values of the RNN and ANN networks were 0.906 and 0.8326, respectively. There was evidence that the LSTM network can learn the continuity pattern between the deflection basin points and enhance the FWD backcalculation accuracy.

Relationship between deflection basin parameters and backcalculated pavement layer moduli

In the case analysed, a glass fibre mesh was applied under the asphalt layer during a rehabilitation treatment. Because only one lane was reinforced, the test section can be used to observe the influence of glass fibre mesh on the relationship between the selected deflection basin parameters (RoC, BLI, MLI, and LLI) and back-calculated pavement layer moduli. The FWD measures were used to determine the bowl of deflection indicators and to back-calculate the layer’s moduli. The values of DBP-s allowed confirmation of the technical condition of pavement construction. The first measures were carried out in 2019 and repeated in 2021; the results were then compared and analysed. Influence was observed on the relationship between the deflection basin and moduli, especially for the base course and subgrade. The reinforced lane showed a better coefficient of determination between DBPs and moduli in 2019, but in 2021 relationships were observed only for LLI and subgrade moduli. The unreinforced lane, however, showed the mentioned relationships in both 2019 and 2021. Because of a relatively small number of measurement points, the presented analyses and observations should be considered as preliminary. Presented results and relationships are another step into developing an alternative approach to determining the initial pavement moduli i.e. to use as a seed moduli.

Evaluating Cumulative Damage Factor (CDF) of 20 Brazilian Airfield Pavement structures using FAARFIELD and BAKFAA

There is a growing concern regarding the structural integrity of Brazilian airport pavements. This concern arises from the fact that they were originally designed to accommodate lighter aircraft during an era of lower traffic. However, with the substantial increase in air traffic and the introduction of heavier aircraft, compounded by the aging infrastructure predominantly built between the 1950s and 1970s, these pavements are now confronted with significant challenges. This research involves collecting data on aircraft characteristics, traffic patterns, and pavement layer properties to update information on the pavement life service of existing Brazilian airfield pavements. Research involves the analysis of determine the Cumulative Damage Factor (CDF) of 20 Brazilian airport runways pavement. Backanalysis of deflection basins obtained from Heavy Weight Deflectometer (HWD) tests is conducted using the BAKFAA software. The FAARFIELD software is then employed to calculate the CDF, incorporating the latest information on aircraft mix and pavement conditions. The study reveals that 65% of the analyzed airport pavements consist of 4 structural layers, with 50% of the structures composed of cement-treated base course layers. Notably, 11 airports exhibit a CDF less than 0.01, suggesting potential overdesigned of pavements for existing aircraft movement. Boa Vista Airport/RR (SBBV) stands out with a CDF of 1.5, prompting recommendations for actions to mitigate pavement degradation. The findings offer valuable insights for future actions, maintenance strategies, and recommendations for the construction of new airports.

Conversions of viscoelastic deflections of asphalt pavement under TSD load to elastic ones for pavement condition assessments

The Traffic Speed Deflectometer (TSD) facility has gained popularity in assessing asphalt pavement conditions due to its efficient testing process and minimal disruption to traffic flow. Current applications of TSD test results resemble those developed for the Falling Weight Deflectometer (FWD) test results, such as analysing the deflection basin shape and back-calculating the pavement layer modulus. However, the TSD facility induces more viscoelastic pavement deflections compared to FWD, primarily due to its rolling load. This research focuses on converting TSD’s viscoelastic deflections under a wider range of loading conditions into elastic deflections under FWD load. This conversion aims to facilitate the use of FWD-based approaches for analysing TSD deflection data. The findings indicate that the converted TSD deflections generate more reasonable evaluation results, further emphasizing the necessity of converting TSD deflections to elastic ones. The models proposed in this research serve as valuable references for performing the viscoelastic-elastic deflection conversion.

Load-Bearing Capacity Assessment of Traffic Superstructures for Roads and Tramways

The load-bearing capacity of traffic superstructures is usually assessed based on deformation measurements. The bearing capacity is defined as the superstructure’s resistance to short-term deformations. However, different measurement concepts and evaluation methods are required for different superstructures to assess the bearing capacity. Therefore, this paper will present various methods for road and tramway superstructures and illustrate them with practical examples.The bearing capacity of road pavements is typically assessed by deflection measurements using the falling weight deflectometer (FWD). This dynamic measurement method applies a falling weight to the road surface, and the resulting deflection basin is recorded briefly. This can then be evaluated. Evaluations of various measurements in Germany are presented.The assessment of load-bearing capacity in the tramway sector is not currently established in German regulations. An individual measurement concept was developed and applied to assess the load-bearing capacity in urban transportation. The measurements aimed to determine the in-situ deformations of the grooved rail with a selected type of fastening during the passing of trams. In addition, the serviceability during operation was checked. The measurements were carried out on an open construction (grass track) and a closed construction (asphalt), as well as in tight curves and on straight track sections. Based on the deformations determined, conclusions can also be drawn about the service life.

RNN-based pavement moduli prediction for flexible pavement design enhancement

In order to facilitate the effective implementation of the MEPDG, researchers concentrate on quantifying local material properties, with a particular emphasis on pavement layer moduli. The layer modulus is a critical parameter necessary for calculating pavement responses (stress, strain, and deflections) resulting from traffic loading. Accurately determining the layer modulus is crucial for enhancing pavement design as it directly impacts the required pavement layer thicknesses and associated costs. Backcalculation is a commonly used method for analyzing Falling Weight Deflectometer (FWD) data to determine pavement layer moduli, with Artificial Neural Networks (ANNs) being the traditional choice. However, ANNs have limitations in terms of convergence accuracy and generalization capability. The aim of this study is to improve the backcalculation of layer moduli to enhance pavement design. By utilizing FWD data, Recurrent Neural Network (RNN) was employed to address the limitations of conventional ANN. Both ANN and RNN networks were developed and trained using identical properties. The findings demonstrate that RNN achieved faster convergence and higher convergence accuracy compared to ANN. The RNN network generated reasonable and precise layer moduli values, exhibiting a determination coefficient (R) of 0.95 in comparison to the measured values, while the ANN network had an R-value of 0.79. The results indicate that the RNN network can learn the continuity pattern between deflection basin points, thereby enhancing the accuracy of FWD backcalculation.

Deflection Basin Detection of Pavements Based on Linear Array Charge-Coupled Device (CCD) Photoelectric Sensors

Deflection is an important indicator of the overall pavement strength, and it is generally detected using the Falling Weight Deflectometer (FWD). In response to the shortcomings of FWD in use, a pavement deflection detection method based on a linear array charge-coupled device (CCD) photoelectric displacement sensor is proposed. Firstly, a detailed description is given of the working principle of the deflection detection photoelectric sensor for the center point of the deflection basin and other points. Secondly, a photoelectric displacement sensor using linear array CCD deflection detection is designed, including a laser, CCD signal processing module, CCD and its driver module, and upper computer communication module. Among them, the EPF10K20TC144-4 device of the FLEX 10K series from ALTERA company is used to generate CCD driving pulses; Two DM54LS245 are selected as the driving interface for CCD photoelectric sensors, and corresponding filtering and signal amplification circuits are designed to address the noise problem of CCD photoelectric signal injection. Finally, the fixed threshold method separates the background and image signals in the CCD photoelectric signal, and the microcontroller is connected to the serial port of the upper computer through the MAX232 chip. The displacement measurement experiment uses the designed linear array CCD photoelectric displacement sensor. The results show that the road deflection basin detection method by the linear array CCD photoelectric displacement sensor fully meets the actual detection requirements and can obtain dynamic deformation information of the tested road surface. It is helpful for a detailed understanding of the changes in the deflection basin of the road surface under load.

Factor analysis evaluation of asphalt pavement performance considering structural strength and hidden cracks

At present, the maintenance work is mainly based on the performance of the asphalt pavement, but the structure distresses inside the pavement is easily overlooked. Therefore, in view of the shortcomings of the evaluation system, this study carried out road nondestructive testing, and obtained performance, deflection basin parameters, and structure distresses of the asphalt pavement and using factor analysis to extract common factors of all indicators, which considers evaluation of performance is more comprehensive and determining the weights according to the contribution rate makes the evaluation results more unbiased. Can provide some guidance for the determination of pavement maintenance time.

Relationship between Fatigue Condition of Asphalt Pavements and Deflection Lag from Traffic Speed Deflectometer

The phase angle is a good indicator of the current fatigue condition of asphalt concrete (AC) layers, but estimating phase angles from drilled core samples is destructive, expensive, and unsuitable for large-scale applications. Similar to the phase angle, a lag between load and response has recently been observed in the deflection basin of the nondestructive traffic speed deflectometer (TSD), i.e., the lag between the loading point and the maximum deflection point, and the deflection lag may be closely related to the phase angle. This study investigated the potential of TSD deflection lag as a nondestructive indicator of pavement fatigue conditions. The relationship between AC phase angle and TSD deflection lag was investigated using the 3D-Move program, and the effects of pavement structure, TSD speed, and test temperature on the deflection lag were also investigated. Field TSD data collected in Tennessee were used to verify the relationship between deflection lag and fatigue cracking. The results show that the lag distance increases uniformly with increasing fatigue levels (phase angles) until fatigue failure occurs. However, the lag distance is also closely related to the asphalt thickness and subgrade modulus; therefore, only the lag distance of pavements with the same structures can be compared to identify the fatigue sections. Overall, the lag distance can be used as an implicit indicator of the fatigue condition of the pavement to predict the initiation and growth of fatigue cracks. Fatigue cracking is expected to occur where the lag distance is relatively large.

Evaluation of the influence of the glass fiber mesh on the deflection basin parameters of a flexible pavement located in the area of mining activity

Evaluation of the influence of the glass fiber mesh on the deflection basin parameters of a flexible pavement located in the area of mining activity

Impact of dynamic loading on pavement deflection measurements from traffic speed deflectometer

Traffic speed deflectometer (TSD) provides an efficient way for evaluating pavement condition. The current principle is to convert measured deflection slopes into the deflection basin using the Area Under The Curve (AUTC) method based on the time-space transform and elastic material theory. This study aims to investigate impact of dynamic loading due to pavement roughness on TSD measurements. Semi-analytical finite element (SAFEM) method was developed to simulate dynamic moving loads on flexible pavements with viscoelastic asphalt layer. The variations of pavement deflection and deflection velocity were found increasing linearly with dynamic loading coefficient (DLC), especially for deflection velocity. The average relative error between the actual deflection basin and the one from the AUTC method could be 15% at DLC of 0.1 with the highest error for the maximum deflection. This indicates the necessity of developing correction method of TSD deflection measurements on rough pavements.

Considerations of Area Parameter of the Deflection Basin on the Structural Evaluation of Flexible Pavements

Considerations of Area Parameter of the Deflection Basin on the Structural Evaluation of Flexible Pavements

T-shaped arrangement of geophones for rapid quantification of asymmetric behaviour of concrete slabs in central FWD tests

ABSTRACT The assessment of asymmetric slab behaviour is out of reach in standard Falling Weight Deflectometer (FWD) tests, because deflections are measured along the driving direction only. Herein, a new T-shaped arrangement of the geophones is proposed. It allows for rapid quantification of asymmetric slab behaviour in central FWD testing of concrete slabs. One geophone is positioned at the centre of impact (= centre of the slab), six along the driving direction, one right and one left of the centre. The ‘Lateral Asymmetry Index (LASIX)’ is introduced as a corresponding dimensionless deflection basin parameter. Its value increases with increasing asymmetric behaviour of the slab. The main research challenge tackled herein is to optimise the radial distance of the lateral geophones from the centre of the slab, such as to maximise the expressiveness of LASIX for the quantification of asymmetric slab behaviour. In this context, FWD tests with measurement of deflections in eight different directions are carried out, and the ‘effective asymmetry index ( A 28 )’ is introduced as another new dimensionless deflection basin parameter. It summarises the asymmetric behaviour based on deflection differences quantified for all 28 pairs of directions which can be combined out of the eight available measurement directions. The optimal radial distance of the lateral geophones from the centre of the slab is found as 1.20 m. Corresponding values of LASIX larger than 8 % refer to coefficients of directional variation of the AREA7 parameter larger than 5%. This indicates directional degradation of the pavement structure resulting from eccentric traffic loads. T-shaped FWD testing requires in situ efforts equivalent to those of standard testing, while allowing for a rapid and reliable quantification of asymmetric behaviour. It allows for the assessment of whether the standard evaluation of uniform moduli of subgrade reaction is realistic or questionable.

Modulus attenuation of semi-rigid asphalt pavement layer in accelerated pavement testing with MLS66

This research aimed to investigate the attenuation mode of the layer modulus of asphalt pavement in accelerated pavement testing (APT). A full-scale experimental section was constructed and tested using the APT facility. Two non-destructive testing (NDT) methods, named falling weight deflectometer (FWD) technique and portable seismic property analyzer (PSPA) test, were used to obtain the layer moduli of asphalt pavement during the APT test. The variation patterns of layer moduli obtained by FWD and PSPA tests were calculated and compared after the temperature was corrected to 20 ℃. It was found that the variation pattern of surface layer modulus based on field FWD measurements was consistent with the one measured from PSPA tests. That is the modulus of the surface layer increases with the APT load repetitions firstly and then decreases with the rise of the repetitions. The modulus values of the surface layer measured from PSPA tests are obviously larger than those backcalculated based on deflection basins. The ratio of the measured surface layer modulus based on the PSPA test to the backcalculated one based on the FWD test ranges between 2.06 and 2.71. The backcalculated base layer modulus always declines with the increasing loading repetitions. The attenuation patterns of the surface layer modulus and the base layer modulus in the damage stage are described as Ea=421100*N-0.6119 and Eb=128000*N-0.1096, respectively.

Bitumen stabilized materials as pavement overlay: Laboratory and field study

Bitumen stabilized materials applied as base courses have been used in many countries. In the southern Brazilian State of Rio Grande do Sul, two test sections were built using such mixes (in 2011 and 2015) as overlay asphalt emulsion base courses and have been under monitoring since then. In 2019, a new test section was built to evaluate mixes of granular materials and reclaimed asphalt pavement stabilized with emulsion and Portland cement as overlay base courses. The main objective of the research reported here was to evaluate the mechanical behavior of such materials and to assess their performance in the field during the years after construction. Indirect tensile strength, monotonic triaxial, and repeated load triaxial tests were carried out to obtain the necessary mechanical parameters. The data collected during the monitoring of the test sections were used to analyze the field performance in terms of permanent deformation. Besides, in situ moduli were obtained using backanalysis of deflection basins. The number of equivalent single axle loads (ESALs) for the analysis period was computed using data collected in toll plazas near the test sections. Using field and laboratory results and the South African TG2 performance model, it was possible to estimate the life of the studied pavements regarding permanent deformation. The TG2 model seems to underestimate the life of pavements with thick (>40 mm) asphalt layers. A model relating rutting to the number of ESALs was obtained, allowing the prediction of rutting of asphalt pavements overlaid by a bitumen stabilized base layer and asphalt wearing courses. Considering the test sections’ performances, the mixes proved to be efficient to improve structural capacity, also reducing the influence of the water level variation on pavement performance.