New Condition Assessment Procedure for Asphalt Pavement Layers, Using Falling Weight Deflectometer Deflections

Xu, Bing; Ranjithan, S. Ranji; Kim, Y. Richard

doi:10.3141/1806-07

Cited by 26 publications

(8 citation statements)

References 2 publications

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“… The surface curvature index (SCI), which is related to the Young modulus of the AC layer (Xu et al, 2002a, 2002b) and defined as where D 0 and D 300 are the deflections measured at the point where the load is applied and at 30.48 cm (12 in.) from it, respectively, in the FWD test. D 900 , which is the deflection measured at 91.44 cm (36 in.)…”

Section: Model Implementation For Pavement Performancementioning

confidence: 99%

“…1. The surface curvature index (SCI), which is related to the Young modulus of the AC layer (Xu et al, 2002a(Xu et al, , 2002b and defined as…”

Section: Selection Of the Input Variablesmentioning

confidence: 99%

“…The study of the individual contributions of the input variables to the output represented by the change in pavement serviceability was not included in the scope of this article. The input parameters were selected based on the sensitivity analysis performed in previous studies by Garg and Thompson (1999) and Xu et al (2002a, 2002b).…”

Section: Comparison With a Multiple Linear Regression Modelmentioning

confidence: 99%

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Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

Bianchini

Bandini

2010

Computer-Aided Civil and Infrastructure Engineering

116

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Government agencies and consulting companies in charge of pavement management face the challenge of maintaining pavements in serviceable conditions throughout their life from the functional and structural standpoints. For this, the assessment and prediction of the pavement conditions are crucial. This study proposes a neuro‐fuzzy model to predict the performance of flexible pavements using the parameters routinely collected by agencies to characterize the condition of an existing pavement. These parameters are generally obtained by performing falling weight deflectometer tests and monitoring the development of distresses on the pavement surface. The proposed hybrid model for predicting pavement performance was characterized by multilayer, feedforward neural networks that led the reasoning process of the IF‐THEN fuzzy rules. The results of the neuro‐fuzzy model were superior to those of the linear regression model in terms of accuracy in the approximation. The proposed neuro‐fuzzy model showed good generalization capability, and the evaluation of the model performance produced satisfactory results, demonstrating the efficiency and potential of these new mathematical modeling techniques.

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Section: Model Implementation For Pavement Performancementioning

confidence: 99%

“…1. The surface curvature index (SCI), which is related to the Young modulus of the AC layer (Xu et al, 2002a(Xu et al, , 2002b and defined as…”

Section: Selection Of the Input Variablesmentioning

confidence: 99%

Section: Comparison With a Multiple Linear Regression Modelmentioning

confidence: 99%

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Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

Bianchini

Bandini

2010

Computer-Aided Civil and Infrastructure Engineering

116

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“…Xu et al (18) developed the Asphalt Pavement Layer Condition Assessment Program (APLCAP), which implements a new integrated procedure for condition assessment from FWD deflections based on dynamic nonlinear FE analysis and calibrated with field measurements (19). To test the prediction accuracy of algorithms incorporated into APLCAP, Xu et al (18) examined the TRB Nonlinear Pavement Analysis Project data sets with APLCAP.…”

Section: Advanced Approaches To Characterizing Nonlinear Pavement Sysmentioning

confidence: 99%

Advanced Approaches to Characterizing Nonlinear Pavement System Responses

Ceylan

Gopalakrishnan

Guclu

2007

Transportation Research Record: Journal of the Transportation R

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The use of falling weight deflectometer–based backcalculation techniques to determine pavement layer moduli is a cost-effective and widely used method for the structural evaluation of an existing pavement. The nonlinear stress-sensitive response of pavement geomaterials has been well established, and mechanistic-based pavement design can be improved by inclusion of these nonlinear material properties. To further the science of nonlinear backcalculation, the TRB Strength and Deformation Characteristics of Pavement Sections Committee has assembled four data sets that can be used to demonstrate the ability to derive stress-dependent moduli for pavement layers. In this study, validated artificial neural network (ANN)–based backcalculation-type flexible pavement analysis models were used to evaluate the TRB Nonlinear Pavement Analysis Project data sets. The Illi-Pave finite element (FE) model, considering nonlinear stress-dependent geomaterials characterization, was utilized to generate a solution database for developing the ANN-based structural models. Such use of ANN models enables the incorporation of needed sophistication in structural analysis, such as FE modeling with proper materials characterization, into routine practical design. This study illustrated the complexities associated with interpreting the backcalculated modulus values. In general, the predicted strains agreed reasonably well with the measured strain values, whereas the predicted stresses did not.

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“…Falling weight deflectometer (FWD) deflections and deflection basin parameters (DBPs) have successfully been used to estimate the pavement structural capacities and the current conditions of existing pavements. In addition to the DBPs, the pavement responses at critical locations in each layer have been proven to be good condition indicators for various distresses (1)(2)(3)(4). These critical pavement responses can be predicted from the DBPs and layer thicknesses on the basis of the statistical regression approach by using the synthetic database developed by the dynamic finite element program (1,2).…”

mentioning

confidence: 99%

Assessment of Pavement Layer Condition with Use of Multiload-Level Falling Weight Deflectometer Deflections

Park

Kim

Park

2005

Transportation Research Record: Journal of the Transportation R

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A condition assessment procedure for pavement layers that uses multiload-level falling weight deflectometer (FWD) deflections is presented. A dynamic finite element program that incorporates a stress-dependent soil model was used to generate the synthetic deflection database. On the basis of the data in this database, the relationships between surface deflections and critical pavement responses, such as the stresses and strains in each layer, have been established. The FWD deflection data, distress survey results, temperature, and laboratory testing results used to develop this procedure were collected from the Long-Term Pavement Performance project database. Research efforts also focused on the effect of the FWD load level on the condition assessment procedure. The results indicate that the proposed procedure can estimate the asphalt concrete (AC), base, and subgrade layer conditions. The AC layer modulus and the tensile strain at the bottom of the AC layer were found to be better indicators of the condition of the AC layer than the deflection basin parameter. It was also found that the structurally adjusted base damage index and base curvature index were good indicators for prediction of the stiffness characteristics of the aggregate base and subgrade, respectively. An FWD test with a load of 71.2 kN or less does not improve the accuracy of this procedure. The results from the study of the nonlinear behavior of a pavement structure indicate that the deflection ratio obtained from multiload-level deflections can predict the type and quality of the base and subgrade materials.

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New Condition Assessment Procedure for Asphalt Pavement Layers, Using Falling Weight Deflectometer Deflections

Cited by 26 publications

References 2 publications

Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

Advanced Approaches to Characterizing Nonlinear Pavement System Responses

Assessment of Pavement Layer Condition with Use of Multiload-Level Falling Weight Deflectometer Deflections

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