Simplified Closed-Form Procedure for Network-Level Determination of Pavement Layer Moduli from Falling Weight Deflectometer Data

El-Raof, Hossam S. Abd; El-Hakim, Ragaa T. Abd; El-Badawy, Sherif M.; Afify, Hafez

doi:10.1061/jpeodx.0000080

“…In addition to road pavement, the HWD can be used for airport pavement because it's loading plate diameter is 45 cm. More diameter than FWD allows applying heavier loads to the pavement surface [7]. HWD applies an impulse load to the surface of pavement with the help of a loading plate and then nine geophones record surface deflections.…”

Section: Heavy Falling Weight Deflectometer (Hwd)mentioning

confidence: 99%

“…Non-Destructive Testing (NDT). The DTs have several disadvantages, such as coring the asphalt, Interfering with the traffic flow, and requiring a long implementation time [7]. Unlike DTs, application of NDTs has gained momentum in recent years because they allow accurate simulation of real conditions, which is not feasible under laboratory conditions.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Karballaeezadeh¹,

Tehrani²,

Mohammadzadeh³

et al. 2020

Preprint

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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.

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“…• The estimation of pavement future performance and evaluation of its results can contribute to maintaining and elongating the pavement service life [6,7]. Evaluation tests for pavements classified into two major categories [8]: 1.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike DTs, application of NDTs has gained momentum in recent years because they allow accurate simulation of real conditions, which is not feasible under laboratory conditions. Considering its possibility of in-site simulation, NDT reduces both costs and time [6,7]. The most common NDT equipment used to evaluate both flexible and rigid pavements is falling weight Deflectometer (FWD) [7,9].…”

Section: Introductionmentioning

confidence: 99%

“…Considering its possibility of in-site simulation, NDT reduces both costs and time [6,7]. The most common NDT equipment used to evaluate both flexible and rigid pavements is falling weight Deflectometer (FWD) [7,9]. In the FWD test, a loading plate and a series of geophones are placed on the surface of pavement, and exposed to 40 KN axle load over a period of 25 to 35 milliseconds.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Karballaeezadeh¹,

Tehrani²,

S.³

et al. 2020

Preprint

2

0

View full text Add to dashboard Cite

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.

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General Procedure for Pavement Maintenance/Rehabilitation Decisions Based on Structural and Functional Indices

El-Raof

¹

,

El-Hakim

²

,

El-Badawy

³

et al. 2019

Recent Developments in Pavement Engineering

2

0

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Simplified Closed-Form Procedure for Network-Level Determination of Pavement Layer Moduli from Falling Weight Deflectometer Data

Cited by 26 publications

References 9 publications

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

General Procedure for Pavement Maintenance/Rehabilitation Decisions Based on Structural and Functional Indices

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