Pavement Condition Data from Traffic Speed Deflectometer for Network Level Pavement Management

Shrestha, Shivesh; Katicha, Samer W.; Flintsch, Gerardo W.

doi:10.1061/9780784482452.039

Cited by 5 publications

(10 citation statements)

References 5 publications

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“…The coefficients for asphalt concrete layer a 1 can be determined by Equation ( 6) and Figure 6, as provided in the 1993 AASHTO Guide [2]. Rhode [20] proposed Equation (7) to calculate the SNeff from the FWD measurements. For an asphalt pavement, Rhode proposed 0.4728, −0.4810, and 0.7581 as k1, k2, and k3, respectively.…”

Section: Approach Of Strains and Effective Structural Number Calibrationmentioning

confidence: 99%

“…Effective Structural Number (SNeff) = k SIP Hp (7) where HP = total thickness (mm); SIP = structural index of pavement = D0 − D1.5HP.…”

Section: Approach Of Strains and Effective Structural Number Calibrationmentioning

confidence: 99%

“…Since the 1980s, the FWD has been one of the most widely used devices for pavement condition assessment in the U.S. In this test, a deflection basin is measured under an impact load using geophones or force balance seismometers [7]. The size of the plate and impact load depends on the material being tested (asphalt, stabilized subgrade, compacted subgrade, etc.).…”

Section: Introductionmentioning

confidence: 99%

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Calibration of Effective Structural Number and Tensile Strain Models Using Traffic Speed Deflectometer (TSD) Data for Enhanced Project-Level Assessment on Flexible and Composite Pavements

Mendez Larrain,

Ali,

Hobson

et al. 2023

Sustainability

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Pavement deterioration models provide the basis for predicting future changes in network conditions, estimating future funding needs, and determining the effectiveness and timing of maintenance and rehabilitation activities. Determining the accurate structural condition of pavements helps identify effective maintenance strategies which enhance the sustainability and service life of pavements. This study aimed to use the Traffic Speed Deflectometer (TSD) and Fast Falling Weight Deflectometer (FFWD) for project-level evaluation of pavements and use pavement properties to calibrate current models that help to predict the structural condition of pavements. Model parameters were calibrated to determine the effective structural number and tensile strains at the bottom of asphalt concrete for asphalt and composite pavements from TSD deflections. Tensile strains from the KENLAYER highlighted varied behaviors for composite pavements. A significant improvement in the calibration was observed for asphalt concrete pavements. While the TSD has higher daily operational costs than FWD, its per-mile cost is significantly lower, making it a viable choice for extensive coverage, even though the quantitative results might differ between the two devices.

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Section: Approach Of Strains and Effective Structural Number Calibrationmentioning

confidence: 99%

“…Effective Structural Number (SNeff) = k SIP Hp (7) where HP = total thickness (mm); SIP = structural index of pavement = D0 − D1.5HP.…”

Section: Approach Of Strains and Effective Structural Number Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Calibration of Effective Structural Number and Tensile Strain Models Using Traffic Speed Deflectometer (TSD) Data for Enhanced Project-Level Assessment on Flexible and Composite Pavements

Mendez Larrain,

Ali,

Hobson

et al. 2023

Sustainability

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“…The condition of an in-service pavement is assessable in two categories including functional and structural. Both functional and structural conditions play an important role in pavement management at the network-level [4]. In most Pavement Management Systems (PMSs), nonstructural indices such as Pavement Condition Index (PCI) are used as pavement indicators to select treatments [3,5] while ignoring the structural conditions of pavement [6].…”

Section: > 𝟏𝟓 Heavy Damage Reconstructionmentioning

confidence: 99%

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

et al. 2020

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Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., Levenberg-Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS model outperforms other models with the promising results of APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.

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“…The condition of an in-service pavement is assessable in two categories including functional and structural. Both functional and structural conditions play an important role in pavement management at the networklevel [4]. In most Pavement Management Systems (PMSs), non-structural indices such as Pavement Condition Index (PCI) are used as pavement indicators to select treatments [3,5] while ignoring the structural conditions of pavement [6].…”

Section: Of 21mentioning

confidence: 99%

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Karballaeezadeh

Zaremotekhases

Shamshirband

et al. 2020

Preprint

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Pavement Condition Data from Traffic Speed Deflectometer for Network Level Pavement Management

Cited by 5 publications

References 5 publications

Calibration of Effective Structural Number and Tensile Strain Models Using Traffic Speed Deflectometer (TSD) Data for Enhanced Project-Level Assessment on Flexible and Composite Pavements

Calibration of Effective Structural Number and Tensile Strain Models Using Traffic Speed Deflectometer (TSD) Data for Enhanced Project-Level Assessment on Flexible and Composite Pavements

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

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