Prediction of In-Service Pavement Structural Capacity Based on Traffic-Speed Deflection Measurements

Elbagalati, Omar; Elseifi, Mostafa A.; Gaspard, Kevin; Zhang, Zhongjie

doi:10.1061/(asce)te.1943-5436.0000891

Cited by 35 publications

(20 citation statements)

References 7 publications

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“…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]. It has recently been proven that there is a statistical relationship between functional and structural conditions [7].…”

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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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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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

View full text Add to dashboard Cite

show abstract

“…Elbagalati et al [7] developed a model to predict pavement structural capacity at an interval of 0.16 km (0.1 mi.) based on the RWD measurements.…”

Section: Existing Structural Parametersmentioning

confidence: 99%

Development of Deflection Parameters to Evaluate the Structural Capacity of Flexible Pavements at the Network Level: Case Study for the State of Texas

et al. 2019

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Pavement deflection has been used widely as a nondestructive technique to evaluate the structural capacity of pavements at both network and project levels. Various transportation agencies use several evaluation methods to evaluate the integrity of the pavement layers. Most of these up-to-date developed indices are exclusively based on either central deflections or one deflection point along FWD deflection bowl. However, no standardized method that utilizes the full FWD deflection bowl is available. This study aims to introduce new comprehensive pavement layer deflection and deflection bowl area parameters that are based on the entire FWD deflection bowl rather than one single deflection point and to relate the developed parameters to the field measured distress data. Thirty-five different pavement sections in the State of Texas were utilized in the study. Two comprehensive deflection parameters and a ranking scale were developed that may be utilized for the overall pavement structural condition evaluation.

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“…On the other side, the pavement performance can also be used in the frames of deterioration determination. Field testing provides much convenience to obtain indirect indicators such as the deflection, friction, and roughness 10,11 …”

Section: Introductionmentioning

confidence: 99%

“…As a direct indicator of the material strength, modulus occurs in many ME models to predict the total number of load repetitions a pavement can bear 3,12 . Deflection, directly controlled by the modulus, is used to calculate indices determining the pavement condition such as the structural number (SN), 13,14 the structural condition index (SCI), 10 the structural index of a pavement (SIP), 15 and the deflection slope index (DSI) 16 . The development of nondestructive testing (NDT) devices such as the falling weight deflectometer (FWD), the rolling wheel deflectometer (RWD), and the traffic speed deflectometer (TSD) contributes to the obtain and applications of these indices.…”

Section: Introductionmentioning

confidence: 99%

Determination of flexible pavement deterioration conditions using Long‐Term Pavement Performance database and artificial intelligence‐based finite element model updating

Deng

Luo

Zhang

et al. 2020

Struct Control Health Monit

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Prediction of In-Service Pavement Structural Capacity Based on Traffic-Speed Deflection Measurements

Cited by 35 publications

References 7 publications

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

Development of Deflection Parameters to Evaluate the Structural Capacity of Flexible Pavements at the Network Level: Case Study for the State of Texas

Determination of flexible pavement deterioration conditions using Long‐Term Pavement Performance database and artificial intelligence‐based finite element model updating

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