Network-Level Pavement Roughness Prediction Model for Rehabilitation Recommendations

Kargah-Ostadi, Nima; Stoffels, Shelley M; Tabatabaee, Nader

doi:10.3141/2155-14

Cited by 63 publications

(18 citation statements)

References 12 publications

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“…The results of parametric analyses were used to evaluate mechanical properties of the Marshall specimens in a quite well manner. Kargah-Ostadi et al 56 used in a network-level pavement management system (PMS) to predict future performance of a pavement section and identify the maintenance and rehabilitation needs. There are also various similar studies in the literature in the way of utilising neural networks and parametric studies in various civil engineering applications [57][58][59] .…”

Section: Published Literature About Artificial Neural Network Applicamentioning

confidence: 99%

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

2012

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The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches.

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Section: Published Literature About Artificial Neural Network Applicamentioning

confidence: 99%

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

2012

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“…In recent years, the application of AI methods in engineering sciences is very common. Methods such as artificial neural networks (ANN) [34][35][36][37][38][39], radial basis function (RBF) [40][41][42][43][44], genetic programming (GP) [45][46][47][48][49], genetic algorithm (GA) [50][51][52][53], gene expression programming (GEP) [24,54,55], support vector machine (SVM) [40,54,[56][57][58], Random Forest (RF) [59][60][61][62][63], Fuzzy systems [64][65][66][67], and regression tree (RT) [68][69][70] have received much attention from engineers. In this paper, authors use the RF and Random Forest optimized by Genetic Algorithm (RF-GA) methods to predict PCI based on IRI.…”

Section: Analysis Phase By Using Artificial Intelligencementioning

confidence: 99%

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Karballaeezadeh¹,

Mohammadzadeh²,

Moazami³

et al. 2020

Preprint

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The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

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“…An extensive research investigated structural deformation as the factors of modified SN, increment of traffic https: //doi.org/10.15405/epms.2019.12.53 Corresponding Author: Zul Fahmi Mohamed Jaafar Selection and peer-review under 538 loads, severity of cracking and thickness of cracked layer, and incremental variation of rut depth. Kargah-Ostadi, Stoffels, and Tabatabaee, (2010) developed the changes in IRI over time roughness prediction model for rehabilitation recommendation using the ANN. The statistical analysis on 20 variables was conducted to determine any significant correlation with IRI.…”

Section: Eqmentioning

confidence: 99%

Pavement Roughness Modeling Using Regression And Ann Methods For LTPP Western Region

Najjar¹,

Uddin²,

Jaafar³

2019

The European Proceedings of Multidisciplinary Sciences

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This study aims to develop pavement roughness models using multiple linear regression equation and artificial neural network (ANN) modeling approaches. The model database uses International Roughness Index (IRI) data included in a national database for the Western region. Datasets for asphalt pavement with bound base at 32 different locations are considered in the analysis. The variables included are IRI, pavement age, design structural number, equivalent single axle load (ESAL), and also a dummy variable for construction number. The LTPP data was used to compare predicted IRI values using the improved linear regression equation with R of 0.573, with different types of ANN models. The ANN models considered are static ANN, feedback ANN, and dynamic ANN. The verifications of the improved dummy regression equation predictions for 39 data points showed only-2.9% difference compared to the mean IRI. Comparisons between the mean predicted IRI values showed that the enhanced dummy regression equation gave a better prediction compared to the ANN model with a minimum error of 37.9%. Out of three ANN models, the feedback ANN provided a better prediction compared to the static and dynamic ANNs. Further analysis showed that the predictions using training all data sets are more accurate with R ranged from 0.740 to 0.827. It is important to consider ESAL and construction number for an accurate and reliable future IRI prediction.

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Network-Level Pavement Roughness Prediction Model for Rehabilitation Recommendations

Cited by 63 publications

References 12 publications

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Pavement Roughness Modeling Using Regression And Ann Methods For LTPP Western Region

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