Parametric Study of Pavement Deterioration Using Machine Learning Algorithms

Fathi, Aria; Mazari, Mehran; Saghafi, Mahdi; Hosseini, Arash; Kumar, Saurav

doi:10.1061/9780784482476.004

Cited by 44 publications

(19 citation statements)

References 17 publications

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“…RFs are able to overcome the overfitting of regression trees [37,73]. RFs need two parameters to be tuned, including the number of regression trees (ntree) and the number of different predictors in each node (mtry) [35,66,[73][74][75][76][77]. These two most critical parameters define the performances of RFs.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

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Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

et al. 2020

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The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.

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Section: Random Forest (Rf)mentioning

confidence: 99%

“…In Iran, Moghadas Nejad et al Focused on the characterization of laboratory-made asphalt concrete samples using image processing and ANN techniques [33,34]. Fathi et al used a hybrid car training method that was a combination of RF and ANN methods to predict the ADI index [35].…”

Section: Introductionmentioning

confidence: 99%

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

et al. 2020

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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%

“…In RF, two main parameters are optimized by using dataset calibration and RMSE [60,72,[79][80][81][82][83]:…”

Section: Random Forest (Rf)mentioning

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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“…Through reviewing the pavement performance models, it was found that the Pavement Condition Index (PCI) [8], International Roughness Index (IRI) [3,[9][10][11][12], Present Serviceability Index (PSI) [5,13], Remaining Service Life (RSL) [14], and Present Serviceability Ratio (PSR) [15] indices were more commonly used by the researchers. In terms of the modelling approach, methods such as artificial neural networks (ANN) [5,12], support vector machine (SVM) [1,11,14,15], radial basis function (RBF) [11], gene expression programming (GEP) [10], regression tree (RT) [13,16], Random Forest (RF) [3,17], and genetic programming (GP) [8] were used in the literature.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement

et al. 2019

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Prediction of the remaining service life (RSL) of pavement is a challenging task for road maintenance and transportation engineering. The prediction of the RSL estimates the time that a major repair or reconstruction becomes essential. The conventional approach to predict RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise operational safety. In this paper, surface distresses of pavement are used to estimate the RSL to address the aforementioned challenges. To implement the proposed theory, 105 flexible pavement segments are considered. For each pavement segment, the type, severity, and extent of surface damage and the pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include falling weight deflectometer (FWD) and ground-penetrating radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include support vector regression (SVR), support vector regression optimized by the fruit fly optimization algorithm (SVR-FOA), and gene expression programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE), scattered index (SI), and Willmott’s index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

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Parametric Study of Pavement Deterioration Using Machine Learning Algorithms

Cited by 44 publications

References 17 publications

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement

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