Machine learning approach for pavement performance prediction

Marcelino, Pedro; Antunes, Maria de Lurdes; Fortunato, Eduardo; Gomes, Marta Castilho

doi:10.1080/10298436.2019.1609673

Cited by 168 publications

(43 citation statements)

References 39 publications

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“…The first three aspects agree entirely with the findings of Marcelino et al [ 63 ] published in November 2019. By reviewing six papers concerning MLAs in the field of road roughness prediction, the authors highlighted the same facts we found on the fourteen studies reported in Table 1 .…”

Section: State-of-the-artsupporting

confidence: 91%

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

Fiorentini

Maboudi

Leandri

et al. 2021

Sensors

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This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km2 in the Tuscany Region, Central Italy. Starting from this basis, in this paper, we propose to integrate the information provided by these MLAs with 10 km of in situ profilometric measurements of the pavement surface roughness and relative calculation of the International Roughness Index (IRI). Accordingly, the aim is to appreciate whether and to what extent there is an association between displacements estimated by MLAs and IRI values. If a dependence exists, we may argue that road regularity is driven by exogenous phenomena and MLAs allow for the replacement of in situ surveys, saving considerable time and money. In this research framework, results reveal that there are several road sections that manifest a clear association among these two methods, while others denote that the relationship is weaker, and in situ activities cannot be bypassed to evaluate the real pavement conditions. We could wrap up that, in these stretches, the road regularity is driven by endogenous factors which MLAs did not integrate during their training. Once additional MLAs conditioned by endogenous factors have been developed (such as traffic flow, the structure of the pavement layers, and material characteristics), practitioners should be able to estimate the quality of pavement over extensive and complex road networks quickly, automatically, and with relatively low costs.

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Section: State-of-the-artsupporting

confidence: 91%

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

Fiorentini

Maboudi

Leandri

et al. 2021

Sensors

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“…A notable number of researchers have used Artificial Neural Networks (ANN) to predict pavement performance (15)(16)(17)(18)(19). Other ML algorithms used in modeling pavement performance include Decision Trees (20,21), Ensemble Trees (22,23), Random Forest (RF) (24)(25)(26), Support Vector Machines (SVM) (20,(27)(28)(29), and Recurrent Neural Networks (30). The description is focused on ANN, RF, and SVM since these algorithms are the ones most frequently used in modeling IRI.…”

Section: Machine Learning Algorithms For Pavement Performance Modelingmentioning

confidence: 99%

Performance of Machine Learning Algorithms in Predicting the Pavement International Roughness Index

Bashar

Torres-Machí

2021

Transportation Research Record: Journal of the Transportation R

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Significant research efforts have documented the capabilities of machine learning (ML) algorithms to model pavement performance. Several challenges, however, limit the implementation of ML by practitioners and transportation agencies. One of these challenges is related to the high variability in the performance of ML models as reported by different studies and the lack of quantitative evidence supporting the true effectiveness of these techniques. The objective of this paper is twofold: to assess the overall performance of traditional and ML techniques used to predict pavement condition, and to provide guidance on the optimal architecture and minimum sample size required to develop these models. This paper analyzes three ML algorithms commonly used to predict International Roughness Index (IRI)—Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)—and compares their performance to traditional techniques. An inverse variance heterogeneity based meta-analysis is performed on 20 studies conducted between 2001 and 2020. The results indicate that ML algorithms capture on average 15.6% more variability than traditional techniques. RF is the most accurate technique with an overall performance value of 0.995. ANN is also identified as a highly effective technique that has been widely used and provides accurate predictions with both small and large sample sizes. For ANN algorithms, a single hidden layer with nodes equal to 0.3–2 times the number of input features is found to be sufficient in predicting pavement deterioration. A minimum sample size equal to 50 times the number of input variables is recommend to model pavement deterioration using ML.

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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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Machine learning approach for pavement performance prediction

Cited by 168 publications

References 39 publications

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

Performance of Machine Learning Algorithms in Predicting the Pavement International Roughness Index

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

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