Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Karballaeezadeh, Nader; Tehrani, Hosein Ghasemzadeh; Mohammadzadeh, Saeed; Shamshirband, Shahaboddin

doi:10.20944/preprints202002.0411.v2

Cited by 4 publications

(4 citation statements)

References 12 publications

(13 reference statements)

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“…There are sporadic studies in which the authors have achieved valuable results with other techniques, such as Random Forest, Boosted Regression Tree, and Support Vector Machine. Reasonably, academic researchers desired to move away from the steady application of Artificial Neural Networks by evaluating the performance of different algorithms that, in the field of pavement [ 70 , 71 , 72 , 73 ], transport [ 74 , 75 ], and road safety engineering [ 76 , 77 , 78 , 79 ], have led to reliable results;…”

Section: State-of-the-artmentioning

confidence: 99%

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-artmentioning

confidence: 99%

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

Fiorentini

Maboudi

Leandri

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

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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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“…e impact of the increase of data dimensionality on data analysis is multifaceted [12]. For example, in nonparametric estimation, the high-dimensionality of data affects the convergence speed of algorithm estimation; in model selection, too many data variables will cause the performance of the model to decline; in regression analysis, the sparsity of high-dimensional data is also one of the hard problems in data forecasting [13].…”

Section: Introductionmentioning

confidence: 99%

Innovation Model of College Education Based on Nonlinear Random Matrix Organization and Management

Luo

Zhang

Jiang

2022

Mathematical Problems in Engineering

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At present, the scientific research and innovation team in colleges and universities has become an important carrier for the development of science and technology and personnel training in China, but in terms of its development status, the role of the scientific research and innovation team has not been really brought into play. Most colleges and universities only have the organizational form of scientific research and innovation teams. Too many scientific research tasks still rely on a small number of backbones. The cohesion of the team is not enough, and the combined force of the team is not manifested. In production practice, more and more high-dimensional data with dimension p approaching or even exceeding the number of samples n are collected and stored. Unlike traditional data analysis, high-dimensional data analysis is more complex and difficult. Layer of scientific research and innovation teams in colleges and universities is often ignored by managers, which is manifested by ignoring the cultural construction of the team. In the eyes of team managers, the time spent on building a team culture is far less than the time it takes to do a few more scientific experiments. University scientific research and innovation team is a multi-level network system. The asymptotic and nonasymptotic theories of random matrices break the framework of classical multivariate statistical analysis and are very suitable for the study of statistical characteristics of high-dimensional data, which can help machine learning algorithms to complete the analysis of high-dimensional data and expand its application range. The research and innovation team in colleges and universities is deeply analyzed from the perspective of nonlinearity. It is believed that the scientific research and innovation team in colleges and universities is a complex nonlinear system in terms of scientific research objects-interdisciplinary research, or from the perspective of personnel composition. Correspondingly, the construction should be considered from a nonlinear perspective in the process, following the systematic, integral, and multi-dimensional characteristics of nonlinear systems. Specifically applied to the construction of the team, first, from a macro perspective, three principles should be paid attention to at the beginning of the creation of the team, and then the specific application should be explained in detail. It is hoped that a new perspective will be put forward in the construction of scientific research innovation teams in colleges and universities to better promote the development of teams.

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Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Cited by 4 publications

References 12 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?

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Innovation Model of College Education Based on Nonlinear Random Matrix Organization and Management

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