Prediction of Asphalt Mixture Resistance Using Neural Network via Laboratorial X-ray Images

Nejad, Fereidoon Moghadas; Mehrabi, Ahmad; Zakeri, Hamzeh

doi:10.12720/jiii.3.1.48-53

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Generally, a hidden layer is sufficient for most analyses, but in highly complex systems, two hidden layers could be used. Each neuron in the hidden layer is connected to all the neurons in its preceding and succeeding layers [48]. The amount of each neuron in the hidden layer and the output layer is determined based on the amount of each neuron in the previous layer, weights, and bias.…”

Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

et al. 2020

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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: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

et al. 2020

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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

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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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“…Generally, a hidden layer is sufficient for most analyses, but in highly complex systems, two hidden layers could be used. Each neuron in the hidden layer is connected to all the neurons in its preceding and succeeding layers (Nejad, Mehrabi, and Zakeri 2015). The amount of each neuron in the hidden layer and the output layer is determined based on the amount of each neuron in the previous layer, weights and bias.…”

Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

“…To do so, the amount of each neuron in the previous layer is multiplied by its weight and then the sum of the weighted values of neurons in the previous layer is obtained and combined with the bias. The obtained value is passed through an activation function and transferred to the next layer (Nejad, Mehrabi, and Zakeri 2015). Various activation functions are used in MLP, including Tansig, Linear, Sigmoid and Tanh.…”

Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Usage Surface Deflection Data for Performance Prediction in Flexible Pavement

Karballaeezadeh¹,

Zaremotekhases²,

Nabipour³

et al. 2019

Preprint

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The conventional method used for calculating pavement condition index (PCI) has two major drawbacks: safety problems during pavement inspection, and human error. This paper proposes a method for removing these problems. The proposed method uses surface deflection data in falling weight Deflectometer test to estimate PCI. The data used in this study were derived from 236 pavement segments taken from Tehran-Qom freeway in Iran. The data set was analyzed using multi layers perceptron (MLP) and radial basis function (RBF) neural networks. These neural networks were optimized by levenberg-marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic (RBF-GA) algorithms. After initial modeling with four neural networks mentioned, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of analysis have been verified by the four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SD). The best reported results belonged to CMIS, including APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.

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Prediction of Asphalt Mixture Resistance Using Neural Network via Laboratorial X-ray Images

Cited by 4 publications

References 9 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

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Usage Surface Deflection Data for Performance Prediction in Flexible Pavement

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