Fuzzy expert system for asphalt pavement performance prediction

Kaur, Devinder; Tekkedil, D. K.

doi:10.1109/itsc.2000.881103

Cited by 14 publications

(7 citation statements)

References 4 publications

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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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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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show abstract

“…The Visual Basic environment is used to build the neural network models. After performing laboratory investigations, the neural network method is used to estimate the experimental results and to evaluate the robustness of the neural network in the estimation of the results [10]. Sodikov in 2005 emphasized the importance of prediction cost highway project with using neural network method.…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical investigation of the properties of the Hot Mix Asphalt Concrete with basalt and glass fiber

Ameri

Nemati

Shaker

et al. 2019

Frattura Ed Integrità Strutturale

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In the recent decades, different kinds of fiber materials are used for improving the asphalt mixture performance. Meanwhile, different kinds of fiber are used excessively due to their desirable physical and chemical properties and their easier application. The main purpose of this research is to evaluate the characteristics of the asphalt mixture while using basalt fiber and glass fiber. In order to provide asphalt samples, these two types of fibers are used in different percentages. In this way, 42 samples (with different percentages of fiber and bitumen) were made using Marshal Hammer. In the next step, while constructing 63 asphalt samples using a gyratory device, then mix asphalt conventional tests include the determination of indirect tensile strength, moisture sensitivity test, and resilient modulus and creep tests performed. The results of this research indicate that using these two types of fibers increased the percentage of optimum bitumen and marshal resistance. At best, adding 0.1% glass fiber resulted in 13% increase in marshal resistance. Finally, ANFIS-GUI was used to estimate the experimental result and the feasibility of employing neural fuzzy network to predict the laboratory data have been evaluated.

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“…The EXpert System in flexible PAVement and overlay design (EXSPAV) is an expert system that is used in overlay design and in the prediction of the residual operating age of old pavements (Khedr and Mikhail, 1996). Kaur and Tekkedil (2000) designed an expert system to forecast the performance of existing pavements. Waheed and Adeli (2000) developed an expert system called PERMIT_EXPERT for the evaluation of superload permit applications.…”

Section: Introductionmentioning

confidence: 99%

Expert System to Control Construction Problems in Flexible Pavements

Mosa

Rahmat

Ismail

et al. 2013

Computer aided Civil Eng

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Highway engineers face complicated problems that are influenced by various conditions during the construction of flexible highway pavements. Identifying such problems and recommending effective solutions demand considerable engineering expertise, which is difficult to obtain at all construction sites. Thus, the development of an expert system can effectively help engineers control and analyze such problems. In addition, an expert system can effectively archive the storage and distribution of expertise among pavement engineers. This article describes the development and evaluation of such an expert system. The first stage in the development of the proposed system was the elicitation of knowledge from written sources and from experts through literature reviews and interviews, respectively. The acquired knowledge was analyzed and classified and then represented in a form containing rules; the rules were subsequently coded as software. This article describes the development and evaluation of the Expert System for the Control of Construction Problems in Flexible Highway Pavements.

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Fuzzy expert system for asphalt pavement performance prediction

Cited by 14 publications

References 4 publications

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Experimental and numerical investigation of the properties of the Hot Mix Asphalt Concrete with basalt and glass fiber

Expert System to Control Construction Problems in Flexible Pavements

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