Prediction of stress intensity factors in pavement cracking with neural networks based on semi-analytical FEA

Wu, Zhenhua; Hu, Sheng; Zhou, Fujie

doi:10.1016/j.eswa.2013.07.063

Cited by 37 publications

(15 citation statements)

References 12 publications

(11 reference statements)

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“…Moreover, most of studies have proved that asphalt in subzero temperatures behaves as a linear elastic and brittle material, and therefore, in this research, asphalt was assumed to be linear and elastic. Tables and present the thickness and mechanical properties of layers . Meanwhile, in contrary of many pervious researches, the loads applied from both front and rear wheels of the vehicle were considered in the analyses.…”

Section: Modellingmentioning

confidence: 99%

“…Tables 1 and 2 present the thickness and mechanical properties of layers. 8,[34][35][36] Meanwhile, in contrary of many pervious researches, the loads applied from both front and rear wheels of the vehicle were considered in the analyses. The distance between the front and rear wheels was considered equal to 3.38 m, and the vehicle weight considered equal to 10 t. The effective contact length of each wheel was 26 cm, and a pressure of 123.8 kPa was applied in the contact line of each wheel with the road to simulate the weight of 10-t vehicle.…”

Section: Modellingmentioning

confidence: 99%

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Investigating the effects of loading, mechanical properties and layers geometry on fatigue life of asphalt pavements

Ziari

Aliha

Mojaradi

et al. 2019

Fatigue Fract Eng Mat Struct

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In this paper, fatigue life, crack growth trajectory, and stress intensity factors of top‐down and bottom‐up cracks have been investigated in a multilayered asphalt pavement using numerical method. Finite element models, Paris law, and maximum tangential stress criteria were used to determine the effects of vehicle position, thickness, and stiffness of layers on fatigue life of pavements. The results show that the minimum fatigue life of pavements with top‐down crack occurs when the wheels are symmetrically located relative to the crack plane. On the other side, in pavements with bottom‐up crack, it occurs when the front tire located on top of the crack. Moreover, the result reveals that the top‐down crack grows slower than bottom‐up crack. Also, the surface layer's stiffness has the most effect on fatigue life of pavement.

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Section: Modellingmentioning

confidence: 99%

Section: Modellingmentioning

confidence: 99%

Investigating the effects of loading, mechanical properties and layers geometry on fatigue life of asphalt pavements

Ziari

Aliha

Mojaradi

et al. 2019

Fatigue Fract Eng Mat Struct

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“…The calculation of SIF was done using semi-analytic in Finite Element Analysis (FEA). The modeling of SIF for the crack propagation was done use neural network [10]. Finally, the analysis of fracture is one prospect for failure analysis [11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation on Mechanical Fracture of PWR Pressure Vessel and Modeling Based on Neural Network

Susmikanti

Himawan²,

Hafid³

et al. 2016

Tri Dasa Mega

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EVALUATION ON MECHANICAL FRACTURE OF PWR PRESSURE VESSEL AND MODELING BASED ON NEURAL NETWORK. The important component of the PWR is a pressure vessel. The material resistance in the pressure vessel needs to be evaluated. One way of evaluation is by the mechanical fracture analysis. The modeling needs to know the phenomena of the analysis result in general. A number of researches have been completed on the calculation of mechanical fracture in the pressure vessel with an internal load. The mechanical fracture was modeled using a neural network approach. In relation to the material resistance of the pressure vessel, which is used in PWR AP1000, the material must be evaluated because of the effect of the load. The modeling is needed to predict the effect of the load. The aim of this study is to evaluate the material resistance through mechanical fracture analysis because of the influence load on the pressure vessel on PWR AP1000. The material, which was observed, is SA 508. This analysis consists of the calculation of stress intensity factor and J-integral with some load at the crack propagation position. The fracture mechanic was analyzed by finite element simulation. The result of Stress Intensity factor and J-Integral was compared with fracture toughness to know the durability of the material. The modeling of J-Integral and Stress Intensity Factor were obtained for some load based on neural network approach. Keywords: Material resistance, mechanical fracture, neural network, PWR, pressure vessel, crack propagation. ABSTRAK EVALUASI FRAKTUR MEKANIK PADA BEJANA TEKAN PWR DAN PEMODELAN BERBASIS NEURAL NETWORK. Komponen penting dari PWR adalah bejana tekan. Ketahanan bahan di bejana tekan perlu dievaluasi. Salah satu cara adalah dengan analisis fraktur mekanik. Pemodelan diperlukan untuk mengetahui fenomena hasil analisis pada umumnya. Terdapat penelitian untuk perhitungan fraktur mekanik dalam bejana tekan dengan beban internal. Penelitian lain adalah hasil dari fraktur mekanik dimodelkan menggunakan pendekatan jaringan syaraf. Sehubungan dengan ketahanan material dari bejana tekan yang digunakan dalam PWR AP1000, bahan harus dievaluasi karena efek dari beban. Pemodelan diperlukan untuk memprediksi pengaruh beban pada bahan dalam bejana tekan. Tujuan dari penelitian ini adalah untuk mengevaluasi ketahanan material melalui analisis fraktur mekanik karena pengaruh beban pada bejana tekan. Bahan yang diamati, adalah SA 508. Analisis ini terdiri dari perhitungan faktor intensitas tegangan dan J-integral dengan beberapa beban pada posisi perambatan retak. Fraktur mekanik dianalisis dengan metode elemen hingga. Hasil faktor intensitas tegangan dan J-Integral dibandingkan dengan ketangguhan patah untuk mengetahui daya tahan material. Pemodelan J-Integral dan faktor intensitas stres diperoleh untuk beberapa beban berdasarkan jaringan saraf. Kata kunci: Ketahanan bahan, teknik patahan, jaringan syaraf, PWR, bejana tekan, perambatan retak.

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“…Their results show the neural network model offers better performance with easier and a faster implementation process. Moreover, ANN and multivariable regression models were used to predict the stress intensity factors (SIFs) in pavement cracking, the results show the advantage of utilizing ANN over multivariable regression models on the prediction accuracy [15]. Felker, et al (2004) used the ANN and statistical analysis approaches to develop the reliable and accurate roughness prediction models for jointed plain concrete pavements, and they found the ANN is able to predict the roughness with reasonably high coefficient of determination, R-squared= 0.90 , whereas R-squared of statistical analysis approach = 0.73 [4].…”

Section: Fig 1: Schematic Of Stress Zone Under the Fwd Loadmentioning

confidence: 99%

ANN Models to Correlate Structural and Functional Conditions in AC Pavements at Network Level

Alharbi¹,

Smadi²

2017

IJAEMS

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Prediction of stress intensity factors in pavement cracking with neural networks based on semi-analytical FEA

Cited by 37 publications

References 12 publications

Investigating the effects of loading, mechanical properties and layers geometry on fatigue life of asphalt pavements

Investigating the effects of loading, mechanical properties and layers geometry on fatigue life of asphalt pavements

Evaluation on Mechanical Fracture of PWR Pressure Vessel and Modeling Based on Neural Network

ANN Models to Correlate Structural and Functional Conditions in AC Pavements at Network Level

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