Nonlinear finite element analysis for structural capacity of railway prestressed concrete sleepers with rail seat abrasion

You, Ruilin; Goto, Keiichi; Ngamkhanong, Chayut; Kaewunruen, Sakdirat

doi:10.1016/j.engfailanal.2018.08.026

Cited by 38 publications

(22 citation statements)

References 17 publications

(18 reference statements)

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“…In this study, customised combinations of different hidden neurons (1,5,10,15,20,25,30), hidden layers (1-2) and LM, BR and SCG algorithms have been employed. Furthermore, each pattern of ANN architectures has been trained for five times to improve the accuracy of models.…”

Section: Performance Evaluation Of Ann Architecturesmentioning

confidence: 99%

“…Research articles have shown a growth in the noise reduction coefficient with an increase in rubber replacing sand. Therefore, replacing aggregates with waste rubber in concrete at an opportune ratio not only supports the improvement of the performance of the concrete but also avoids the environmental pollution and waste of resources caused by conventional treatments [9,10]. The use of waste rubber can help engineers and stakeholders achieve a system's zero emission status in a faster and safer manner.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

et al. 2021

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Not only can waste rubber enhance the properties of concrete (e.g., its dynamic damping and abrasion resistance capacity), its rational utilisation can also dramatically reduce environmental pollution and carbon footprint globally. This study is the world’s first to develop a novel machine learning-aided design and prediction of environmentally friendly concrete using waste rubber, which can drive sustainable development of infrastructure systems towards net-zero emission, which saves time and cost. In this study, artificial neuron networks (ANN) have been established to determine the design relationship between various concrete mix composites and their multiple mechanical properties simultaneously. Interestingly, it is found that almost all previous studies on the ANNs could only predict one kind of mechanical property. To enable multiple mechanical property predictions, ANN models with various architectural algorithms, hidden neurons and layers are built and tailored for benchmarking in this study. Comprehensively, all three hundred and fifty-three experimental data sets of rubberised concrete available in the open literature have been collected. In this study, the mechanical properties in focus consist of the compressive strength at day 7 (CS7), the compressive strength at day 28 (CS28), the flexural strength (FS), the tensile strength (TS) and the elastic modulus (EM). The optimal ANN architecture has been identified by customising and benchmarking the algorithms (Levenberg–Marquardt (LM), Bayesian Regularisation (BR) and Scaled Conjugate Gradient (SCG)), hidden layers (1–2) and hidden neurons (1–30). The performance of the optimal ANN architecture has been assessed by employing the mean squared error (MSE) and the coefficient of determination (R2). In addition, the prediction accuracy of the optimal ANN model has ben compared with that of the multiple linear regression (MLR).

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Section: Performance Evaluation Of Ann Architecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

et al. 2021

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“…Similarly to other prestressed concrete (PC) members, the prestress of concrete sleepers will be lost after being transformed [34][35][36][37][38]. The prestress loss consists of both instantaneous losses and time dependent losses.…”

Section: The Calculation Of Prestress Lossesmentioning

confidence: 99%

Evaluation of remaining fatigue life of concrete sleeper based on field loading conditions

You

Kaewunruen

2019

Engineering Failure Analysis

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The main functions of the sleepers in ballasted track include transferring loads and maintaining railway geometry. Sleepers can be manufactured using timber, concrete, steel or other engineered materials, but concrete is most used commonly around the world. There is much research on the impact load characteristics and the ultimate load carrying capacity of prestressed concrete sleepers, but research on the fatigue life of prestressed concrete sleepers is limited. A prestressed concrete sleeper's fatigue damage is mainly due to the repeated load of wheel-rail interaction. Fatigue failure is a time-dependent limited state where a concrete sleeper accumulates damage to a failure point. Concrete sleepers suffer fatigue loading throughout their whole lives, and the loadcarrying capacity of concrete sleepers degrades as well. Therefore, fatigue life assessment is an important and complicated research topic. Field loading conditions, material time-dependent and dynamic properties, and the bending moments of prestressed concrete sleepers are analysed in this paper. This study also presents a fatigue life assessment method for concrete sleepers, and provides a study case based on field loading conditions and the time-dependent behaviour of material. This paper will improve concrete sleeper maintenance and inspection criteria, and will also provide the flexibility of a design principle for the concrete sleeper.

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“…In order to simulate a real ballast condition, a polymeric material can be placed between the rail seats of sleepers as an alternative test support specified in Australian Standard AS1085.19J [33][34][35][36][37][38]. It is found that 5 layers of rubber conveyor matting used in the mining industry is enough to avoid excessive vibrations from the floor.…”

Section: Experimental Modal Analysismentioning

confidence: 99%

Mode shape curvature squares method for crack detection in railway prestressed concrete sleepers

Janeliukštis

Ručevskis

Kaewunruen

2019

Engineering Failure Analysis

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Nonlinear finite element analysis for structural capacity of railway prestressed concrete sleepers with rail seat abrasion

Cited by 38 publications

References 17 publications

Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

Evaluation of remaining fatigue life of concrete sleeper based on field loading conditions

Mode shape curvature squares method for crack detection in railway prestressed concrete sleepers

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