ANN-Based Fatigue Strength of Concrete under Compression

Abambres, Miguel; Lantsoght, Eva O. L.

doi:10.3390/ma12223787

Cited by 27 publications

(20 citation statements)

References 64 publications

(78 reference statements)

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“…Unlike Section 4.2 where the crack features are explicitly used as NN variables, the fatigue damage in this section describes the state of health of the material. It may be represented by the degradation of residual stiffness 171 and the reduction of static strength 172 . Some studies use guided waves and AE signals for damage detection and monitoring.…”

Section: Review Of Nn Applications In Fatiguementioning

confidence: 99%

Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

135

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Neural network (NN) models have significantly impacted fatigue‐related engineering communities and are expected to increase rapidly due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles.

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Section: Review Of Nn Applications In Fatiguementioning

confidence: 99%

Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

135

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“…Although most research work is focused on predicting the compressive strength, there are notable works handling other properties of concrete. Predictions of mechanical properties of hardened concrete such as flexural strength [34] for modified zeolite additive mortar, or [36] for hybrid composites, elastic modulus of recycled aggregate concrete [70], Poisson's ratio of lightweight concrete [71], fatigue strength [72], freeze-thaw durability [73], and electrical property prediction [74], showed to be useful. There have also been investigations focused on the properties of fresh concrete such as drying shrinkage [42], structural properties such as chloride permeability [75,76] and diffusivity [77], air void content [78], as well as the dependency of compressive strength on the concrete microstructure [79].…”

Section: Anns For Prediction Of Concrete Materials Behaviormentioning

confidence: 99%

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Kekez

Kubica

2021

Materials

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Prominence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed and price, as well as carbon footprint. Artificial neural networks represent a new and efficient tool in achieving optimal concrete mixtures according to its intended function. This paper addresses concrete mix design and the application of artificial neural networks (ANNs) for self-sensing concrete. The authors review concrete mix design methods and the development of ANNs for prediction of properties for various types of concrete. Furthermore, the authors present developments and applications of ANNs for prediction of compressive strength and flexural strength of carbon nanotubes/carbon nanofibers (CNT/CNF) reinforced concrete using experimental results for the learning process. The goal is to bring the ANN approach closer to a variety of concrete researchers and possibly propose the implementation of ANNs in the civil engineering practice.

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“…The accuracy and efficiency of stress calculation can be improved by adjusting the conditions and parameters of the finite element model. In the future, artificial intelligence algorithms, such as artificial neural networks (ANNs), can be introduced in the structural stress calculation of cement concrete [ 56 , 57 , 58 ]. A database of stress analysis can be developed through field tests and numerical simulations, after which the artificial intelligence algorithm model can be trained based on the database to achieve stress prediction.…”

Section: Recommendations For Fatigue Models Of Airfield Concrete Pavements In the Futurementioning

confidence: 99%

Fatigue Models for Airfield Concrete Pavement: Literature Review and Discussion

Yuan

et al. 2021

Materials

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The fatigue model plays an important role in the mechanistic–empirical design procedure of airfield pavement. As for cement concrete pavement, the fatigue model represents the relationship between the stress and the number of load repetitions. To further understand the fatigue model, a literature review was performed in this paper along with the discussion. In this paper, the developed fatigue models available now were classified as the full-scale testing-based fatigue model and the concrete beam testing-based fatigue model, according to the data source. Then, the regression analysis process and stress calculation method of each fatigue model were summarized. Besides, the fatigue model proposed by the Federal Aviation Administration (FAA) was compared with the fatigue model of the Civil Aviation Administration of China (CAAC). The design thicknesses using the two models were obtained based on the finite element analysis. The results show that the designed slab using the fatigue model of FAA is thicker than that of CAAC, meaning that the fatigue model of FAA is comparatively conservative. Moreover, it can be concluded that the differences in the slab thickness become more significant with the increase in the wheel load and the foundation strength. Finally, the recommendation was proposed to refine the fatigue model in the future study from three aspects: data source, stress calculation method, and regression analysis process.

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ANN-Based Fatigue Strength of Concrete under Compression

Cited by 27 publications

References 64 publications

Fatigue modeling using neural networks: A comprehensive review

Fatigue modeling using neural networks: A comprehensive review

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Fatigue Models for Airfield Concrete Pavement: Literature Review and Discussion

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