Neuro-fuzzy systems in determining light weight concrete strength

Tosee, Seyed Vahid Razavi; Nikoo, Mehdi

doi:10.1007/s11771-019-4223-3

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…The main benefit of using computer technology with an ANN testing method to solve a predetermined problem is that there is no need to derive mathematical dependencies, because in the process of ANN learning, appropriate values are assigned to subsequent variables, and then the solution obtained in the process of experimental research is implemented [21,22]. In the presented work, the results of LWC tests, obtained earlier in the laboratory, were analyzed.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete

et al. 2021

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Predicting the properties of concrete before its design and application process allows for refining and optimizing its composition. However, the properties of lightweight concrete are much harder to predict than those of normal weight concrete, especially if the forecast concerns the insulating properties of concrete with artificial lightweight aggregate (LWA). It is possible to use porous aggregates and precisely modify the composition of lightweight concrete (LWC) with specific insulating properties. In this case, it is advisable to determine the parameters of the components and perform preliminary laboratory tests, and then use theoretical methods (e.g., artificial neural networks (ANNs) to predict not only the mechanical properties of lightweight concrete, but also its thermal insulation properties. Fifteen types of lightweight concrete, differing in light filler, were tested. Lightweight aggregates with different grain diameters and lightweight aggregate grains with different porosity were used. For the tests, expanded glass was applied as a filler with very good thermal insulation properties and granulated sintered fly ash, characterized by a relatively low density and high crushing strength in the group of LWAs. The aim of the work is to demonstrate the usefulness of an ANN for the determination of the relationship between the selection of the type and quantity of LWA and porosity, density, compressive strength, and thermal conductivity (TC) of the LWC.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete

et al. 2021

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Multi-factor model to predict surface chloride concentration of concrete based on fuzzy logic system

Guo

Yao

2022

Case Studies in Construction Materials

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Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: A review

Sesugh,

Onyia,

Fidelis

2024

Turkish Journal of Engineering

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Concrete is one of the most common construction material used all over the word. In estimating the strength properties of concrete, laboratory works need to be carried out. However, researchers have adopted predictive models in order to minimize the rigorous laboratory works in estimating the compressive strength and other properties of concrete. Self-compacting concrete which is an advanced form of construction is adopted mainly in areas where vibrations may not be possible due to complexity of the form work or reinforcement. This work is targeted at predicting the compressive strength of self-compacting concrete using artificial intelligence techniques. A comparative performance analysis of all techniques is presented. The outcomes demonstrated that training in a Deep Neural Network model with several hidden layers could enhance the performance of the suggested model. The ANN model, possesses a high degree of steadiness when compared to experimental results of concrete compressive strength. ANN was observed to be a strong predictive tool, as such is recommended for formulation of many civil engineering properties that requires predictions. Much time and resources are saved with artificial intelligence models as it eliminates the need for experimental test which sometimes delay construction works.

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Neuro-fuzzy systems in determining light weight concrete strength

Cited by 3 publications

References 20 publications

Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete

Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete

Multi-factor model to predict surface chloride concentration of concrete based on fuzzy logic system

Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: A review

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