An intelligent hybrid system for predicting the tortuosity of the pore system of fly ash concrete

Boukhatem, Bakhta; Rebouh, Redouane; Zidol, Ablam; Chekired, Mohamed; Tagnit-Hamou, Arezki

doi:10.1016/j.conbuildmat.2019.02.005

Cited by 22 publications

(2 citation statements)

References 29 publications

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“…(hollow square in scatter plot) match well with the curve for the predicted permeability according to the attenuation when the tortuosity was 10 (green curve). In impermeable concrete materials, the value of tortuosity is in the range of 10-25 when the porosity is in the range of 6-10% [29]. The difference between the measured and predicted permeabilities was calculated to be under 2.58×10 -9 m/s with 1.42×10 -9 m/s of standard deviation.…”

Section: Table 4 Results Of Ffrc Test Using Concrete Specimensmentioning

confidence: 94%

Permeability Monitoring of Underground Concrete Structures using Elastic wave Characteristics with Biot’s Model

Lee,

Kim,

Hong

et al. 2024

Preprint

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Underground environments are preferred for the construction of important infrastructures to overcome social problems, such as the tunnel or disposal of radioactive waste at deep depth. Concrete is primarily used as a structural material owing to its high strength and cost efficiency. To utilize these structures safely in the long term, the permeability of the concrete should be monitored. If the permeability is increased due to crack generation in a concrete medium, groundwater can flow into the underground structure. However, permeability monitoring is challenging at depths of 500 m–1 km under high-temperature, high-pressure, and limited space conditions. In this study, a theoretical model was developed to predict the permeability of concrete using compressive elastic waves. To derive the relationship between the permeability and elastic wave characteristics, Biot’s model, which can associate the characteristics of P-waves with the properties of porous media, was applied. The P-wave velocity and attenuation were investigated according to the permeability of the concrete based on Biot’s model. Subsequently, the concrete specimens were prepared to measure the permeability, P-wave velocity, and attenuation, and the permeability results from the experiment were compared to those obtained from the model for validation. Biot’s model is expected to be useful for permeability monitoring based on the characteristics of the elastic waves in underground structures.

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Section: Table 4 Results Of Ffrc Test Using Concrete Specimensmentioning

confidence: 94%

Permeability Monitoring of Underground Concrete Structures using Elastic wave Characteristics with Biot’s Model

Lee,

Kim,

Hong

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…However, different factors such as the geometry and the heterogeneity of the cementitious materials, the presence of steel reinforcement and moisture, make these methods less precise. Boukhatem et al [19] used a soft computing approach to predict the transport tortuosity of the pore system of fly ash concrete by building an intelligent hybrid system. In their system, a genetic algorithm was used to optimize the structure and the hyper-parameters of the network.…”

Section: Introductionmentioning

confidence: 99%

Determination of geometrical parameters of the microstructure of a porous medium: Application to cementitious materials

Bourbatache

Bennai

Zhao

et al. 2020

International Communications in Heat and Mass Transfer

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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An intelligent hybrid system for predicting the tortuosity of the pore system of fly ash concrete

Cited by 22 publications

References 29 publications

Permeability Monitoring of Underground Concrete Structures using Elastic wave Characteristics with Biot’s Model

Permeability Monitoring of Underground Concrete Structures using Elastic wave Characteristics with Biot’s Model

Determination of geometrical parameters of the microstructure of a porous medium: Application to cementitious materials

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

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