Modelling the hardened properties of steel fiber reinforced concrete using ANN

Mahesh, R.; Sathyan, Dhanya

doi:10.1016/j.matpr.2021.08.311

Cited by 10 publications

(1 citation statement)

References 9 publications

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“…Each hidden layer contains the same number of neurons. This choice allows for the reduction of parameters number that can be changed in the model [35]. In our system, the ANN is trained under mean-square-error (MSE) criterion with feed forward back-propagation algorithm (BP).…”

Section: Ann Architecturementioning

confidence: 99%

Inverse identification of pipeline steel mechanical state by means of coupled magnetic measurements and artificial neural networks

Maazaz

Hubert

Zemmouri

2023

NDT & E International

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Section: Ann Architecturementioning

confidence: 99%

Inverse identification of pipeline steel mechanical state by means of coupled magnetic measurements and artificial neural networks

Maazaz

Hubert

Zemmouri

2023

NDT & E International

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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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Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review

Kazemi,

Shafighfard,

Yoo

2024

Arch Computat Methods Eng

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Modelling the hardened properties of steel fiber reinforced concrete using ANN

Cited by 10 publications

References 9 publications

Inverse identification of pipeline steel mechanical state by means of coupled magnetic measurements and artificial neural networks

Inverse identification of pipeline steel mechanical state by means of coupled magnetic measurements and artificial neural networks

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

Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review

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