A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prophecy of Mechanical and Durability Characteristics of Sustainable Geopolymer Based on Artificial Neural Network
The goal of current research on cementations and concrete is to develop long-term solutions to urgent issues such rising carbon emissions or related corrosive attacks on reinforced concrete structures. Geopolymer concrete is regarded as an eco-friendly substitute because of its exceptional qualities in terms of sustainability and carbon emission reduction. Cement ranks third among all materials that release greenhouse gases into the atmosphere, according to the Global Cement and Concrete Association. Numerous studies have led to the development of a new type of concrete called geopolymer concrete, which aims to lessen the harmful effects of cement on the environment. Geopolymer concrete's engineering qualities, such its compressive strength, are frequently determined via testing procedures that call for costly equipment, lengthy sample preparation times, and huge quantities of raw materials. One of the most important elements in guaranteeing the quality of concrete is its compressive strength. Based on data gathered through experiments, artificial neural networks (ANN) and deep neural networks (DNN) are among the techniques that are examined. To examine the predictive accuracy of compressive strength, three multilayer network models were built using the Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) algorithms. Rice husk ash geopolymer paste at two distinct ages (7 and 28 days), respectively.
Prophecy of Mechanical and Durability Characteristics of Sustainable Geopolymer Based on Artificial Neural Network
The goal of current research on cementations and concrete is to develop long-term solutions to urgent issues such rising carbon emissions or related corrosive attacks on reinforced concrete structures. Geopolymer concrete is regarded as an eco-friendly substitute because of its exceptional qualities in terms of sustainability and carbon emission reduction. Cement ranks third among all materials that release greenhouse gases into the atmosphere, according to the Global Cement and Concrete Association. Numerous studies have led to the development of a new type of concrete called geopolymer concrete, which aims to lessen the harmful effects of cement on the environment. Geopolymer concrete's engineering qualities, such its compressive strength, are frequently determined via testing procedures that call for costly equipment, lengthy sample preparation times, and huge quantities of raw materials. One of the most important elements in guaranteeing the quality of concrete is its compressive strength. Based on data gathered through experiments, artificial neural networks (ANN) and deep neural networks (DNN) are among the techniques that are examined. To examine the predictive accuracy of compressive strength, three multilayer network models were built using the Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) algorithms. Rice husk ash geopolymer paste at two distinct ages (7 and 28 days), respectively.
Prophecy of Mechanical and Durability Characteristics of Sustainable Geopolymer Based on Artificial Neural Network
Lecture Notes in Civil Engineering
Nehdi, Moncef (editor) / Rahman, Rahimi A. (editor) / Davis, Robin P. (editor) / Antony, Jiji (editor) / Kavitha, P. E. (editor) / Jawahar Saud, S. (editor) / Prabhakar, Prajjwal (author) / Kumar, Rohit (author)
International Conference on Structural Engineering and Construction Management ; 2024 ; Angamaly, India
2024-12-29
22 pages
Article/Chapter (Book)
Electronic Resource
English
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