Compressive Strength Prediction of Concrete Under Sulfate Attack Using Coupled Machine Learning Methods

One of the most significant factors affecting the durability of concrete is sulfate attack. In this paper, to predict the compressive strength (CS) of concrete under sulfate attack, three coupled machine learning methods (SSA-BP, PSO-BP and NGO-BP) were developed by coupling BP neural networks (BPNN...

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Vydané v:	Iranian journal of science and technology. Transactions of civil engineering Ročník 49; číslo 2; s. 1577 - 1590
Hlavní autori:	Jin, Libing, Liu, Peng, Fan, Tai, Wu, Tian, Wang, Yuhang, Wu, Qiang, Xue, Pengfei, Zhou, Pin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.04.2025 Springer Nature B.V
Predmet:	Accuracy Algorithms Artificial intelligence Artificial neural networks Back propagation networks Civil Engineering Compressive strength Durability Energy consumption Engineering Environmental conditions Foraging behavior Learning algorithms Machine learning Neural networks NGOs Nongovernmental organizations Optimization algorithms Optimization techniques Particle swarm optimization Research Paper Search algorithms Sulfates Swarm intelligence Concrete Compressive strength Sulfate attack Intelligent optimization algorithm Machine learning
ISSN:	2228-6160, 2364-1843
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