With the rapid development of artificial intelligence technology, machine learning is gradually becoming an important tool for concrete material design and performance prediction.
Traditional concrete research mainly relies on empirical formulas, experimental mixing, and engineering experience.
However, concrete is a typical multi-component, multi-scale, strongly nonlinear material, and its performance is influenced by various factors such as cement, fly ash, slag, water, aggregates, additives, age, curing conditions, and fiber or nanomaterial content.
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