In today’s rapidly developing construction industry, the demand for concrete as a core building material continues to rise.
Subsequently, the massive accumulation of construction demolition waste and the increasing depletion of natural aggregate resources are driving the development of recycled concrete technology under the dual pressure of environmental protection and resource scarcity.
Although the application of recycled concrete aggregates (RCA) can effectively alleviate this dilemma, the compressive strength, as a key indicator of concrete quality, has the disadvantages of high consumables, high cost, and long cycle in traditional compression testing.
This article will share a study on using machine learning technology to predict the compressive strength of recycled concrete, providing a more efficient solution for engineering practice.
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