Machine Learning-Based Prediction of Compressive Strength in Additive Manufacturing of Concrete Technology (2025-10)¶

Automated robotic systems are used to layer by layer deposit concrete in 3D printing. This new way of building has many benefits, such as being more environmentally friendly, using less material, saving money, and building faster. By leveraging ML and deep learning algorithms, including ANN, decision trees, and support vector machines, predictions of compressive strength are improved, ensuring enhanced structural performance and reliability. Additive manufacturing, commonly known as 3D printing, is an advanced digital technique used to construct three-dimensional structures with exceptional precision. In 3D concrete technology, the adoption of machine learning methods has introduced substantial benefits over traditional construction approaches. Machine learning models such as random forest, artificial neural networks (ANN), support vector regression (SVR), and linear regression facilitate accurate and efficient predictions of compressive strength and structural integrity. This technology employs automated robotic systems to deposit concrete layers systematically, offering sustainability, reduced material waste, cost efficiency, and faster construction timelines.