Volume 1, Issue 1 — Year 2024 — Article e100005

Sina Aminbakhsh¹; Amin Tohidi²

1. Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1955847781, Iran
2. Department of Mining Engineering, Amirkabir University of Technology, Tehran 1591634311, Iran

Accurate prediction of the uniaxial compressive strength (UCS) of concrete is crucial for ensuring the safety, durability, and performance of structures in construction. This study presents a predictive model using a multilayer perceptron (MLP), to estimate UCS based on key input parameters such as water-cement ratio, aggregate size, curing time, water and cement content. The MLP model was trained and validated using a dataset comprising 120 cubic laboratory-tested concrete samples (15cm × 15cm × 15cm) with varying compositions for normal construction materials. Performance of the model was evaluated using statistical metrics (split into training and testing sets as 70%-30%), showing that the MLP-based approach provides accurate and reliable predictions compared to traditional regression models. The proposed method offers a practical, efficient tool for geotechnical engineers to assess concrete strength, potentially reducing the need for extensive experimental testing and enhancing quality control in concrete production.

Construction materials, Multilayer perceptron, Artificial intelligence, Concrete, MLP