Comparative Assessment of Concrete Compressive Strength Prediction at Industry Scale Using Embedding-based Neural Networks, Transformers, and Traditional Machine Learning Approaches

TL;DR

This study compares various AI models for predicting concrete compressive strength at an industry scale, finding that embedding-based neural networks provide the most accurate predictions, supporting automated quality control in construction.

Contribution

It introduces and evaluates embedding-based neural networks for concrete strength prediction using a large industry dataset, demonstrating superior accuracy over traditional models.

Findings

Embedding-based neural networks outperform other models in prediction accuracy.

The models achieve a mean 28-day prediction error of approximately 2.5%.

Results support automated quality control in large-scale construction.

Abstract

Concrete is the most widely used construction material worldwide; however, reliable prediction of compressive strength remains challenging due to material heterogeneity, variable mix proportions, and sensitivity to field and environmental conditions. Recent advances in artificial intelligence enable data-driven modeling frameworks capable of supporting automated decision-making in construction quality control. This study leverages an industry-scale dataset consisting of approximately 70,000 compressive strength test records to evaluate and compare multiple predictive approaches, including linear regression, decision trees, random forests, transformer-based neural networks, and embedding-based neural networks. The models incorporate key mixture design and placement variables such as water cement ratio, cementitious material content, slump, air content, temperature, and placement conditions. Results indicate that the embedding-based neural network consistently outperforms traditional machine learning and transformer-based models, achieving a mean 28-day prediction error of approximately 2.5%. This level of accuracy is comparable to routine laboratory testing variability, demonstrating the potential of embedding-based learning frameworks to enable automated, data-driven quality control and decision support in large-scale construction operations.