Enhancing Concrete Compressive Strength Prediction with Deep Learning: A Comparative Analysis of Model Architectures

Gregorius Airlangga(1*),

(1) Universitas Katolik Indonesia Atma Jaya, Indonesia
(*) Corresponding Author

Abstract


The imperative to predict concrete compressive strength accurately is a crucial aspect of modern civil engineering, with significant implications for the safety and cost-effectiveness of construction projects. This research explores the application of deep learning techniques to enhance predictive accuracy in this domain. We conducted a comprehensive comparative analysis of five machine learning models: a Basic neural network model, a Dropout model, a Batch Normalization model, a Deep Dense Neural Network (Deep DNN), and a Convolutional Neural Network (CNN). Utilizing a dataset reflective of various concrete mixtures and their corresponding compressive strengths, each model underwent rigorous evaluation through a five-fold cross-validation scheme. Performance metrics, including Mean Squared Error (MSE) and R-Squared (R²), were computed to assess each model's predictive capabilities. The results indicated that models employing batch normalization and deeper architectures provided superior predictive performance, suggesting that these features are instrumental in understanding the complex relationships between the components of concrete mixtures. The Batch Normalization and Deep DNN models demonstrated remarkable accuracy and consistency, surpassing traditional and CNN models. This study not only enhances the current understanding of material property prediction through machine learning but also paves the way for the development of more efficient and robust predictive tools in civil engineering. The findings underscore the transformative potential of deep learning in material science, emphasizing its ability to deliver nuanced and precise predictions for critical engineering properties.

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DOI: https://doi.org/10.30645/kesatria.v5i3.459

DOI (PDF): https://doi.org/10.30645/kesatria.v5i3.459.g454

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