Real-time prediction of early concrete compressive strength using AI and hydration monitoring

Adam Marchewka; Patryk Ziółkowski; Sebastián García Galán

doi:10.1038/s41598-025-97060-w

Real-time prediction of early concrete compressive strength using AI and hydration monitoring

Abstrakt

The continuous evolution of construction technologies, particularly in reinforced concrete production, demands advanced, reliable, and efficient methodologies for real-time monitoring and prediction of concrete compressive strength. Traditional laboratory methods for assessing compressive strength are time-intensive and can introduce delays in construction workflows. This study introduces a comprehensive framework for a system designed to predict early-age compressive strength of concrete through continuous monitoring of the cement hydration process using a custom artificial intelligence (AI) model. The system integrates a network of temperature sensors, communication modules, and a centralized database server to collect, transmit, and analyze real-time data during the concrete curing process. The AI model, a deep neural network leverages this data to generate accurate strength predictions. The system architecture emphasizes scalability, robustness, and integration with existing construction management systems. Empirical results indicate that the proposed system achieves high predictive accuracy, with an R2 value of 0.996 and RMSE of 0.143 MPa, offering a robust tool for real-time decision-making in construction. This study also critically evaluates the system's performance, identifying key strengths such as predictive accuracy and real-time processing capabilities, and addresses challenges related to wireless communication reliability and sensor power supply. Recommendations are provided for enhancing system precision, improving communication technologies, optimizing power management, and ensuring scalability across diverse construction contexts. The developed system, which is part of the "CONCRESENSE" project and protected under European patent number 245107 (2024), represents a significant advancement in construction technology, with substantial implications for enhancing the safety, efficiency, and quality of reinforced concrete structures.

Cytowania

Autorzy (3)

Adam Marchewka dr inż.
- Politechnika Bydgoska
Patryk Ziółkowski dr inż.
Sebastián García Galán Prof.
- University of Jaén

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Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

Scientific Reports nr 15,
ISSN: 2045-2322

Język:

angielski

Rok wydania:

2025

Opis bibliograficzny:

Marchewka A., Ziółkowski P., García Galán S.: Real-time prediction of early concrete compressive strength using AI and hydration monitoring// Scientific Reports -,iss. 15 (2025),

DOI: