Marchewka Adam, Ziolkowski Patryk, García Galán Sebastián
Institute of Telecommunications and Computer Sciences, University of Science and Technology, Bydgoszcz, Poland.
Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233, Gdansk, Poland.
Sci Rep. 2025 May 2;15(1):15463. doi: 10.1038/s41598-025-97060-w.
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 R 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.
建筑技术的不断发展，尤其是在钢筋混凝土生产方面，需要先进、可靠且高效的方法来实时监测和预测混凝土抗压强度。传统的实验室评估抗压强度的方法耗时较长，可能会导致施工流程延误。本研究引入了一个全面的系统框架，该系统旨在通过使用定制的人工智能（AI）模型持续监测水泥水化过程来预测混凝土的早期抗压强度。该系统集成了温度传感器网络、通信模块和中央数据库服务器，以便在混凝土养护过程中收集、传输和分析实时数据。人工智能模型是一个深度神经网络，利用这些数据生成准确的强度预测。系统架构强调可扩展性、稳健性以及与现有施工管理系统的集成。实证结果表明，所提出的系统具有较高的预测精度，R值为0.996，均方根误差为0.143MPa，为施工中的实时决策提供了一个强大的工具。本研究还严格评估了系统的性能，确定了预测准确性和实时处理能力等关键优势，并解决了与无线通信可靠性和传感器电源相关的挑战。针对提高系统精度、改进通信技术、优化电源管理以及确保在不同施工环境中的可扩展性提出了建议。所开发的系统是“CONCRESENSE”项目的一部分，并受到欧洲专利号245107（2024）的保护，代表了建筑技术的重大进步，对提高钢筋混凝土结构的安全性、效率和质量具有重大意义。
