Ziolkowski Patryk, Niedostatkiewicz Maciej, Kang Shao-Bo
Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdansk, Poland.
School of Civil Engineering, Chongqing University, Chongqing 400045, China.
Materials (Basel). 2021 Mar 28;14(7):1661. doi: 10.3390/ma14071661.
Concrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete mixtures. The concrete mix design methods currently used in engineering practice are joint analytical and laboratory procedures derived from the Three Equation Method and do not perform well enough for the needs of modern concrete technology. This often causes difficulties in predicting the final properties of the designed mix and leads to precautionary oversizing of concrete properties for fear of not providing the required parameters. A new approach that would make it possible to predict the newly designed concrete mix properties is highly desirable. The answer to this challenge can be methods based on machine learning, which have been intensively developed in recent years, especially in predicting concrete compressive strength. Machine learning-based methods have been more or less successful in predicting concrete compressive strength, but they do not reflect well the variability that characterises the currently used concrete mixes. A new adaptive solution that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete is proposed herein. In presented study, a machine learning model was built with a deep neural network architecture, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were calculated according to contemporary design methods (Bolomey and Fuller method), and a comparative analysis was conducted. It was found out that the new algorithm performs significantly better than that without adaptive features trained on the same dataset. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process.
混凝土配合比设计是混凝土技术中最关键的问题之一。这一过程旨在配制出一种能生产出具有所需特性和质量的混凝土的配合比。当代对混凝土的要求不仅涉及其结构性能，而且越来越多地涉及其生产过程和环境友好性，这迫使混凝土生产商使用化学和技术上都很复杂的混凝土混合物。目前工程实践中使用的混凝土配合比设计方法是源自三方程法的联合分析和实验室程序，对于现代混凝土技术的需求而言，其表现还不够理想。这常常在预测设计配合比的最终性能时造成困难，并因担心无法提供所需参数而导致对混凝土性能进行预防性的过度设计。非常需要一种能够预测新设计的混凝土配合比性能的新方法。应对这一挑战的答案可能是基于机器学习的方法，近年来这些方法得到了深入发展，尤其是在预测混凝土抗压强度方面。基于机器学习的方法在预测混凝土抗压强度方面或多或少取得了成功，但它们不能很好地反映当前使用的混凝土混合物所具有的变异性。本文提出了一种新的自适应解决方案，通过纳入给定批次混凝土的两个观测值，能够根据混凝土配合比的主要成分组成来估计混凝土抗压强度。在本研究中，构建了一个具有深度神经网络架构的机器学习模型，在一个广泛的混凝土配方数据库上进行训练，并转化为一个数学公式。对根据当代设计方法（博洛米和富勒法）计算的四种混凝土配合比配方进行了测试，并进行了对比分析。结果发现，新算法的性能明显优于在相同数据集上训练的无自适应特征的算法。所提出的算法可作为混凝土配合比设计过程中的混凝土强度检查工具。
