Boukhelf Fouad, Targino Daniel Lira Lopes, Benzaama Mohammed Hichem, Lima Babadopulos Lucas Feitosa de Albuquerque, El Mendili Yassine
Builders Lab, Builders Ecole d'Ingénieurs, ComUE NU, 1 rue Pierre et Marie Curie, 146110 Epron, France.
Graduate Program in Civil Engineering-Structures and Civil Construction (PEC), Department of Structural Engineering and Civil Construction (DEECC), Technology Center (CT), Federal University of Ceará (UFC), Bloco 733, Campus do Pici s/n, Fortaleza 60440-900, CE, Brazil.
Materials (Basel). 2023 Jan 19;16(3):943. doi: 10.3390/ma16030943.
In this paper, an artificial neural network (ANN) model is proposed to predict the hydration process of a new alternative binder. This model overcomes the lack of input parameters of physical models, providing a realistic explanation with few inputs and fast calculations. Indeed, four mortars are studied based on ordinary Portland cement (CEM I), cement with limited environmental impact (CEM III), and glass powder (GP) as the cement substitution. These mortars are named CEM I + GP and CEM III + GP. The properties of the mortars are characterized, and their life cycle assessment (LCA) is established. Indeed, a decrease in porosity is observed at 90 days by 4.6%, 2.5%, 12.4%, and 7.9% compared to those of 3 days for CEMI, CEMIII, CEMI + GP, and CEMIII + GP, respectively. In addition, the use of GP allows for reducing the mechanical strength in the short term. At 90 days, CEMI + GP and CEMIII + GP present a decrease of about 28% and 57% in compressive strength compared to CEMI and CEMIII, respectively. Nevertheless, strength does not cease increasing with the curing time, due to the continuous pozzolanic reactions between Ca(OH) and silica contained in GP and slag present in CEMIII as demonstrated by the thermo-gravimetrical (TG) analysis. To summarize, CEMIII mortar provides similar performance compared to mortar with CEMI + GP in the long term. This can later be used in the construction sector and particularly in prefabricated structural elements. Moreover, the ANN model used to predict the heat of hydration provides a similar result compared to the experiment, with a resulting R² of 0.997, 0.968, 0.968, and 0.921 for CEMI, CEMIII, CEMI + GP, and CEMIII + GP, respectively, and allows for identifying the different hydration modes of the investigated mortars. The proposed ANN model will allow cement manufacturers to quickly identify the different hydration modes of new binders by using only the heat of hydration test as an input parameter.
本文提出了一种人工神经网络（ANN）模型，用于预测新型替代胶凝材料的水化过程。该模型克服了物理模型输入参数不足的问题，只需少量输入就能给出合理的解释，且计算速度快。实际上，研究了四种基于普通硅酸盐水泥（CEM I）、环境影响有限的水泥（CEM III）以及用作水泥替代品的玻璃粉（GP）的砂浆。这些砂浆分别命名为CEM I + GP和CEM III + GP。对这些砂浆的性能进行了表征，并建立了它们的生命周期评估（LCA）。实际上，与3天时相比，90天时CEMI、CEMIII、CEMI + GP和CEMIII + GP的孔隙率分别降低了4.6%、2.5%、12.4%和7.9%。此外，使用GP在短期内会降低机械强度。90天时，CEMI + GP和CEMIII + GP的抗压强度与CEMI和CEMIII相比分别降低了约28%和57%。然而，由于热重（TG）分析表明，GP中所含的二氧化硅与CEMIII中所含的矿渣之间持续发生火山灰反应，强度并不会随着养护时间而停止增长。总之，从长期来看，CEMIII砂浆与CEMI + GP砂浆具有相似的性能。这一点随后可应用于建筑行业，特别是预制结构构件。此外，用于预测水化热的ANN模型与实验结果相似，CEMI、CEMIII、CEMI + GP和CEMIII + GP的决定系数R²分别为0.997、0.968、0.968和0.921，并且能够识别所研究砂浆的不同水化模式。所提出的ANN模型将使水泥制造商仅通过将水化热测试作为输入参数，就能快速识别新型胶凝材料的不同水化模式。
