Zhao Yan, Huang Ziyan, Zhao Huilong, Xu Zhen, Chang Wei, Liu Bai
School of Civil Engineering and Architecture, Wuyi University, Wuyishan City, Fujian Province, China.
Nanping Highway Development Center, Nanping City, Fujian Province, China.
PLoS One. 2025 Jul 7;20(7):e0326652. doi: 10.1371/journal.pone.0326652. eCollection 2025.
High strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compressive strength and material components. In this study, two artificial neural network (ANN) models-the BP and Elman networks were used to predict the compressive strength of ultra-high-performance lightweight concrete (UHPLC), based on a robust database of 115 test datasets from previous studies. The investigated parameters included the cement grade (Grade 42.5 and Grade 52.5), cement content (352 kg/m3-938 kg/m3), silica fume content (0 kg/m3-350 kg/m3), fly ash content (0 kg/m3-220 kg/m3), microsphere content (0 kg/m3-624 kg/m3), lightweight sand types (pottery sand, expanded perlite sand, and expanded shale lightweight sand), lightweight sand content (0 kg/m3-769 kg/m3), sand type (quartz sand, river sand), sand content (0 kg/m3-1314 kg/m3), water (90 kg/m3-395 kg/m3), water reduce (0 kg/m3-42.8 kg/m3), steel fiber content (0 kg/m3-234 kg/m3). Correlation analysis and sensitive analysis indicated that lightweight sand content and sand content had the most significant effects on UHPLC compressive strength, followed by water content. Conversely, fly ash content and lightweight sand type had minimal impact. The developed ANN models for UHPLC compressive strength demonstrated high predictive accuracy for both training and testing datasets, which the RMSE of BP network and Elman network were 0.226 and 0.160, respectively, while R2 of both two developed models were more than 0.98. Additionally, UHPLC exhibited a higher compressive strength-to-density ratio than high-strength concrete, ultra-high-performance concrete, and even Q235 steel. Three strategies were proposed for creating ultra-high-performance lightweight composites: optimizing packing density and lowering the water-binder ratio, along with careful selection of lightweight aggregates.
高强度和轻量化是混凝土发展的关键趋势。由于抗压强度与材料成分之间存在复杂的关系，要在这些性能之间取得平衡以生产出具有高结构效率（强度重量比）的混凝土具有挑战性。在本研究中，基于先前研究的115个测试数据集组成的可靠数据库，使用了两种人工神经网络（ANN）模型——BP网络和Elman网络来预测超高性能轻质混凝土（UHPLC）的抗压强度。研究的参数包括水泥等级（42.5级和52.5级）、水泥用量（352 kg/m³ - 938 kg/m³）、硅灰用量（0 kg/m³ - 350 kg/m³）、粉煤灰用量（0 kg/m³ - 220 kg/m³）、微珠用量（0 kg/m³ - 624 kg/m³）、轻质砂类型（陶砂、膨胀珍珠岩砂和膨胀页岩轻质砂）、轻质砂用量（0 kg/m³ - 769 kg/m³）、砂类型（石英砂、河砂）、砂用量（0 kg/m³ - 1314 kg/m³）水（90 kg/m³ - 395 kg/m³）、减水剂（0 kg/m³ - 42.8 kg/m³）、钢纤维用量（0 kg/m³ - 234 kg/m³）。相关性分析和敏感性分析表明，轻质砂用量和砂用量对UHPLC抗压强度影响最为显著，其次是水用量。相反，粉煤灰用量和轻质砂类型影响最小。所开发的用于UHPLC抗压强度的ANN模型在训练和测试数据集上均显示出较高的预测准确性，BP网络和Elman网络的均方根误差（RMSE）分别为0.226和0.160，而两个开发模型的决定系数（R²）均大于0.98。此外，UHPLC的抗压强度与密度之比高于高强度混凝土、超高性能混凝土，甚至Q235钢。提出了三种制备超高性能轻质复合材料的策略：优化堆积密度、降低水胶比以及精心选择轻质骨料。
