Zhang Qianhui
School of Civil Engineering and Architecture, Zhejiang Guangsha Vocational and Technical University of construction, Dongyang, 322100, Zhejiang Province, China.
Sci Rep. 2025 Jul 1;15(1):21420. doi: 10.1038/s41598-025-89595-9.
This study explores mix proportion design and mechanical property prediction of EPS lightweight structural concrete using orthogonal experimentation and machine learning models. The research systematically analyzed the effects of EPS content, water-to-binder ratio, and POM fiber content on compressive strength, splitting tensile strength, thermal conductivity, and frost resistance. Key findings reveal that EPS content significantly enhances thermal insulation and frost resistance but reduces mechanical strength. POM fibers were shown to improve tensile strength and frost resistance by limiting crack propagation. A novel dataset was established and utilized in performance prediction using XGBoost, optimized with Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). Among these, SOA-XGBoost achieved the highest prediction accuracy and stability. The optimal mix proportion, combining 35% EPS, 0.21 water-to-binder ratio, and 0.65% POM fiber content, was identified, providing an effective balance between mechanical and thermal performance. The proposed framework offers valuable insights and methodologies for optimizing lightweight concrete and serves as a reference for other composite materials in engineering applications.
本研究采用正交试验和机器学习模型探索了EPS轻质结构混凝土的配合比设计和力学性能预测。该研究系统分析了EPS含量、水胶比和POM纤维含量对抗压强度、劈裂抗拉强度、导热系数和抗冻性的影响。主要研究结果表明，EPS含量显著提高了保温性能和抗冻性，但降低了力学强度。结果表明，POM纤维通过限制裂缝扩展提高了抗拉强度和抗冻性。建立了一个新的数据集，并将其用于使用XGBoost进行性能预测，该模型通过海鸥优化算法（SOA）、鲸鱼优化算法（WOA）和粒子群优化（PSO）进行了优化。其中，SOA-XGBoost实现了最高的预测精度和稳定性。确定了最佳配合比，即35%的EPS、0.21的水胶比和0.65%的POM纤维含量，在力学性能和热性能之间实现了有效平衡。所提出的框架为轻质混凝土的优化提供了有价值的见解和方法，并为工程应用中的其他复合材料提供了参考。
