Self-Compacting Concrete (SCC) offers remarkable benefits in modern engineering. However, traditional SCC design faces challenges, necessitating a reduction in carbon emissions for enhanced sustainability and a shift towards multi-performance collaborative design. This study proposed an intelligent and interpretable approach for multi-objective low-carbon SCC design. By combining machine learning and optimization algorithm, key factors including sustainability, rheology, workability, strength, durability, and cost were simultaneously addressed. Partial Dependence Plots was employed for model interpretation and feature impacts reveal. The proposed three-objective optimization exhibited superior efficiency, achieving comprehensive optimization efficiencies of 42.3%. In the context of meeting other performance requirements, C40 and C50 SCC optimized using this method exhibited a significant reduction of 18.9% and 10.1% in embodied carbon. This study aims to establish a versatile intelligent framework, rather than a specific model, capable of adapting to future iterations with evolving high-quality data, to achieve multi-objective SCC collaborative design. This advancement contributes to intelligent and low-carbon practices in concrete science and the construction industry.

中文翻译：

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考虑耐久性的低碳自密实混凝土数据驱动多目标设计

自密实混凝土 (SCC) 在现代工程中具有显着的优势。然而，传统的 SCC 设计面临着挑战，需要减少碳排放以增强可持续性，并转向多性能协作设计。本研究提出了一种智能且可解释的多目标低碳 SCC 设计方法。通过结合机器学习和优化算法，同时解决了可持续性、流变性、可加工性、强度、耐用性和成本等关键因素。部分依赖图用于模型解释和特征影响揭示。所提出的三目标优化表现出优越的效率，综合优化效率达到42.3%。在满足其他性能要求的情况下，采用该方法优化的C40和C50 SCC的隐含碳分别显着降低了18.9%和10.1%。本研究旨在建立一个通用的智能框架，而不是一个特定的模型，能够适应未来迭代和不断发展的高质量数据，以实现多目标SCC协同设计。这一进步有助于混凝土科学和建筑行业的智能和低碳实践。