Synthetic Dataset for Sequential Learning-Based Optimisation of Bio-Ash Binder Formulations under Seasonal Availability Constraints

This dataset accompanies the study on sequential learning–based optimisation of bio-ash–cement binder formulations under seasonally varying material availability. It provides a fully synthetic but chemically inspired benchmark design space for evaluating data-driven optimisation strategies in cementitious materials research. The dataset comprises 5,006 unique binder formulations, each defined by the mass fractions of cement and five bio-based ash components (A1–A5). Ash components represent generic bio-ash types derived from agricultural residues (e.g. rice husk ash, cassava peel ash), and their internal proportions are systematically varied under mass-balance constraints. Cement content ranges from 0 to 100 wt% in discrete steps. To reflect dynamic supply conditions, the dataset includes season-specific ash usage metrics for four seasons (S1–S4), expressing the fraction of available ash resources consumed by each formulation. A synthetic compressive strength value is assigned to every formulation using a nonlinear scoring function based on chemically inspired descriptors, with added noise to generate a structured yet non-trivial optimisation landscape. These strength values do not represent calibrated physical predictions and are intended solely as a hidden objective function for benchmarking sequential learning algorithms. The dataset is designed for in silico benchmarking, reproducibility studies, and methodological comparisons of optimisation and active learning strategies. It enables systematic evaluation of algorithmic performance without the need for physical experiments. Column description Index_Sample: Unique sample identifier A1_m% – A5_m%: Mass fractions (wt%) of five bio-ash components Cement_m%: Mass fraction (wt%) of cement in the binder Ash_Usage_Percentage_S1 – S4: Normalised ash usage metrics for four seasonal availability scenarios Compressive_Strength (MPa): Synthetic compressive strength value used as optimisation target本数据集配套于一项基于序列学习的胶凝材料配合比优化研究，该研究聚焦于物料供应随季节波动条件下的生物灰-水泥基胶凝配方。本数据集提供了一个完全合成但基于化学原理的基准设计空间，用于评估胶凝材料研究领域的数据驱动优化策略。 本数据集包含5006组独特的胶凝配方，每组配方均由水泥与五种生物灰组分（A1–A5）的质量分数定义。生物灰组分代表源自农业废弃物（如稻壳灰、木薯皮灰）的通用生物灰类型，其内部比例在质量平衡约束下被系统地调整。水泥含量以离散步长在0至100 wt%范围内变化。 为反映动态供应条件，本数据集包含针对四个季节（S1–S4）的季节特异性灰分使用指标，用以表征每组配方消耗的可用灰分资源占比。本数据集基于化学启发的描述符，通过非线性评分函数为每组配方赋予合成抗压强度值，并添加噪声以生成一个结构化且具有一定复杂度的优化空间。这些强度值并非经过校准的物理预测结果，仅作为用于序列学习算法基准测试的隐式目标函数。 本数据集旨在用于虚拟（in silico）基准测试、可重复性研究，以及优化与主动学习策略的方法学对比，可在无需开展物理实验的前提下实现算法性能的系统性评估。 列说明 Index_Sample：唯一样本标识符 A1_m% – A5_m%：五种生物灰组分的质量分数（wt%） Cement_m%：胶凝材料中水泥的质量分数（wt%） Ash_Usage_Percentage_S1 – S4：四种季节供应场景下的归一化灰分使用指标 Compressive_Strength (MPa)：用作优化目标的合成抗压强度值