This paper employs a data-driven approach to determine the impact of concrete mixture compositions on the temporal evolution of chloride in concrete structures. This is critical for assessing the service life of civil infrastructure subjected to aggressive environments. The adopted methodology relies on several simple and complex standalone machine learning (ML) algorithms, with the primary objective of establishing confidence in the unbiased prediction of the underlying hidden correlations. The simple algorithms include linear regression (LR), k-nearest neighbors (KNN) regression, and kernel ridge regression (KRR). The complex algorithms entail support vector regression (SVR), Gaussian process regression (GPR), and two families of artificial neural networks, including a feedforward network (multilayer perceptron, MLP) and a gated recurrent unit (GRU). The MLP architecture cannot explicitly handle sequential data, a limitation addressed by the GRU. A comprehensive dataset is considered. The performance of ML algorithms is evaluated, with KRR, GPR, and MLP exhibiting high accuracy. Given the diversity of the adopted concrete mixture proportions, the GRU was unable to accurately reproduce the response in the test set. Further analyses elucidate the contributions of mixture compositions to the temporal evolution of chloride. The results obtained from the GPR model unravel latent correlations through clear and explainable trends. The MLP, SVR, and KRR also provide acceptable estimates of the overall trends. The majority of mixture components exhibit an inverse relation with chloride content, while a few components demonstrate a direct correlation. These findings highlight the potential of surrogate approaches for describing the physical processes involved in chloride ingress and the associated correlations, toward the ultimate goal of enhancing the service life of civil infrastructure.

翻译：本文采用数据驱动方法研究混凝土配合比组成对结构中氯离子时变演化规律的影响，这对于评估严酷环境下土木基础设施的使用寿命至关重要。所采用的方法依赖于多种简单与复杂的独立机器学习算法，主要目标是建立对潜在隐藏相关性无偏预测的可信度。简单算法包括线性回归、K近邻回归与核岭回归。复杂算法涵盖支持向量回归、高斯过程回归以及两类人工神经网络：前馈网络（多层感知机）与门控循环单元。多层感知机架构无法显式处理序列数据，此局限性由门控循环单元解决。研究采用了综合性数据集进行评估，核岭回归、高斯过程回归与多层感知机均表现出较高精度。由于所采用混凝土配合比比例的多样性，门控循环单元未能准确复现测试集的响应。进一步分析阐明了配合比组成对氯离子时变演化的贡献规律。基于高斯过程回归模型的结果通过清晰可解释的趋势揭示了潜在相关性。多层感知机、支持向量回归与核岭回归亦能对总体趋势提供可接受的估计。多数配合比组分与氯离子含量呈负相关，而少数组分呈现正相关。这些发现凸显了代理方法在描述氯离子侵蚀物理过程及相关性方面的潜力，最终服务于延长土木基础设施使用寿命的目标。