Abstract This paper develops and employs an ensemble machine learning (ML) model for prediction of surface chloride concentration (Cs) of concrete, which is an essential parameter for durability design and service life prediction of concrete structures in marine environment. For this purpose, a database containing 642 data-records of field exposure data of Cs (along with the associated mixture proportion parameters, environmental conditions and exposure time) is established based on extensive literature surveying, which covers splash, tidal, and submerged zones in various areas in the world. The database is used to train five standalone ML models, that is, linear regression (LR), Gaussian process regression (GPR), support vector machine (SVM), multilayer perceptron artificial neural network (MLP-ANN) and random forests (RF) models, as well as an ensemble weighted voting-based ML model, and subsequently used to compare their prediction performances. It is shown that, by meta-heuristically combining predictions of RF, MLP-ANN, and SVM, the ensemble ML model produces higher accuracy of prediction compared to all standalone ML models tested in this study. The prediction performances of eight conventional quantitative models for Cs prediction are also analyzed based on the testing dataset selected for ML. The results show that adoption of more diverse datasets and consideration of more factors in conventional models can improve their prediction performance. The ensemble ML model established on a large database, can easily consider the twelve influencing factors (which is difficult for conventional models) in the database, and has superior prediction performance, yet better time-efficiency, compared to conventional models.

中文翻译：

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使用集成机器学习预测海洋混凝土表面氯化物浓度

摘要 本文开发并采用集成机器学习 (ML) 模型来预测混凝土表面氯化物浓度 (Cs)，这是海洋环境中混凝土结构耐久性设计和使用寿命预测的重要参数。为此，在大量文献调查的基础上，建立了一个包含 642 条 Cs 野外暴露数据记录的数据库（以及相关的混合比例参数、环境条件和暴露时间），涵盖了飞溅、潮汐和淹没区。世界的各个领域。该数据库用于训练五个独立的ML模型，即线性回归（LR）、高斯过程回归（GPR）、支持向量机（SVM）、多层感知器人工神经网络（MLP-ANN）和随机森林（RF）楷模，以及基于集成加权投票的 ML 模型，随后用于比较它们的预测性能。结果表明，通过元启发式结合 RF、MLP-ANN 和 SVM 的预测，与本研究中测试的所有独立 ML 模型相比，集成 ML 模型产生更高的预测精度。还基于为 ML 选择的测试数据集分析了用于 Cs 预测的八种常规定量模型的预测性能。结果表明，在传统模型中采用更多样化的数据集和考虑更多因素可以提高其预测性能。建立在大型数据库上的集成ML模型，可以轻松考虑数据库中的十二个影响因素（传统模型难以做到的），具有优越的预测性能，