Prediction of surface chloride concentration of marine concrete using ensemble machine learning. (October 2020)

• Record Type: Journal Article
• Authors: Cai, Rong; Han, Taihao; Liao, Wenyu; Huang, Jie; Li, Dawang; Kumar, Aditya; Ma, Hongyan
• Is Part Of: Cement and concrete research. Volume 136(2020)
• Publication Date: 2020-10
• Subjects: Surface chloride concentration -- Marine concrete -- Ensemble machine learning -- Voting method; Cement -- Periodicals; Cement -- Research -- Periodicals; Concrete -- Periodicals; Concrete -- Research -- Periodicals; Ciment -- Périodiques; Béton -- Périodiques; Cement; Concrete; Periodicals; 620.135
• DOI: 10.1016/j.cemconres.2020.106164
• Languages: English
• ISSNs: 0008-8846

Abstract

Abstract: This paper develops and employs an ensemble machine learning (ML) model for prediction of surface chloride concentration ( C s ) 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 C s (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 C s 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. Highlights: 642 records of field exposure data of surface chloride concentration in marine concrete are collected; An ensemble machine learning model is developed to predict the surface chloride concentration; The ensemble model is more accurate than standalone machine learning models and conventional quantitative models; Effects of seven factors on surface chloride concentration are predicted using the machine learning model.