Title

Prediction of Surface Chloride Concentration of Marine Concrete using Ensemble Machine Learning

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.

Department(s)

Electrical and Computer Engineering

Second Department

Materials Science and Engineering

Third Department

Civil, Architectural and Environmental Engineering

Research Center/Lab(s)

INSPIRE - University Transportation Center

Comments

National Science Foundation, Grant 1761697

Keywords and Phrases

Ensemble Machine Learning; Marine Concrete; Surface Chloride Concentration; Voting Method

International Standard Serial Number (ISSN)

0008-8846

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Elsevier, All rights reserved.

Publication Date

01 Jul 2020

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