The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches

authored by: Xiaoying Zhuang, Shuai Zhou
Abstract: Advances in machine learning (ML) methods are important in industrial engineering and attract great attention in recent years. However, a comprehensive comparative study of the most advanced ML algorithms is lacking. Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared. Six ML algorithms, including the Support Vector Regression (SVR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), Artificial Neural Network (ANN), Bayesian Ridge Regression (BRR) and Kernel Ridge Regression (KRR), are adopted for the relationship modeling to predict crack closure percentage (CCP). Particle Swarm Optimization (PSO) is used for the hyper-parameters tuning. The importance of parameters is analyzed. It is demonstrated that integrated ML approaches have great potential to predict the CCP, and PSO is efficient in the hyper-parameter tuning. This research provides useful information for the design of the bacteria-based self-healing concrete and can contribute to the design in the rest of industrial engineering.
Organisation(s): Institute of Continuum Mechanics
External Organisation(s): Ton Duc Thang University
Chongqing University
Type: Article
Journal: Computers, Materials and Continua
Volume: 59
Pages: 57-77
No. of pages: 21
ISSN: 1546-2218
Publication date: 2019
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Biomaterials, Modelling and Simulation, Mechanics of Materials, Computer Science Applications, Electrical and Electronic Engineering
Electronic version(s): https://doi.org/10.32604/cmc.2019.04589 (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{35aad46c165f4d228829e75d239a55b3,
title = "The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches",
abstract = "Advances in machine learning (ML) methods are important in industrial engineering and attract great attention in recent years. However, a comprehensive comparative study of the most advanced ML algorithms is lacking. Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared. Six ML algorithms, including the Support Vector Regression (SVR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), Artificial Neural Network (ANN), Bayesian Ridge Regression (BRR) and Kernel Ridge Regression (KRR), are adopted for the relationship modeling to predict crack closure percentage (CCP). Particle Swarm Optimization (PSO) is used for the hyper-parameters tuning. The importance of parameters is analyzed. It is demonstrated that integrated ML approaches have great potential to predict the CCP, and PSO is efficient in the hyper-parameter tuning. This research provides useful information for the design of the bacteria-based self-healing concrete and can contribute to the design in the rest of industrial engineering.",
keywords = "Bacteria, Crack closure percentage, Machine learning, Prediction, Self-healing concrete",
author = "Xiaoying Zhuang and Shuai Zhou",
note = "Funding information: Acknowledgement: This work was supported by Sofa-Kovalevskaja-Award of Alexander von Humboldt-Foundation.",
year = "2019",
doi = "10.32604/cmc.2019.04589",
language = "English",
volume = "59",
pages = "57--77",
journal = "Computers, Materials and Continua",
issn = "1546-2218",
publisher = "Tech Science Press",
number = "1",
}