Details
| Original language | English |
|---|---|
| Pages (from-to) | 57-77 |
| Number of pages | 21 |
| Journal | Computers, Materials and Continua |
| Volume | 59 |
| Issue number | 1 |
| Publication status | Published - 2019 |
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
ASJC Scopus subject areas
- Materials Science(all)
- Biomaterials
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Mechanics of Materials
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Electrical and Electronic Engineering
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In: Computers, Materials and Continua, Vol. 59, No. 1, 2019, p. 57-77.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches
AU - Zhuang, Xiaoying
AU - Zhou, Shuai
N1 - Funding information: Acknowledgement: This work was supported by Sofa-Kovalevskaja-Award of Alexander von Humboldt-Foundation.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Bacteria
KW - Crack closure percentage
KW - Machine learning
KW - Prediction
KW - Self-healing concrete
UR - http://www.scopus.com/inward/record.url?scp=85064869529&partnerID=8YFLogxK
U2 - 10.32604/cmc.2019.04589
DO - 10.32604/cmc.2019.04589
M3 - Article
AN - SCOPUS:85064869529
VL - 59
SP - 57
EP - 77
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
SN - 1546-2218
IS - 1
ER -