Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring.

Sofija Kekez,Jan Kubica

doi:10.1039/d0ra03450a

Sofija Kekez, Jan Kubica

Open Access

https://doi.org/10.1039/d0ra03450a

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Journal: RSC advances	Publication Date: Jan 1, 2020
Citations: 13	License type: CC BY 3.0

Affiliation: Silesian University of Technology

Abstract

Carbon nanotube/concrete composite possesses piezoresistivity i.e. self-sensing capability of concrete structures even in large scale. By incorporating smart materials in the structural health monitoring systems the issue of incompatibility between monitored structure and the sensor is surpassed since the concrete element fulfills both functions. Machine learning is an attractive tool to reduce model complexity, so artificial neural networks have been successfully used for a variety of applications including structural analysis and materials science. The idea of using smart materials can become more attractive by building a neural network able to predict properties of the specific nanomodified concrete, making it more cost-friendly and open for unexperienced engineers. This paper reviews previous research work which is exploring the properties of CNTs and their influence on concrete, and the use of artificial neural networks in concrete technology and structural health monitoring. Mix design of CNT/concrete composite materials combined with the application of precisely trained artificial neural networks represents a new direction in the evolution of structural health monitoring of concrete structures.

Highlights

Both nanotechnology and neural networks were in the making during the rst half of the twentieth century
This paper reviews previous research work which is exploring the properties of carbon nanotubes (CNTs) and their influence on concrete, and the use of artificial neural networks in concrete technology and structural health monitoring
Mix design of CNT/ concrete composite materials combined with the application of precisely trained artificial neural networks represents a new direction in the evolution of structural health monitoring of concrete structures

Summary

Introduction

Both nanotechnology and neural networks were in the making during the rst half of the twentieth century. Piezoresistivity is de ned as the change of electrical resistivity under the applied strain, and in order to obtain a piezoresistive cement-based material, an electrically conductive material such as carbon nanotubes has to be incorporated into a cementitious matrix.[124,154] The piezoresistive behavior of CNT networks was rst presented with CNT/polymer composites, whereupon application of mechanical load, the con guration of the networks was affected resulting in a change of the electrical resistance.[36,38,40,98,104]. Real-time effective and long-distance monitoring of concrete structures is possible to obtain by developing a cementitious material representing a good host for the intricate branches of long continuous CNT neurons on one side and on the other – trained neural network capable of disregarding negligible data and processing relevant information about the structure's condition, so it may generate a 3D model showing possible damage and current state of structural elements. Future investigation of the authors will focus on developing the methodology of production of self-sensing CNT/ concrete composites and specifying, modeling and training the ANN by using the obtained results of sensitivity testing

Conclusions

Brain theory