Journal
ENGINEERING STRUCTURES
Volume 148, Issue -, Pages 924-935Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2017.07.022
Keywords
Structural health monitoring; Sensor network; Damage detection; Nanocomposite conductive concrete; Resistor mesh model; Damage localiiation; Smart concrete
Categories
Funding
- Italian Ministry of Education, University and Research (MIUR) through Relevant National Interest (PRIN) [2015MS5L27]
- National Science Foundation [1069283]
- Ministerio de Economia y Competitividad of Spain
- Consejeria de Economia, Innovacion, Ciencia y Empleo of Andalucia (Spain) [DPI2014-53947-R, P12-TEP-2546]
- FPU from Spanish Ministry of Education [FPU13/04892]
- Integrative Graduate Education and Research Traineeship (IGERT) in Wind Energy Science, Engineering and Policy (WESEP) at Iowa State University.
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Interest in self-sensing structural materials has grown in recent years due to their potential to enable continuous low-cost monitoring of next-generation smart-structures. The development of cement based smart sensors appears particularly well suited for structural health monitoring due to their numerous possible field applications, ease of use, and long-term stability. Additionally, cement-based sensors offer a unique opportunity for monitoring of civil concrete structures because of their compatibility with new and existing infrastructure. In this paper, we propose the use of a computationally efficient resistor mesh model to detect, localize and quantify damage in structures constructed from conductive cement composites. The proposed approach is experimentally validated on non-reinforced and reinforced specimens made of nanocomposite cement paste doped with multi-walled carbon nanotubes under a variety of static loads and damage conditions. Results show that the proposed approach is capable of leveraging the strain-sensing and damage-sensitive properties of conductive cement composites for real-time distributed structural health monitoring of smart concrete structures, using simple and inexpensive electrical hardware and with very limited computational effort. (C) 2017 Elsevier Ltd. All rights reserved.
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