Development of a Structural Health Monitoring Tool for Underwater Concrete Structures

2021 ◽  
Vol 147 (10) ◽  
pp. 04021135
Author(s):  
B. H. J. Pushpakumara ◽  
G. A. Thusitha
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Concrete Structures ◽  
Monitoring Tool ◽  
Structural Health ◽  
Underwater Concrete

scholarly journals Existing concrete structures: Life management, testing and structural health monitoring

2020 ◽  
Vol 21 (4) ◽  
pp. 1212-1212
Author(s):  
Alfred Strauss ◽  
Sylvia Kessler ◽  
Maria Pina Limongelli ◽  
Konrad Bergmeister
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Concrete Structures ◽  
Structural Health ◽  
Life Management

scholarly journals The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool

2019 ◽  
Vol 9 (23) ◽  
pp. 5064 ◽  
Author(s):  
Marco Civera ◽  
Matteo Ferraris ◽  
Rosario Ceravolo ◽  
Cecilia Surace ◽  
Raimondo Betti
Keyword(s):  
Experimental Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Speech Processing ◽  
Speaker Recognition ◽  
Vibration Analysis ◽  
Monitoring Tool ◽  
Mel Frequency Cepstral Coefficients ◽  
Structural Health ◽  
Cepstral Coefficients

Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). This approach, while efficient and still very common in applications, such as speech and speaker recognition, has been followed by other more advanced and competitive techniques for the same aims. The Teager-Kaiser Energy Cepstral Coefficients (TECCs) is one of these alternatives. These features are very closely related to MFCCs, but provide interesting and useful additional values, such as e.g., improved robustness with respect to noise. The goal of this paper is to introduce the use of TECCs for damage detection purposes, by highlighting their competitiveness with closely related features. Promising results from both numerical and experimental data were obtained.

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

RSC Advances ◽  
2020 ◽  
Vol 10 (39) ◽  
pp. 23038-23048
Author(s):  
Sofija Kekez ◽  
Jan Kubica
Keyword(s):  
Machine Learning ◽  
Carbon Nanotube ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Concrete Structures ◽  
Concrete Technology ◽  
Structural Health

Carbon nanotube/concrete composite possesses piezoresistivity i.e. self-sensing capability of concrete structures even in large scale.

STRUCTURAL HEALTH MONITORING OF CORROSION POTENTIAL IN CONCRETE BRIDGE DECKS

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Hani Nassif ◽  
Chaekuk Na ◽  
Hasan Al-Nawadi ◽  
Adi Abu-Obeida ◽  
William Wilson
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Corrosion Potential ◽  
Silver Chloride ◽  
Bridge Decks ◽  
Concrete Structures ◽  
Concrete Bridge ◽  
Accelerated Corrosion ◽  
Structural Health ◽  
Concrete Bridge Decks

Structural Health Monitoring (SHM) of concrete structures during construction, as well as over its service life, has recently become more attractive to owners and consulting engineers. With the introduction of new materials and construction methods, various types of concrete structures are being instrumented with monitoring devices to determine their performance and response to various loading conditions. Among many other objectives, this includes monitoring concrete performance at the serviceability and durability limit states. This paper is an overview of an on-going program for the SHM of concrete bridge decks in the State of New Jersey focusing on field performance. Three types of corrosion sensors are instrumented to monitor the corrosion activities in concrete decks; one is the silver-silver chloride electrode and the other two are multi element probe (MEP) corrosion sensors. Other types of MEPs were also instrumented on bridge decks during reconstruction in late 1990s to monitor the corrosion potential of the bridge decks. Various types of sensors are installed in precast panels during fabrication as well as in-situ cast concrete decks during and after construction. Moreover, a laboratory-based accelerated corrosion testing program is also performed on concrete specimens using various types of rebars. This ongoing study is aimed at correlating laboratory-accelerated corrosion results with long-term performance of the steel in concrete bridge decks under field conditions.

scholarly journals Post-processing algorithms for distributed optical fiber sensing in structural health monitoring applications

2020 ◽  
pp. 147592172092155 ◽  
Author(s):  
Mattia Francesco Bado ◽  
Joan Ramon Casas ◽  
Judit Gómez
Keyword(s):  
Structural Health Monitoring ◽  
Optical Fiber ◽  
Health Monitoring ◽  
Optical Fiber Sensors ◽  
Post Processing ◽  
Monitoring Tool ◽  
Fiber Sensors ◽  
Structural Health ◽  
Distributed Optical Fiber

Distributed optical fiber sensors are measuring tools whose potential related to the civil engineering field has been discovered in the latest years only (reduced dimensions, easy installation process, lower installation costs, elevated reading accuracy, and distributed monitoring). Yet, what appears clear from numerous in situ distributed optical fiber sensors monitoring campaigns (bridges and historical structures among others) and laboratory confined experiments is that optical fiber sensors monitorings have a tendency of including in their outputs a certain amount of anomalistic readings (out of scale and unreliable measurements). These can be both punctual in nature and spread over all the monitoring duration. Their presence strongly affects the results both altering the data in its affected sections and distorting the overall trend of the strain evolution profiles, thus the importance of detecting, eliminating, and substituting them with correct values. Being this issue intrinsic in the raw output data of the monitoring tool itself, its only solution is computer-aided post-processing of the strain data. This article discusses different simple algorithms for getting rid of such disruptive anomalies using two methods previously used in the literature and a novel polynomial-based one with different levels of sophistication and accuracy. The viability and performance of each are tested on two study case scenarios: an experimental laboratory test on two reinforced concrete tensile elements and an in situ tunnel monitoring campaign. The outcome of such analysis will provide the reader with both clear indications on how to purge a distributed optical fiber sensors-extracted data set of all anomalies and on which is the best-suited method according to their needs. This marriage of computer technology and cutting edge structural health monitoring tool not only elevates the distributed optical fiber sensors viability but also provides civil and infrastructures engineers a reliable tool to perform previously unreachable levels of accuracy and extension monitoring coverage.

Sign in / Sign up

Export Citation Format

Share Document