Blockchain-Enabled Internet of Things for Unsupervised Structural Health Monitoring in Potential Building Structures

2021 ◽  
pp. 158-170
Author(s):  
Wael Mohammad Alenazy
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Structural Health Monitoring ◽  
Internet Of Things ◽  
Health Monitoring ◽  
Remote Monitoring ◽  
Building Structures ◽  
Stress Strain ◽  
Structural Health ◽  
Automated Decision Making

The integration of internet of things, artificial intelligence, and blockchain enabled the monitoring of structural health with unattended and automated means. Remote monitoring mandates intelligent automated decision-making capability, which is still absent in present solutions. The proposed solution in this chapter contemplates the architecture of smart sensors, customized for individual structures, to regulate the monitoring of structural health through stress, strain, and bolted joints looseness. Long range sensors are deployed for transmitting the messages a longer distance than existing techniques. From the simulated results, different sensors record the monitoring information and transmit to the blockchain platform in terms of pressure points, temperature, pre-tension force, and the architecture deems the criticality of transactions. Blockchain platform will also be responsible for storage and accessibility of information from a decentralized medium, automation, and security.

Safety 4.0 for the Most Dangerous Industrial Objects

2021 ◽  
Author(s):  
Igor Razuvaev
Keyword(s):  
Decision Making ◽  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Low Temperatures ◽  
Storage Tanks ◽  
Data Verification ◽  
Structural Health

Abstract Isothermal Storage Tanks (IST) contains tens thousands tons of the liquefied gases (propane, ethane, ethylene, etc.) at very low temperatures. These are the most dangerous industrial objects. In the report the Integrated Structural Health Monitoring (ISHM) Systems for the management of the integrity of these tanks in real time is considered. The structure of the ISHM Systems, NDT methods, technical characteristics, data verification procedures, a decision-making algorithm and practical results are described.

scholarly journals Wind turbine lifetime extension decision-making based on structural health monitoring

2019 ◽  
Vol 143 ◽  
pp. 611-621 ◽  
Author(s):  
T. Rubert ◽  
G. Zorzi ◽  
G. Fusiek ◽  
P. Niewczas ◽  
D. McMillan ◽  
...  
Keyword(s):  
Decision Making ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Structural Health ◽  
Lifetime Extension

scholarly journals A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates

2020 ◽  
pp. 147592172090454 ◽  
Author(s):  
Manuel A Vega ◽  
Michael D Todd
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Informed Decision ◽  
Informed Decision Making ◽  
Bayesian Neural Network ◽  
Bayesian Neural Networks ◽  
Structural Health

Many physics-based and surrogate models used in structural health monitoring are affected by different sources of uncertainty such as model approximations and simplified assumptions. Optimal structural health monitoring and prognostics are only possible with uncertainty quantification that leads to an informed course of action. In this article, a Bayesian neural network using variational inference is applied to learn a damage feature from a high-fidelity finite element model. Bayesian neural networks can learn from small and noisy data sets and are more robust to overfitting than artificial neural networks, which make it very suitable for applications such as structural health monitoring. Also, uncertainty estimates obtained from a trained Bayesian neural network model are used to build a cost-informed decision-making process. To demonstrate the applicability of Bayesian neural networks, an example of this approach applied to miter gates is presented. In this example, a degradation model based on real inspection data is used to simulate the damage evolution.

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