Previous study has proved that using electromechanical impedance (EMI) instrumented bar-type corrosion measuring probe can realize the quantitative assessment of the corrosion amount. To gain more insights into the working mechanism and design better probes, this work examined a new type of corrosion measuring probe based on the conical rod, and evaluated their performance. Theoretical model of this type of new probes was established based on one dimensional piezo-elasticity theory, and the electrical impedance was derived to obtain first-order resonant and anti-resonant frequencies in longitudinal vibration mode. Two experiments were performed to validate the feasibility of the probe for corrosion measurement, including the artificial uniform corrosion experiment and the accelerated corrosion test. Comparisons between the theoretical predictions and the experimental results from the artificial uniform corrosion experiment were made, and good agreement was found. Effects of piezoelectric patch thickness and cone angle on first resonant and anti-resonant frequencies were also analyzed. In addition, a wireless impedance measurement system was preliminarily realized, which is very promising in developing the low cost and high accuracy online real-time monitoring technology for the pipeline corrosion monitoring.
This work presents an offshore pipeline scour monitoring sensor network system based on active thermometry. The system consists of thermal cables, data acquisition unit, and data processing unit. As the thermal cables emit heats, the distributed DS18B20 digital temperature sensors record temperature information over time. The scour-induced exposure and free spanning can be identified by analyzing the temperature curves. Pipeline exposure and free-spanning experiments were carried out in laboratory, whose results show that the system is able to give overall information about the development of pipeline scour. Difference values analysis reveals the changing patterns of heat transfer behavior for line heat source in sediment and water scenarios. Two features, magnitude and temporal instability, are extracted from temperature curves to better differentiate sediment and water scenarios. Based on these two features, K-means clustering algorithm is adopted for pattern classification of the system, which was implemented in MATLAB and facilitated the automatic detection of the scour monitoring sensor network system. The proposed sensor network has the advantages of low cost, high precision and construction flexiblility, providing a promising approach for offshore pipeline scour monitoring, especially suitable for nearshore environment.
Structural health monitoring (SHM) is of great significance for post-earthquake damage assessment. Smartphone-based monitoring techniques provide the possibility to perform crowdsensing for all buildings in urban regions after an earthquake. However, this idea still faces many difficulties and is hard to realize. Fortunately, the development of game engines provides the opportunity for simulating this kind of experiment. The main objective of this study was to use Unity to simulate the whole process when a city is struck by an earthquake that consists of one main shock and one aftershock. During the emergency response, the citizens and the “city brain” in Unity, named Ground Eye, cooperate to finish the task of taking refuge and collecting data for regional damage assessment. Some basic assumptions were made first. Then the city model was established in Unity, and the behaviors of the citizens were directed by the behavior tree artificial intelligence (AI). OpenSees was utilized to determine the monitoring demand and simulate the monitoring results. A GUI was built to exhibit the data during the whole process. The results show that the evacuation and monitoring plan is feasible. The simulation framework presented in this paper can be used in other SHM application scenarios.
Corrosion induced thickness loss in metallic structures is a common and crucial problem in multiple industries. Therefore, it is important to accurately monitor the corrosion amount of the structure. Traditional corrosion monitoring methods are mainly based on electrochemical methods, and most of them are unable to quantify the corrosion amount. In our previous work, a new type of corrosion sensing mechanism based on the electromechanical impedance instrumented circular piezoelectric-metal transducer was proposed, in which the peak frequencies in the conductance signatures decrease linearly with the increase of the corrosion induced thickness loss. However, only the uniform corrosion with even metal thickness decrease was considered in the previous study. In this paper, the capability of the proposed sensing mechanism for the quantification and prediction of pitting corrosion was investigated using one-dimensional convolutional neural networks (1D CNN). Finite element modeling of the pitting corrosion was performed and the probability distribution of the corrosion pits was considered. In the experimental setup, corrosion pits were generated on the corrosion sensor using mechanical drilling. The 1D CNN was adopted to explore the regression relationship between the EMI signatures of the sensor and the mass loss induced by pitting corrosion. The results show that the proposed method has achieved high accuracy in the quantitative prediction of pitting corrosion. This paper lays the technical foundation for real-time and quantitative monitoring of pitting corrosion for metallic structures.
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