Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks

Abstract

The bolted spherical joint (BSJ) has wide applications in various space grid structures. The bar and the bolted sphere are connected by the high-strength bolt inside the joint. High-strength bolt is invisible outside the joint, which causes the difficulty in monitoring the bolt looseness. Moreover, the bolt looseness leads to the reduction of the local stiffness and bearing capacity for the structure. In this regard, this study used the electro-mechanical impedance (EMI) technique and back propagation neural networks (BPNNs) to monitor the bolt looseness inside the BSJ. Therefore, a space grid specimen having bolted spherical joints and tubular bars was considered for experimental evaluation. Different torques levels were applied on the sleeve to represent different looseness degrees of joint connection. As the torque levels increased, the looseness degrees of joint connection increased correspondingly. The lead zirconate titanate (PZT) patch was used and integrated with the tubular bar due to its strong piezoelectric effect. The root-mean-square deviation (RMSD) of the conductance signatures for the PZT patch were used as the looseness-monitoring indexes. Taking RMSD values of sub-frequency bands and the looseness degrees as inputs and outputs respectively, the BPNNs were trained and tested in twenty repeated experiments. The experimental results show that the formation of the bolt looseness can be detected according to the changes of looseness-monitoring indexes, and the degree of bolt looseness by the trained BPNNs. Overall, this research demonstrates that the proposed structural health monitoring (SHM) technique is feasible for monitoring the looseness of bolted spherical connection in space grid structures.

Sensor Fault Diagnosis for Impedance Monitoring Using a Piezoelectric-Based Smart Interface Technique

“…Traditionally, a piezoelectric sensor (e.g., PZT) is bonded directly to the host structure's surface to perform a required impedance measurement. To detect structural damage, the measured impedance Sensors 2020, 20, 510 2 of 20 signature is statistically compared with the signature of the undamaged state by using statistical damage metrics [10][11][12]. However, the direct attachment of the PZT often leads to weak EM impedance responses and further results in difficulties in predetermining effective frequency bands for damage detection tasks [13][14][15].…”

Review of annual progress of bridge engineering in 2019

“…It is an important development direction in the future to deeply integrate the new generation of information technology to develop the new generation of bridge damage monitoring and detection technology (Bao and Li 2019). Xu et al (2019a) and Yin et al (2019) respectively constructed different neural network models and established a complex mapping relationship between monitoring data and structural damage state to rapidly identify the structural damage state. Xu et al (2019d) proposed a structure crack identification method based on computer vision and artificial intelligence, and realized the remote intelligent monitoring of shallow fatigue cracks on the structure surface.…”

Detecting of the Crack and Leakage in the Joint of Precast Concrete Segmental Bridge Using Piezoceramic Based Smart Aggregate

“…Inspection via non-destructive methods is a choice to detect these defects mentioned above based on a fixed time interval [ 20 , 21 , 22 ]. To provide real-time information on structural well being, structural health monitoring (SHM) has received much attention recently [ 23 , 24 , 25 ]. In practical engineering, the commonly used methods for detecting the cracks in the concrete include image-based methods [ 26 , 27 , 28 ], radar-based methods [ 29 , 30 ], ultrasonic [ 31 , 32 , 33 ], and impact echo (IE) [ 34 , 35 ], among others.…”