Abstract
Increase in the demand for reliable structural health information led to the development of Structural Health Monitoring (SHM). Prediction of upcoming accidents and estimation of useful life span of a structure are facilitated through SHM. While data sensing is the core of any SHM, tracking the data anytime anywhere is a prevailing challenge. With the advancement in information technology, the concept of Internet of Things (IoT) has made it possible to integrate SHM with Internet to track data anytime anywhere. In this paper, a SHM platform embedded with IoT is proposed to detect the size and location of damage in structures. The proposed platform consists of a Wi-Fi module, a Raspberry Pi, an Analog to Digital Converter (ADC), a Digital to Analog Converter (DAC), a buffer, and piezoelectric (PZT) sensors. The piezoelectric sensors are mounted as a pair in the structure. Data collected from the piezoelectric sensors will be used to detect the size and location of damage using a proposed mathematical model. Implemented on a Raspberry Pi, the proposed mathematical model will estimate the size and location of structural damage, if any, and upload the data to Internet. This data will be stored and can be checked remotely from any mobile device. The system has been validated using a real test bed in the lab.
Highlights
Structural Health Monitoring (SHM) is a vital tool to improve the safety and maintainability of critical structures such as bridges and buildings
The instantaneous baseline damage detection based on Wavelet Transformation (WT) and cross correlation (CC) analysis is carried out by the digital signal processor (DSP) module of low-power piezoelectric guided waves system [28]
Results show that the proposed Internet of Things (IoT) SHM platform successfully checked if the sheet is healthy or not with 0% error
Summary
SHM is a vital tool to improve the safety and maintainability of critical structures such as bridges and buildings. SHM provides real time and accurate information about the structural health condition. It is a process of nondestructive evaluations to detect location and extent of damage, calculate the remaining life, and predict upcoming accident. Integration of the diverse theories has helped to improve the efficiency and performance of the SHM systems and to reduce the computational time and costs [2]. The combination of SHM, cloud computing, and the IoT enabled ubiquitous services and powerful processing of sensing data streams beyond the capability of traditional SHM system. A complete SHM platform embedded with IoT is proposed to detect the size and location of damage in structures
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