Health Monitoring of Historical Structures by Installing Various Sensors

Abstract

BAPS Hindu Mandir temple is a stone un-reinforced structure that will be constructed in Abu Dhabi according to the ancient Indian methods (Shilpa Shastra principle). The temple is designed to be entirely sculpted from stone, and thousands of stone pieces will be shipped from India to the UAE. This type of structure is the first of its kind in the country. Baps Mandir is a stone structure that is not covered by modern design standards. The structural performance needs to be evaluated to detect the damage early to mitigate life and economic losses. Accordingly, a structural health monitoring system (SHM) is a crucial modern technology to detect structural damage under static and dynamic loading conditions.

SHM system consists of three major elements: sensors location, data acquisition system and data processing system. Reliable data from the sensors is required to identify structural health accurately. In this study, the Main Shikar tower (MST) was chosen to allocate the optimal sensor placement. The MST was modelled separately in the ETABS. The elastic lateral load analysis concept was used to compute the stiffness of springs at the boundary conditions. The optimal sensor placement was allocated for the MST by computing the importance of each node and the correlation in two different methods. The temple is still under construction; therefore, four seismometers were installed on the site to evaluate the structural performance during construction. MySQL database and Python programming language were used in this study to process and analyze the acquired signals from the installed sensors. Fourier analysis was applied to compute the acceleration time series by deriving velocity records in the frequency domain. In addition, the classical spectral ratio method was used to estimate the site resonance frequency. The results from the field testing were compared with the simulation model for structural performance verification.

Date of Award	Dec 2022
Original language	American English
Supervisor	Tadahiro Kishida (Supervisor)