Structural health monitoring of a 250‐m super‐tall building and operational modal analysis using the fast Bayesian FFT method

Abstract

This paper presents the work on the structural health monitoring design and operational modal analysis of a 250-m super-tall building situated in Shanghai, China. The building is a steel-concrete composite structure with a steel composite frame-concrete core tube system. At the 21st and 36th-38th floors, outrigger trusses and ring-shaped trusses are set to strengthen this structure.Because the height of this structure is overlimited and its lateral stiffness in the vertical direction is nonuniform, a SHM system was designed to monitor the structural condition and evaluate its safety. The SHM system is presented in this paper, and the instrumented equipment includes accelerometers and tilt sensors. The locations of sensors were well arranged so that SHM could be conducted using the least amount of sensors. On the basis of the system, an ambient vibration test was carried out to perform the analysis with four setups designed to investigate the modal parameters in the X and Y directions. The Fast Bayesian FFT method was employed to perform the operational modal analysis. The first 10 modes were identified. The modal parameters obtained by the Bayesian method are studied and discussed, and they are compared with the results obtained by other methods. Long-term monitoring of this super-tall building was also carried out to investigate the change of modal parameters in different stages. The results of this study are expected to provide a reference for model updating, damage detection, and SHM of this high-rise building.

Ensemble learning‐based structural health monitoring by Mahalanobis distance metrics

“…Under such circumstances, civil structures may seriously suffer from damage and subsequently trigger catastrophic failure and even collapse. To ensure the health and safety of such structural systems and reduce economic and human losses caused by the occurrence of damage, structural health monitoring (SHM) is a great necessity for every society with any culture, geographical location, and economic development 1–5 . In fact, SHM is an emergent and diagnostic tool for damage detection and structural condition assessment.…”

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

“…Generally, conventional signal processing techniques such as Fast Fourier Transform (FFT) [1][2][3], and Short-Time Fourier Transform (STFT) [4,5] are employed to analyze the linear and stationary signals. However, the responses of structures in the real world are inherently nonlinear according to structural health monitoring systems [6,7] and non-stationary.…”

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

“…Whilst the latter is time-consuming and not suitable for quantitative analysis, image analysis-based detection techniques, on the other hand, can be quite challenging and fully dependent on the quality of images taken under different real-world situations (e.g., light, shadow, noise, etc.). In recent years, researchers have experimented with the application of a number of soft computing and machine learning-based detection techniques as an attempt to increase the level of automation of asset condition inspection [13,14,15,16,17,18,19,20]. The notable efforts include; structural health monitoring with Bayesian method [13], surface crack estimation using Gaussian regression, support vector machines (SVM), and neural networks [14], SVM for wall defects recognition [15], crack-detection on concrete surfaces using deep belief networks (DBN) [16], crack detection in oak flooring using ensemble methods of random forests (RF) [17], deterioration assessment using fuzzy logic [18], defect detection of ashlar masonry walls using logistic regression [19,20].…”

“…In recent years, researchers have experimented with the application of a number of soft computing and machine learning-based detection techniques as an attempt to increase the level of automation of asset condition inspection [13,14,15,16,17,18,19,20]. The notable efforts include; structural health monitoring with Bayesian method [13], surface crack estimation using Gaussian regression, support vector machines (SVM), and neural networks [14], SVM for wall defects recognition [15], crack-detection on concrete surfaces using deep belief networks (DBN) [16], crack detection in oak flooring using ensemble methods of random forests (RF) [17], deterioration assessment using fuzzy logic [18], defect detection of ashlar masonry walls using logistic regression [19,20]. The literature also includes a number of papers devoted to the detection of defects in infrastructural assets such as cracks in road surfaces, bridges, dams, and sewerage pipelines [21,22,23,24,25,26,27,28,29,30].…”