The actual wind load information is helpful in evaluating the health status of high‐rise structures. However, as a type of distributed load, the wind load is very difficult to be measured directly. A possible solution is to reconstruct it from the structural response measurements. This is often an ill‐posed inverse problem. In this article, such ill posedness is solved by using a stable input estimator. With the help of the proposed application‐oriented algorithm selection guidance, a type of state and input estimator is formulated. This type of estimator is designed based on the Kalman filter scheme, and is capable of estimating the unknown inputs and the system states within one sampling time. This actually facilitates the online simultaneous reconstruction of the wind load and the structural responses. The 600 m tall Canton Tower is situated in a typhoon active area, and a structural health monitoring system has already been integrated onto this tower. These two points make the Canton Tower an ideal test bed for validating the above illustrated online reconstruction strategy for the wind load and the structural responses. An operational modal analysis (OMA) is first performed to identify the modal properties of the Canton Tower under the Typhoon Nanmadol in 2011. Then a reduced‐order finite element model (FEM) of the Canton Tower is updated according the OMA results. Finally, the equivalent fluctuating lateral loads and moments, which act on the nodes of the reduced‐order FEM are reconstructed using the acceleration measurements recorded during Tyhoon Kai‐tak in 2012. The reconstruction results are validated by comparing the simultaneously reconstructed structural acceleration with the corresponding sensor measurements. The mean component of the loads and moments are calculated using the real‐time wind speed measurements and the available aerodynamic force coefficients. It is noted here that the focus of this article is not to develop a totally new theory, but rather to explore the application of a state and input estimator in the foreground to a practical complex structure.

Recently self-sensing cementitious composite has demonstrated its strong potentiality for structural health monitoring of civil infrastructures because of its low-cost, long-term stability and compatibility with concrete structures. In this paper, we propose novel hybrid nanocarbon materials engineered cement-based sensors (HNCSs) with high-sensitivity, which are fabricated with self-sensing cementitious composites containing electrostatic self-assembled CNT/NCB composite fillers. The mechanical property and sensing performance of the HNCSs are pre-characterized under static and dynamic compressive loadings. The HNCSs are then integrated into a five-story building model via custom-made clamps to verify the feasibility for dynamic response measurements. Results show that the developed sensors have satisfactory mechanical property and excellent pressure-sensitive reproducibility and stability. With clamps holding on the building model, the HNCSs perform satisfactorily under sinusoidal excitations in the frequency range from 2 to 40 Hz. In addition, the modal frequencies and their changes of the building model caused by ‘damage’ simulated through adding additional masses identified by the HNCSs are favorably consistent with the counterparts acquired by accelerometers and strain gauges, indicating that the developed HNCSs have great potential for structural modal identification and damage detection applications.

Operational modal analysis of Canton Tower by a fast frequency domain Bayesian method supertall structure;ambient vibration;modal identification;Bayesian method;uncertainty; The Canton Tower is a high-rise slender structure with a height of 610 m. A structural health monitoring system has been instrumented on the structure, by which data is continuously monitored. This paper presents an investigation on the identified modal properties of the Canton Tower using ambient vibration data collected during a whole day (24 hours). A recently developed Fast Bayesian FFT method is utilized for operational modal analysis on the basis of the measured acceleration data. The approach views modal identification as an inference problem where probability is used as a measure for the relative plausibility of outcomes given a model of the structure and measured data. Focusing on the first several modes, the modal properties of this supertall slender structure are identified on non-overlapping time windows during the whole day under normal wind speed. With the identified modal parameters and the associated posterior uncertainty, the distribution of the modal parameters in the future is predicted and assessed. By defining the modal root-mean-square value in terms of the power spectral density of modal force identified, the identified natural frequencies and damping ratios versus the vibration amplitude are investigated with the associated posterior uncertainty considered. Meanwhile, the correlations between modal parameters and temperature, modal parameters and wind speed are studied. For comparison purpose, the frequency domain decomposition (FDD) method is also utilized to identify the modal parameters. The identified results obtained by the Bayesian method, the FDD method and a finite element model are compared and discussed.

Research on structural health monitoring (SHM) is nowadays evolving from SHM‐based diagnosis towards SHM‐based prognosis. The structural strain response, as a localized response, has gained growing attention for application to structural condition assessment and prognosis in that continuous strain measurement can offer information about the stress experienced by an in‐service structure and is better suited to characterize local deficiency and damage of the structure than global responses. As such, accurate forecasting of the structural strain response in real time is essential for both structural condition diagnosis and prognosis. In this paper, a Bayesian modeling approach embedding model class selection is proposed for dynamic forecasting purpose, which enables the probabilistic forecasting of structural strain response and bears a strong capability of modeling the underlying non‐stationary dynamic process. As opposed to the classical time series models, the proposed Bayesian dynamic linear model (BDLM) accommodates both stationary and non‐stationary time series data and delineates the time‐dependent structural strain response through invoking different hidden components, such as overall trend, seasonal (cyclical), and regressive components. It in turn paves an effective way for incorporating the newly observed time‐variant data into the model framework for structural response prediction. By embedding a novel model class selection paradigm into the BDLM, the proposed algorithm enables simultaneous model class selection and probabilistic forecasting of strain responses in a real‐time manner. The utility of the proposed approach and its forecasting accuracy are examined by using the real‐world monitoring data successively collected from a three‐tower cable‐stayed bridge.