In model updating, damage detection and structural health monitoring (SHM), modal identification is usually the first step to calculate the modal properties, such as natural frequencies, damping ratios and mode shapes, of the targeted structure utilizing the measured time-domain responses. Ambient vibration test provides an efficient and economical mean to collect structural vibration responses for the purpose of modal identification since no artificial excitation is required. This paper presents the work on the ambient vibration test and modal identification of a recently constructed boat-shaped building situated in Hong Kong. The building is not an isolated structure, and it is connected with a secondary structure in its fourth and fifth floors. To identify detail mode shapes, sixty-two measurement points were desired. They were located in the three staircases (to minimize the effect on the normal operation of the building) and on a typical floor of the building. Due to the large number of measurement points coving almost all floors of the building, multiple setups with moving references-approach were designed. Some common problems encountered in the field test were discussed in this paper. A recently developed fast Bayesian FFT method was employed to perform the modal identification. This method could identify the most probable value (MPV) of modal parameters efficiently and also calculate the associated posterior uncertainty analytically, providing an important tool to evaluate the accuracy of modal parameters. The first nine modes were identified including five translational modes, two torsional modes, one combined mode and one vertical mode. Due to the connections with other building, torsional behavior was observed in some translations modes. Based on the MPV and the associated uncertainty, the variability of modal parameters across different setups is also addressed. Some interesting feathers of this structure were studied and investigated, providing the baseline for the future SHM and condition assessment of this building. (C) 2017 Elsevier Ltd. All rights reserved.