Large-scale structures are subjected to environmental loads or frequent seismic motion with irremediable effect. These loads have often multidirectional actions on structures, and it couples their responses and leads to multiple-input multiple-output (MIMO) problem. The complexity of MIMO model and the relative time-delays in sensing networks are among major sources of error in dynamic properties identification in large scale structures. This study proposed a parametric-time domain method to reduce the negative effect of these problems. For this purpose, the contribution of each input in the output signals is determined using QR decomposition and converts a MIMO problem into multiple single-input multiple-output (SIMO) ones. In this regard, an Autoregressive Moving Average with eXogenous (ARMAX) model is implemented on decoupled signals for modal identification. Further, for time synchronization of records, a cross-correlation function has been used to achieve more precise results. The method was employed real strong-motion response recorded by different sensors at a high rise 64-story concrete building. Results demonstrate the promising precision of the proposed algorithm for identifying current structural modal properties under real earthquake excitations. Hence, structures can be monitored efficiently along seismic experiences to detect any possible variations in their structural features. The comparison between the output of the proposed method and previous study indicates a considerable improvement on accuracy of the estimated model property particularly on mode shapes.autoregressive moving average with eXogenous input (ARMAX), cross-correlation function, MIMO, QR decoupling, tall building, time lag | INTRODUCTIONProgressive deterioration in civil structures due to aging under the effects of environmental conditions has become a worldwide concern. In addition, natural and man-made hazards such as typhoons, earthquakes, and explosions can also cause damage or intensify existing damages. A productive approach like Structural Health Monitoring (SHM) can utilize damage pattern recognition to deal with these adversarial phenomena. [1,2] Nowadays, there is unanimity on the significance of monitoring and managing the health of civil infrastructure systems, and the principal advantage from the new paradigm of infrastructure SHM is to manage health and make it possible to detect deterioration and damage at their initiation to prevent catastrophic failure. [3,4] Recently, rapid progress in high performance computers and smart sensor technologies have made SHM more feasible in using high computational demand algorithms and a dense array of sensors. [5] Various forms of SHM have been employed for assisting owners of old infrastructure considering their safety and economic operation. [6] In this regard, many efforts have been done which most of them were focusing on