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Sanchez J, Benaroya H (2014) Review of force reconstruction techniques. Īzam SE, Chatzi E, Papadimitriou C, Smyth A (2017) Experimental validation of the Kalman-type filters for online and real-time state and input estimation. Int J Numer Method Biomed Eng 33(12):e2889. Zhang D, Han X, Zhang Z, Liu J, Jiang C, Yoda N, Meng X, Li Q (2017) Identification of dynamic load for prosthetic structures. Zhong J, Liu H, Yu L (2019) Sparse regularization for traffic load monitoring using bridge response measurements. Niu Y, Fritzen CP, Jung H, Buethe I, Ni YQ, Wang YW (2015) Online simultaneous reconstruction of wind load and structural responses-theory and application to Canton Tower. Some related issues are discussed as well. Illustrated results show that the proposed method can be used for identifying the dynamic forces in long-time duration and saving the computing time. A cluster constructed from three personal computers is used for implementation of the proposed method. Numerical simulations are carried out on a frame structure and a truss structure, respectively. In the last step, the identified results calculated from all the sub-problems are fused via a weighted average method. Sparse regularization such as weighted l 1-norm regularization method is introduced for ensuring that the identified result is sparse and stable. Herein, influences of unknown initial conditions are considered. Then the next step focuses on solving the sub-problems which can be executed in parallel. In the first step, moving time window is applied for splitting an original problem into several sub-problems in time domain. The proposed method is implemented via three continues steps, i.e., partition of parallel computing tasks, solution of parallel computing tasks and fusion of the identified results. To reduce the computing time, a parallel computing-oriented method is proposed in this study for dealing with a long-time duration problem of force identification. Rapid identification of dynamic forces is an important research subject.