A novel inverse procedure for load identification based on improved artificial tree algorithm

This paper presents an accurate and effective method, namely, the improved artificial tree algorithm based on the Green's kernel function method (IAT-GKFM), to identify the load in time domain. The forward problem of load identification is constructed by using the Green's kernel function method. The forward problem is discretized using the time domain Galerkin method, where a matrix form for load identification is formed. The IAT algorithm is proposed to solve the inverse multi-dimensions problem in the inverse stage, which aims to minimize the measuring dispersion between the calculated response and the actual response. Several numerical examples are conducted. It is demonstrated that the IAT with high performance can provide more optimum results than those of other compared algorithms. Using this optimized strategy, the loads acting on a simple plate and a vehicle roof are reconstructed successfully. The superiority of IAT-GKFM may motivate the improvement of the other inverse problems.

Automated summary

This paper introduces an improved artificial tree algorithm, IAT-GKFM, based on the Green's kernel function method to accurately identify loads in the time domain. The proposed IAT algorithm is able to solve inverse multidimensional problems effectively, demonstrating better performance than other compared algorithms. Through numerical examples, the superiority of IAT-GKFM is shown in successfully reconstructing loads on simple plates and vehicle roofs.