Resumen en Inglés:

Abstract With the development of science and technology in recent decades, numerous optimization algorithms have emerged and been successfully applied in various fields. Particle swarm optimization (PSO) is a well-established evolutionary algorithm commonly used for optimization tasks. However, similar to other evolutionary algorithms, PSO has two main limitations that can hinder its performance. The first limitation is premature convergence, which can result in suboptimal solutions. The second limitation is the high computational time since PSO employs all particles in the swarm for each iteration. To overcome these limitations, in this work, we propose coupling a reduction model technique, specificially, Orthogonal Diagonalization (OD) with a hybrid algorithm combining Genetic Algorithm (GA) and PSO, termed HGAPSO-OD. To evaluate the effectiveness of the proposed approach, a large-scale railway bridge, calibrated based on field measurements, is used as a case study. The results demonstrate that HGAPSO-OD not only increases the accuracy but also reduces computational time of GA and traditional PSO.

Resumen en Inglés:

Abstract The fragment dispersion properties of metal shells under internal explosive loading are important topics in the fields of public safety and defence technology. The internal explosion driving phenomenon of square charge structures is common in the design of innovative warheads, the protection of improvised explosive devices (IEDs), the storage of hazardous materials, etc., and it is essential to investigate their fragment dispersion characteristics. In this study, the effect of the eccentric initiation on the circumferential fragment speed of the square charge structure was investigated via high-speed impulse X-ray experimental technique and numerical simulating methods. Furthermore, a high-order polynomial calculation formula for the circumferential fragment dispersion speed of the square charge structure under different eccentric coefficients for initiation was established based on the numerical and experimental data. The results of this study would provide references for the disposal of IEDs and the design of warheads.

Resumen en Inglés:

Abstract The process that involves the continuous monitoring and analysis of a structure's behavior and performance is known as Structural Health Monitoring (SHM). SHM typically involves the use of sensors and other monitoring devices to collect data, such as displacements, strains, accelerations, among others. These data are analyzed using advanced algorithms and machine learning techniques to identify any signs of abnormal behavior or deterioration. This paper presents a numerical and experimental study of a slender frame subjected to five levels of structural changes under impact loading. The dynamic responses were recorded by four accelerometers and used to build models based on Sparse Auto-Encoders (SAE). Such models can identify each of the five structural states in an unsupervised way. A new strategy to define the hyperparameters of the SAE is presented, which proved to be adequate in the experiments conducted. Finally, the experimental data set is made available to the scientific community to serve as a benchmark for validating SHM methodologies to identify structural changes from dynamic measurements.