ABSTRACT
The article presents a novel application of the most up-to-date computational approach, i.e., artificial intelligence, to the problem of the compression of closed-cell aluminium. The objective of the research was to investigate whether the phenomenon can be described by neural networks and to determine the details of the network architecture so that the assumed criteria of accuracy, ability to prognose and repeatability would be complied. The methodology consisted of the following stages: experimental compression of foam specimens, choice of machine learning parameters, implementation of an algorithm for building different structures of artificial neural networks (ANNs), a two-step verification of the quality of built models and finally the choice of the most appropriate ones. The studied ANNs were two-layer feedforward networks with varying neuron numbers in the hidden layer. The following measures of evaluation were assumed: mean square error (MSE), sum of absolute errors (SAE) and mean absolute relative error (MARE). Obtained results show that networks trained with the assumed learning parameters which had 4 to 11 neurons in the hidden layer were appropriate for modelling and prognosing the compression of closed-cell aluminium in the assumed domains; however, they fulfilled accuracy and repeatability conditions differently. The network with six neurons in the hidden layer provided the best accuracy of prognosis at MARE≤2.7% but little robustness. On the other hand, the structure with a complexity of 11 neurons gave a similar high-quality of prognosis at MARE≤3.0% but with a much better robustness indication (80%). The results also allowed the determination of the minimum threshold of the accuracy of prognosis: MARE≥1.66%. In conclusion, the research shows that the phenomenon of the compression of aluminium foam is able to be described by neural networks within the frames of made assumptions and allowed for the determination of detailed specifications of structure and learning parameters for building models with good-quality accuracy and robustness.
ABSTRACT
It is a common situation that seismic excitations may lead to collisions between adjacent civil engineering structures. This phenomenon, called earthquake-induced structural pounding, may result in serious damage or even the total collapse of the colliding structures. Filling the gap between two buildings erected close to one another by using visco-elastic materials can be considered to be one of the most effective methods to avoid seismic pounding. In this paper, a new polymer-metal composite material made of polyurethane and closed-cell aluminum foam is proposed as a pounding energy absorber for protection against earthquake hazards. The composite was created in two versions, with and without an adhesive interface. A series of experiments which reflect the conditions of seismic collision were performed: quasi-static compression, dynamic uniaxial compression and low-cycle dynamic compression with 10 loops of unloading at 10% strain. The composite material's behavior was observed and compared with respect to uniform material specimens: polymer and metal foam. The experimental results showed that the maximum energy absorption efficiency in the case of the new material with the bonding layer was improved by 34% and 49% in quasi-static and dynamic conditions, respectively, in comparison to a sole polymer bumper. Furthermore, the newly proposed composites dissipated from 35% to 44% of the energy absorbed in the cyclic procedure, whereas the polymer specimen dissipated 25%. The capacity of the maintenance of the dissipative properties throughout the complete low-cycle loading was also satisfactory: it achieved an additional 100% to 300% of the energy dissipated in the first loading-unloading loop.
