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Physics-Informed Neural Networks (PINNs) are being applied to forward and inverse problems in various science and engineering disciplines, integrating physical principles into the learning process. In line with this, the previously proposed Physics-Informed Parallel Neural Networks (PIPNNs) framework addresses the inverse structural identification problem of continuous structural systems, particularly for handling inherent discontinuities in the system such as interior supports and dissimilar element properties. The PIPNNs framework accommodates structural discontinuities by dividing the computational domain into subdomains, each uniquely represented through a parallelized and interconnected Neural Network (NN) architecture. However, these parallel NNs pose a challenge due to the increased number of terms in the loss function, making network training more complex. The main contribution of this paper is …