No abstract

Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for output-only system identification and input estimation of dynamic systems. Using merely sparse output-only measurements, the proposed PINNs architectures furnish a novel approach to input, state, and parameter estimation of linear and nonlinear systems with multiple degrees of freedom. These architectures are comprised of parallel and sequential PINNs that act upon a set of ordinary differential equations (ODEs) obtained from spatial discretization of the partial differential equation (PDE). The performance of this framework for dynamic system identification and input estimation was ascertained by extensive numerical experiments on linear and nonlinear systems. The advantage of the proposed approach, when compared with system identification, lies in its computational efficiency. When compared with traditional Artificial Neural Networks (ANNs), this approach requires substantially smaller training data and does not suffer from generalizability issues. In this regard, the states, inputs, and parameters of dynamic state-space equations of motion were estimated using simulated experiments with “noisy” data. The proposed framework for PINN showed excellent great generalizability for various types of applications. Furthermore, it was found that the proposed architectures significantly outperformed ANNs in generalizability and estimation accuracy.

Shape memory alloys (SMAs) have been utilized as an alternative to conventional materials for seismic retrofit applications in the last two decades. The effectiveness of SMAs to provide further improvement in seismic behavior of structures was demonstrated satisfactorily. Three 2/ 3-scaled, one-bay and one-storey substandard RC frames were constructed to represent the seismically vulnerable buildings with several deficiencies. The first RC frame was tested as a reference specimen under quasi-static reversed cyclic loading, which caused flexural plastic hinges at the column ends. Then, the rest of the two RC frames were upgraded with superelastic copper-aluminum-manganese (CuAlMn) alloy bars and conventional steel bars with the aim of enhancing seismic performance of substandard RC frames. The upgrading materials were attached to the RC frames through a retrofitting mechanism to provide tension-only retrofitting bars. Hence, the RC frame retrofitted by conventional steel bars was exposed to residual displacements after they yielded. The SMA-upgraded frame showed flag shaped hysteresis curve due to its superelastic behavior and caused considerable reduction in the residual displacement of RC frames.

Shape memory alloys (SMAs) have been getting much attention by many researchers in a variety of application areas due to their unique properties of superelasticity (SE) and shape memory effect (SME). They have the ability to recover large inelastic deformations upon heating (SME) and stress removal (SE). In recent years, structural engineers have been dealing with these smart materials to incorporate into civil engineering applications such as rebar in the reinforcement of concrete structures, repairing, retrofitting, base isolation system, dampers for vibrational control, etc. To overcome and mitigate the possible seismic risk of the structure under consideration, understanding the material characteristics of SMAs under various loading conditions is one of the critical steps. In this study, the mechanical properties of two popular SE SMAs, i.e. copper-aluminum-manganese (Cu-Al-Mn) and nickel-titanium (Ni-Ti), were investigated in detail. Moreover, the mechanical properties of the conventional rebar steel were also identified for comparison purposes. Room temperature monotonic and incremental cyclic tests were applied on dog-bone shaped Steel, Cu-Al-Mn and Ni-Ti tensile coupon specimens the obtain and compare their mechanical characteristics. The results showed that Cu-Al-Mn and Ni-Ti materials exhibited a significant re-centering ability upon unloading with negligible and comparable residual deformations whereas the Steel experienced higher permanent plastic deformations with almost 3% recovery at the same amount of deformation. In addition, the decrease in the amount of dissipated energy for Cu-Al-Mn and Ni-Ti for consecutive cyclic motion is much less than conventional steel. Test results were also evaluated in terms of cyclic performance of materials, residual strain, recovery capacity, dissipated energy and equivalent viscous damping. Experimental outcomes highlighted the potential usage of SMAs in seismic applications and supply basis information for continued research.