Viscous damping wall (VDW) is an effective velocity-dependent damper, which can dissipate earthquake energy by shear strain of viscous material. The damping force equation of VDW can only be obtained from regression of VDW dynamic test results, as velocity exponent of power-law material can not be obtained from rheometers. In this study, a Dynamic Sandwich-Type Shear test was designed matching the design working conditions of VDW. A series of experiments with different frequencies and strain amplitudes were conducted. Simple data procession methods were proposed to calculate velocity exponent and storage/loss modulus of polymer from experimental data. Comparison with methods adapted from those for VDW formula validated that radial linear regression method was proper to separate stress components and pivotal point method was accurate to evaluate velocity exponent. The velocity exponents obtained varied from 0.8 to 1, with various loading frequency and strain amplitude. Finally, the differences between the results of storage/loss modulus in DSTS test and ARES test were compared. Due to the extrusion effect caused by engineering working condition, the storage and loss modulus obtained by DSTS test was larger than the modulus obtained by ARES test.

Damping has attracted increasing attention as an indicator for structural health monitoring (SHM), owing to its sensitivity to subtle damage that may not be reflected in natural frequencies. However, the practical application of damping-based SHM remains limited by the accuracy and robustness of damping identification methods. Enhanced Frequency Domain Decomposition (EFDD), a widely used operational modal analysis technique, offers efficiency and user-friendliness, but suffers from intrinsic deficiencies in damping identification due to bias introduced at several signal-processing stages. This study proposes to improve EFDD by integrating neural networks, replacing heuristic parameter choices with data-driven modules. Two strategies are explored: a step-wise embedding of neural modules into the EFDD workflow, and an end-to-end grid-weight framework that aggregates candidate damping estimates using a lightweight multilayer perceptron. Both approaches are validated through numerical simulations on synthetic response datasets. Their applicability was further validated through shaking-table experiments on an eight-storey steel frame and a five-storey steel–concrete hybrid structure. The proposed grid-weight EFDD demonstrated superior robustness and sensitivity in capturing early-stage damping variations, confirming its potential for practical SHM applications. The findings also revealed that the effectiveness of damping-based indicators is strongly influenced by the structural material system. This study highlights the feasibility of integrating neural network training into EFDD to replace human heuristics, thereby improving the reliability and interpretability of damping-based damage detection.

The loss factor is an important index for evaluating the damping capacity of non-Newtonian viscous polymers used in viscous damping walls (VDWs). The modulus ratio method (MRM) and the tangent of the loss angle (TLA) are commonly used to calculate this parameter. However, deviations may arise when using the MRM or TLA to evaluate the damping capacity of a polymer exhibiting the power-law effect. To resolve this problem, a simplified equation for the loss factor that considers the power-law effect (SELF-PL) was proposed based on its energy definition. Then, a series of dynamic sandwich-type shear (DSTS) tests was conducted to measure the loss factor of a polymer (Oppanol-polyisobutylene B12, abbreviated as PIB-B12) for VDWs. Based on the DSTS test data, the accuracy of the SELF-PL was verified, and the error of the MRM caused by the power-law effect was investigated. Finally, an error equation for the MRM was derived to quantify its applicable range. The strain rate amplitude is found to be the most influential factor affecting a power-law material. The loss factor of PIB-B12 decreases with increasing loading frequency, while it increases with increasing strain amplitude. The relative error of the MRM exhibits a roughly linear relationship with the power-law exponent.

Monolithic precast reinforced concrete (RC) frames with cast-in-place joint cores and grouted-sleeve splices are increasingly adopted in seismic regions, yet connection degradation is not carried through to system-level fragility: existing studies take hinge properties from code tables or one test, or stop at the joint. This study closes that gap with a reproducible transfer route in which the degrading, pinched M–θ hysteresis identified from a refined joint model becomes structure-level hinge parameters, with every intermediate quantity reported so that the route can be reproduced elsewhere. The joint model is an explicit finite-element interface-spring/contact strategy with Mohr–Coulomb interface behavior, tension cut-off, local spring failure, and sleeve–grout–rebar bond slip. Incremental dynamic analysis and fragility assessment of a six-story monolithic precast frame and a comparable cast-in-place frame show larger inter-story drift demands in the precast frame. At PGA = 0.40 g, its collapse exceedance probabilities are higher by 0.73 and 0.44 percentage points in X and Y, respectively. Global performance is broadly comparable, but the precast frame shows a consistent, modest unfavorable tendency caused by connection pinching and reduced ultimate rotation. Interface treatment, sleeve grouting quality, and connection-level ductility should therefore be explicitly considered in seismic performance assessment and design.