Structural health monitoring (SHM) is important for rapid post-earthquake condition assessment and resilience-oriented management of civil structures. Among system identification methods, wave-based approaches are attractive because they are sensitive to localized stiffness changes and may reduce some limitations of global modal indicators, including potential influences associated with soil-structure interaction. However, many artificial-intelligence (Al)-based identification methods remain difficult to interpret physically, which limits their reliability in engineering applications. This study proposes an interpretable physics-consistent neural framework (PCNF) for wave-based system identification of buildings. The PCNF is derived directly from the layered Timoshenko beam formulation, in which the state transition of each structural layer is mapped onto a neural computational graph with physically meaningful trainable parameters. Structural parameter identification is therefore reformulated as a physics-guided gradient-based learning problem. The PCNF is applied to a 54-story office building in Los Angeles using records from nine earthquakes. The identified layer-wise shear-wave velocities exhibit coefficients of variation not exceeding 5
Wind loads and earthquakes are assumed to be uncorrelated in earthquake-prone areas; therefore, their interaction is ignored by existing design guidelines. However, the fact that strong earthquake events are commonly followed by strong to moderate aftershocks, and that wind is constantly flowing at high speeds, increases the probability of their joint occurrence, thus making current structural design assumptions questionable. This paper investigates the effect of the combined action of strong winds and vertical and horizontal earthquakes on the seismic response of reinforced concrete (RC) structures. Three reinforced concrete buildings that are representative of low-, medium- and high-rise buildings were analyzed in the nonlinear range using a lumped mass and distributed mass model. It is concluded that the effects of wind load and the vertical component were significant. These effects can significantly affect the axial forces in the columns but do not significantly affect base shears. It is essential to include the combined effect of earthquakes and wind to accurately predict the ductility of the structures and their respective expected failure mechanisms under multi-hazard load conditions.
The present study aims to evaluate the effect of the lintel beam above the window opening in perforated infilled frames by the finite element modeling of a number of specimens with and without lintel beams, calibrated against four large-scale single-story single-bay steel moment-resisting frames that were constructed in Tasnimi and Mohebkhah (2011)’s study. Box sections were used as lintel beams, while roller supports were used in the specimens without a lintel beam. It was demonstrated that the absence of this component in the numerical modeling of infilled frames with window openings led to significant errors in estimating ultimate load and its corresponding displacement, which were higher in specimens with larger openings. It also resulted in considerable errors in predicting failure modes in the infills, which can cause changes in the principal stress field of the infills. Our results suggest that there is no need to have four compressive struts for the macro-modeling of the infilled frames with central openings. The location of the lintel beam influenced the position of the struts, particularly in the samples with larger openings. The specifications of the compressive struts depend on the drift applied to the structure.
Seismic vulnerability assessment in rural areas is crucial for mitigating earthquake-induced losses and promoting sustainable development. In China’s rural regions, most buildings are self-constructed with limited seismic resilience. While traditional field surveys accurately assess individual building vulnerability, they are time-consuming and expensive, making them unsuitable for extensive rural vulnerability evaluations. To address this issue, this study presents a rapid and accurate method for estimating the seismic vulnerability of rural buildings, using the rural areas of Weinan City, China, as a case study. First, several villages were randomly selected for field investigation. The seismic vulnerability of rural buildings was assessed according to EMS-98 standards. A proxy model linking building attributes to vulnerability was established by using machine learning (ML) and applied to the whole study area. Finally, the vulnerability index method of RISK-UE was employed to evaluate the seismic risk and the possible damage under different seismic intensities. The results show that the vulnerability class of rural buildings in the rural Weinan area is dominated by C, with an average VIM index of 0.67, indicating that the overall seismic performance needs to be strengthened. With increasing seismic intensity (from VII to X), the number of potentially displaced households increases from 7446 (3.39
To investigate the canyon topography-stratigraphy effect on the dynamic response of a bridge, an analytical solution for a simply-supported bridge crossing a semi-cylindrical weathered canyon under SH waves is proposed. The simply-supported bridge is modelled as an elastic shear beam, which is supported by two rigid abutments with each having a rigid foundation embedded in half-space. The dynamic canyon-bridge interaction problem is physically decomposed into one scattering problem and two radiation problems. The proposed solution is verified by degenerating to a past exact solution for a single simply-supported bridge model in flat half-space. It is found that the surficial inhomogeneous soil layer may significantly amplify the seismic response of the bridge and foundations. The intensity of the topographic-stratigraphic amplification is influenced by the inhomogeneity and thickness of the surficial layer of the weathered canyon, as well as the frequency component and obliquity of the incident SH waves.
