The beam-only connected reinforced concrete shear wall (BRW) is used as a reinforcing component to enhance the seismic performance of concrete-filled, double-skin steel tube (CFDST) frames. The effects of the BRW on seismic risks of CFDST frames are investigated. Three performance levels of limit states are defined and described according to the typical failure of test specimens and the existing definition of current guidance. A simplified numerical model is calibrated for CFDST frame-BRW structures, and test results validate it. Nonlinear dynamic analyses on a nine-story CFDST-BRW building are performed to investigate the effects of BRW on reducing the seismic risk of CFDST buildings. The results show that the BRW reduces the probability of collapse of the CFDST frame to 2.76% in 50 years, which can effectively reduce the probability of different degrees of damage in the service cycle of the structure. The results provide information for risk-informed decision-making on the design of CFDST frame-BRW structures.

A simple analytical model for calculating the vibration frequencies of anchor span strands in suspension bridges is proposed in this study. The vibration frequencies of strands are important indicators of tension force and critical in construction control. Most existing methodologies either ignore the connecting rods in anchor spans or require a complex finite-element analysis to ensure accurate calculations. A simple model is proposed for rapid and efficient analysis that includes the geometric and material properties of connecting rods. The calculation results of the proposed model are compared with and verified against those of existing solutions in literature and finite-element simulations. Afterward, the proposed method is applied to the Nanxi Yangtze suspension bridge. The effects of the length and material parameters of connecting rods on the calculation of strand tensions are parametrically discussed. Conclusions and recommendations for future strand tension analysis and control are also presented.

Phenolic photodissociation at the air–water interface proceeds orders of magnitude faster than in bulk water, yet the structural origins of this acceleration remain insufficiently understood. Here, we present a descriptor‐level analysis supporting a dual‐control picture, in which phase‐dependent photodissociation reflects both πσ*‐related dark‐state accessibility and the local solvent's capacity to accommodate transferred electron density. We construct a structure‐resolved, statistics‐driven framework that bypasses snapshot‐level multireference conical‐intersection searches by identifying solvent‐side dark‐state acceptor orbitals {σ _p *}, constructing their energy distribution ε(σ _p *), and linking it to local microenvironment descriptors that quantify coordination saturation and directional constraint. Truncated cluster models prove unreliable because boundary microstates dominate acceptor selection and mask the intrinsic interface–bulk contrast. Periodic slab models remove this bias: the interfacial ε(σ _p *) distribution is shifted lower by approximately 0.7 eV and substantially broadened relative to bulk, predominantly through within‐motif energy‐window shifts rather than differences in hydrogen‐bond topology. Low‐coordination, weakly constrained microenvironments correlate systematically with lower ε(σ _p *), and small‐system SA‐CASSCF diagnostics support the same trend direction. Together, these descriptor‐level signatures indicate that the air–water interface favors both dark‐state access and transferred‐electron stabilization, providing transferable inputs for multiphase photochemical modeling and strategies for tuning interfacial reactivity through control of defect‐rich microstate supply.

Bond strength degradation of corroded reinforced concrete (RC) structures involves complex mechanical interactions that are difficult to capture through experimental or purely data-driven methods alone. This study proposes a physics-informed neural network (PINN) framework that embeds a thin-walled cylinder rust expansion model as differentiable soft constraints in the loss function, achieving deep integration of mechanical priors with data-driven learning. The PINN predicts the bond strength τ_u alongside intermediate physical quantities including rust expansion stress σ_r, hoop stress σ_θ, and damage variable D_θ. A four-stage progressive training strategy is adopted to balance data fitting and physical consistency. The results show that the proposed PINN improves R² by 5.49–15.66% and reduces SMAPE by 33.91–58.87% compared to XGBoost, RF, and SVM, while achieving an average prediction error of 1.66% in experimental validation. Under the cover thickness extrapolation scenario (15 mm → 10 mm), the PINN maintains prediction errors within 1.5%, significantly outperforming both ML models and empirical formulas. The framework’s interpretability is further demonstrated through decomposition of the bond strength into frictional and mechanical interlock components, providing mechanistic insight into the corrosion-induced failure process.