Hysteretic lateral force-displacement (HLFD) models are important for efficient structural analysis under cyclic loading (e.g., earthquakes). This paper proposes a novel machine learning (ML)-based HLFD model, referred to as ML-HLFD, to characterize the relationship between lateral force and displacement of reinforced concrete (RC) columns with different properties (e.g., geometry, and material properties). To this end, a database including 498 experimental results is collected for model training, validation, and testing purposes. The ML-HLFD first uses a support vector machine (SVM) to classify the different failure modes (i.e., flexure failure, flexure-shear failure, and shear failure). After that, an artificial neural network (ANN) is trained for obtaining the implicit mapping between inputs (i.e., the properties of RC column) and outputs (i.e., the crucial parameters of selected HLFD models). The performance of the ML-HLFD models is studied by (1) cross-validation; and (2) comparisons with experiments, a classical fiber-element model, and an existing analytical model, which demonstrate the accuracy and efficiency of ML-HLFD models under a wide range of scenarios.

In recent decades, precast concrete buildings have undergone significant development, attracting considerable academic attention to their mechanical performances. Unlike cast-in-situ buildings, precast buildings are assembled on site by connecting precast components using mechanical devices or on-site casted joints, which makes the connections particularly important for overall structural performances. This study presents a comprehensive review of the mechanical performances of precast buildings, with a specific focus on various types of connections and their structural properties. This study reviews the mechanical performances of building connections using dry, wet, and/or hybrid methods between pre-manufactured components, e.g., beam–column joints, wall–panel connections, and column/wall–foundation connections. Both experimental and numerical investigations are reviewed. The paper provides a valuable reference regarding the mechanical performances of precast concrete buildings.

For problems involving rate constitutive equations, such as rate-independent elasto-plasticity, consistent or algorithmic tangent moduli (operators) play an important role in preserving the asymptotic quadratic rate of convergence of incremental-iterative solution schemes based on Newton’s method. Furthermore, consistent (algorithmic) tangent moduli are required in structural response sensitivity analysis based on the direct differentiation method. This paper focuses on the derivation of the consistent tangent moduli for a pressure independent multi-yield-surface J₂ (Von Mises) plasticity model that has been used extensively in nonlinear constitutive modeling of soil materials, but can be used for other materials as well. Application examples are provided to validate the consistent tangent moduli derived herein, and to compare the rate of convergence and computational time of nonlinear incremental-iterative analyses performed using the consistent and continuum tangent moduli, respectively.

With the increasing construction of large-scale wind turbines in seismically active coastal areas, the survivability of these high-rated-power offshore wind turbines (OWTs) in marine and geological conditions becomes extremely important. Although research on the dynamic behaviors of OWTs under earthquakes has been conducted with consideration of the soil-structure interaction, the attention paid to the impact of earthquake-induced seabed liquefaction on OWTs supported by large-diameter monopiles remains limited. In view of this research gap, this study carries out dynamic analyses of a 10 MW OWT under combined wind, wave, and earthquake loadings. This study uses a pressure-dependent multisurface elastoplastic constitutive model to simulate the soil liquefaction phenomenon. The results indicate that the motion of the large-diameter monopile leads to more extensive soil liquefaction surrounding the monopile, specifically in the zone near the pile toe. Moreover, compared with earthquake loading alone, liquefaction becomes more severe under the coupled wind and earthquake loadings. Accordingly, the dynamic responses of the OWT are apparently amplified, which demonstrates the importance of considering the coupling loadings. Compared with wind loading, the effect of wave loading on the dynamic response and liquefaction potential is relatively insignificant.

Peridynamics (PD) has been increasingly used to study the damage behaviors of reinforced concrete (RC) structures due to its strong capacity in analyzing discontinuous problems. In this paper, bond-based peridynamics (BPD) is enhanced for refined analysis of bond-slip behaviors between concrete and ribbed steel rebar in the pullout test. The enhancement of BPD includes the following: (1) an axial-shear interaction (ASI) model for refined simulation of bond-slip behaviors between steel ribbed rebar and concrete; (2) a nonlinear uniaxial concrete model to avoid excessive compression of concrete bonds; (3) a novel gradually weakening fictitious element (GWFE) approach to improve the stability and convergence of the Newton algorithm in solving the PD equations, e.g., in case of negative concrete stiffness; (4) a horizon updating approach for rebuilding bond interactions when large displacements occur in the bond-slip process; and (5) a parallel computing approach to improve the computational efficiency of refined analysis. The enhanced BPD method is implemented in an open-source finite-element software, OpenSees, and verified by a rebar pullout test. The type of steel rebar rib, strength of concrete, cross-area of steel, and cyclic loading condition are investigated in detail regarding their effects on the stress redistribution and mesoscale crack propagation of the RC members. The results demonstrate that the enhanced BPD modeling method presented herein is capable of fine simulation of bond-slip behaviors in the pullout test, e.g., the strength deterioration, stiffness degradation, and cyclic response.