Experimental investigation and modeling of low cycle fatigue behaviour of Ni-Ti shape memory alloy (SMA) rebar

Interpretable machine learning models for predicting seismic performance in the plastic hinge region of flexural DSCW components

Abstract Currently, with the increasing demands for manufacturing precision and production efficiency, the number of layers and the overall thickness of multilayered clamping high-pressure vessels have dramatically increased. Building upon previous studies in linear elastic calculations, this research takes into account the strain hardening of metallic materials and adopts a bilinear hardening elastic-plastic model. Analytical expressions for the elastic-plastic stress distribution, initial yield pressure pyield, and full yield pressure py of multilayered cylinders with interlaminar gaps are derived. In the derivation process, consistent with prior elastic studies, the calculation method for the equivalent radius Req in the plastic region is proposed. Finite element verification shows that the analytical stress calculation results align well with the finite element analysis, demonstrating the high accuracy of the proposed calculation method.

• An adaptive stable node-based smoothed upper bound FEA method with discontinuous velocity fields is proposed. • A SOCP formulation using the MC criterion, integrated with adaptive mesh refinement is developed. • Numerical examples validate the method's superior accuracy and computational efficiency. • The effects of interface roughness on failure modes and stability are systematically analyzed. An adaptive stable node-based smoothed upper bound finite element limit analysis (adaptive SNS-UBFELA) method is proposed for soil-structure interaction problems, in which discontinuous velocity fields along the soil-structure interface are considered. A second-order cone programming formulation is developed based on the Mohr-Coulomb yield criterion and the associated flow rule, and an adaptive mesh refinement scheme is incorporated to improve accuracy and efficiency. The method is implemented in MATLAB with the MOSEK solver and validated through several numerical examples. Results show that the proposed approach predicts plastic regions and limit loads in good agreement with analytical solutions, slip-line field theory, and adaptive T6-UBFELA, while providing higher accuracy and numerical robustness than adaptive and adaptive NS-UBFELA. Although slightly less accurate than T6-UBFELA, SNS-UBFELA achieves superior computational efficiency. Numerical examples involving footings and anchors further demonstrate that soil-structure interface roughness significantly influences failure modes and stability, highlighting the necessity of incorporating discontinuous velocity fields in numerical analysis. Overall, adaptive SNS-UBFELA is an efficient and robust tool for soil-structure interaction analysis in geotechnical engineering.

In seismic events, the ductility shortage of RC pier columns, rooted in insufficient deformation capacity of plastic hinge zones, remains a pivotal factor triggering structural failure. Superelastic Shape Memory Alloy Fibers-Reinforced Engineered Cementitious Composites (SMAF-ECC), a novel composite, features strain hardening, self-recovery, and high energy dissipation. Applying SMAF-ECC to plastic hinge zones of pier columns effectively enhances ductility and seismic performance, enabling rapid post-seismic repair. This paper proposes a novel RC pier with SMAF-ECC wrapped plastic hinge zones. One conventional RC pier and three SMAF-ECC wrapped pier columns (SMAF contents: 0 %, 0.5 %, 0.75 %) were tested under low-cycle reciprocating loads. The test results showed that SMAF-ECC/RC pier column exhibited outstanding performance in terms of energy dissipation capacity, self-recovery capacity, and possessed good bearing capacity. At 0.50 % SMAF volume content, compared with conventional RC pier column, the energy consumption, displacement recovery, peak load, and yield load of SMAF-ECC/RC pier column are increased by 50.24 %, 71.04 %, 14.25 %, and 21.08 %, respectively. Moreover, the ductility coefficient reaches up to 4.21. Compared with ECC/RC pier column, the energy consumption, displacement recovery, peak load, and yield load of SMAF-ECC/RC pier column are increased by 36.01 %, 38.68 %, 9.69 %, and 13.51 %, respectively. Furthermore, the evolution of bearing capacity with SMAF content was found to be governed by the competition between the tensile enhancement effect of SMAF and a sectional stiffness counter effect, explaining the optimal performance at 0.5 % content. Finally, a mechanism-informed theoretical model for the horizontal bearing capacity of pier columns was established, explicitly considering the tensile contribution of SMAF-ECC. The calculated results agree well with the experimental data, with a correlation coefficient of 0.9299 and an average relative error of 5.77 %, providing a novel technical approach and theoretical basis for seismic enhancement of SMAF-ECC/RC pier columns.

This article represents an experimental examination on the seismic performance of fiber reinforcement polymer (FRP) warp confined FRP reinforced concrete (FCFRC) columns. A total of six specimens were prepared, among which four specimens were confined with GFRP sheets in their plastic hinge regions, and the other two were left unconfined. Each specimen was tested under a combination of constant axial load and cyclic lateral displacement load. The main objective of this experimental study was to investigate the effect of hoop spacing and the effect of confinement layers of GFRP wraps applied at the plastic hinge zone on seismic performance. The confinement effect of FRP hoops and wraps on the seismic performance of the columns was evaluated in terms of failure modes, hysteresis behavior, skeleton curve, residual drift ratio, and energy dissipation capacity. The experimental results revealed that employing the GFRP wraps in plastic hinge regions effectively improved the load bearing capacity by 52.9%, energy dissipation capacity 3.6 times, and stiffness degradation of specimens, hence improving the seismic performance. It also had a positive impact on the residual drift ratio of FCFRC columns, which decreased by up to 75%. Additionally, decreasing the hoop spacing was also found effective in improving the seismic performance of the structure.

This study presents the restoring force characteristics and seismic reinforcement effect when traditional mud walls are reinforced with steel bar braces. First, cyclic horizontal loading experiments are carried out on 1P and 2P mud walls equipped with steel bar braces, and the obtained restoring force characteristics are studied. The bolt holes in the gusset plates at the end of the steel bar braces are loose holes, and the braces become effective after the mud wall yields. Using the obtained restoring force characteristics, a calculation of response and limit strength is carried out for SDOF models.

Abstract This study presents a nonlinear solution for cavity expansion on the basis of unified strength theory, and considers the influence of strain-softening and drained condition. By assuming that the small-strain in elastic region, and large-strain in softening and plastic flow region, instead of, the assumption of elastic region and plastic region were usually used in calculation. The initial stress may be anisotropic due to the influence of the initial consolidation of geomaterials. Besides, a drained cylindrical cavity expansion analysis definitely will be more suitable for the interpretation of in situ soil testing in geomaterials with very high permeability. In the end, some data are conducted to verify the suitability of this study. The research results provide a theoretical basis for the analysis of cavity expansion in geomaterials with very high permeability, and have a certain reference value for similar engineering designs.

Computational mechanics is one of the techniques used to predict and optimize material behavior and structural performance. However, modeling a complex material model and achieving an accurate response in finite element analysis (FEA) remains a challenge. This study investigates the mechanical material properties of 3D-printed polylactic acid (PLA) by integrating tensile testing and FEA to optimize material behavior. The tensile testing was conducted on three different raster orientations (0°, 45°, and 90°), and the resultant stress-strain data were used to calibrate FEA models. For FEA nonlinear material modeling, isotropic elasticity was combined with a multilinear plasticity model, where the yield stress values were determined by using the strain offset method. Six different strain offsets (SOs), i.e., 0%, 0.007%, 0.01%, 0.02%, 0.05%, and 0.2%, were analyzed to evaluate their impact on the accuracy of the FEA results against the experimental results. The results highlight a significant influence of strain offset selection on the plastic region estimation and overall accuracy. The commonly used 0.2% strain offset method (SOM) significantly overestimated the plastic region, while 0% strain offset provided the most accurate simulation response. These results emphasize the importance of selecting the correct yield stress value for 3D-printed nonlinear material modeling in FEA simulations.

ABSTRACT We characterize the enhancement in toughness and ductility for a composite polyvinyl alcohol (PVA) infused with Molybdenum disulfide quantum dots (MoS 2 QDs) generated using hydrothermal synthesis. The elastic stress–strain properties of the system are characterized using a uniaxial tensile test which exhibits a significantly large strains and a considerably large of plastic region at an intermediate concentration of the infused QDs within the range 146–219 mM. Maximum toughness and efficiency is also achieved for the nanocomposite in the above mentioned concentrations of MoS 2 QDs. These experimental results are also consistent with the numerical simulation in the fiber bundle model (FBM) where the span of the plastic region shows the same non‐monotonic behavior with the disorder strength along with highest stability during failure process at the same point where toughness attains the maximum value. These results offer valuable insights into optimizing the mechanical properties of PVA‐MoS 2 QD nanocomposites. This will be beneficial for potential applications that require a combination of strength and elasticity, making these materials ideal candidates for structural applications that demand both load‐bearing capacity and flexibility.

A technique for determining the mechanical properties of aluminum alloys by instrumented indentation with a ball indenter was developed. The technique is based on the correlation between the maximum equal strain during specimen tension and the indentation strain hardening parameter in the plastic region. This made it possible to obtain a formula for calculating the ratio of yield strength to ultimate strength using the strain hardening parameter. The values of the loading ratio necessary to achieve the maximum Brinell hardness, which is proportional to the ultimate tensile strength with a constant conversion factor for the tested aluminum alloys, were established. The ultimate tensile strength value and the ratio of yield strength to ultimate tensile strength allow calculating the yield strength of the alloy, which is usually determined quite difficult by indentation according to other known methods. Given the unambiguous correlation between the ratio of yield strength to ultimate tensile strength and the strain hardening parameter, it is proposed to use it as a diagnostic parameter in the estimation the degree of fragility of structural materials. The higher this parameter, the more prone the material is to brittle fracture. The proposed method for determining mechanical properties by instrumented indentation is quite simple and easily responds to automation, which increases the productivity of monitoring the mechanical properties of aluminum alloys.

This article aims to analyze the thermo-elasto-plastic displacement and stresses in a rotating thick-walled cylindrical shell under plane strain and transient temperature conditions. The material is assumed to be homogeneous and isotropic, following Hooke’s law in the elastic region. In the plastic region, the Tresca yield criterion combined with the associated flow rule and a linear hardening assumption is employed, and two possible plastic regions are investigated. The results of the analysis indicate that the circumferential stress attains the maximum value among the stress components, with its peak occurring at the inner surface of the shell. The Tresca stress remains critical at the inner surface at all time instants, demonstrating that yielding always initiates from this location. These findings underscore the crucial role of the inner radius in the structural integrity of rotating shells, providing a suitable framework for predicting behavior and determining operational limits in such systems.

Three-dimensional finite element analyses were carried out to assess the impact of various types of lateral stiffeners on the response of steel beams. Hot-rolled simply supported H-steel beams were modeled in Abaqus and strengthened with centrally located vertical, V-shaped, inverted V-shaped, single X-shaped, or doubled X-shaped stiffeners. All these stiffeners possess a similar quantity of steel by varying the length and thickness of the stiffeners. The behavior of beams was studied in the elastic phase, hardening phase, necking phase, and failure. The yield stress, ultimate load, deflection value, and hardening in the three phases were also examined. It has been found that the findings indicate that altering the configuration of the stiffener, while maintaining its location and steel volume, can influence the response of the strengthened beam either favorably or adversely. Two stiffeners raised the yield load by 9.6%, the ultimate load by 10.8%, and elastic storage energy by 70% above the reference beam. One kind of stiffener increases in the plastic region, two types drop somewhat, and two others decrease significantly. The necking region shows a rise of 237% in one threshold and 36% to 90% for the other beams compared to the reference beam. Furthermore, the software provides a definitive indication of the kind of stiffener and the degree of its advantage, while simultaneously revealing the type of stiffener that is not advantageous.

The FeNiCrMn alloy gasket is vital for the sealing performance of the engine cylinder head-block interface and thus engine reliability. The transition temperature at which the plastic region disappears in the FeNiCrMn alloy gasket remains ambiguous. Molecular dynamics (MD) simulations show that lowering temperature suppresses plastic deformation under tension but improves compressive performance, while strain rate has negligible effects on elastic and strength properties. Based on MD data, a machine learning (ML) model achieved high prediction accuracy ( ). Mathematical analysis (MA) further identified critical temperatures of (tension) and 526K (compression), beyond which tensile plasticity vanishes and compressive behavior exhibits the opposite trend. A combined MD-ML-MA framework was employed to investigate the mechanical properties and critical temperature of the FeNiCrMn alloy gasket. MD simulations assessed tensile and compressive responses across temperatures and strain rates. The resulting dataset was used to train an ML neural network with a backpropagation algorithm for predictive modeling, while MA quantified the plastic region m(T), enabling determination of critical temperature thresholds.

Abstract Prestressed bridges have been a key element of transportation infrastructure for decades. Despite an expected service life of around 100 years under optimal design and maintenance, many structures exhibit damage that compromises their integrity or leads to failure. A common cause is material degradation resulting from inadequate design or environmental exposure, with corrosion being the most critical factor affecting the mechanical properties of prestressing reinforcement. This paper investigates the effect of corrosion on the nonlinear behaviour of standardised bridge girders and the overall capacity of the structure. To demonstrate the impact of reduced mechanical properties of the reinforcement, a nonlinear analysis was carried out using Atena software, in which degradation of material characteristics was simulated. The effect of corrosion was incorporated by modifying the stress–strain diagram of the prestressing reinforcement, up to complete elimination of its plastic region. This significantly affected both strain capacity and the ductility of failure of the load-bearing member. The results reveal a pronounced reduction in load-bearing capacity and a transition toward brittle failure in advanced stages of corrosion, underlining the critical importance of accurate diagnostics when making decisions related to maintenance and rehabilitation of existing bridges.

Grouted corrugated metal duct (GCMD) connections have emerged as a promising technology for prefabricated bridge piers, offering a balance between constructability and structural continuity. However, their mechanical behavior under seismic loading, particularly concerning joint-level stress redistribution, damage evolution, and energy dissipation, remains insufficiently quantified in the literature. This study presents a high-fidelity nonlinear finite element model developed in ABAQUS to simulate the cyclic response of a GCMD-connected prefabricated bridge pier and compares it with a monolithic cast-in-place counterpart. The concrete damaged plasticity (CDP) model, calibrated with the Ding–Yu constitutive law, is employed to capture concrete damage under repeated loading. The corrugated duct is explicitly modeled as a shell element coupled with longitudinal reinforcement, and surface-to-surface contact with Coulomb friction (μ = 1.0) is used to represent interfacial interactions. Results show that both piers exhibit similar failure modes localized in the plastic hinge region at the base, though the prefabricated pier displays a 10–20% larger damage zone due to local stiffness enhancement from the duct. While the prefabricated system achieves 5–8% higher peak load capacity and approximately 10% greater peak resistance, the monolithic pier demonstrates superior self-centering capability and more uniform energy dissipation, as evidenced by fuller hysteretic loops. The close agreement in skeleton curves and reinforcement stress distribution (with only a 3–5% stress difference between duct and bar) confirms the effectiveness of load transfer through the GCMD joint. This work provides a validated numerical framework for performance assessment of GCMD connections and offers practical insights for their adoption in seismic regions.

Genomic plasticity and phylogeny of sheeppox and goatpox viruses reveal progressive host and terrestrial adaptation.

Reinforced concrete (RC) structural walls are commonly used as lateral-load resisting systems for mid- and high-rise buildings. Due to their relatively large stiffness and high strength, there is a consensus in the earthquake engineering community that well-designed RC walls exhibit superior performance under strong earthquakes. The expected energy dissipation mechanism of cantilever walls is flexural yielding concentrated at the bottom of the wall (i.e., the plastic hinge region). However, the bottom walls of high-rise buildings usually experience complex force and moment combinations, making them prone to considerable damage under extreme conditions, which can cause irreparable failure and interruption of functionality. Therefore, improving the seismic performance of bottom RC walls is one of the critical issues for high-rise buildings in earthquake-prone regions. First, the current design practices of axial compressive behavior, global instability, and minimum reinforcement ratio for RC walls were summarized. Design considerations for improving the seismic performance of RC walls under extreme conditions were discussed. Subsequently, three typical failure modes (axial compressive failure, global instability, and shear failure) of the RC wall and the improvement measures were validated through finite element analyses. Numerical results demonstrate that the embedded steel plate can efficiently improve the performance of walls under high axial compressive force and large shear force conditions, providing one of the successful solutions for improving seismic resilience of RC walls.

Abstract The complexity of engineering problems generally makes it challenging to obtain explicit physical formulations and sufficient monitoring data. For the issue of shallow tunnelling-induced deformation, it is difficult to obtain an explicit analytical solution due to the anisotropy of in situ stress, asymmetric free surface boundary, and complicated elastic–plastic response. Physics-informed neural networks (PINNs) have demonstrated excellent capability in resolving boundary value problems. In this context, a data-driven and physics-informed neural network is developed to predict tunnelling-induced deformation. The underlying elastic–plastic governing equations and boundary constraints of a shallow-buried tunnel are encoded into a deep neural network framework, which is divided into two independent regions according to the Mohr–Coulomb yield criterion, ensuring that constitutive relations are executed in elastic and plastic regions separately. To mitigate nonlinearity and stress concentration effects, a global adaptive sampling strategy guided by probability distributions is introduced, which ensures a more efficient approach to enforcing physics laws. Comparisons between the analytical and PINN-based solutions of deformation induced by deep tunnel excavation demonstrate the robust performance of the proposed model, especially with the implementation of a global adaptive sampling strategy. For the solution of shallow tunnelling-induced ground deformation, the adaptive PINN model can accurately reproduce the elastic–plastic ground settlement fields with sparse labelled data. The data mining and physical information exchange character of the proposed adaptive PINN model demonstrates the promising potential of the scientific machine learning method in addressing engineering issues.

Abstract This study focuses on stabilizing the calculations involved in topology optimization for elastoplastic material models, specifically the optimization process which often faces challenges due to the discontinuity of the slope in the stress–strain curve at the yield point. Traditional approaches to topology optimization for elastoplastic material models sometimes suffer from stagnation at mechanically irrational solutions and fluctuations in design variables, caused by the non-smooth characteristic of the stress–strain relationship. Since gradient-based methods used in topology optimization require the evaluated functions to be differentiable, it is crucial to ensure that the stress–strain curve which effects the functions transitions smoothly between the elastic and plastic regions. To address this issue, this study proposes the use of the subloading surface model, which enables a smooth transition from the elastic to the plastic region, thereby stabilizing the optimization process. The objective function is formulated to maximize the energy absorption capacity of the structure, and sensitivity formulas are derived analytically based on the subloading surface model. The numerical examples show improved convergence performance, indicating the potential applicability of structural designs under other materially nonlinear constitutive models and geometrically nonlinear conditions.