Welding, as a critical process for achieving permanent material joining through localized heating or pressure, is extensively applied in mechanical manufacturing and transportation industries, significantly enhancing the assembly efficiency of complex structures. However, the associated localized high temperatures and rapid cooling often induce uneven thermal expansion and contraction, leading to complex stress evolution and residual stress distributions that compromise dimensional accuracy and structural integrity. In this study, we propose a combined heat source model based on the geometric characteristics of the weld pool to simulate the arc welding process of large flange shafts made of Fe-C-Mn-Cr low-alloy medium carbon steel. Simulations were performed under different welding durations and shaft diameters, and the model was validated through experimental welding tests. The results demonstrate that the proposed model accurately predicts weld pool geometry (depth error of only 2.2%) and temperature field evolution. Meanwhile, experimental and simulated deformations are presented with 95% confidence intervals (95% CI), showing good agreement. Residual stresses were primarily concentrated in the weld and heat-affected zones, exhibiting a typical “increase–steady peak–decrease” distribution along the welding direction. A welding duration of 90 s effectively reduced residual stress differentials perpendicular to the welding direction by 19%, making it more suitable for medium carbon steel components of this scale. The close agreement between simulation and experimental data verifies the model’s reliability and indicates its potential applicability to the welding simulation of other large-scale critical components, thereby providing theoretical support for process optimization.

To utilize garbage fly ash (GFA) as a resource, this research proposes a method for preparing GFA with higher reactivity through carbonation and applies it to the production of foamed concrete. The effects of CO₂-cured GFA substitution rate and foam volume on slump flow, rheological properties, mechanical strength, thermal conductivity, water absorption rate, and water resistance coefficient of foam concrete are clarified. The results show that an increase in the CO₂-cured GFA substitution rate from 0 to 100% improves the slump flow by 10.8%~34.5% and decreases the plastic viscosity by 4.8%~36.4% and yield stress by 5.6%~28.1%. The higher carbonized GFA substitution rate can prolong the initial setting time with the largest amplitude of 30.4%. In addition, increasing the CO₂-cured GFA substitution rate improves the mechanical strengths, water resistance, thermal conductivity, and solidification of heavy metals. When the CO₂-cured GFA substitution rate is 100%, the 28-day compressive strength, 28-day flexural strength, water absorption rate, water resistance coefficient, thermal conductivity, leached Zn, and leached Cr of foam concrete are 18 MPa, 3.6 MPa, 20.7%, 0.46, 0.69 W·m⁻¹·K⁻¹, 9.4 × 10⁻⁵ mg/mL, and 8.6 × 10⁻⁵ mg/mL, respectively. Moreover, more foam volume improves the fresh-mixed performance of foam concrete while reducing the mechanical strength, water resistance property, thermal conductivity, and solidification of heavy metals. It is found that the technical approach for preparing foamed concrete containing CO₂-cured GFA with 40% foam volume can achieve its large-scale use.

This study develops a unified continuum damage mechanics (CDM) model for high-cycle fatigue life prediction of large manually arc-welded flange shafts manufactured from 45Mn steel (quenched and tempered) under combined bending–torsion loading. Fatigue tests revealed consistent crack initiation at the weld toe, with multiaxial loading reducing fatigue life by 35–42% compared to pure bending. The CDM parameters were calibrated against experimental data and implemented through an ABAQUS 2021 UMAT subroutine, achieving prediction errors below 5%—significantly outperforming conventional nominal and hotspot stress methods. For high-cycle fatigue conditions, a simplified CDM model neglecting plastic damage maintained engineering accuracy while improving computational efficiency by 3–5 times. The damage variable D = 0.9 was identified as a universal threshold for accelerated damage progression. These findings provide quantitative basis for multiaxial fatigue design and structural health monitoring of large welded components.

Basalt fibers possess high tensile strength and excellent corrosion resistance, properties that may enhance the chloride resistance of recycled aggregate concrete (RAC) structures. Nevertheless, the effects of basalt fibers on RAC structures under chloride attack remain poorly understood. This study investigates mass loss and the deterioration of key mechanical properties in basalt fiber-reinforced RAC composite slab–column assemblies (RAC composite assemblies) subjected to NaCl freeze–thaw cycles (F-Cs) and dry–wet alternations (D-As) and further explores the damage mechanisms of the concrete matrix through microscopic characterization. The results show that, compared with NaCl F-Cs, NaCl D-As have a more pronounced impact on the performance degradation of RAC composite slab–column assemblies. Moreover, basalt fibers effectively mitigate the deterioration of RAC composite assemblies in chloride-rich environments, particularly under NaCl D-As, where their protective effect is more evident. At 2.5 vol% fiber content, impact toughness peaked at an 83.7% improvement after 30 D-As, while flexural toughness showed a maximum enhancement of 773.6% after 100 F-Cs. Scanning electron microscopy energy-dispersive spectroscopy (SEM-EDS) analysis revealed a marked increase in Cl content within RAC, with NaCl D-As causing more severe erosion than NaCl F-Cs. Additionally, basalt fibers significantly inhibited chloride ion penetration and associated erosion in RAC. These findings provide valuable insights into utilizing basalt fibers to enhance the durability of RAC in coastal infrastructure exposed to chloride attacks. Further research on long-term performance and fiber parameter optimization is needed to support practical implementation.

Large flange shafts are the core load-bearing and connecting components of high-end equipment, and their welding multi-physical fields directly affect the quality and service safety of the components. Traditional experiments and finite element methods suffer from long cycles and low efficiency, which can hardly meet the demand for rapid prediction. Aiming at the fast and accurate prediction of welding temperature, deformation and residual stress, this study combines thermal–mechanical coupled finite element simulation with machine learning to construct and compare a variety of prediction models. A dataset is built based on simulation data from 100 groups of process parameters. Overfitting is reduced through strategies including early stopping and dropout, and models such as MLP, RF, RBF-SVR, TabNet, XGBoost, and FT-Transformer are established and verified through 10-fold cross-validation. The results show that the MLP model performs best in the prediction of temperature, deformation and residual stress, and is in good agreement with the simulation values. The prediction errors of the peak temperature of the weld and base metal are below 5%, and the errors of deformation and residual stress are controlled within 10%. The average error of peak residual stress is about 6 MPa, and the deviation of most samples is less than 5 MPa. The RF model ranks second in accuracy, with an average error of about 6.5 MPa for peak residual stress, showing a satisfactory interpretability and engineering applicability. RBF-SVR and TabNet can meet basic prediction requirements. Under the small-sample condition in this work, XGBoost and FT-Transformer present relatively large errors and a weak generalization ability, making it difficult to achieve high-precision prediction. The MLP model established in this paper can effectively reproduce the evolution of welding multi-physical fields and supports the rapid prediction and process optimization of large flange shaft welding. The generalization ability and practical performance of the model can be further improved by expanding the dataset and experimental verification in the future.