沈阳建筑大学

Abstract This paper investigated the dynamic compressive properties of ultrahigh-toughness cementitious composites (UHTCCs) under real-time high-temperature conditions. A series of dynamic compression mechanical tests were conducted using a split Hopkinson pressure bar system on UHTCC specimens at five different real-time temperatures (20°C, 200°C, 400°C, 600°C, and 800°C). The dynamic compressive stress–strain curves of UHTCC were obtained, and the coupled effects of temperature and strain rate on the peak strength, peak strain, and energy absorption of UHTCC were analyzed. The results indicated that both the dynamic compressive strength and energy absorption capacity of UHTCC decreased with increasing temperature, while they increased with increasing strain rate. A comparison with the test results of UHTCC after pre-thermal treatment revealed that, except for the specimens at 200°C, the reduction rules in compressive strength and energy absorption for the other specimen groups due to the high temperature in these two cases was similar. This study proposed fitting models for the dynamic compressive strength and energy absorption of UHTCC at high temperatures, and proposed a method for converting post-temperature test results into the real-time high-temperature test results. The modified Zhu-Wang-Tang model was established to construct the dynamic compression constitutive relationship of UHTCC under real-time high-temperature conditions.

MAX-phase materials are promising candidates for thermal barrier coating applications, owing to their ultra-high thermal stability, high hardness, and excellent thermal shock resistance. The exploration of new MAX phases is therefore of great significance for developing advanced thermal barrier coating materials. The present work theoretically predicted three novel boron-based Nb2AB (A = Si, Ge, Sn) compounds using swarm intelligent optimization combined with first-principles calculations. Nb2GeB and Nb2SnB are confirmed to adopt the typical MAX-phase P63/mmc structure, whereas Nb2SiB crystallizes in an orthorhombic Pnma structure. These structures are verified to be dynamically stable at ambient pressure and thermodynamically stable from 0 to 1500 K, supporting their experimental feasibility. Notably, orthorhombic Pnma_Nb2SiB exhibits larger bulk modulus (214 GPa), shear modulus (137 GPa), Young’s modulus (338 GPa), and higher melting temperature (2919 K) than the conventional P63/mmc phase, with its melting point approaching that of 8YSZ (2973 K). The minimum thermal conductivity of the presently designed Nb2AB compounds ranges from 0.52 to 0.85 W/m·K, which is significantly lower than that of 8YSZ (2.34 2.21 W/m·K). Although their thermal expansion coefficients are slightly lower than that of 8YSZ, the outstanding overall mechanical and thermodynamic properties render Nb2AB compounds promising for thermal barrier coating applications. The present results are expected to provide valuable theoretical guidance for the experimental synthesis of Nb2AB compounds and further rational design of novel MAX-phase materials.

The degradation of mechanical properties in corroded reinforced concrete (RC) beams presents a major challenge for assessing structural durability. To address this issue, this study proposes an integrated machine learning (ML) framework to predict the mechanical properties of such beams. First, a database of 464 samples was established, including 12 input parameters and 2 output parameters, followed by correlation analysis of the inputs. On this basis, the applicability of existing design codes and empirical models was evaluated. Subsequently, eight ML models were trained, with their hyperparameters optimized via Bayesian optimization (BO) to enhance prediction accuracy. The Categorical Boosting (CatBoost) model was identified as the most accurate, and its hyperparameters were further optimized using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for improved performance. Results show the PSO-optimized CatBoost model achieves the highest prediction accuracy to date: for the flexural strength test set, the coefficient of determination (R2) is 0.984 and root mean square error (RMSE) is 3.1602; for the deflection test set, R2 is 0.975 and RMSE is 0.6259. Compared with design codes, flexural strength test set R2 increases by 27.3 % and RMSE decreases by 72.8 %; versus traditional models like Support Vector Regression (SVR), R2 rises by 5.4 % and RMSE drops by 43.5 %. Additionally, SHapley Additive exPlanations (SHAP) analysis reveals geometric parameters (beam height, beam width) dominate flexural strength, while elastic stiffness and beam length drive deflection. Finally, a user-friendly graphical user interface (GUI) was developed for rapid mechanical property assessment of corroded RC beams.

Wearable flexible sensors are widely used in health monitoring but often face mechanical damage and thermal degradation during prolonged use. This study presents a high-performance waterborne polyurethane elastomer based on synergistic dynamic hydrogen and disulfide bonding. The elastomer is synthesized via a prepolymer method using polytetramethylene glycol (PTMG) as the soft segment and 3,3 ',5,5 '-tetrabromobisphenol A (TBBA) with 2,2 '-diaminodiphenyl disulfide (DTDA) as chain extenders, enabling precise structural control. FTIR and TG analyses confirm its structure and thermal properties. An optimal TBBA:DTDA ratio of 4:1 achieves a balance between mechanical strength and self-healing, with tensile strength of 15.87 MPa, elongation at break of 1316.07%, and healing efficiency above 89%. Incorporating hydroxylated multi-walled carbon nanotubes (MWCNTs) significantly improves conductivity. At 2 wt% loading, the composite maintains strong mechanical performance and enhanced healing efficiency (> 91%), with tensile strength of 11.38 MPa and elongation of 1049.82%. Thermal stability is improved, with T-90% rising to 418.81 degrees C, residual carbon to 8.08%, and T-g increased from 50.36 degrees C to 53.66 degrees C. The resulting films exhibit high sensitivity (GF 0.7-3.93) and stable cyclic behavior, enabling reliable monitoring of various human motions.

Numerous safety hazards in construction projects can readily cause safety accidents. While collaborative governance among stakeholders is vital for construction safety, it is hampered by interest-related factors. Evolutionary game theory is an excellent tool for analyzing participants' behavioral decisions based on interest factors, and it is employed in this study to explore strategies for promoting collaborative governance. However, existing studies rarely mention the concept of collaborative governance of construction safety risks, seldom focus on construction payment disputes between owners and contractors, and barely take into account the differences in interests and decisions faced by stakeholders under projects of varying costs. Based on this, an evolutionary game model among the government, owner and contractor is established by taking China's construction industry as an example, and MATLAB numerical simulation is conducted. First, the heterogeneity of the laws of strategy evolution under different cost levels was verified. Subsequently, cost levels were divided into two major categories and four subcategories based on strategy evolution results, and sensitivity analysis was conducted for each corresponding scenario. It was found that rewards for owners and contractors are barely effective, while cutting government regulatory costs and boosting positive governmental incentives generally play a positive role. The effects of penalties for inadequate safety investment and safety accidents on collaboration differ across project costs. Nevertheless, collaborative governance can be achieved via reasonable parameter optimization. This study addresses the critical issue of interest factors hindering collaborative governance, and provides a critical perspective for promoting construction safety and the sustainability of the construction industry. Cost-stratified analysis reduces overly definitive suggestions, offering valuable insights for both theory and practice.