Machine learning is a powerful means for the rapid development of high-performance functional materials. In this study, we presented a machine learning workflow for predicting the corrosion resistance of a self-healing epoxy coating containing ZIF-8@Ca microfillers. The orthogonal Latin square method was used to investigate the effects of the molecular weight of the polyetheramine curing agent, molar ratio of polyetheramine to epoxy, molar content of the hydrogen bond unit (UPy-D400), and mass content of the solid microfillers (ZIF-8@Ca microfillers) on the low impedance modulus (lg|Z|_0.01Hz) values of the scratched coatings, generating 32 initial datasets. The machine learning workflow was divided into two stages: In stage I, five models were compared and the random forest (RF) model was selected for the active learning. After 5 cycles of active learning, the RF model achieved good prediction accuracy: coefficient of determination (R ²) = 0.709, mean absolute percentage error (MAPE) = 0.081, root mean square error (RMSE) = 0.685 (lg(Ω·cm²)). In stage II, the best coating formulation was identified by Bayesian optimization. Finally, the electrochemical impedance spectroscopy (EIS) results showed that compared with the intact coating ((4.63 ± 2.08) × 10¹¹ Ω·cm²), the |Z|_0.01Hz value of the repaired coating was as high as (4.40 ± 2.04) × 10¹¹ Ω·cm². Besides, the repaired coating showed minimal corrosion and 3.3% of adhesion loss after 60 days of neutral salt spray testing.

Shield tunneling is one of the most important technologies for building of underground engineering. Many grouting holes were prefabricated for the requirement of backfill grouting, which is easy to induce local damages and potential disasters, such as leakage and cracking. Accordingly, an integrated workflow for damage detection and stability evaluation was performed based on nondestructive testing (NDT) and numerical simulation. As a case study, this method was applied to an underwater shield tunnel. Firstly, Ground Penetrating Radar (GPR) was used to detect the conditions in grouting holes. Then, the infrared camera was used to determine the damaged positions induced by grouting holes. According to NDT results, the numerical models were developed to analyze the mechanical behaviors of structure. It indicated the geophysical inversion results are consistent with field conditions. The influence area increases with a significant value of water pressure, and stress magnitude would increase to 45KPa if the increment of water pressure reaches to 10KPa. As a promising application, structure stability was evaluated in the light of analytical results.

Settlement behavior plays an important role for the stability of underwater tunnel due to the different responses of fractured surrounding rock to external load. In contrast to the traditional analysis method based on continuum mechanics, the presented numerical model using improved hybrid finite element was performed to study the settlement behaviors of structure, and structural health monitoring system (SHMS) was introduced for field verification. The Nanjing Yangtze River tunnel, a typical underwater shield tunnel was selected as a case study for numerical simulation and real-time monitoring. First, an improved numerical model was developed on the basic of our previous research, which considers the impact of natural geological fractures on structure stability. Then, numerical investigation was applied to study the displacement of settlement under different boundary conditions. The differences between intact and fractured surrounding rock were discussed, which denote the response of fractured surrounding rock is significantly larger. To verify the numerical results, SHMS was employed in this project to monitor its mechanical behaviors. On the basis of the mass monitoring data, the analytical method was introduced to investigate the response of tunnel settlement to water pressure, which agreed well with the results obtained from the model of fractured surrounding rock.

A small fraction of Tidal Disruption Events (TDE) produce relativistic jets, evidenced by their non-thermal X-ray spectra and transient radio emission. Here we present milliarcsecond-resolution imaging results on TDE J1644+5734 with the European VLBI Network (EVN). These provide a strong astrometric constraint on the average apparent jet velocity βapp < 0.27, that constrains the intrinsic jet velocity for a given viewing angle.