The effect of nano grain surface layer generated by ultrasonic impact on the fatigue behaviors of a titanium alloy Ti3Zr2Sn3Mo25Nb (TLM) was investigated. Three vibration strike-numbers of 24,000 times, 36,000 times and 48,000 times per unit are chosen to treat the surface of TLM specimens. Nanocrystals with an average size of 30 nm are generated. The dislocation motion plays an important role in the transformation of nanograins. Ultrasonic surface impact improves the mechanical properties of TLM, such as hardness, surface residual stress, tensile strength and fatigue strength. More vibration strike numbers will cause a higher enhancement. With a vibration strike number of 48,000 times per square millimeter the rotating-bending fatigue strength of TLM at 107 cycles is improved by 23.7%. All the fatigue cracks initiate from the surface of untreated specimens, while inner cracks appear after the fatigue life of 106 cycles with the ultrasonic surface impact. The crystal slip in the crack initiation zone is the main way of growth for microcracks. Crack cores are usually formed at the junction of crystals. The stress intensity factor of TLM titanium alloy is approximately 7.0 MPa·m1/2.

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Predicting surface settlement in deep foundation pit engineering plays a central role in the safety of foundation pit construction. Recently, static or dynamic methods are usually applied to predict ground settlement in deep foundation pit projects. In this work, we propose a model combining wavelet noise reduction and radial basis neural network (XW-RBF) to reduce noise interference in monitoring data. The results show that the XW-RBF model predicts an average relative error of 0.77 and a root average square error of 0.13. The prediction performance is better than the original data prediction results with noise structure and has higher prediction accuracy. The noise data caused by the interference of construction and the surrounding environment in the original data can be removed via the wavelet noise reduction method, with the discreteness of the original data reducing by 30%. More importantly, our results show that the XW-RBF model can reflect the law of data change to predict the future data trend with high credibility. The findings of this study indicate that the XW-RBF model could optimize the deep foundation pit settlement prediction model for high accuracy during the prediction, which inspires the potential application in deep foundation pit engineering.

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