The parameters of underground structure have the characteristics of large spatial heterogeneity and strong uncertainty. Recently, people gradually begin to use the idea of probability to analyze the safety of underground structures. However, few articles analyze the dynamic response of structural reliability. In this paper, the reliability of tunnel roof under blasting disturbance is studied, and the influence of the number of disturbances and the distribution of random variables on the structural reliability is analyzed. The prediction model of structural reliability is proposed. The sensitivity of different parameters to the structural reliability is discussed. The results show that the traditional method of evaluating the structural safety status is more dependent on the design value of each parameter. However, the design value does not represent the true state of the structure in a completely accurate way, which will result in a certain deviation between the assessment of the structural safety state and the actual state. Single disturbance has a limited impact on the structural reliability, while repeated disturbances can still have a noticeable effect on the structural reliability. When the disturbance reaches a certain number of times, the reliability of the structure can drop sharply. This paper proposes the prediction function of structure reliability according to the decay of structure reliability after blasting disturbance. Only a small number of parameters are needed to predict the structure reliability at different times. The error between the forecast results and the actual results is about 1%. With the increase of the number of blasting disturbances, the sensitivity coefficient of the structural resistance factor gradually decreases, and the sensitivity coefficient of the loaded effect gradually increases. The control of the blasting strength should be gradually increased to ensure the structural safety. Even if the mean and mean square error of random variables are the same, different distribution forms will have a great impact on the calculation results.

Deep buried tunnel has a complex stress environment and large stress level. At present, it is difficult to dynamic monitoring for such tunnels. In order to make the safety evaluation and prediction of tunnel closer to the engineering practice, this paper deduces the tunnel reliability evaluation algorithm. The time-varying reliability of deep tunnel excavation process is analyzed and predicted. Based on the similarity theory, the actual deep tunnel excavation is simulated by indoor model test, the stress and deformation are monitored. The results show that after the tunnel excavation, the reliability of the structure does not decrease gradually, but increases slowly first and then decreases sharply. With the increase of the buried depth of the tunnel, the structural reliability decreases gradually. When the buried depth reaches a certain depth, the structural reliability will decrease rapidly. The influence of different parameters on the structural reliability has different sensitivity. The sensitivity of friction angle is the highest, and the sensitivity of gravity is the lowest. The sensitivity has little effect with the change of tunnel excavation time and tunnel buried depth. The accuracy of this method is verified by the comparative analysis of model box tests.

Suspension bridge is a flexible structure. The weight, location and number of traffic on the bridge will affect the geometric alignment of the bridge. Although the finite element software can analyze the geometric nonlinearity of the bridge, the calculation of random load combination in finite element software is huge. In this paper, based on the deformation coordination, static equilibrium and constitutive model of the bridge, the force expression considering the geometric nonlinearity of the bridge is derived. Finite element software is used to simulate the internal force of components of suspension bridge under train load and vehicle load. The accuracy is verified by comparing the finite element results. Considering the random number of traffics, the random location of traffics, and the random weight of traffics, this paper analyzes the stress state of bridge under the random process of train load and vehicle load respectively. The method in this paper can be well used to analyze the actual stress state of the bridge, and provide a theoretical basis for related calculations.

To reduce temperature-induced measurement effects and the complexity of temperature compensation in long-term structural strain monitoring, this study proposes a machine-vision-based strain measurement method with reduced temperature sensitivity and develops a vision strain sensor incorporating fused-silica sensing elements. By integrating low-thermal-expansion fused-silica sensing elements with a dual-target visual measurement configuration, together with template matching and sub-pixel localization algorithms, the sensor achieves high-precision structural strain measurement. Static loading tests demonstrate strong agreement between the sensor and a linear variable differential transformer (LVDT), with a maximum root mean square error of 4.908 με. The sensor also exhibits good zero-drift stability and repeatability. Temperature cycling tests further show that, over the range of -20 °C to 50 °C, the sensor exhibits an average equivalent temperature sensitivity coefficient of 1.32 με/°C, indicating that it can effectively suppress temperature-induced additional responses at the sensor level and reduce reliance on post-compensation. A 20-day monitoring test on a steel member further demonstrates that the strain variation measured by the sensor agrees well with the theoretical thermal strain of the member in both overall trend and magnitude, thereby verifying its stable operational capability and its ability to characterize thermally induced structural strain responses. This method provides a feasible technical route for long-term continuous strain measurement in structural health monitoring with reduced dependence on temperature compensation.