The landslide can destroy all kinds of constructions, and seriously hinder people's production and life as well as the development of national economy. Bolt is one of the main methods for slope treatment, but it is difficult to monitor its construction quality and anchoring effect directly. With the rise and development of MEMS (Micro-electro mechanical system) technology, MEMS sensors, with the advantages of small size, low cost and high precision, quickly come out from the conventional monitoring methods and provide new possibilities for the monitoring field in geological engineering. In this paper, based on MEMS sensors, a model test was designed to explore the stability of the slope after treatment by bolts. Natural river sands were used to prepare slopes with angle of 45° through the air-plluviation method. In addition, the tests were divided into two groups (with or without bolts). MEMS sensors were set up in the slope to wirelessly and continually capture the acceleration, angular velocity and angle of slope sliding triggered by simulated rainfall in real-time. It was found that: with no treatment, the acceleration and angle in the interior and the bottom of the slope gradually changed during rainfall, while those parameters in the rear and the surface of the slope had no significant change, which indicated that the slope creep mainly occurred in the interior and the bottom of the slope before failure. When landslides occurred, the movement monitoring indexes in the interior and the bottom of the slope suddenly changed, followed by those in the rear and the surface of the slope, which means that when the sandy slope slides, the interior and the bottom of the slope slides first, and then the rear and the surface of the slope surface fail. This is a typical retrogressive landslide. After the slope was treated by bolts, only creep could be observed during long-term rainfall, and the acceleration and angle in the bottom, interior and surface of the slope gradually changed, while almost no change was found in the rear of the slope, which shows that under rainfall conditions, overall creep occurs for the slope after reinforcement, the slope angle decreases, and there is no landside. The experimental results prove that MEMS sensors can realize low-cost, high-precision, continuous real-time monitoring of slope, and can capture gradual changes of movements before failure and the sudden change when landslide occurs. It should play a certain role in the study of landslide mechanism and landslide warning, and has a broad application in the field of geological engineering monitoring. 相似文献
The first-order second-moment method (FOSM) reliability analysis is commonly used for slope stability analysis. It requires the values and partial derivatives of the performance function with respect to the random variables for the design. Such calculations can be cumbersome when the performance functions are implicit. Implicit performance functions are normally encountered when the slope is geologically complicated and the limit equilibrium method (LEM) is used for the stability analysis.
To address this issue, this paper presents a support vector machine (SVM)-based reliability analysis method which combines the SVM with the FOSM. This method employs the SVM method to approximate the implicit performance functions, thus arriving at SVM-based explicit performance functions. The SVM method uses a small set of the actual values of the performance functions obtained via the LEM for complicated slope engineering. Using the SVM model, a large number of values and partial derivatives of the performance functions can be obtained for conventional reliability analysis using the FOSM. Examples are given to illustrate the proposed SVM-based slope reliability analysis. The results show that the proposed approach is applicable to slope reliability analysis which involves implicit performance functions. 相似文献