Considering the differential settlement in the junction between the structure perpendicular to the dike and the body and foundation of dike (called the earth-rock junction in this paper) during runtime, an experimental investigation of optical fiber sensor monitoring was conducted. Based on the sensing mechanism of single-mode optical fiber bending loss, the experiment focused on the influence of the bending radius of an optical fiber on the bending loss. In view of the characteristics of the differential settlement in the earth-rock junction, we designed a butterfly-type optical fiber sensor and composite optical fiber sensor for monitoring device in monitoring the differential settlement to enlarge the monitoring range and improve the sensibility of optical fiber sensor. Based on the research on the working principle and bending properties of the composite optical fiber monitoring device, we conducted experiments on the bending of the composite optical fiber sensor monitoring device and the use of the device for monitoring the differential settlement. These experiments verified the feasibility of the composite optical fiber sensor monitoring device at monitoring the differential settlement in the earth-rock junction.

Structural modal identification has become increasingly important in health monitoring, fault diagnosis, vibration control, and dynamic analysis of engineering structures in recent years. Based on an analysis of traditional optimization algorithms, this paper proposes a novel sensor optimization criterion that combines the effective independence (EFI) method with the modal strain energy (MSE) method. Considering the complex structure and enormous degrees of freedom (DOFs) of modern concrete arch dam, a quantum genetic algorithm (QGA) is used to optimize the corresponding sensor network on the upstream surface of a dam. Finally, this study uses a specific concrete arch dam as an example and determines the optimal sensor placement using the proposed method. By comparing the results with the traditional optimization methods, the proposed method is shown to maximize the spatial intersection angle among the modal vectors of sensor network and can effectively resist ambient perturbations, which will make the identified modal parameters more precise.

For earth-rock dams influenced by rainstorms, seepage status monitoring is very important and provides the basis for the safe and effective operation of earth-rock dams. The most influential factors concerning the seepage of earth-rock dams are the reservoir water level, precipitation, temperature, and timeliness, and the influence of the reservoir water level and precipitation on the seepage of an earth-rock dam exhibits hysteretic effects. The reservoir water level of an earth-rock dam abruptly increases and may exceed the historically highest water level, therein causing new deformations of the earth-rock dam or even plastic deformation. Thus, the permeability coefficient for parts of an earth-rock dam changes, and we present the exceeded water level factor. Considering the complexity of the seepage monitoring of earth-rock dams, based on the hysteretic reservoir water level and precipitation, temperature, timeliness, and the exceeded water level factor, a statistical model based on an explicit function and an artificial wavelet neural network model based on an implicit function are established. Based on these two models, an integrated monitoring model based on maximum entropy theory is established. At the end of this paper, three monitoring models are used for the seepage monitoring of a measuring point of an earth-rock dam influenced by rainstorms, and the results show that the three monitoring models obtain satisfactory predication accuracy.