A novel deep displacement sensor based on the electromagnetic induction theory is investigated and designed, which can directly convert the varied sliding displacement and tilt angle at any depth within the landslide mass to the variation of mutual inductance, so it has advantages, such as simple sensor structure, high sensitivity, accurate positioning of sliding surface and position, a remote, as well as real-time and automatic monitoring toward the underground landslide mass over the conventional deep displacement monitoring methods. The structure design, sensing principle, and theory modeling for the proposed sensor are presented. In order to improve the sensor's performance, the complicated relationship between the landslide mass's sliding magnitude and direction, the sensor's geometric parameters, and its corresponding mutual inductance were derived by theoretical modeling. Furthermore, a series of ground-based testing experiments and theoretical modeling simulation are conducted and compared in detail, which not only initially shows the design feasibility and modeling effectiveness for the proposed sensor, but is also useful to give an in-depth understanding of the sensor property and optimize the sensor design.

Deep displacement observation is one basic means of landslide dynamic study and early warning monitoring and a key part of engineering geological investigation. In our previous work, we proposed a novel electromagnetic induction-based deep displacement sensor (I-type) to predict deep horizontal displacement and a theoretical model called equation-based equivalent loop approach (EELA) to describe its sensing characters. However in many landslide and related geological engineering cases, both horizontal displacement and vertical displacement vary apparently and dynamically so both may require monitoring. In this study, a II-type deep displacement sensor is designed by revising our I-type sensor to simultaneously monitor the deep horizontal displacement and vertical displacement variations at different depths within a sliding mass. Meanwhile, a new theoretical modeling called the numerical integration-based equivalent loop approach (NIELA) has been proposed to quantitatively depict II-type sensors’ mutual inductance properties with respect to predicted horizontal displacements and vertical displacements. After detailed examinations and comparative studies between measured mutual inductance voltage, NIELA-based mutual inductance and EELA-based mutual inductance, NIELA has verified to be an effective and quite accurate analytic model for characterization of II-type sensors. The NIELA model is widely applicable for II-type sensors’ monitoring on all kinds of landslides and other related geohazards with satisfactory estimation accuracy and calculation efficiency.

Landslide is a very common and destructive geo-hazard, and displacement monitoring of it is integral for risk assessment and engineering prevention. Given the shortcomings of current landslide displacement monitor technologies, a new three-dimensional underground displacement monitoring technology is proposed based on the double mutual inductance voltage contour method. The underground displacement measuring device mainly consists of an information processing unit and sensing array, connected by power and RS-485 communication lines. An underground displacement measurement model to convert the double mutual inductance voltages and the inter-axis angle into the relative displacement between adjacent sensing units is established based on the interval-interpolation and contour-modeling. Under the control of the information processing unit, the relative displacement between any two adjacent sensing units can be calculated through the underground displacement measurement model, so as to obtain the total displacement from underground depth to surface, and the measurement data can be further sent to the Internet of things cloud platform through the 4G module; thus the remote real-time monitoring of underground displacement three-dimensional measurement for the rock and soil mass from underground depth to the surface is realized. The measurement model is verified by building an experimental platform to simulate the underground displacement of rock and soil mass. The experimental results show that for each measuring unit, when the horizontal displacement and vertical displacement are within the measurement range of 0–50 mm, the maximum measurement error will not exceed 1 mm, which can meet the accuracy requirements of underground displacement monitoring of landslide.

The two-archive 2 algorithm (Two_Arch2) is a manyobjective evolutionary algorithm for balancing the convergence, diversity, and complexity using diversity archive (DA) and convergence archive (CA). However, the individuals in DA are selected based on the traditional Pareto dominance which decreases the selection pressure in the high-dimensional problems. The traditional algorithm even cannot converge due to the weak selection pressure. Meanwhile, Two_Arch2 adopts DA as the output of the algorithm which is hard to maintain diversity and coverage of the final solutions synchronously and increase the complexity of the algorithm. To increase the evolutionary pressure of the algorithm and improve distribution and convergence of the final solutions, an -domination based Two_Arch2 algorithm ( -Two_Arch2) for many-objective problems (MaOPs) is proposed in this paper. In -Two_Arch2, to decrease the computational complexity and speed up the convergence, a novel evolutionary framework with a fast update strategy is proposed; to increase the selection pressure, -domination is assigned to update the individuals in DA; to guarantee the uniform distribution of the solution, a boundary protection strategy based on indicator is designated as two steps selection strategies to update individuals in CA. To evaluate the performance of the proposed algorithm, a series of benchmark functions with different numbers of objectives is solved. The results demonstrate that the proposed method is competitive with the state-of-the-art multi-objective evolutionary algorithms and the efficiency of the algorithm is significantly improved compared with Two_Arch2.