To address the sensor placement challenges in ultra-slender and flexible structures such as deep-sea mining risers, the traditional effective independence method (EI) is improved by using the modal strain energy coefficient and the strain modal energy coefficient in this paper. A stepwise optimization and recombination strategy is further employed to extend the method to the scenarios of multi-type sensor placement. Based on this approach, a numerical simulation of a 6000-meter-class riser is conducted to explore the placement schemes of acceleration and strain sensors in such scenarios. Finally, for the five candidate schemes, three types of criteria are proposed to evaluate and select the optimal configuration. The results demonstrate that a combination of 21 acceleration sensors and 24 strain sensors can achieve a relatively ideal layout effect, which can reduce the maximum value of off-diagonal elements in the MAC matrix to below 0.1. The relevant research conclusions can provide a theoretical reference for the engineering practice of optimizing the placement of deep-sea mining riser sensors.

In order to solve the problem that the traditional intelligent optimization algorithm is easy to fall into the local optimal solution in structural damage identification, which leads to many misjudged units and poor identification accuracy in damage identification, an iterative structural damage identification method is proposed.To accelerate algorithm convergence and avoid failing into local optimum, a clustering particle swarm algorithm based on K-means is proposed. The results of traditional damage identification methods are improved by iteration, and the results of damage identification are updated iteratively to obtain accurate damage elements and damage severity. The three-legged offshore wind turbine structure is used as an example. Firstly, the effect of non-iterative methods on identifying structural damage is investigated with or without noise pollution. Secondly, the structural damage identification effect of the iterative damage identification methods under the influence of noise-free and noise pollution is studied. Thirdly, the convergence and stability of the proposed method are discussed. Finally, the proposed methods are verified by physical model experiment. The results show that the proposed iterative K-means clustering particle swarm algorithm could obtain accurate damage location and damage severity compared with the traditional structural damage identification methods, has good noise robustness, and has fewer iterations and a stable identification effect.