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Optimal arrangement of structural sensors in soft rock tunnels based industrial IoT applications
Computer Communications ( IF 2.766 ) Pub Date : 2020-03-25 , DOI: 10.1016/j.comcom.2020.03.037
Chao Lin; Cheng-lin Zhang; Jie-hua Chen

In recent years, with the rapid development of China’s economy, many civil engineering structures such as high-rise buildings, bridges, dams and tunnel have increased rapidly. Sensor optimization layout, as an important technology of Structural Health Monitoring (SHM), has become one of the research hotspots in related fields and disciplines. Due to the limitations of civil engineering structures and real economic conditions, it is not possible to place sensors on all structural degrees of freedom. Therefore, it is of great theoretical and practical significance to study the optimal layout of sensors for civil engineering. The optimal layout of the sensor is to evaluate and monitor the civil engineering structure through a reasonably arranged limited sensor, which is a combinatorial optimization problem. At present, the algorithms for solving this problem are traditional optimization algorithms, sequence methods and intelligent optimization algorithms. Both traditional optimization algorithms and sequence methods can only obtain suboptimal solutions, which cannot guarantee the accuracy of sensor placement. In view of this, this paper proposes a sensor optimization arrangement based on the intelligent algorithm emerging in recent years. This paper introduces several common intelligent optimization algorithms, and proposes a hybrid intelligent algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. By comparing and analyzing the convergence of each algorithm, the results show that the hybrid algorithm has certain advantages over the single intelligent algorithm. In this paper, the hybrid intelligent algorithm is used to optimize the arrangement of the sensor. The experimental results show that the optimal layout of the sensor has certain rationality and feasibility.
更新日期:2020-03-26

 

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