Optimal Placement of Wireless Sensor Nodes for Bridge Dynamic Monitoring Based on Improved Particle Swarm Algorithm

Abstract

The issue of optimal placement of wireless sensor networks (WSNs) is one of the major challenges for dynamic monitoring of bridges. It should be solved based on combining the effective monitoring of the dynamic performance of a bridge with the energy consumption of WSNs. Thus, the relationship between the bridge modal and the energy consumption of wireless networks is derived. Optimizing sensor location is achieved by using the improved wavelet particle swarm algorithm, which overcomes the disadvantage of the conventional particle swarm algorithm by applying mutation operator to the selected particles with certain small probability. Based on the comparison between the ANSYS simulation results and the measured bridge data, as well as the energy distribution diagram of WSNs, the proposed method is shown to reflect the dynamic performance of the test bridge well. Moreover, the method effectively controls the energy consumption of WSNs and achieves reasonable optimization effects. Therefore, this method can be used for dynamic monitoring of wireless sensors in various bridge types.

Freeze–Thaw Damage Model of Polypropylene Fiber Reinforced Cement Stabilized Waste Construction Slurry under Uniaxial Action

“…(1) PSO was successfully used to identify parameters of the meso random damage model for the FCMS during F-T cycles, retaining wall design [27], concrete frame design [28] and bridge engineering [29,30]. However, civil engineering materials are polyphase materials [37][38][39] and are based on the no free lunch theorem [31].…”

“…When establishing the meso random damage model, the parameters of the model need to be identified according to experimental data. The particle swarm optimization (PSO) algorithm is a commonly used parameter identification calculation method [26], which has application for retaining wall, concrete and bridge engineering, and can obtain better calculation results [27][28][29][30]. According to the no free lunch theorem [31], when an algorithm is optimized to solve a class of problems, it will inevitably reduce the effect on other problems.…”

Wireless Sensor Network Optimization: Multi-Objective Paradigm

“…Wireless sensor networks are being used for monitoring and surveillance applications in various practical scenarios including warehouse monitoring, cargo fleet monitoring, home monitoring, human activity monitoring, health monitoring, industrial process monitoring and infrastructure monitoring [94][95][96]. A multi-objective optimization strategy has been proposed in [97] for dynamic monitoring of the bridge. The authors applied the proposed scheme on the dynamic monitoring of a bridge in Quzhou, China.…”

Wireless Sensor Network Optimization: Multi-Objective Paradigm

“…Wireless sensor networks are being used for monitoring and surveillance applications in various practical scenarios including warehouse monitoring, cargo fleet monitoring, home monitoring, human activity monitoring, health monitoring, industrial process monitoring and infrastructure monitoring [ 94 – 96 ]. A multi-objective optimization strategy has been proposed in [ 97 ] for dynamic monitoring of the bridge. The authors applied the proposed scheme on the dynamic monitoring of a bridge in Quzhou, China.…”