## 1 Introduction
[20] proposed a new method for the model updating of large railway bridges using orthogonal diagonalization (OD) combined with improved particle swarm optimization (IPSO). Scholars in various fields have solved many problems by using the advantages of the PSO algorithm; however, the standard PSO algorithm has the defects of easily falling into local optimums and premature convergence. To this end, some scholars have also proposed many improvement strategies. [PERSON] et al. [21] proposed an improved particle swarm optimization algorithm and simulated three benchmark functions. The results showed that IPSO has a better global optimization ability than the basic PSO algorithm. [PERSON] et al. [22] proposed a new state assessment method for reinforced concrete (RC) bridge superstructures based on fuzzy C-mean clustering using a particle swarm optimization (FCM-PSO) algorithm. It has the advantage of the PSO algorithm in global optimization. [PERSON] et al. [23] proposed a new method to identify damage in structures using particle swarm optimization (PSO) combined with artificial neural networks (ANNs), which avoids the possibility that ANNs will get trapped in local minima while seeking the best solution. When solving specific engineering problems, especially when solving combinatorial optimization problems such as the optimal placement of sensor measurement points, the two random weights of social learning and individual cognition in the PSO algorithm will directly affect the search speed and search accuracy of the optimal solution of the sensor measurement point arrangement scheme. Considering that the search performance of the PSO algorithm depends on the balance between its global search and local improvement ability, this paper will improve the inertia weight \(\omega\) in the algorithm to make up for the defect of the linear decline in \(\omega\) so that the particles take into account both local and global searches in the optimization process and improve the efficiency of the algorithm. In the beginning, it is anticipated that the particles can search the whole space quickly at a fast speed to find the general position of the optimal solution and then conduct a fine search of the general position at a slow speed, finally converging to the optimal solution. The rest of this paper is organized as follows. In Section 2, aiming at the problem that the standard particle swarm optimization algorithm is prone to falling into local optimal and premature convergence due to the influence of weight, the PSO is improved, and an improved particle swarm optimization algorithm based on random weight (IPSOARW) is proposed. In Section 3, on the basis of setting the fitness function, the optimization strategy of the number and location of measurement points is proposed for the large-span steel beam structure. In Section 4, the optimization strategy of the number and location of measurement points in Section 3 is applied to the deck of the steel tube truss girder bridge (STTGB). The number and position of measurement points in the modal test of the STTGBbridge deck are optimized by taking the vibration data of the deck as the optimization data. In Section 5, based on the optimized number and position of bridge deck measurement points obtained in Section 4, the bridge deck measurement points are arranged according to the uniform distribution method and the IPSOARW distribution method and the modal test is completed. The test results based on the two methods are compared and analyzed, and the feasibility and superiority of the measurement point arrangement strategy based on IPSOARW are verified. The purpose of this paper is to provide a reference for the dynamic test of long-span steel beam structures, save test costs and improve test efficiencies on the basis of ensuring the accuracy of the test results.