# Research on Non-Random Vibration Analysis of Concrete Pump Truck Boom Based on Dynamic Excitation
## 1 Introduction  
High-precision and high-efficiency concrete pumping technology has become a major need in the engineering industry due to the quick growth of contemporary large-scalebuilding construction technology. However, the pumping truck boom's vibration issue has a major impact on pouring accuracy during the actual construction process, particularly when complex dynamic excitation is present at high altitudes, and the boom structure displays complex non-random vibration behavior [1; 2; 3]. In addition to causing pouring position deviation, this vibration may lower the overall construction quality and safety. The majority of conventional vibration analysis techniques are grounded in probability theory, which uses random variables to characterize the dynamic uncertainty of parameters including geometric dimensions, material qualities, loads, and structure boundary conditions [4; 5]. Researchers have commenced the investigation of non-random vibration analysis techniques to manage uncertainty analysis under constrained sample settings. Conventional stochastic vibration analysis depends on extensive sample data to develop precise probability distribution models. Researchers frequently regard the parameters of a system as stochastic variables and examine its stress, displacement-deformation, and stability attributes under random excitation to evaluate the system's reliability [6; 7; 8]. Source [9] presents an effective evaluation technique that integrates finite element analysis (FEA) with time-dependent dependability. The research focused on the impact of stochastic variables on the long-term efficacy and safety of structures, successfully facilitating rapid and precise predictions of structural performance at various temporal intervals through the implementation of agent models and Monte Carlo simulations. Source [10] established a comprehensive analytical framework that integrates probabilistic methods, finite element analysis (FEA), and Monte Carlo simulation techniques to address uncertainties in vibrating systems, thereby enhancing the accuracy of analysis and the prediction of the response of bilaterally vibrating systems subjected to random vibrations. Nonetheless, stochastic vibration analysis utilizing probabilistic models necessitates substantial data support, and the actual application of stochastic models is constrained in engineering due to challenges in data collecting. The dynamic characteristics of boom structures in mechanical equipment, such as concrete pump trucks, cranes, and excavators, are considerably influenced by the intricate working environment and fluctuating operating conditions. During the pumping of concrete, the friction within the conveying pipeline induces a substantial vibrational response, hence complicating the study of structural vibrations. These intricate and multi-source uncertain excitations frequently appear to be beyond the scope of conventional stochastic analytic techniques. The creation of the stochastic process model must be based on a large amount of sample observation data of the dynamic parameters in order to gather the probability distribution information of the parameters. This is challenging or prohibitively expensive for many real-world engineering challenges. However, research has demonstrated that incorrect subjective probability assumptions for these parameters can result in significant variations in the estimation of structural response and reliability. This can easily lead engineers to design structural parameters irrationally and misjudge the actual outcomes. As a result, the non-stochastic analysis approach employed in this article is better able to characterize and forecast the system's dynamic response, making it easier for engineers to evaluate the structural performance and design parameters.