Application of wireless sensors and deep learning algorithms in comfort analysis of wooden structures

Abstract

In many buildings with wooden structures, the service life and comfort of the floor will be affected by the walking load of the human body. The vibration effect generated by the human walking on the floor will affect the comfort that the floor brings to people. This article primarily focuses on the study of the correlation between floor vibration and human walking load in wooden buildings. It provides an analysis and summary of research findings from both domestic and international sources. Furthermore, the data collected is utilized to establish a model for estimating human walking load. The advent of information technology has witnessed significant transformative changes spurred by shifts in societal needs. Notably, the establishment of wireless sensor networks stands as a significant milestone within the field of information technology, highlighting advancements in science and technology. The evolution of wireless sensor networks has proven instrumental in various sectors within China, including but not limited to the military, environmental monitoring, biological research and medical systems, disaster monitoring, and energy allocation. This paper uses deep learning algorithms to analyze the node deployment problems of wireless sensor networks, summarizes a reasonable node deployment plan through specific research and practice, and uses collaborative deep learning methods to improve the operating efficiency of the network. In addition, this article also tested the vibration effect of wood structure buildings, analyzed different influencing factors, and provided a good reference point for the formulation of wood structure comfort standards.