Deformation Detection Model of High-Rise Building Foundation Pit Support Structure Based on Neural Network and Wireless Communication

Abstract

The reasonable selection and optimized design of the deep foundation pit support scheme is directly related to the safety, construction period, and cost of the entire project. Here, based on a large number of theoretical results in many related fields, relevant influencing factors are systematically analyzed, and advanced mathematical algorithms such as neural networks are introduced according to the relevant characteristics of building deep foundation pit support construction. First of all, this paper designs and implements deep foundation pit construction safety risk technology based on wireless communication and BIM technology and analyzes and describes the framework and function of the foundation pit construction safety risk identification system. Secondly, we use neural network algorithms to study the deformation prediction of the foundation pit supporting structure, which can describe the expression method of the above safety knowledge. Finally, the differences and benefits of this method and traditional methods are compared through experiments, which show that this technology can pave the way for the construction of deep foundation pit construction safety risk knowledge.

Metro Engineering Project Schedule Optimization Based on Wireless Network Communication and BIM Model Algorithm

“…e reliability method of the construction schedule is to regard the various variables that affect the construction schedule as random variables, such as human factors, material supply, capital supply, technical level, management level, construction conditions and environment, design changes, and risk factors. And the laws of these complex variables can be studied by probability and statistics methods [20]. In actual engineering, the impact of construction schedule is a complex issue, and actual schedule prediction may involve a variety of complex probability distribution methods, such as normal distribution, logarithmic distribution, and exponential distribution.…”

Expression and Analysis of Uncertainty in Deep Foundation Pit Design Scheme Decision-Making

“…Chen et al [36] adopted an improved AHP tailored for railway station foundation pits, facilitating practical decision-making in support type selection. On a technological front, Ding [37] introduced a deformation detection model grounded in neural networks and wireless communication, pushing the envelope of real-time monitoring of high-rise building foundation pit support structures. Complementing these advances, Yang et al [38], by leveraging finite element analysis and on-site monitoring data, offered a thorough examination of deformation patterns in large deep foundation pits located in soft soil regions, laying a solid groundwork for subsequent designs.…”

Research on the Optimal Design of Retaining Piles of a Wide Metro Tunnel Foundation Pit Based on Deformation Control

“…Therefore, understanding and controlling deformation is not only a technical requirement but also a safety imperative. Most scholars have explored the deformation curve using branch simulation methods, optimizing design parameters by analyzing these curves [1,2,4,[6][7][8][9][10][11][12][13][14]. These methods focus on fine-tuning the design to achieve minimal deformation while maintaining structural integrity.…”

“…In recent years, there has been significant progress in the prediction and calculation methods for foundation pit retaining structures, incorporating artificial intelligence techniques. These include meta-heuristic algorithms like the CO2 algorithm (Arama et al, 2020 [30]), grey wolf algorithm (Kalemci et al, 2020 [31]), and particle swarm optimization [16,32], as well as machine learning algorithms [6,9,[32][33][34][35]. However, these heuristic and machine learning algorithms primarily focus on identifying general patterns through large datasets, often lacking insights into the physical mechanisms underlying foundation pit engineering.…”