Дослідження стійкості різних типів баштових кранів та методів для підвищення безпеки експлуатації

Abstract

UA: В статті проаналізовано проблему забезпечення стійкості баштових кранів різних типів (з поворотною/неповоротною вежею) при дії робочих та зовнішніх навантажень. Розглянуто методи підвищення безпеки: комп'ютерне моделювання, адаптивне управління, компенсація навантажень. Запропоновано використання штучного інтелекту для керування стійкістю.

EN: This article delves into the critical issue of stability in different types of tower cranes, specifically comparing those with rotating (slewing) towers and those with non-rotating (top-slewing) towers. It explores various methods to enhance the operational safety of these essential construction machines. Tower cranes, while indispensable in modern construction and heavy industries, are inherently susceptible to stability-related accidents due to their height, slenderness, and the dynamic nature of the loads they handle. This study comprehensively analyzes the influence of structural characteristics, operational loads, and a range of external forces on crane stability. The research explores factors such as wind loading, dynamic forces generated during operation (e.g., load swing, sudden stops), and even more extreme events like impact waves. The research methodology combines a thorough review of existing literature, standards, and regulations with advanced computational analysis and experimental validation. The literature review encompasses relevant standards, such as the EN 13001 series, Ukrainian national regulations, and research publications focusing on crane stability, dynamic load analysis, and safety improvement methods. This review identifies gaps in traditional calculation methods, particularly their limitations in accurately representing complex load combinations and the behavior of non-standard or modified crane configurations. To overcome these limitations, the study leverages 3D modeling and finite element analysis using industrystandard software. Detailed models of both rotating and non-rotating tower cranes are developed, allowing for a precise simulation of stress distributions, deformation patterns, and potential failure points under various loading scenarios. These models incorporate the specific geometric parameters, material properties, and connection details of the cranes. The finite element analysis approach allows for a significantly more accurate assessment of stress concentrations, particularly in critical areas such as the tower-to-base connection, compared to traditional methods of structural mechanics. The comparative analysis highlights key differences in the stability characteristics of the two crane types. The location of the center of gravity, the response to dynamic loads (especially during slewing operations), and the impact of wind forces are meticulously examined. The findings indicate that cranes with rotating towers exhibit a larger degree of load sway and potentially greater vulnerability to certain types of dynamic instability. To validate the theoretical and computational findings, experimental studies are conducted using a scaleddown (1:20) laboratory model of a KB-403 tower crane. A custom-designed computer program controls the model's movements, allowing for the precise simulation of various operational scenarios and the collection of empirical data on crane behavior. Based on the combined theoretical, computational, and experimental results, the research proposes a novel design concept: a tower crane with a load-compensating mechanism using a movable counterweight. The study outlines an algorithm for controlling the position of this counterweight, dynamically adjusting it based on real-time sensor data (load weight, jib extension, wind speed and direction, tower inclination, and stress levels in critical components). Furthermore, the article explores the potential integration of artificial intelligence and machine learning techniques to create an adaptive control system for enhanced stability. This system would utilize data from a network of sensors to predict and mitigate potential instability issues in real-time. The importance of ongoing data collection and continuous refinement of the artificial intelligence models is emphasized to ensure long-term reliability and effectiveness.