# Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model
## 1 Introduction  
Tower cranes are among of the most used equipment in construction projects, playing a critical role in transporting materials and building components on construction sites [1]. The operations of tower cranes largely depend on the experience and manual path planning of crane operators. Poor judgment and inadequate path planning by operators as a result of human inaccurate distance calculation and restricted view on the construction sites can significantly increase safety risks, potentially leading to severe construction accidents, such as lifted objects striking already constructed parts of a building or falling on workers. Several severe crane accidents have happened globally during the past several years, e.g., in August 2023, a devastating crane collapse occurred in Thane, a city in Western India, resulting in 20 fatalities and three injuries [2]. In December 2021, a tower crane collapse inTurin, Italy, led to three deaths and three injuries [3]. Between 2011 and 2020, the United States recorded 129 construction crane accidents [4]. In Australia, crane-related accidents in the construction sector resulted in 47 fatalities between 2003 and 2015, accounting for 8.5% of all construction industry fatalities [4; 5]. In China, tower crane accidents result in an average of 1.17 fatalities per year, and the consequences of such accidents are often more severe than typical construction incidents [6]. Many fatalities involving cranes and ground workers are caused by contact with objects and equipment, particularly crane loads and parts [7]. Due to the nature of high-altitude operations, it is difficult for ground workers to consistently monitor the lifted loads above them, making the risk of falling objects a critical safety issue that needs to be addressed. Currently, ensuring the safety of tower crane operations relies heavily on manual monitoring, which is both time-consuming and labor-intensive [8]. To address these challenges, researchers have explored various solutions. In the field of sensor-based monitoring, [PERSON] et al. [9] developed a digital twin-based monitoring system for port cranes that integrates multi-source heterogeneous data from various sensors, enabling real-time operation monitoring and control program simulation. Similarly, [PERSON] et al. [10] proposed a shape-performance integrated digital twin framework that combines multiple sensors with analytical, numerical, and AI models to enable real-time structural analysis and predictive maintenance of cranes. Meanwhile, with the increasing availability and affordability of surveillance cameras on construction sites, there has been increasing interest in leveraging computer vision technology to monitor and assess operational safety [11; 12; 13]. [PERSON] et al. [14] were the first to utilize video data from cameras mounted above cranes to identify hazardous areas using Mask Region-based Convolutional Neural Network (Mask R-CNN) and pixel coordinate distance conversion methods. [PERSON] et al. [15] employed a convolutional neural network (CNN) to detect whether workers on construction sites were wearing safety helmets in surveillance videos, thus reducing the risk of injury. [PERSON] et al. [16] developed a system to automatically detect and three-dimensionally (3D) locate the position of tower crane hooks using cameras. Although these studies have contributed to the identification of potential safety risks during tower crane operations, they have seldom provided solutions for mitigating those risks. For example, while operators may be informed of the presence of workers near a load, there is no guidance on how to adjust crane movements to avoid endangering those workers. Path planning technology has been widely adopted across various crane applications, demonstrating its maturity and versatility in the industry. In offshore applications, time-optimal trajectory planning methods have been developed specifically for offshore cranes operating on non-inertial platforms.