Adaptive Risk Metric Framework for Tower Crane Safety in High-Rise Construction Projects

The construction industry is constrained by problems such as frequent safety accidents, low efficiency, and backward informatization (with an informatization rate of only 0.03%), and the traditional management model is characterized by obvious "three highs" (high risk of aerial work, high waste of energy-intensive building materials, and high difficulty in efficient collaboration). The smart construction site system integrates technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Building Information Modeling (BIM) to realize the intelligent management of all elements (personnel, machinery, materials, methods, and environment) in the construction process. This paper analyzes the application of the system in scenarios such as environmental monitoring, violation identification, and construction simulation, and verifies its effectiveness with cases: UWB positioning technology shortens the rescue time by more than 70%, and tower crane monitoring achieves "zero accidents" while improving efficiency by 20%. The research shows that the system can reduce the safety accident rate by more than 40% and improve management efficiency by about 35%. In response to problems such as data silos and high costs in promotion, the system needs to develop towards intelligence and integration in the future to help the high-quality transformation of the construction industry.

Abstract Given the multi-source and uncertainty of tower crane construction safety risks, this study proposes a comprehensive risk analysis method that integrates disaster chain theory (DCT), analytic hierarchy process (AHP), entropy weight method (EWM), and cloud model. Firstly, based on the disaster chain theory, the topology of risk factors is established, and the key risk factors such as equipment failure, operation error, environmental interference, and management defects in the operation of tower cranes are systematically identified. An evaluation system consisting of 4 first-level indicators and 16 second-level indicators was constructed through the topological mapping of the disaster chain, and the AHP-EWM combined weighting method was used to integrate the subjective and objective weights to effectively balance the expert experience and data objectivity. The cloud model theory is introduced to deal with the ambiguity and randomness in risk assessment, and the mapping relationship between the risk level and the cloud characteristic parameters are established to achieve the conversion from qualitative concept to quantitative evaluation. The case application shows that the risk factors with higher weight ranking include: fatigue/deformation of steel structure, and failure of safety devices. This study innovatively combines dynamic weight optimization with uncertainty modeling to provide a visual risk assessment method for tower crane construction companies.

The tower crane operators play a crucial role in construction operations, and their work efficiency directly affects the success of the construction projects. However, the heavy and repetitive nature of their tasks often leads to operator fatigue, which in turn compromises both performance and safety. This study investigates the impact of incentive mechanisms on the work efficiency of tower crane operators by analyzing the electroencephalogram (EEG) signals of the operators collected. The multifractal detrended fluctuation analysis (MF-DFA) method is employed to examine the Hurst index range and multifractal spectrum width of the EEG signals under two incentive modes: a reward–punishment incentive mode and a no reward–punishment mode. The research results show that there are significant differences between the two incentive modes (p < 0.05). The Hurst index range and multifractal spectrum width of the EEG signals under the reward-punishment mode are greater than those under the no reward-punishment mode. Furthermore, during different working stages, the lifting efficiency of prefabricated components is consistently higher in the reward–punishment incentive mode. This indicates that drivers under the incentive mode have less mental fatigue and higher work efficiency. Additionally, in the incentive mode, the lifting efficiency of prefabricated components is significantly higher, demonstrating the practical benefits of such incentives in improving work performance. From this study, it can be concluded that the reward-punishment incentive mechanism effectively improves the work efficiency of tower crane operators by positively influencing their cognitive state. Based on the EEG signals, the impact of incentive mechanisms on human performance can be better understood, and valuable insights can be provided for optimizing the safety and operational efficiency of construction sites.

Tower cranes are very important in construction works as they are the devices that can lift heavy loads to very high places and take up to a required place. Nevertheless, their mechanical nature, particularly the load swinging and power usage, are sources of big problems in ensuring the safety of both the personnel and the equipment as well as in the running of the system. Mostly, traditional analytical models have difficulties in fully describing the nonlinearity and complexity of the relationships among crane parameters and performance and this is kind of a challenge that calls for data-driven approaches. This paper suggests a mixed strategy of neural networks and metaheuristic optimization to accomplish the prediction and the optimization of the power consumption in tower crane systems. To build up the model, the Container Crane Controller dataset, which is open to the public, containing speed, load angle, and corresponding power was used. As the database was a small-sample one, the authors resorted to data augmentation to extend the training set and enhance the model's ability to generalize. A multilayer perceptron (MLP) network with two hidden layers (32 and 16 neurons, respectively) was set up to learn the nonlinear mapping that takes speed and angle as inputs and power as output.On testing the network secured a high value of determination coefficient (R²), which is a strong confirmation of its capability to reflect the basic dynamics accurately. After the training, the neural network was combined with the Differential Evolution (DE) algorithm to figure out the best operating points for maximum power output.A study of convergence illustrated that DE operations got stable solutions very quickly and did not encounter the problem of local minima. Among the visualizations, such as contour plots and three-dimensional surfaces, the interaction between speed, angle, and power was unveiled, while the convergence curve gave an insight into the stability and reliability of the optimization process.The results confirm that the proposed hybrid approach not only enhances predictive accuracy but also provides practical insights into safe and energy-efficient operation of tower cranes. This paper gives a proof of neural network and evolutionary optimization combination as a very effective method to achieve better crane performance. Moreover, this performance-enhancing methodology can be further employed in the future studies to development of other construction machinery.

SHM-YOLO: Detection of occluded small objects in top-down views from tower cranes under adverse rain and snow conditions

Prefabricated building projects represent industrialized and intelligent construction through factory production, standardized design, and mechanized assembly. This study presents a multi-agent simulation approach to model the prefabricated construction process, allowing for the concurrent optimization of the prefabricated component (PC) splitting design and the construction organization plan through iterative simulation. (1) Employing a questionnaire survey, it identifies critical factors affecting schedule and cost from a design–construction coordination perspective. (2) Based on these findings, an agent-based model was developed incorporating PC installation, crane operations, and storage yard spatial constraints, along with interaction rules governing these agents. (3) Data interoperability was achieved among Revit, NetLogo3D and Navisworks. This integrated environment offers project managers digital management of design and construction plans, simulation support, and visualization tools. Simulation results confirm that a hybrid resource allocation strategy utilizing both tower cranes and mobile cranes enhances resource leveling, accelerates schedule performance, and improves cost efficiency.

The roof structure of the Islamic Center Jambi building was initially planned entirely with Wide Flange (WF) steel. However, due to time limitations and tower crane capacity, stages 2–4 were redesigned into a Space Frame system to accelerate construction. This study aimed to analyze the cost and time comparison between a full WF structure (stages 1–4) and a WF–Space Frame combination (WF stage 1, Space Frame stages 2–4). Cost analysis was based on the 2023 Public Works Unit Price Analysis (AHSP), while scheduling was evaluated using the Critical Path Method (CPM). Results showed that the WF–Space Frame combination reduced costs by 11.989% (IDR 35.308 billion vs. 40.118 billion) and shortened project duration by 64 days (348 vs. 412). Therefore, the WF–Space Frame system proved more efficient in both cost and time, making it a more optimal alternative.

Dynamic risk assessment of tower crane operations by integrating functional resonance analysis method and Bayesian network

The construction industry faces workforce challenges including an aging workforce, declining interest among younger generations, and gender disparities, with women comprising less than 11% of workers. While automation and teleoperation emerge as strategies to address workforce shortages, psychological barriers—low self-efficacy (SE) and gender stereotypes (GS)—continue to limit prospective groups, particularly women, from choosing construction as a career path. This study explores the impact of training at various immersion levels on boosting SE and mitigating GS across gender groups. Using a between-subjects experimental design, participants were randomly assigned to two immersion levels of VR technology with balanced gender distribution and completed two sessions of tower crane operation simulations. SE and GS were assessed before and after the intervention. Results demonstrate that training with both VR immersion levels significantly increased SE across all participants, with women showing greater improvements than men—effectively eliminating pre-existing gender gaps. For GS, while main changes were not statistically significant, analyses revealed marginally significant interaction effects. Post-hoc comparisons showed that women and those in the immersive VR condition demonstrated significant improvements post-training. This research provides valuable insights for developing inclusive training approaches in increasingly automated construction environments, potentially helping address industry-wide labor shortages.

This study presents a comparative analysis of construction site layout planning using the NSGA-II and hybrid NSGA-II-MOPSO multi-objective optimization algorithms. The objective is to simultaneously minimize material transportation distance and maximize on-site safety by optimizing the spatial arrangement of auxiliary facilities and tower cranes. Various layout solutions were generated and evaluated based on risk zones, facility proximity, and crane operational parameters. The results demonstrate that the hybrid NSGA-II-MOPSO algorithm produces a more diverse and superior set of Pareto-optimal solutions compared to the standard NSGA-II. A balanced solution found offers a practical trade-off between efficiency and safety, making it a suitable candidate for implementation. The study highlights the effectiveness of evolutionary algorithms in solving complex CSLP problems and provides a decision-making framework for selecting layouts based on project-specific priorities.

In this article, the prescribed-time tracking control problem is taken into consideration for a class of strict feedback single-input-single-output(SISO) systems with unknown nonlinear items. A novel 3-segment piecewise parametric function is introduced in the prescribed time. By utilising the unique positive definite solution of the parametric Lyapunov equation (PLE), a linear state feedback controller with a time-varying gain is designed to achieve prescribed-time output tracking control with the impact of unknown nonlinear terms attenuated. The existing parametric function has a disadvantage that it is not differentiable in some time spots, therefore it is modified to be differentiable and bounded during the whole time span. It is proved that with the proposed linear time-varying controller, the Lyapunov-like function is bounded, which implies that all the state signals, control signals, and the output tracking error are bounded not only before the prescribed time but also beyond the prescribed time. Finally, the simulation results and simulation comparisons on a second-order nonlinear system verify the effectiveness of the proposed control mechanism. Moreover, the proposed controller is applied to control a tower crane, which demonstrates the feasibility of the proposed method in applications.

Comprehensive tower crane dynamics: An experimental dataset of trajectories and payload oscillations for system identification and machine learning-based control.

Digital Safety Navigation for Tower Crane Climbing Process from a Systematic Perspective: Risk Identification, Safety Information Requirements, and Related Intelligent Platform

To investigate the photosynthetic responses of needles with different ages (current-year and annual-year) in Pinus koraiensis to sunflecks along the vertical gradient of canopy, we conducted an experiment at the Changbai Mountain Forest Ecosystem Positioning Station utilizing a canopy tower crane platform. We selected current-year and annual-year needles from the upper (mean height: 23.26 m), middle (16.55 m), and lower (11.15 m) layers of the canopy of dominant species P. koraiensis, to simulate photosynthetically active radiation (PAR) abruptly increasing from a shaded state (50 μmol·m-2·s-1) to a sunfleck state (1200 μmol·m-2·s-1) and then decreasing back to 50 μmol·m-2·s-1 by using Li-6400 portable photosynthesis system. We measured the physiological response of needles during photosynthetic induction and photosynthetic recovery, as well as stomatal dynamic process. The results showed that leaf age and canopy height had significant effects on photosynthetic induction. During photosynthetic induction, the highest maximum reaction rate (RSmax) was recorded in current-year needles at the lower canopy (18.41 nmol·m-2·s-1) and in annual-year needles at the upper canopy (17.39 nmol·m-2·s-1). During photosynthetic recovery, current-year needles at the lower canopy showed the highest RSmax(2.93 nmol·m-2·s-1), while annual-year needles at the upper canopy exhibited the highest RSmax(0.66 nmol·m-2·s-1). This result indicated complementary strategies in sunfleck response between needle ages across canopy layers. There was a significant vertical gradient in stomatal kinetics. In the stomatal opening stage, the RSmax for both current-year and annual-year needles was significantly higher at the upper canopy compared to the lower canopy (145.5% faster and 104.4% faster, respectively). During the stomatal closing stage, the RSmax was lower for both needle ages at the upper canopy compared to the lower canopy (40.2% slower and 34.5% slower, respectively). These stomatal dynamics indicated that upper-canopy needles utilized sunflecks more effectively. Collectively, the complementary strategies in sunfleck utilization across needle ages and canopy heights would contribute to maximizing the overall photosynthetic carbon assimilation capacity of the broad-leaved Korean pine forest canopy.

Sway Angle Sensor System for Full-Scale Tower Cranes Based on the Phase Difference of Sound Waves

The complexity of skyscraper construction has increased with urban densification and the growing demand for vertical infrastructure. Building Information Modelling (BIM) has emerged as a transformative tool to address coordination delays, safety risks, and rising costs. This study delves into the application of Building Information Modeling (BIM) in high-rise construction projects, with a focused examination of its role in optimizing foundation design, regulating slipform construction processes, and enhancing site safety management. It begins by outlining the structural and procedural characteristics of tall buildings and common operational challenges. BIMs role in supporting slipform construction is examined, highlighting benefits in scheduling, real-time quality control, and IoT integration. The research also assesses BIMs safety management capabilities, including risk analysis, evacuation planning, and safety training facilitation. Furthermore, the study demonstrates BIMs effectiveness in logistical planning, such as optimizing tower crane placement. Finally, the article discusses current barriers to widespread BIM adoption and proposes strategies for broader implementation. Findings confirm that BIM significantly improves decision-making, efficiency, and safety in high-rise construction.

The article presents the results of a study aimed at improving approaches to the assessment of the reliability of construction lifting equipment, particularly tower cranes. The author analyzes modern methods for selecting and calculating reliability indicators, with particular attention paid to the four key reliability properties: dependability (faultlessness), durability, maintainability, and storability. Priority indicators for the technical assessment of machines used seasonally or under intensive conditions are identified.The study reviews approaches proposed by leading experts in the field of reliability and examines such indicators as the probability of failure-free operation, failure flow rate, reliability margin coefficient, durability coefficient, as well as integral indices of maintainability and operational readiness of machinery.An analysis of technical specifications for over 100 models of tower cranes manufactured by leading global companies (Liebherr, Potain, Terex, Jaso) was conducted. Based on the collected statistical data, the dependencies of structural mass and total installed engine power on the lifting capacity of the cranes were established. Corresponding equations were derived, which can serve as an engineering basis for preliminary design and forecasting of the technical parameters of similar equipment

This study aims to assess workers' compliance with Occupational Safety and Health (OSH) implementation and risk control using Hazard Identification Risk Assessment and Risk Control (HIRARC) and Job Safety Analysis (JSA) methods on tower cranes to reduce workplace accident risks. Using a quantitative descriptive method through questionnaires, interviews, and field observations, the study found that workers' compliance is generally high but still not optimal, requiring further actions such as regular monitoring, safety patrols, and preventive measures. Risk identification using HIRARC identified 21 high-risk variables and 1 medium-risk variable. Risk control was implemented through the Hierarchy of Control and JSA methods, helping reduce potential hazards and improve safety performance on tower crane operations in the workplace.

Abstract. Problem. Ensuring the safe operation of tower cranes and preventing emergency situations remains a critical issue in construction and industrial applications. Despite the implementation of mandatory safety measures regulated by relevant standards, the breakage of one branch of a double-rope sling can still occur due to dynamic loads during crane operation or human errors, such as incorrect load securing by a slinger or inattention by the crane operator. Additionally, hidden internal defects or unnoticed structural damage in the sling pose further risks. The main challenge is the occurrence of chaotic oscillations of the load, which negatively affect the stability of the crane and overall work safety. Goal. The objective of this study is to analyze the dynamic behavior of a tower crane load in the event of a sling breakage and to develop a mathematical model that accurately describes load oscillations under these conditions. Methodology. A method based on dynamic modeling using differential-algebraic equations is applied to simulate the breakage modes of cable systems. This approach enables a more precise representation of the load behavior when a sling branch fails. Results. The study demonstrates that the proposed method significantly enhances the accuracy of the mathematical model of a triple mathematical pendulum, making it closely resemble the real oscillations of the load during a sling breakage. The approach exhibits high sensitivity to changes in load behavior and ensures a rapid response to a rope failure. Originality. This research introduces a refined dynamic modeling technique for analyzing the effects of a sling breakage on a tower crane load. The developed model effectively captures the complexity of load oscillations, offering an improved understanding of crane operation under emergency conditions. Practical value. The results of this study can be utilized in the development and operational procedures of tower cranes to enhance safety. The proposed method provides accurate data on load behavior and enables timely detection of a rope break, making it a highly effective and reliable tool for improving work safety compared to existing approaches.