Crane Operation Research Articles

Developing an advanced safety device for bridge-type cranes

Introduction. Experimentally revealed disadvantages of domestic complex safety devices in the part of load capacity limitation and parameters registration are described. The reasons why it is inadmissible to re-equip cranes under the control of foreign safety systems to the existing domestic devices are explained. It is suggested to achieve the safety of such cranes through the development of safety devices that take into account the dynamic characteristics of a particular crane and control algorithms of its drives.Materials and methods. The study was done using the developed safety device realizing the functions of load capacity limitation, automatic determination of the parameters of the load capacity limiter algorithm, determination of the crane operation intensity. The description of these algorithms in relation to cranes equipped with frequency control system is given. Experimental determination of operational parameters of the device is carried out on an overhead crane equipped with the ControlPro safety system (KoneCranes).Results. The use of the device allowed to decrease the dynamic coefficient in the whole range of lifted loads masses. A twofold reduction of the dynamic component was obtained when lifting near-nominal loads. The accuracy of characteristic number determination was 1.3%.Discussion and conclusion. It is demonstrated that the developed algorithm of load capacity limitation allowed not only to increase crane security, but also to reduce the loading on the crane, in comparison with the standard safety system. The accuracy of operating parameters determination in conditions of real technological process satisfies the requirements of normative documentation. Thus, the possibility of duplicating or replacing some functions of the standard safety system by using the developed device is shown.

Causes of crane safety risk in the Vietnamese construction industry

Crane is one of the most important machineries in the construction field. They are used to move large or heavy loads on most construction sites. However, crane operations in construction are very dangerous activities and contribute to a big ratio of serious accidents around the world and in Vietnam. The paper aimed to identify and evaluate crane safety risk causes as well as groups of causes in the Vietnamese construction industry. The paperevaluated from the perspective of various related people involved in crane activities, namely drivers, managers,and workers. A well-structured questionnaire using a five-point Likert scale was produced and sent to collectdata from 60 valid crane-related practitioners. The paper finds 59 specific causes that give rise to crane safety risks in the Vietnamese construction industry. However, there are noteworthy differences in answers from managers, drivers, and workers. The managers, drivers, and workers showed the most common safety risks“Stress of crane workers due to time pressure”, “Insufficient safety awareness and behavior of crane workers”, and “Subcontractor does not establish safety funds for the construction safety”, respectively. They pointed out that “Crane manufacturers with bad manufacturing quality” are the most severe cause of safety risk. The managers, drivers, and workers also showed the highest safety risk level of cause “Stress of crane workers due to time pressure”, “Insufficient supervise of main contractor for the crane foundation and installation task”, and “Government lacks a good implementation of certificate management and graduation for crane operators”,respectively. Crane management of construction site-related causes is the most common safety risk and the highest safety risk level. Regulatory bodies and stakeholders-related causes have the highest degree of severity.

Semantic Web–assisted progress monitoring of crane operations in construction projects

The rising importance of cranes in modern construction has led to the need for more efficient crane monitoring and operational management. Previous studies have focused on acquiring crane monitoring data through Internet of Things (IoT) devices. However, they offer limited data reasoning capacity and only understand a few particular construction activities with distinct patterns. These limitations restrict the applicability and generalisability of crane monitoring systems in real-world projects. This study proposes a Semantic Web-based method to enhance the reasoning of crane monitoring data by correlating as-is and as-planned information of crane operations from different IoT devices and information systems. The proposed method was validated through laboratory experiments, where the transient crane behaviours during a 242.7-second lift operation were accurately detected with an average error of 0.32 seconds, and all recognised lifts were successfully matched to the six lift orders. The outcome of this study is expected to advance crane lift monitoring and management practices, leading to increased crane utilisation and project performance.

Automatic Planning for Cable Crane Operations in Concrete Dam Construction

Automatic Planning for Cable Crane Operations in Concrete Dam Construction

Research on the Application of Stewart Platform for Wave Compensation

In the field of ocean engineering, the efficiency and safety of ship operation are significantly affected by waves. Faced with the instability and safety risks of ship crane operation under complex sea conditions, a design and research method of efficient wave compensation device based on Stewart platform is proposed in this paper, aiming at developing a compensation system that can respond in real time and effectively counteract the effects of waves, so as to improve the safety and efficiency of offshore operations. Aiming at the limitation of traditional wave compensation technology, this paper adopts the BP neural network PID control algorithm combined with Stewart platform. Utilizing the Stewart platform's six-degree-of-freedom motion capability, fast compensation of wave motion is achieved. On this basis, the BP neural network PID control strategy is introduced to optimize the response speed and compensation accuracy of the system, and realize the efficient and accurate compensation of wave motion under complex sea conditions.

Efisiensi Penggunaan CBC Calculator sebagai Alat Bantu Pengukuran Produktivitas Metode Crew Balance Chart

Work productivity in construction project operations is often measured using non-scientific methods due to the relatively short time required. Productivity is typically calculated based on daily production output, which is measured in the afternoon. One scientific method for measuring productivity is the Crew Balance Chart (CBC). This study aims to evaluate the efficiency that can be achieved through an Android-based application specifically designed to streamline data processing and reduce measurement time. The method used is a comparative analysis of productivity measurement duration by comparing standard CBC methods with the tools, using a tower crane operation as a case study. The findings reveal an efficiency increase of 142.1%. The use of the Android-based application reduced measurement duration by 1,11 hours, from 3,76 hours to 2,64 hours. It is important to note that the application assists only in the data processing stage, while data collection and analysis continue to follow standard CBC procedures.

Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model

Tower cranes are the most used equipment in construction projects, and the path planning of tower crane operations directly affects the safety performance of construction projects. Traditional tower crane operations rely on only the driving experience and manual path planning of crane operators. Poor judgement and bad path planning may increase safety risks and even cause severe construction safety accidents. To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. A prototype based on the theoretical model was constructed and tested on actual construction projects. Through experimental scenarios, it was found that each tower crane operation poses safety risks to 3–4 workers on average, and the proposed prototype can significantly reduce the safety risks of dropped loads from tower crane operations affecting ground workers and important equipment. A comparison between the proposed model and other regular algorithms was also conducted, and the results show that compared with traditional RRT and APF algorithms, the proposed model reduces the average maximum collision times by 50. This research provides a theoretical model and a preliminary prototype to provide dynamic path planning and reduce safety risks in tower crane operations. Future research will be conducted from the aspects of multiple device monitoring and system optimization to increase the analysis speed and accuracy, as well as on human–computer interactions between tower crane operators and the path planning guidance model.

A hybrid speed optimization strategy based coordinated scheduling between AGVs and yard cranes in U-shaped container terminal

In automated container terminals, the uninterrupted operation of the yard crane is crucial to guarantee the working efficiency of the terminal. The interaction efficiency between Automated Guided Vehicles (AGVs) and Yard Cranes (YCs) significantly impacts overall operational effectiveness. In this paper, coordinated scheduling between AGVs and YCs within a U-shaped container terminal is analyzed. A hybrid speed optimization strategy is suggested for AGVs to reduce energy consumption. It combines stage and continuous speed optimization methods. The continuous strategy is used for urgent tasks, optimizing completion time at a higher energy cost. For less time-sensitive tasks, the stage strategy reduces completion time while balancing energy consumption. Subsequently, a mixed-integer time-energy coordinated scheduling model is developed to minimize both the maximum completion time and energy. A chaotic Inverse Learning-based Slime-Mold Genetic Algorithm (LSMAGA) is proposed that utilizes PWLCM mapping and inverse learning for population initialization, combined with a genetic algorithm. This approach is designed to optimize the task configurations of AGVs and YCs, while also addressing the AGV speed optimization strategy. The effectiveness and advantages of the proposed algorithm are validated through a series of small-scale task experiments. Compared to other algorithms, the proposed algorithm demonstrates excellent convergence speed. Large-scale task experiments involving 200–3000 tasks further confirm the effectiveness of the hybrid strategy. This approach reduces the maximum completion time by an average of 8.21% and 11.45%, respectively, with only a minor increase in energy consumption. Also the maximum completion time of the stage speed strategy in this paper is reduced by 15.51% on average compared to other speed strategies. This enhancement significantly boosts the operation efficiency of the container terminal. Additionally, the impact of the AGV speed change strategy on energy consumption time during co-scheduling is investigated.

Optimizing floating crane operations for efficient bulk product transshipments on inland waterways

In the realm of global supply chains, the optimization of floating crane operations for bulk product transshipment via inland waterways emerges as a crucial necessity to address economic, operational, and environmental imperatives. This research identifies a significant gap in existing methodologies for the scheduling, routing, and assignment of floating cranes, which are essential for improving efficiency and sustainability in maritime logistics. To bridge this gap, we propose the Reinforcement Learning Variable Neighbourhood Strategy Adaptive Search (RL-VaNSAS) algorithm, a novel integration of reinforcement learning with variable neighbourhood search strategies. This advanced model aims to holistically minimize energy consumption, labor costs, and penalty costs, while simultaneously enhancing service efficiency. Through rigorous simulations, RL-VaNSAS was benchmarked against conventional methods such as Differential Evolution (DE), Genetic Algorithm (GA), and the original Variable Neighbourhood Search Adaptive Strategy (VaNSAS), revealing its superior capability in significantly reducing annual energy costs to $1,211,948, labor costs to $270,948, penalty costs to $19,948, and operational hours to 12,087. Demonstrating notable advancements in operational efficiency and cost reduction, RL-VaNSAS offers a sustainable solution to the dynamic challenges of maritime logistics, characterized by fluctuating vessel arrivals and diverse cargo requirements. The findings illuminate the critical need for innovative optimization techniques in enhancing the sustainability and efficiency of maritime logistics operations. RL-VaNSAS not only fills the identified research gap but also sets a new standard for future endeavors in global supply chain management, underlining the importance of adopting advanced optimization strategies for sustainable production and economic growth.

Teleoperated forwarder with VR: impact of latency on operator log handling performance

ABSTRACT This study uses a full scale cut to length forwarder prototype to explore crane log handling during teleoperation (remote-control). Visual feedback is provided from stereo cameras into a VR headset. Details of the system are provided along with measurements on key properties. The system allows for an artificial latency (the delay between a command and the observed response) to be introduced. A setup to allow repeated crane operations is used to evaluate operator performance at different latencies and also to compare teleoperation with in-machine operation. The results indicate that teleoperation of crane log handling can achieve comparable performance to traditional in-machine operation. Notably, the performance is consistent even at transmission latencies that surpass those expected in 4 G internet connections. This suggests a potential for remotely operating forwarders from both on-site and potentially off-site locations. This research did not delve into actual forwarding work or the implications of wireless connection bandwidth or alternative technologies like Wi-Fi or 5 G. Future investigations should consider these factors, along with the requirement for enhanced camera systems to ensure holistic machine visibility and consequently, safe teleoperation.

Sustainable Synchronization of Truck Arrival and Yard Crane Scheduling in Container Terminals: An Agent-Based Simulation of Centralized and Decentralized Approaches Considering Carbon Emissions

Background: Container terminal congestion is often measured by the average turnaround time for external trucks. Reducing the average turnaround time can be resolved by controlling the yard crane operation and the arrival times of external trucks (truck appointment system). Because the truck appointment system and yard crane scheduling problem are closely interconnected, this research investigates synchronization between the approaches used in truck appointment systems and yard crane scheduling strategies. Rubber-tired gantry (RTG) operators for yard crane scheduling operations strive to reduce RTG movement time as part of the container retrieval service. However, there is a conflict between individual agent goals. While seeking to minimize truck turnaround time, RTGs may travel long distances, ultimately slowing down the RTG service. Methods: We address a method that balances individual agent goals while also considering the collective objective, thereby minimizing turnaround time. An agent-based simulation is proposed to simulate scenarios for yard crane scheduling strategies and truck appointment system approaches, which are centralized and decentralized. This study explores the combined effects of different yard scheduling strategies and truck appointment procedures on performance indicators. Various configurations of the truck appointment system and yard scheduling strategies are modeled to investigate how those factors affect the average turnaround time, yard crane utilization, and CO2 emissions. Results: At all levels of truck arrival rates, the nearest-truck-first-served (NTFS) scenario tends to provide lower external truck turnaround times than the first-come-first-served (FCFS) and nearest-truck longest-waiting-time first-served (NLFS) scenario. Conclusions: The decentralized truck appointment system (DTAS) generally shows slightly higher efficiency in emission reduction compared with centralized truck appointment system (CTAS), especially at moderate to high truck arrival rates. The decentralized approach of the truck appointment system should be accompanied by the yard scheduling strategy to obtain better performance indicators.

Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality

Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency.

A fast prediction method of fatigue life for crane structure based on Stacking ensemble learning model

To quickly obtain the fatigue life of cranes in service, the metal structure that determines the crane life is anchored. Meanwhile, the fast prediction method of fatigue life of crane metal structures based on the Stacking ensemble learning model is proposed. Firstly, in line with the structural stress method, the global rough model of the metal structure is established by the co-simulation technology to obtain the fatigue damage regions of the structure. The local fine model is constructed by local cutting and boundary condition transplantation to determine the critical weld at the failure regions. Secondly, through weld definition, equivalent structural stress acquisition, and fatigue life calculation, the sample data set with lifting load and trolley running position as input and fatigue life cycle times as output is constructed. Then, the Stacking integrated learning model combining gradient boosting, ridge regression, Extra Trees, and linear is built. On this basis, combined with the Miner theory, the rapid prediction of crane fatigue life is realized. Finally, the proposed method is applied to the QD40t × 22.5 m × 9 m general bridge crane. The results show that the life sample set constructed by the structural stress method is more accurate and reasonable than the nominal, hot spot, and fracture mechanics methods. The life prediction results of the Stacking integration model were improved by 6.3 to 49.2% compared to the single model. The method has theoretical and practical significance in reducing accidents and ensuring the safe operation of cranes.

Low-cost IoT-Based sensors dashboard for monitoring the state of health of mobile harbor cranes: Hardware and software description

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is "immersed" in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the payloads data of the six months was collected and was used to calculate the working efficiency of the load and unload process of the crane. The results demonstrated that while maintenance costs were decreased, the crane load/unload procedure was improved. The SESCO TRANS company crane operators approved the developed approach and appreciated the achieved results.

A geometric model with stochastic error for abnormal motion detection of portal crane bucket grab

Abnormal swing angle detection of bucket grabs is crucial for efficient harbor operations. In this study, we develop a practically convenient swing angle detection method for crane operation, requiring only a single standard surveillance camera at the fly-jib head, without the need for sophisticated sensors or markers on the payload. Specifically, our algorithm takes the video images from the camera as input. Next, a fine-tuned ‘the fifth version of the You Only Look Once algorithm’ (YOLOv5) model is used to automatically detect the position of the bucket grab on the image plane. Subsequently, a novel geometric model is constructed, which takes the pixel position of the bucket grab, the steel rope length provided by the Programmable Logic Controller (PLC) system, and the optical lens information of the camera into consideration. The key parameters of this geometric model are statistically estimated by a novel iterative algorithm. Once the key parameters are estimated, the algorithm can automatically detect swing angles from video streams. Being analytically simple, the computation of our algorithm is fast, as it takes about 0.01 s to process one single image generated by the surveillance camera. Therefore, we are able to obtain an accurate and fast estimation of the swing angle of an operating crane in real-time applications. Simulation studies are conducted to validate the model and algorithm. Real video examples from Qingdao Seaport under various weather conditions are analyzed to demonstrate its practical performance.

Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System

Mobile cranes are essential for transporting heavy materials at construction sites, but their operation carries significant safety risks, particularly due to the potential for overturning accidents. These accidents can be classified into two main categories: mechanical accidents, which are caused by factors such as outrigger failure, excessive load weight, and operator skill, and environmental accidents, which arise from ground subsidence due to groundwater and sinkholes. While numerous studies have addressed the causes and prevention of mechanical accidents, there has been a lack of research focusing on the prevention of environmental accidents. This study presents the development of an Electrical Resistivity Measurement System (ERMS) designed to prevent overturning accidents caused by ground subsidence at mobile crane work sites. The ERMS, mounted on a mobile crane, continuously monitors the ground conditions in real time and predicts the likelihood of ground subsidence to prevent accidents. Unlike typical buried electrode methods, the proposed system features a foldable electrode mechanism and a water supply device, thereby making installation and removal more efficient. Furthermore, it uses a ground stability determination algorithm that qualitatively assesses soft ground conditions, which are the primary cause of ground subsidence. The performance of the ERMS was validated through comparisons with commercial equipment, and its applicability was further confirmed through field tests conducted at mobile crane installations. The ERMS is expected to significantly reduce the risk of accidents caused by ground subsidence during mobile crane operations and to contribute to enhancing overall safety in construction environments.

Aluminum exposure levels in workers at electrolytic production

Introduction. Occupational exposure to aluminum has been established to lead to accumulation of metal in tissues and create a risk of functional impairment in the central nervous system. The aim of the work was to assess the levels of external and internal aluminum exposure in workers at the electrolytic production of aluminum under modern occupation conditions. Materials and methods. Two hundred fifty measurements of the average shift aluminum oxide concentration were analyzed at various stages of the technological process. The urine aluminum concentration urine was determined by the atomic absorption method. Results. The aluminum oxide concentration in the housings with the unbaked anode technology varied from 0.59 to 17.95 mg/m3. The MPC was exceeded at the electrolyzer workplace in 10% of measurements, the anode maker — in 40%, and the crane operator – in 50%. In housings with a baked anode, the aluminum oxide concentration in all measurements did not exceed the MPC. The highest aluminum emission was observed in occupational groups associated with unbaked anodes. A trend model was constructed for the dependence of urine aluminum concentration on the aluminum dioxide level in the air, which has the form of an exponential curve. The bend in the curve begins with an air aluminum dioxide content of about 4.2 mg/m3. Limitations. The study is limited by the number of examined workers who underwent periodic medical examination. Conclusion. The results of biomonitoring showed the elimination of aluminum with urine to reflect the level of exposure to the toxicant. The equation of the dependence of the urine aluminum concentration on the air aluminum dioxide level was calculated.

Enhancing Situational Awareness for Remote Control of Ship Cranes

Abstract Cargo handling in ship operations poses risks due to heavy loads, necessitating innovation to mitigate accidents. Automation and remote control offers safer and more efficient operations by moving people from hazardous situations. Smaller and remote ports often rely on geared ships, which are typically manually operated. A first step towards automation is to remotely control these cranes. In remote control, the operator needs to gain situational awareness through sensor inputs. Previous research has shown that there is a need to investigate the sensor configuration and placement to provide situational awareness, particularly considering the placement of cargo onboard. This study seeks to identify needed sensor configurations for situational awareness in remote control of ship cranes using a simulator framework. A modular crane simulator framework has been developed to evaluate situational awareness for different sensor configurations. The sensor configuration has been evaluated with feedback from a crane expert. This research has identified potential sensor configurations for enhancing situational awareness in remote crane operations. The developed simulator framework has demonstrated its flexibility and efficiency as a platform for testing and optimizing cargo handling operations.

TPE-Optimized DNN with Attention Mechanism for Prediction of Tower Crane Payload Moving Conditions

Accurately predicting the payload movement and ensuring efficient control during dynamic tower crane operations are crucial for crane safety, including the ability to predict payload mass within a safe or normal range. This research utilizes deep learning to accurately predict the normal and abnormal payload movement of tower cranes. A scaled-down tower crane prototype with a systematic data acquisition system is built to perform experiments and data collection. The data related to 12 test case scenarios are gathered, and each test case represents a specific combination of hoisting and slewing motion and payload mass to counterweight ratio, defining tower crane operational variations. This comprehensive data is investigated using a novel attention-based deep neural network with Tree-Structured Parzen Estimator optimization (TPE-AttDNN). The proposed TPE-AttDNN achieved a prediction accuracy of 0.95 with a false positive rate of 0.08. These results clearly demonstrate the effectiveness of the proposed model in accurately predicting the tower crane payload moving condition. To ensure a more reliable performance assessment of the proposed AttDNN, we carried out ablation experiments that highlighted the significance of the model’s individual components.

Comprehensive Analysis of Input Shaping Techniques for a Chain Suspended from an Overhead Crane

Abstract This work investigates the use of input shaping techniques for controlling oscillations in a chain suspended from an overhead crane, aiming to enhance crane operations without sacrificing speed or safety. The study focuses on two specific input shaping strategies: step-input and polynomial-input shapers. The step-input shaper applies discrete changes at determined intervals, whereas the polynomial-input shaper utilizes a continuous function to adjust the crane's input throughout its operation. Numerical simulations assess the effectiveness of these techniques in reducing oscillations across various vibration modes of the chain and examine their impact on overall system performance metrics such as maneuver time, kinetic energy, robustness, and operational smoothness. Results from comprehensive simulations indicate that both input shapers significantly eliminated residual vibrations found in time-reduced inputs. The polynomial-input shaper demonstrated enhanced performance in decreasing the residual kinetic energy, and it promoted smoother operation and better adaptability to variations in chain length.