Multiple Cranes Research Articles

Consensus Tracking and Vibration Control for Multiple Overhead Cranes With Flexible Cables Under Time-Varying Actuator Faults

Consensus Tracking and Vibration Control for Multiple Overhead Cranes With Flexible Cables Under Time-Varying Actuator Faults

Control of Cooperative-Transportation Dynamics in Twin-Lift Cranes Suspending a Liquid Container

Bulky and heavy liquid containers may need to be transported by multiple cranes. Unfortunately, the interaction between swing effects from the multiple suspension cables and fluid sloshing in the container may corrupt efficiency and safety of the cooperative operation. Although significant work has been devoted to understanding the swing dynamics of industrial cranes and sloshing dynamics in liquid containers, less effort has been directed at the interaction between swing and sloshing dynamics in multiple cranes transporting a liquid container. A dynamic model of the motion of twin cranes carrying a liquid container has been derived. The complicated interaction between twin-lift swing and fluid sloshing has been captured by the analytical model. Additionally, a novel control method has been proposed to reduce coupled oscillations. Numerous experiments have been performed to validate the effectiveness of the dynamic model and oscillation-control method.

Multi-Objective Optimization of Tasks Scheduling Problem for Overlapping Multiple Tower Cranes

The scheduling of tower crane operations is a complex process. Overlapping areas between tower cranes often lead to increased collision possibilities, resulting in additional tower crane operation complexity. Single objectives related to time or economic aspects were always considered in dealing with this issue, which neglected other objectives and the relationships between different objectives. Therefore, this article proposes a novel method for the schedule of prefabricated component lifting tasks on the construction site, integrating the multi-objective optimization model with the decision-making method with the aim of minimizing energy consumption costs and minimizing the amplitude of the costs among multiple tower cranes. A non-dominated sorting genetic algorithm-III (NSGA-III) written in Python is used as the multi-objective optimization algorithm—which considers the selection of tasks for each tower crane and the order of lifting for each tower crane and technique for order preference by similarity to an ideal solution (TOPSIS), and is applied as the decision-making method for ranking the Pareto front. Then, a green construction production and education integration training building construction project located in Jinan, China is used as the case study to verify that the method is practical and reasonable. The results show that conflicts can be effectively avoided, energy consumption costs reduced, and equipment utilization increased by rationally distributing lifting tasks among multiple overlapping tower cranes. And among the top 11 solutions, the lifting tasks and priorities for tower crane 1 are close to the same. In contrast, the task lifting for tower crane 2 was assigned based on the balance of the energy consumption costs of the two tower cranes. The discovery of this article is helpful to eliminate collisions, interference, and frequent start and stop of several tower cranes, so as to realize the safe, stable, and efficient operation of the construction site.

Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

Discretized Cell Modeling for Optimal Layout of Multiple Tower Cranes

Multiple tower crane layout planning (MTCLP) refers to the selection of the types, quantities, and positions of tower cranes and material storage positions. In a construction project, choosing tower cranes with a larger covering radius and lifting capacity can reduce the transportation time of materials. However, it can also simultaneously raise the rental cost and the risk of crane crashes. Determining an optimal multiple tower crane layout plan to achieve high construction efficiency with low cost is still a challenge. Mathematically, MTCLP is a complex combinatorial problem controlled by a number of variables, such as site configuration, building layout, material storage positions, and workload. To precisely describe irregular shaped sites and buildings, this research partitioned the site into unit cells and proposed a cell-based optimization model. For the optimal crane layout plan, the problem was formulated as a mixed integer linear problem (MILP). The model’s mathematical constraints comprised coverage of the given positions, the crane’s safety distance, sufficient crane capacity, and the utilization ratio. The objectives were to increase the crane coverage ratio while reducing the cost and the working areas overlapped by more than two cranes. Two case studies are presented to demonstrate the effectiveness of the model. The first construction project consisted of seven buildings with irregular shapes. By strictly forbidding the areas to be overlapped by more than three tower cranes, the model successfully obtained the optimal crane layout plan using the Gurobi Optimizer. Compared with the contractor’s layout plan, the optimal plan reduced the rental cost by 10.7% and the area overlapped by more than three tower cranes by 4.56%. In the second project, alternative plans were identified by the Gurobi Optimizer and ε-constraint method to enhance confidence in selecting the ultimate crane layout plan. The optimization procedure involved (1) optimizing the layout plan with minimum crane cost and maximum coverage ratio, (2) identifying all possible layouts as the Pareto fronts when the coverage ratio could not be raised without increasing the crane cost, and (3) minimizing the areas of crane overlap covered by more than three tower cranes. The effectiveness of the proposed model is also proved by comparing the differences in layout plans caused by “regular” and “urgent” lifting demands.

Dynamic human systems risk prognosis and control of lifting operations during prefabricated building construction

Prefabricated building construction (PBC) involves tedious lifting operations that require multiple cranes to work simultaneously in dynamic workspaces. Such operations involve frequent interactions among human, cyber, and physical environments, creating challenges for risk prognosis and control in dynamic contexts. Unfortunately, human errors pose challenges for achieving resilient lifting operation control. A dynamic human systems risk prognosis and control (DHS RP & C) approach is thus necessary for 1) capturing human errors and 2) controlling risks of human anomalies proactively. This study critically reviews opportunities and challenges for establishing the proposed approach. Challenges exist as 1) how to collect human/team behavior data during lifting operations, 2) how to analyze these data for comprehending the impacts of human factors, and 3) how to respond to operational contingencies with risk control measures. In the end, the authors established a research roadmap for guiding future research activities toward automated lifting operations in PBC.

Service scheduling optimization for multiple tower cranes considering the interval time of the cross-tasks.

The key issues that have always affected the production yield of the construction industry are delays and cost overruns, especially when dealing with large-scale projects and super-high buildings in which multiple tower cranes with overlapping areas are often deployed because of urgent due date and limited space. The service scheduling of tower cranes, which act as the crucial site equipment for lifting and transporting materials, is one of the main problems not only related to the construction progress and project cost but also affecting equipment health, and it may bring security risks. The current work presents a multi-objective optimization model for a multiple tower cranes service scheduling problem (MCSSP) with overlapping areas while achieving maximum interval time of cross-tasks and minimum makespan. For the solving procedure, NSGA-Ⅱ is employed with double-layer chromosome coding and simultaneous coevolutionary strategy design, which can obtain a satisfactory solution through effectively allocating tasks within overlapping areas to each crane and then prioritizing all the assigned tasks. The makespan was minimized, and stable operation of tower cranes without collision was achieved by maximizing the cross-tasks interval time. A case study of the megaproject Daxing International Airport in China has been conducted to evaluate the proposed model and algorithm. The computational results illustrated the Pareto front and its non-dominant relationship. The Pareto optimal solution outperforms the results of the single objective classical genetic algorithm in terms of overall performance of makespan and interval time of cross-tasks. It also can be seen that significant improvement in the time interval of cross-tasks can be achieved at the cost of a tiny increase in makespan, which means effective avoidance of the tower cranes entering the overlapping area at the same time. This can help eliminate collision, interference and frequent start-up and braking of tower cranes, leading to safe, stable and efficient operation on the construction site.

Location Optimization of Tower Cranes on High-Rise Modular Housing Projects

In high-rise modular housing complex projects, tower crane layout planning is the key to ensuring the efficient lifting of components during construction. To improve the lifting efficiency of the cranes and control costs, the layout plan should minimize the distance the tower cranes must move the prefabricated units. The distance between the trailer holding the components, the tower crane, and the structure under construction should be kept to a minimum. However, most current studies consider the relative positions of the tower crane and the trailer without fully considering the movement efficiency of the trailer, and when multiple trailers and multiple tower cranes are involved, the optimization scheme is more complicated. In this study, a mathematical model based on mixed integer linear programming (MILP) is built to determine the type and location of tower cranes as well as the location of trailers to solve the problem of situating multiple tower cranes in a high-rise modular housing complex project. Finally, the validity and practicality of the model are demonstrated with case studies.

Estimating and visualizing the exposure to tower crane operation hazards on construction sites

As an increasing number of building components are modularized and prefabricated, tower cranes are required to lift larger and heavier loads. This situation increased the risk of crane failure and the severity of crane accidents, challenging the effectiveness of conventional reactive safety management approaches. Thus, early hazard recognition and proactive control (Prevention through Design, PtD) are necessitated to manage Tower Crane Operation (TCO) hazards. This study proposes an automatic PtD method to manage the Hazard Exposure (HE) brought by one or multiple tower crane operations on construction sites in the construction planning stage by leveraging a path-finding algorithm and Building Information Modelling (BIM)-enabled spatial–temporal analyses. The dynamic distribution of TCO hazards is estimated based on safe and practical lift paths generated by a path-finding algorithm, and visualized as HE heatmaps in BIM models. Three application scenarios of the HE estimation and visualization are explored, including managing critical lifts, improving daily hazard communication on site, and contextualizing scheduling/planning tasks with hazard information. Estimating and visualizing HE in these scenarios are expected to allow planners and safety managers arrange activities and workspaces to minimize the risks of TCO hazards. The contributions of the paper include (1) proposing a generic energy-based quantification model for different types of TCO hazards, (2) increasing the spatial–temporal granularity of the HE estimation and visualization, and (3) promoting proactive controlling measures over the TCO hazards through planning, scheduling, and the coordination of crane operations with other on-site activities.

Optimization of service scheduling problem for overlapping tower cranes with cooperative coevolutionary genetic algorithm

PurposeIn regarding to operational efficiency and safety improvements, multiple tower crane service scheduling problem is one of the main problems related to tower crane operation but receives limited attention. The current work presents an optimization model for scheduling multiple tower cranes' service with overlapping areas while achieving collision-free between cranes.Design/methodology/approachThe cooperative coevolutionary genetic algorithm (CCGA) was proposed to solve this model. Considering the possible types of cross-tasks, through effectively allocating overlapping area tasks to each crane and then prioritizing the assigned tasks for each crane, the makespan of tower cranes was minimized and the crane collision avoidance was achieved by only allowing one crane entering the overlapping area at one time. A case study of the mega project Daxing International Airport has been investigated to evaluate the performance of the proposed algorithm.FindingsThe computational results showed that the CCGA algorithm outperforms two compared algorithms in terms of the optimal makespan and the CPU time. Also, the convergence of CCGA was discussed and compared, which was better than that of traditional genetic algorithm (TGA) for small-sized set (50 tasks) and was almost the same as TGA for large-sized sets.Originality/valueThis paper can provide new perspectives on multiple tower crane service sequencing problem. The proposed model and algorithm can be applied directly to enhance the operational efficiency of tower cranes on construction site.

Optimal consensus control of multi-agent systems based on the state-dependent riccati equation

This paper is concerned with optimal consensus control of a multi-agent system, leaderless or leader-follower control. The hierarchical state-dependent Riccati equation (SDRE) controller was the main framework. The Lyapunov-based method is used for solving time-varying systems. Not only positive definite solution of the related algebraic Riccati equation is realized by the SDRE method based on Lyapunov, but also the negative definite solution is realized. Furthermore, the leader-follower tracking structure is developed based on two nonlinear differential equations; the feedforward differential equation and the state-dependent Riccati differential equation, which consider the error in the cost function through the co-sate equation. The application of the proposed controller structure in controlling the lifting system of multiple 3D cranes is studied. The theoretical results have been verified by simulating the leaderless and leader-follower consensus control problem.

Multiobjective Scheduling for Cooperative Operation of Multiple Gantry Cranes in Railway Area of Container Terminal

Railway station plays an important role in improving the operation efficiency of rail-sea intermodal container terminal. The cooperative scheduling of multiple gantry cranes (GCs) can reduce the production and operation cost of railway station. Most studies look into discarding the conflict schemes of GCs, which may obtain excellent scheduling results. These existing conflict-free strategies can not contribute to enhancing the cooperative scheduling performance of multiple GCs. This study integrates container trucks into multiple GCs scheduling environment to eliminate those conflicts, and proposes a mixed-integer programming model considering cooperation between multiple GCs and trucks. The model aims to simultaneously minimize the makespan of the container handling system in station, the total empty travel time of GCs and the total energy consumption of both cranes and trucks. To solve the proposed model, a conflict-free operation strategy for multiple GCs based on hybrid indirect loading and unloading (CFHI) is proposed. CFHI strategy is implemented according to the cooperation between GCs and trucks. An effective multi-objective artificial bee colony algorithm (EMOABC) based on CFHI and fuzzy correlation entropy (FCE) is developed. Within the developed algorithm, an encoding/decoding method based on CFHI is designed to represent and decode the population solutions. The FCE is adopted to evaluate and select the better solutions for next iteration evolution. The effectiveness of the proposed CFHI strategy is verified by comparing it with two popular equipment allocation strategies. Extensive experimental results show that proposed EMOABC is effective to the proposed model. Our findings here have significant implications for the cooperative operation of multiple GCs considering energy consumption in container terminal.

Port Equipment Downtime Prediction and Lifetime Data Analysis: Evidence from a Case Study

Prediction of downtime and lifetime data for gantry cranes in a container terminal is a crucial concern for port terminals due to the requirement for maintenance planning and capital expenditure. Correct estimation of lifetime behavior for gantry cranes is complex since multiple cranes are involved, each with different costs, capacities; installation, and retirement dates. This paper develops statistically-oriented predictions for the lifetimes of container terminals company fleet of gantry cranes. Data records on downtime for cranes were collected and analyzed using Weibull, normal, and Rayleigh distributions regarding a port in southwestern Nigeria. The downtime, probability density function, cumulative density function, reliability, and hazard rate were analyzed for three shape functions of Weibull, β=0.5, 1, and 3. The same was analyzed for Rayleigh and normal distribution functions. The mean downtime was 30.58 hrs. The highest PDF, CDF, R(t) for all β =0.5, 1, and 3, were 0.26, 0.78, .030 and 13.13, respectively. However, the least values for these parameters are 0.01, 0.71, 0.25, and 0.04, respectively. These values are means for thirty data points and concern the Weibull distribution function. For the Rayleigh distribution, the mean PDF, CDF, R(t) and h(t) are 0.002, 0.042, 0.958 and 0.002 while they are 0.002, 0.456, 0.542 and 35.755 for the normal distribution. This article provides new insights into the lifetime analysis of gantry cranes in a container terminal.

BIM-based 4D mobile crane simulation and onsite operation management

Heavy industrial construction, particularly when modularization is adopted, requires thorough heavy-lift studies to manage the extensive use of mobile cranes in congested and dynamic site layouts. This study introduces an integrated 4D crane simulation and onsite operation management framework for multiple concurrent mobile crane operations in the BIM environment with the support of a comprehensive database. Unlike similar recent efforts, the proposed framework is built upon previously developed crane planning and optimization systems, which enables generating accurate operation plans both in micro (objects) and macro scales (site). This framework addresses five different components regarding onsite crane operation management, including BIM-based multi-crane lift animation, scheduling and cost analyses, motion controlling in the BIM environment, safety monitoring and clash detection, and spatiotemporal site analyses. The developed framework's functionality is validated by implementing it into a portion of a real project in Alberta, Canada, through three different cases.

Optimization of multiple-crane service schedules in overlapping areas through consideration of transportation efficiency and operational safety

Tower crane scheduling is a classic conundrum. It is further complicated to prevent collisions and reduce idle transportation time among multiple overlapping tower cranes. Unlike previous research attempts, this study aims to provide an optimal solution to this multiple crane service scheduling problem (MCSSP). Firstly, the MCSSP was translated to a Mixed Integer Linear Programming (MILP) model. Then, the model was optimized by 1) distributing the lifting requests in overlapping areas to the proper tower cranes; 2) selecting the appropriate supply location to serve each lifting request; and 3) arranging the lifting sequences of each tower crane to complete the requests. Compared with previous methods, the proposed MILP model (solved using Gurobi™) can result in the saving of 6.54%–18.07% of total operation costs meanwhile achieving the non-collision goal. The findings of this research can be deployed in optimizing efficiency and safety in the real-life scheduling of multiple overlapping tower cranes.

Performance estimation of a passing-crane automated storage and retrieval system

Storage and retrieval automation has progressed rapidly. One such popular storage and retrieval system deploys two passing aisle-bound cranes. Each crane can access every location in the rack. To pass the other crane and prevent collision, each crane has to timely move the platform to an appropriate level and simultaneously rotate it. We develop a queuing model with preemptive-resume interrupted service to estimate the system response time (and hence throughput capacity) while considering two I/O point positions, random storage, and a crane assignment policy where all requests are shared between the cranes. The analytical models are validated with simulation based on the data from real cases. We find that a design with I/O points located in the middle of the rack will increase the interference, but it has a high relative throughput because of the reduced expected travel time. Compared with a system with one crane, a two-crane system has interference, but it can improve the system efficiency, especially in large systems with high job arrival rates. The model can be extended to other systems where multiple cranes are used in a single travel aisle with crane interference, e.g. passing cranes operating in a container stack lane.

An A-Star algorithm for semi-optimization of crane location and configuration in modular construction

Nowadays, the use of heavy mobile cranes in on-site construction of industrial projects has become inevitable. Due to the high rental cost of such cranes, an optimized plan is essential for the multiple crane lifts. This plan reduces the operating costs of the crane by minimizing waste time and lessens the potential of failures and accidents. However, developing an efficient lift plan can be challenging and time-consuming, considering the large number of lift options. This article presents a novel framework based on an informed search algorithm called A-star, which provides a semi-optimum lift plan based on a predetermined lifting sequence. This system talks to a comprehensive database to provide initial data. The proposed framework is applied successfully and validated in an actual modular construction project in Alberta, Canada. The results show a significant reduction in the total cost compared with the previously used lift planning algorithm.

Crane operation planning in overlapping areas through dynamic supply selection

The planning of tower crane operations is a complex process that has significant impact on the time, cost, productivity, and safety of construction projects. This complexity pertains to the involvement of multiple tasks, cranes, and material supply locations, as well as their interactions with each other. Overlapping areas between cranes also lead to increased collision possibilities, resulting in additional crane operation complexity. To increase crane operation productivity while maintaining safety, proper linkage between tasks, cranes, and material supply locations is important. Past research has linked tasks to supply points based on minimized individual task operating time, such that a task is linked to a supply point that provides the minimum amount of time for task completion. This approach may not be as efficient as possible for construction sites with overlapping crane areas, as it may increase total project time although time for each individual task is at a minimum. To address this concern, this research develops a Java-based, agent-based modeling (ABM) simulation tool to investigate the effects of dynamic supply selection on crane efficiency. This ABM applies a dynamic supply selection system and tests numerous scenarios to ensure the most efficient linkage between tasks, cranes, and supply locations. To evaluate the capabilities of the developed method, it has been applied to a real-world construction project. The results of this case study indicate that effective crane operation planning, conducted through dynamic supply location selection, with respect to tasks and cranes, can have a significant impact on productivity and the total schedule time of crane tasks.

Optimized Planning Approach for Multiple Tower Cranes and Material Supply Points Using Mixed-Integer Programming

AbstractIt is common to implement multiple tower cranes on building construction projects. The plan for usage of multiple tower cranes should be optimized for better project performance, such as re...

Spatio-temporal planning for tower cranes in construction projects with simulated annealing

Tower crane planning plays an important role in the progress and cost of construction projects. Finding the optimal tower crane planning scheme from a large number of potential candidates is typically regarded as a complex combinatorial optimization problem. To tackle this issue, researchers have attempted to develop mathematical models combined with various optimization algorithms. However, a major limitation is that most previous studies only focused on a specific stage of a construction project rather than all stages or used two-dimensional project data (i.e., quantity and place) without time dimension, neglecting the dynamic nature and changes that occur during the different construction stages. Another problem is that the service period of tower cranes as well as the height ranking among them, which are highly important when multiple tower cranes are used, were rarely taken into account. To address those challenges, this study proposes a spatio-temporal planning model for tower cranes in construction projects with simulated annealing. The interacting planning objects, consisting of number, type, location, service period, and height ranking, are incorporated into a mathematical model, and it pays particular attention to estimating delays caused by waiting for new lifting tasks and avoiding collisions among tower cranes. The capabilities of the proposed planning model are demonstrated through an example of a high-rise building project by testing all feasible initial solutions. The results show that the calculated optimal solutions can provide the savings ranging from 23.27% to 41.73% depending on the different cases, with an average saving of 31.10% of the total cost when compared to the initial solutions.