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•Model-free VRFT applied to ADRC combined with fuzzy control is proposed.•Least-squares algorithm specific to VRFT is replaced with Grey Wolf Optimizer.•The fuzzy control system stability is employed in the design approaches.•Model-free optimal tuning of controllers for tower crane systems is done.•Experimentally validated model-free controllers are offered. This paper proposes the Virtual Reference Feedback Tuning (VRFT) of a combination of two control algorithms, Active Disturbance Rejection Control (ADRC) as a representative data-driven (or model-free) control algorithm and fuzzy control, in order to exploit the advantages of data-driven control and fuzzy control. The combination of Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control (PDTSFC) tuned by Virtual Reference Feedback Tuning results in two novel data-driven algorithms referred to as hybrid data-driven fuzzy ADRC algorithms. The main benefit of this combination is the automatic optimal tuning in a model-free manner of the parameters of the combination of Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control called ADRC-PDTSFC. The second benefit is that the suggested combination is time saving in finding the optimal parameters of the controllers. However, since Virtual Reference Feedback Tuning generally works with linear controllers to solve a certain optimization problem and the fuzzy controllers are essentially nonlinear, this paper replaces the least-squares algorithm specific to Virtual Reference Feedback Tuning with a metaheuristic optimization algorithm, i.e. Grey Wolf Optimizer. The fuzzy control system stability is guaranteed by including a limit cycle-based stability analysis approach in Grey Wolf Optimizer algorithm to validate the next solution candidates. The hybrid data-driven fuzzy ADRC algorithms are validated as controllers in terms of real-time experiments conducted on three-degree-of-freedom tower crane system laboratory equipment. To determine the efficiency of the new hybrid data-driven fuzzy ADRC algorithms, their performance is compared experimentally with that of two control algorithms, namely Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control, whose parameters are optimally tuned by Grey Wolf Optimizer in a model-based manner using the nonlinear process model. [Display omitted]

For 5-DOF varying-cable-length tower cranes, a novel adaptive controller is presented with multi-variable state constraints in this paper. To meet safety and transportation requirements, it is essential to ensure that all actuated state constraints are satisfied theoretically and practically. For this purpose, some auxiliary terms are designed elaborately to constrain all actuated state variables within suitable ranges. Considering parametric uncertainties, an adaptive controller is proposed to estimate uncertain/unknown friction-related parameters as well as compensate for friction. Notably, an elaborate adaptive law is introduced to precisely estimate unknown payload masses through online identification. To the best of our knowledge, this article should be the first closed-loop controller considering for 5-DOF tower cranes that all actuated state variables are restricted within preset limits. The Lyapunov technique and LaSalle’s invariance theorem are employed to prove the system’s stability theoretically. Hardware experiments are carried out to demonstrate the satisfactory control performance and significant robustness of the proposed controller.

Challenging and dangerous material-handling applications in construction have motivated the study of the dynamics and control of tower cranes. Manipulating tower cranes is difficult while moving large-size payloads because of the inevitable payload swing and twisting about the cables. Although significant work has been directed at reducing the swing of point-mass loads, much less effort has been directed at limiting the payload twisting. A nonlinear dynamic model of tower cranes carrying distributed-mass beams is described. Furthermore, an open-loop control method is proposed to reduce both the swing and twisting of the payloads during slewing motions. Simulations and experiments demonstrate the theoretical dynamic behavior of the model and validate the effectiveness of the proposed control method.

In this paper, a novel fault accommodation approach is designed for double-pendulum tower crane systems with both actuator drift and loss of efficiency. Importantly, a unique disturbance effect indicator is introduced to purposely judge the advantages and disadvantages of disturbances’ effects (including actuator faults, unknown/uncertain dynamics, unmodeled dynamics, and external disturbances) on the double-pendulum tower crane system. By employing the estimated disturbance, an adaptive neural network-triggered tracking strategy is subsequently developed. Additionally, utilizing the Lyapunov method and Barbalat’s lemma, the entire system stability is theoretically proven without any linearization around the equilibrium of original complicated nonlinear dynamics of tower cranes. The designed control strategy is not only able to deal with the double-pendulum swing dynamics, but also introduces a disturbance indicator for the first time to improve the tracking control performance by the positive disturbance effect. Several experimental results indicate that the designed strategy can achieve graceful degradation in tracking performance for the fault-tolerant system by employing the beneficial actuator faults, unknown/uncertain dynamics, unmodeled dynamics, and external disturbances while eliminating detrimental ones.

Tower cranes are well-known underactuated systems, where the design of controllers for them with time-varying rope length was weak in the past because of their complex dynamic characteristic. The payload oscillation will become worse when the jib slew angle, the trolley position and the rope length are changed simultaneously. The proposed method is designed based on robust adaptive sliding mode control via tracking nonzero initial reference trajectories, in which frictions and lumped disturbances in the crane system are eliminated, and unknown payload mass is effectively estimated online. Lyapunov technique is combined with LaSalle’s invariance theorem to design controller and analyze stability. Various and strict simulations are applied, which validate the effectiveness and extreme robustness of the proposed method.

Effective tower crane layout planning is essential for the success of construction projects. Traditional optimization algorithms, which often provide a single optimal solution, may not always reveal the global optimum, leaving room for doubt. This paper introduces the competitor algorithm, a novel multi-optimal solution approach inspired by the competitive learning paradigm within classroom settings. This algorithm is designed to provide users with a diverse set of competitive solutions, while avoiding falling into local optima. This strategic diversification ensures that users are equipped with a comprehensive range of options, empowering them to make confident, informed decisions. Furthermore, we have streamlined the positioning range for tower cranes, transitioning from a two-dimensional plane to a one-dimensional segmented line, thus eliminating the need to explore extensive, non-competitive regions. The competitor algorithm’s performance was validated through practical application, showcasing both its stability and optimization prowess, thereby confirming its reliable utility in real-world scenarios.

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.

Purpose Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process. Design/methodology/approach The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance. Findings The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization. Originality/value In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent. Practical implications The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.

PurposeThe site layout has a significant impact on the efficiency of construction operations. Planning an effective site layout partly involves identifying and positioning temporary facilities such as tower cranes and areas on the jobsite for materials storage. This study proposes an approach to optimizing the type and location of the tower crane and material supply point on construction sites.Design/methodology/approachThe problem is formulated into an integer linear programming (ILP) model considering the total cost of material transportation as the objective function and site conditions as constraints. The efficacy of the approach is demonstrated by finding the optimum site layout for a numerical example. The proposed model is validated and verified using two methods.FindingsResults indicate that the proposed model successfully identifies the type and location of the tower crane and the location of material supply point, leading to approximately 20% cost reduction compared with when such features of a site layout are decided solely based on experience and educated guesses of the construction manager.Originality/valueThe primary contribution of this study is to present a modified linear mathematical model for site layout optimization that exhibits improved performance compared with previous models. The type and location of the tower crane and the material supply point as decision variables are extracted directly from solving the proposed model. The proposed model will help enhance time and cost efficiency on construction sites.

PurposeThis research is aimed at predicting tower crane accident phases with incomplete data.Design/methodology/approachThe tower crane accidents are collected for prediction model training. Random forest (RF) is used to conduct prediction. When there are missing values in the new inputs, they should be filled in advance. Nevertheless, it is difficult to collect complete data on construction site. Thus, the authors use multiple imputation (MI) method to improve RF. Finally the prediction model is applied to a case study.FindingsThe results show that multiple imputation RF (MIRF) can effectively predict tower crane accident when the data are incomplete. This research provides the importance rank of tower crane safety factors. The critical factors should be focused on site, because the missing data affect the prediction results seriously. Also the value of critical factors influences the safety of tower crane.Practical implicationThis research promotes the application of machine learning methods for accident prediction in actual projects. According to the onsite data, the authors can predict the accident phase of tower crane. The results can be used for tower crane accident prevention.Originality/valuePrevious studies have seldom predicted tower crane accidents, especially the phase of accident. This research uses tower crane data collected on site to predict the phase of the tower crane accident. The incomplete data collection is considered in this research according to the actual situation.