Automatic tower crane layout planning system for high-rise building construction using generative adversarial network

Li, Rongyan; Chi, Hung-Lin; Peng, Zhenyu; Li, Xiao; Chan, Albert P.C.

doi:10.1016/j.aei.2023.102202

Cited by 10 publications

(8 citation statements)

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“…Augmentation methods include lighting, brightness control, and regular picture improvement for the best possible clarity. [91]. While the reported accuracy in controlled ▪ Relatively demanding in terms of computational resources owing to the presence of residual layers.…”

Section: D Vehicle Wheel Under Real World Condition With Deep Learningmentioning

confidence: 99%

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Akande,

Alabi,

Oyinloye

2024

J. Comput. Theor. Appl.

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Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.

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Section: D Vehicle Wheel Under Real World Condition With Deep Learningmentioning

confidence: 99%

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Akande,

Alabi,

Oyinloye

2024

J. Comput. Theor. Appl.

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“…Establish a coordinate system according to the sitting position of the tower crane, set the position of the tower crane to O m = (o m1 , o m2 , o m3 ), set the coordinates of the supply point to S i = (s i1 , s i2 , s i3 ), and set the coordinates of the demand point to D j = d j1 , d j2 , d j3 , as shown in Figure 3. Table 3 summarizes the corresponding parameters of Formulas ( 6)- (10) and hook travel time. The tower crane location; s i1 , s i2 , s i3…”

Section: Estimate Tower Crane Movement Time Between Two Locationsmentioning

confidence: 99%

“…imum value ℎ of the height difference between the supply and demand point positions plus twice the lifting height, and 𝑉 represents the moving speed of the hook in the vertical direction, as shown in Equation (9). As shown in Equation (10), the total travel time of the hook is related to the coordinated movement of the hook in the horizontal and vertical directions, the complexity of the construction environment, and the construction site conditions. Use parameter 𝜂 to indicate the operator's level of operation in two directions of motion.…”

Section: Estimate Tower Crane Movement Time Between Two Locationsmentioning

confidence: 99%

“…Generally speaking, there are three main aspects that significantly affect the operational efficiency and safety of multiple tower cranes: (i) the selection and layout of multiple tower cranes on construction sites [6][7][8][9][10][11][12][13][14][15]; (ii) the improvement of path and motion scheduling [4,[16][17][18][19][20]; and (iii) the lifting schedule for each tower crane [5]. In the early stage of construction, it is necessary to choose a reasonable type of tower crane and carry out a reasonable layout of multiple tower cranes.…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain the optimal tower crane selection and layout plan, the problem is usually expressed as a mixed integer linear problem (MILP) [6,13,21]. Some of the optimization methods used to study this problem include mixed integer linear programming [22], the upgraded sine cosine algorithm (USCA) [8], the firefly algorithm [11], simulated annealing [23], the particle swarm optimization algorithm (PSO) [24], the automatic tower crane layout planning (TCLP) system [10], building information modeling (BIM), and the geographic information system (GIS); these methods assist in scheduling the layout of multiple tower cranes on construction sites. Layout planning reduces the probability of conflicts between tower cranes by reducing the size of overlapping areas, but does not eliminate the possibility of collisions when overlapping areas exist [25].…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Zhao,

Cui

et al. 2024

Buildings

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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.

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