Search Results (4)

Single-sided formwork supporting systems (SFSSs) play a crucial role in the urban construction of retaining walls using cast-in-place concrete. By supporting the formwork from one side, an SFSS can minimize its spatial footprint, enabling its closer placement to boundary lines without compromising structural integrity. However, existing SFSS designs struggle to achieve a balance between mechanical performance and lightweight construction. To address these limitations, an innovative instrumented SFSS was proposed. It is composed of a panel structure made of a panel, vertical braces, and cross braces and a supporting structure comprising an L-shaped frame, steel tubes, and anchor bolts. These components are conducive to modular manufacturing, lightweight installation, and convenient connections. To facilitate the optimal design of this instrumented SFSS, a physics-constrained generative adversarial network (PC-GAN) approach was proposed. This approach incorporates three objective functions: minimizing material usage, adhering to deformation criteria, and ensuring structural safety. An example application is presented to demonstrate the superiority of the instrumented SFSS and validate the proposed PC-GAN approach. The instrumented SFSS enables individual components to be easily and rapidly prefabricated, assembled, and disassembled, requiring only two workers for installation or removal without the need for additional hoisting equipment. The optimized instrumented SFSS, designed using the PC-GAN approach, achieves comparable deformation performance (from 2.49 mm to 2.48 mm in maxima) and slightly improved component stress levels (from 97 MPa to 115 MPa in maxima) while reducing the total weight by 20.85%, through optimizing panel thickness, the dimensions and spacings of vertical and lateral braces, and the spacings of steel tubes. This optimized design of the instrumented SFSS using PC-GAN shows better performance than the current scheme, combining significant weight reduction with enhanced mechanical efficiency. Full article

In our modern society, there has been a noticeable increase in pollution due to the trend of post-use handling of items. This necessitates the adoption of recycling and remanufacturing processes, advocating for sustainable resource management. This paper aims to address the issue of disassembly line balancing. Existing disassembly methods largely rely on manual labor, raising concerns regarding safety and sustainability. This paper proposes a human–machine collaborative disassembly approach to enhance safety and optimize resource utilization, aligning with sustainable development goals. A mixed-integer programming model is established, considering various disassembly techniques for hazardous and delicate parts, with the objective of minimizing the total disassembly time. The CPLEX solver is employed to enhance model accuracy. An improvement is made to the Q-learning algorithm in reinforcement learning to tackle the bilateral disassembly line balancing problem in human–machine collaboration. This approach outperforms CPLEX in both solution efficiency and quality, especially for large-scale problems. A comparative analysis with the original Q-learning algorithm and SARSA algorithm validates the superiority of the proposed algorithm in terms of convergence speed and solution quality. Full article

Large-volume waste products, such as refrigerators and automobiles, not only consume resources but also pollute the environment easily. A two-sided disassembly line is the most effective method to deal with large-volume waste products. How to reduce disassembly costs while increasing profit has emerged as an important and challenging research topic. Existing studies ignore the diversity of waste products as well as uncertain factors such as corrosion and deformation of parts, which is inconsistent with the actual disassembly scenario. In this paper, a partial destructive mode is introduced into the mixed-model two-sided disassembly line balancing problem, and the mathematical model of the problem is established. The model seeks to comprehensively optimize the number of workstations, the smoothness index, and the profit. In order to obtain a high-quality disassembly scheme, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The proposed model and algorithm are then applied to an automobile disassembly line as an engineering illustration. The disassembly scheme analysis demonstrates that the partial destructive mode can raise the profit of a mixed-model two-sided disassembly line. This research has significant application potential in the recycling of large-volume products. Full article

The two-sided disassembly line is popular for its high-efficiency disassembly of large-volume end-of-life products. However, in the process of two-sided disassembly, some parts and components need to be disassembled in parallel, and the uncertainty of disassembly time lacks certain research. This paper constructs a fuzzy multiobjective two-sided disassembly line balance problem model based on parallel operation constraint, which aims to reduce the balance loss rate, smoothness index, and energy consumption of disassembly activities. A multiobjective flatworm algorithm based on the Pareto-dominance relationship is developed. To increase the diversity of feasible solutions in the evolution process and accelerate the convergence of Pareto-optimal solutions to prevent the random search of solution space, growth, splitting and regeneration mechanisms are embedded in the algorithm. The working mechanism and efficiency of the multiobjective flatworm algorithm are proved on a series of two-sided disassembly cases, and the excellent performance of the proposed model and algorithm are demonstrated by an actual automobile two-sided disassembly line. Full article