2026· Research on Engineering Structures and Materials· 0 citations
Abstract
This paper proposes a hybrid optimization algorithm that fuses multiple methods to address the weak global exploration ability, frequent local optima, and poor engineering adaptability in masonry layout optimization of building infill walls. The method builds a multilayer cooperative framework. It first uses the Genetic Algorithm to create a diverse population. It then applies Simulated Annealing to perform probabilistic jumping optimization. After that, it introduces sparse A search to verify topological feasibility. It finally relies on a cooperative mechanism of Adaptive Whale Optimization and iterative local search to explore the solution space in depth. Experiments on the simultaneous localization and mapping–building information modeling coupled dataset and the building information modeling component multimodal dataset show that the algorithm reaches a standard block utilization rate of 98.76 percent. It also keeps the cutting loss rate as low as 2.79%and achieves a peak stagger-joint compliance rate of 97.11%. In irregular wall scenarios, it reduces cost by up to 33.87%. The results show that this algorithm improves the optimization quality and engineering applicability of masonry layout and provides reliable technical support for precise construction and efficient material use of building infill walls.
Previous studies on construction site layout often simplified the site as a rectangle, with little consideration of adaptability to complex terrain and multiple functional constraints. An optimization method was developed for irregular construction sites, based on Systematic Layout Planning (SLP) and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), to address the limited adaptability of traditional methods in multi-objective and multi-constraint scenarios. A mathematical model for site layout was constructed using a rasterization method, with transportation time, transportation cost, and noise level as the optimization objectives. High-quality initial populations were generated by quantifying logistics and non-logistics relationships using the SLP method. The NSGA-II algorithm was enhanced with an adaptive penalty function, two-point crossover encoding, dynamically adjusted crossover and mutation probabilities, and a population restart mechanism. This improved its global search efficiency and convergence performance in complex solution spaces. Case validation results indicate that SLP-INSGA-II outperforms NSGA-II and SLP-NSGA-II while maintaining comparable performance to INSGA-II on some indicators. Without degrading overall optimization performance, incorporating SLP-based engineering priors can enhance search guidance, leading to layout solutions that are both feasible and engineering-interpretable. This study provides a modeling and solution approach for layout optimization in irregular construction sites.
To address the demand for efficient and automated removal of surface crack defects in special steel bars, this paper proposes a grinding path planning method based on hybrid ACO–SA optimization. A geometric model of surface cracks is established, and the key factors influencing grinding efficiency are analyzed. Crack defects are represented as line segments with defined start and end coordinates, which effectively preserves defect continuity and overcomes the limitations of scattered point-based representations. To enhance optimization performance and avoid premature convergence, a task-oriented grinding path planning framework based on a hybrid ant colony optimization (ACO) and Simulated Annealing (SA) strategy is developed, combining the global search capability of ACO with the local optimization strength of SA. Experimental results for 10 m long special steel bars containing 100 randomly distributed cracks demonstrate that the proposed method achieves an average planning time of 9.4 s and a maximum path fluctuation of 324 mm, ensuring both computational efficiency and path stability. Validation using real surface defect data further confirms the effectiveness and practicality of the proposed approach for industrial grinding applications. The proposed method provides an effective solution for automated grinding of surface cracks in special steel bars and offers practical potential for automated defect repair in the steel manufacturing industry.
Ziliang Li, Ruolei Wang, Hongjian Tan et al.· Applied Sciences· 0 citations
Modular interior design increasingly requires the simultaneous improvement of spatial efficiency, f unctional p erformance, a nd a rtistic q uality. I n s mart-building e nvironments, i nterior layouts also influence indoor electromagnetic wave propagation, wireless-sensor placement, and antenna-network coverage, making quantitative layout optimization relevant to engineering design as well as spatial art practice. This study proposes a multi-objective optimization framework for modular interior art design and spatial utilization improvement. A parametric module system was constructed to describe geometric, material, and visual attributes, and a dual-objective optimization model was developed to maximize both spatial utilization and an integrated artistic evaluation index. An improved NSGA-II algorithm was applied to generate Pareto-optimal solutions, and TOPSIS was used to rank candidate schemes under different preference settings. The results show that the top balanced solution achieved a composite score of 0.871, while deviation from verified real-world projects remained within 2.1%. Mean spatial utilization increased from 58.6% to 80.1%, and all artistic evaluation dimensions exceeded the acceptable threshold. The findings indicate that, within the spatial utilization range of 0.55–0.75, artistic and spatial objectives can reach a favorable trade-off and partial synergy.
