Fabrication drawings are essential for design evaluation, lean manufacturing, and quality detection of precast concrete (PC) components. Due to the complicated shape of PC components, the fabrication drawing needs to be customized to determine manufacturing dimensions and relevant assembly connections. However, the traditional manual drawing method is time-consuming, labor-intensive, and error-prone. This paper presents a BIM-based framework to automatically generate the readable drawing of PC components using building information modeling (BIM) and multi-agent reinforcement learning (MARL). Firstly, an automated generation method is developed to transform BIM model to view block. Secondly, a graph-based representation method is used to create the relationship between blocks, and a reward mechanism is established according to the drawing readability criterion. Subsequently, the block layout is modeled as a layout optimization problem, and the internal spacing and position of functional category blocks are regarded as agents. Finally, the agents collaborate and interact with the environment to find the optimal layout with the guidance of a reward mechanism. Two different algorithms are utilized to validate the efficiency of the proposed method (MADQN). The proposed framework is applied to PC stairs and a double-sided shear wall to demonstrate its practicability.

As a revolutionary design approach, generative design could offer promising solutions for intelligent design. Considering the high expenses and poor efficiency inherent in traditional interior design, this paper proposes a customized style interior design (CSID) framework based on Generative Adversarial Network (GAN). The CSID-GAN is a two-stage generative model that could first generate rational interior layout schemes and then personalize the style to achieve desired design outcomes. To this end, various loss functions are incorporated to train the generative model for different design tasks. The dataset-model twining method is iteratively utilized to create more diverse design proposals, enabling the model to extensively learn personalized design concepts. Moreover, for evaluating the design results, a comprehensive assessment system is developed at each stage, and the rationality and applicability of this assessment system were validated. Finally, CSID-GAN is employed in optional style design tests for different house layouts. The experimental results have verified the practical feasibility of CSID-GAN.

Bubble diagrams containing key features and information are used for generative design of floor plans. The lack of reliable methods for automatically generating bubble diagrams significantly affects the smoothness of layout generation systems. To improve the time-consuming and unstable acquisition process, a novel method based on graph neural networks (GNNs) is proposed to generate various residential bubble diagrams. First, a dual-branch graph neural network (DBGNN) is introduced to learn the feature patterns of heterogeneous links, including connectivity and adjacency relations. Then, decentralized node sampling (DNS) and centralized node sampling (CNS) are proposed to enhance the local feature learning of DBGNN. Subsequently, a variational graph autoencoder (VGAE) is used to learn the implicit distribution of topological patterns, enabling the model to generate diverse outputs. Experimental results show that the proposed model performs excellently in two link prediction tasks, achieving 92.39% ACC-Door and 78.84% ACC-Wall, while also generating 50 distinct bubble diagrams, validating the effectiveness of the proposed method and demonstrating its outstanding application value.

Detecting construction quality issues is crucial for an infrastructure project. In this study, a data-driven intelligent system is proposed to improve the efficiency of quality detection and management. First, an intelligent system framework including mobile devices, application software, and deep learning algorithms is developed. Second, an improve YOLOv5 (HA-YOLO) based on the hybrid attention is proposed to improve the inference speed and accuracy of defect detection. In addition, an apparent quality dataset from the construction site is analyzed and produced. Finally, the stability of the intelligent detection system is tested under various shooting conditions to demonstrate its effectiveness and robustness. The experimental results on public datasets (NEU-DET) show that HA-YOLO achieves 79.8 mAP with a maximum improvement of 13.8% over other series models. In our task, the HA-YOLO improves the detection accuracy by 2.7% and 3.8% in the comparison with YOLOv5x and YOLOv8 algorithms, respectively.