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Objective-directed deep graph generative model for automatic and intelligent highway interchange design
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-21 , DOI: 10.1016/j.autcon.2025.105982
Chenxiang Ma, Chengcheng Xu
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-21 , DOI: 10.1016/j.autcon.2025.105982
Chenxiang Ma, Chengcheng Xu
Highway interchanges have traditionally been designed through a time-consuming manual process. To enhance efficiency and effectiveness, this paper develops an objective-directed automatic and intelligent interchange design method using graph conditional variational autoencoder. Based on interchange graph representation and augmentation techniques, data are collected from diverse interchanges types and converted into graphs that store design parameters. Aiming at graph reconstruction and fitting data distribution, proposed model learns to generate optimized interchanges by embedding design objectives including throughput and total ramp length. For evaluation, predictors are used to directly output interchange properties, enabling the quick screening of structures. Results demonstrate significant improvements with generated designs showing up to 7.67 % increased throughput and 27.63 % reduced total ramp length compared to traditional methods. The generated set contains a high proportion of valid, novel and unique interchanges. These advancements highlight the potential for generative model in creating more efficient and valid interchanges.
中文翻译:
面向目标的深度图生成模型,用于自动和智能高速公路立交桥设计
传统上,高速公路立交桥是通过耗时的手动过程设计的。为了提高效率和有效性,本文开发了一种基于图条件变分自动编码器的目标导向自动智能交换设计方法。基于交换图表示和增强技术,从不同的交换类型收集数据,并将其转换为存储设计参数的图形。针对图重建和拟合数据分布,所提出的模型通过嵌入包括吞吐量和总斜坡长度在内的设计目标来学习生成优化的交换。对于评估,预测因子用于直接输出交换属性,从而能够快速筛选结构。结果表明,与传统方法相比,生成的设计显著提高了吞吐量,总升温长度缩短了 27.63%。生成的 set 包含高比例的有效、新颖和唯一的 interchange。这些进步凸显了生成模型在创建更高效和有效的立交桥方面的潜力。
更新日期:2025-01-21
中文翻译:
面向目标的深度图生成模型,用于自动和智能高速公路立交桥设计
传统上,高速公路立交桥是通过耗时的手动过程设计的。为了提高效率和有效性,本文开发了一种基于图条件变分自动编码器的目标导向自动智能交换设计方法。基于交换图表示和增强技术,从不同的交换类型收集数据,并将其转换为存储设计参数的图形。针对图重建和拟合数据分布,所提出的模型通过嵌入包括吞吐量和总斜坡长度在内的设计目标来学习生成优化的交换。对于评估,预测因子用于直接输出交换属性,从而能够快速筛选结构。结果表明,与传统方法相比,生成的设计显著提高了吞吐量,总升温长度缩短了 27.63%。生成的 set 包含高比例的有效、新颖和唯一的 interchange。这些进步凸显了生成模型在创建更高效和有效的立交桥方面的潜力。