Longitudinal barriers are considered as passive safety systems designed to shield hazards located at roadsides against motor vehicle impacts. Since these barriers are manmade obstacles, they also pose a threat to drivers using the road. Recent motorcyclist accidents with longitudinal barriers have proven that a particular barrier successfully protecting vehicle occupants may wound or kill motorcyclists due to its components. For this reason, sharp and blunt edges in steel longitudinal barrier parts, such as posts are usually shielded against contact from unprotected motorcyclists during a high-speed impact event. In recent years, more longitudinal barriers have been designed with motorcyclists in mind and these motorcycle protection barriers have become wide spread especially on urban high-speed roads. However, since the development of these barriers are fairly new compared to conventional longitudinal barriers, there is limited guidance on their design criteria, such as thickness, geometry, connections. For this purpose, this paper intends to provide an artificial neural network metamodeling-based design optimization methodology to an existing continuous motorcycle protection barrier design to make it more competitive in terms of weight and thus, cost. As a result of this study, the optimized barrier has become 34% more economical compared to its original design while its protection level remained intact.

纵向屏障被认为是被动安全系统，旨在保护位于路边的危险免受机动车辆的影响。由于这些障碍物是人造障碍物，它们也对使用道路的司机构成威胁。最近带有纵向护栏的摩托车事故已经证明，成功保护车辆乘员的特定护栏可能会因其部件而伤害或杀死摩托车手。出于这个原因，钢制纵向屏障部件（例如立柱）中的锋利和钝边缘通常会在高速撞击事件中被屏蔽，以免与未受保护的摩托车手接触。近年来，更多的纵向护栏被设计为考虑到摩托车手，这些摩托车保护护栏已经广泛传播，尤其是在城市高速道路上。然而，由于与传统的纵向屏障相比，这些屏障的开发是相当新的，因此对其设计标准（例如厚度、几何形状、连接）的指导有限。为此，本文旨在为现有的连续摩托车保护屏障设计提供一种基于人工神经网络元建模的设计优化方法，使其在重量和成本方面更具竞争力。作为这项研究的结果，优化后的屏障与原始设计相比经济性提高了 34%，同时其保护水平保持不变。本文旨在为现有的连续摩托车保护屏障设计提供一种基于人工神经网络元建模的设计优化方法，使其在重量和成本方面更具竞争力。作为这项研究的结果，优化后的屏障与其原始设计相比经济性提高了 34%，同时其保护水平保持不变。本文旨在为现有的连续摩托车保护屏障设计提供一种基于人工神经网络元建模的设计优化方法，使其在重量和成本方面更具竞争力。作为这项研究的结果，优化后的屏障与原始设计相比经济性提高了 34%，同时其保护水平保持不变。