Application of Machine Learning to Bridge Design

机器学习在桥梁设计中的应用

• 批准号: RGPIN-2020-05778
• 负责人: Gauvreau, Paul
• 金额: $ 1.89万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749344
• 关键词: Application; Machine; Learning; Bridge; Design

The primary objective of the proposed research program is to develop an innovative methodology, based on machine learning, that will enable engineers to extract knowledge efficiently from previously constructed bridges and apply this knowledge seamlessly in support of an enhanced bridge design process that is adaptable not only to the design of familiar systems but also of systems that incorporate novel elements. The use of machine learning will create a more rational basis for the use of correlation in design and hence will provide a means for designers to determine, in function of the amount and quality of underlying data, the extent to which correlation can be relied on as a basis for validating design decisions. The first step in the methodology is to apply available methods of machine learning to support a replicative design process, i.e., one in which the designs generated by machine learning models reflect the features of the data used to train the models. Because a suitable dataset does not yet exist, one will be created as part of the work in this research program. The second step will be to adapt these models to support a creative design process, i.e., one in which the designs generated by machine learning models contain features that are not found in the original data. The strategy that will be employed in this step will be to augment the original data with a sufficient number of additional entries that reflect the design intent. The assumption to be confirmed through this work is that a relatively small number of additional entries will be sufficient to re-train the models to enable them to support the design of bridges with these new features. This assumption is based on the a high degree of consistency among the structural systems used for bridges, as well as the capacity for machine learning algorithms to fit highly complex data with great accuracy. Machine learning is intrinsically correlative yet the primary basis of bridge design, checks of demand and capacity, is based on causation, i.e., an application of physical principles. The third step in the methodology will be to investigate and characterize mathematically the relation between designs generated by machine learning on the basis of correlation and their validity as established on the basis of causation. The proposed research program is the first systematic study of the application of machine learning to bridge design. It is also one of the few studies of how to enable machine learning, an intrinsically replicative technique, to support a meaningful creative process. The proposed research thus has the potential to set the direction of future research and development in this field, which will has the potential to bring about a major transformation in the practice of bridge design.

拟议的研究计划的主要目标是基于机器学习的创新方法，该方法将使工程师能够从先前构造的桥梁中有效提取知识，并无缝地应用这些知识，以支持增强的桥梁设计过程，该过程不仅可以适应熟悉的系统的设计，而且还适用于熟悉的系统。机器学习的使用将为在设计中使用相关性而创建更合理的基础，因此将为设计人员确定基础数据的数量和质量，即可以依靠相关性作为验证设计决策的基础的程度。该方法的第一步是应用机器学习的可用方法来支持复制设计过程，即机器学习模型生成的设计反映了用于训练模型的数据的特征。由于尚不存在合适的数据集，因此将创建一个研究计划中的工作的一部分。 第二步将是调整这些模型以支持创意设计过程，即机器学习模型生成的设计包含在原始数据中未找到的功能。在此步骤中将采用的策略是用足够数量的其他反映设计意图的其他条目来增强原始数据。通过这项工作确认的假设是，相对较少的其他条目将足以重新培训模型，以使它们能够用这些新功能支持桥梁的设计。该假设基于用于桥梁的结构系统之间的高度一致性，以及机器学习算法的能力，可以非常准确地拟合高度复杂的数据。 机器学习本质上是相关性的，但桥梁设计的主要基础是需求和能力的检查，基于因果关系，即应用物理原理的应用。该方法的第三步将是根据与因果关系确定的相关性及其有效性所产生的设计之间的关系和数学表征。 拟议的研究计划是机器学习在桥接设计中的应用的首次系统研究。这也是如何启用机器学习（一种本质上复制技术）来支持有意义的创造过程的少数研究之一。因此，拟议的研究有可能在该领域建立未来的研发方向，这将有可能在桥梁设计实践中进行重大转变。