Modelling truck loads on secondary rural highway networks

安全可靠的二级公路网络是加拿大农村社区和工业的关键经济推动力。随着路面和桥梁设计和管理方法的不断进步，技术、方法和资源的限制限制了这些方法所需的卡车载荷数据的可用性。这些限制尤其适用于低流量的农村高速公路。此外，公众对透明决策的期望越来越高，迫使公共机构使用质量和代表性的卡车装载数据，而不是更简单，有时不切实际的装载近似值。今天，土木工程师需要卡车载荷数据，当他们：规划和优先安排道路养护活动；2.为新建道路或大型修复工程设计路面；3.评估卡车荷载对桥梁寿命的影响；4.制订具成本效益的道路基建资产管理策略；和5。评估卡车负载是否符合法规限制。未来五年拟议的研究目标是开发和验证一种成本效益高的建模方法，以估计农村公路上的卡车负荷。这有助于我支持农村公路基础设施数据驱动设计和管理的长期研究目标。具体而言，拟建研究的短期目标是：1。系统分析区域主要产业（如农业、林业、石油）产生的卡车交通特征和模式；2.评估一种尚未在加拿大部署的新型监测技术，该技术可以唯一识别卡车的轴配置和车身类型（例如，料斗、平板甲板、油罐车）；和3。应用空间分析和机器学习工具，在没有直接测量的情况下，预测二级公路网各位置的卡车负荷分布。本文的研究成果对加拿大的土木工程研究和实践具有重要意义。现代分析工具和新技术的应用为加拿大研究人员和从业人员建立了一个重要的知识库，他们认识到将其他司法管辖区的某些方法和发现转移到加拿大独特的工业和监管环境中的挑战。此外，所提出的研究方法提供了这种新的知识，而不需要实施复杂和昂贵的新的卡车负荷监测程序。虽然这些好处本身意义重大，但与更知情的决策和培训更知情和更合格的决策者所产生的无法量化的好处相比，这些好处就显得微不足道了。最终，拟议的研究能够提供更安全、更可靠的农村公路基础设施，以经济有效地满足加拿大工业、社区和公民的需求。

Safe and reliable secondary highway networks are critical economic enablers for rural communities and industries across Canada. As pavement and bridge design and management approaches continue to advance, technological, methodological, and resource constraints limit the availability of the truck load data that these approaches require. These constraints are especially pertinent for low-volume, rural highways. Moreover, growing public expectations for transparent decision-making compel public agencies to use quality and representative truck load data, rather than more simplistic and sometimes unrealistic load approximations. Today, civil engineers need truck load data when they: 1.plan and prioritize road maintenance activities; 2.design pavement for new road construction or major rehabilitation projects; 3.evaluate the effects of truck loads on the life of bridges; 4.formulate cost-effective strategies for managing road infrastructure assets; and 5.assess the compliance of truck loads relative to regulatory limits. The goal of the research proposed for the coming five-year period is to develop and validate a cost-effective modelling approach to estimate truck loads on rural highways. This contributes to my long-term research goal to support the data-driven design and management of rural highway infrastructure. Specifically, the short-term objectives of the proposed research are: 1.to systematically analyze truck traffic characteristics and patterns generated by predominant regional industries (e.g., agriculture, forestry, petroleum); 2.to evaluate a new monitoring technology, yet to be deployed in Canada, which uniquely identifies the axle configuration and body type (e.g., hopper, flat deck, tanker) of trucks; and 3.to apply spatial analysis and machine learning tools to predict truck load distributions at locations on the secondary highway network without direct measurement. The outcomes of the proposed research are novel and significant for civil engineering research and practice in Canada. The application of modern analytical tools and new technologies builds an essential knowledge base for Canadian researchers and practitioners who recognize the challenges of transferring certain methods and findings from other jurisdictions to the unique Canadian industrial and regulatory context. Moreover, the proposed research approach provides this new knowledge without the implementation of complex and costly new truck load monitoring programs. While significant on their own, these benefits pale in comparison to the unquantified benefits derived from more informed decision-making and from the training of more informed and highly qualified decision-makers. Ultimately, the proposed research enables the provision of safer and more reliable rural highway infrastructure that cost-effectively meets the demands of Canadian industries, communities, and citizens.