Modelling truck loads on secondary rural highway networks

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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.