Data-driven decision support for in-service and post-hazard infrastructure management

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Around the world, public infrastructure maintenance has been underfunded for years. Canada is no exception, the aging of existing infrastructure has lead to an alarming number of bridges that are in critical condition. In addition, cities located on the west coast of Canada and along the Saint-Lawrence Basin face the threat of earthquakes which could have catastrophic impacts on our already vulnerable infrastructure. Public infrastructures are essential components of economic development. Yet, society cannot afford at short and medium term, to mitigate the risks of ageing infrastructure and hazard vulnerability by strengthening or replacing all deficient structures; we face the challenge of stretching even further the life of the deficient infrastructure. A path to mitigating these challenges is data-driven technologies for supporting management decisions regarding the condition and safety of structures during their service life and following extreme natural hazards such as earthquakes. Sensing technologies are now inexpensive and are commercially available for measuring structural behaviours such as displacement, strain, tilt, acceleration, and environmental effects. Comparatively, our capacity to interpret data is still in its infancy. Factors such as the complexity of the interactions between a structure and its operating environment, the errors caused by the operation in harsh conditions, and the sheer volume of data acquired are all sources of false alarms. Our capacity to autonomously filter out false alarms currently limits our capacity at detecting anomalies. The contributions envisaged are part of a long-term research program; its goal is to provide society with solutions for big-data analytics enabling the sustainable management of ageing infrastructure through massively-distributed structural health monitoring (SHM). The three objectives of this research proposal are (O1) create a generic framework for modelling the in-service behaviour of structures, detecting anomalies, and autonomously taking data-driven decisions; (O2) Create a data-driven decision support system for rapid post-earthquakes structural safety assessment; (O3) Develop an open-source computational platform enabling empirical studies, collaborative research, and technology transfer. Note that for Objective 1, “autonomous data-driven decisions” refers to (a) the capacity of the framework to learn by itself the baseline response of a structure, (b) when a potential anomaly is detected, to decide by itself if this deviation from the baseline response requires the attention of an engineer, and (c) adapt autonomously to changing conditions caused, for example, by degradation, climate changes, or maintenance activities. All three objectives share the same methodological background relying on specialized structural engineering knowledge, Bayesian methods, and Machine Learning.