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4TU.
Resilience Engineering

Cristina Torres-Machi

I am an Assistant Professor at the University of Colorado Boulder, where I lead the IRI - Innovation for Resilient Infrastructure research group. I have a dual PhD degree from the Polytechnic University of Valencia in Spain and the Catholic University of Chile. Prior to joining the University of Colorado in 2017, I worked as a research associate at the Center for Pavement and Transportation Technology (CPATT) at the University of Waterloo, Canada.

I have more than 10 years of experience working on advancing the current management of infrastructure assets, with a focus on highway projects. My research seeks to enhance the condition and resilience of infrastructure systems by developing data-driven, risk-based, and cost-effective methodologies to optimize decision-making in the management of infrastructure. I have authored more than 60 publications including journal articles, book chapters, and conference proceedings and my work has received more than 250 citations. I have participated in successful projects funded by public and private agencies for the measurement and prediction of pavement performance, the multi-objective optimization of maintenance programs, and the development and implementation of pavement management systems. Different organizations including universities and the private sector have recognized my research contributions. I received the Abertis International Award on Transportation Research (2016) and the award for PhD dissertation excellence by the Catholic University of Chile (2015) and the Polytechnic University of Valencia (2017). I serve as Vice-Chair of the ASCE Infrastructure Systems Committee, and I am also an active member of the Transportation Research Board Committees on Pavement Management Systems (AKT10) and Transportation Asset Management (AJE30).

As part of the Young Resilience Fellowship, I will collaborate with Dr. João Santos on flood resilient pavements. The primary objective of this collaboration is to quantify the impact of flooding on pavement deterioration. This research will combine traditional ground-based condition data with novel remote sensing data and leverage the capabilities of advanced machine learning algorithms to assess the impact of flooding events on pavement deterioration. With flood events being more frequent and severe globally, this research will provide a better understanding on how these events accelerate pavement deterioration and inform mitigation strategies to enhance the resilience of our transportation system.