Alessandro Fascetti, PhD

Assistant Professor

726 Benedum Hall | 3700 O’Hara Street | Pittsburgh, PA 15261 P: 412-624-9884

fascetti@pitt.edu

My research team investigates the physical processes that contribute to the degradation of the civil infrastructure of the Country at different length and time scales. The broader question and societal impact of our research lie in the definition of science-based decisionmaking procedures for the formal evaluation of the state of infrastructure networks at the national scale, by defining active and passive control mechanisms to guide the operation of the systems in ordinary (service life) and extraordinary (natural and manmade hazards) conditions. The ultimate goal is to provide a comprehensive framework for the classification and evaluation of the resilience of the transportation and geotechnical infrastructure of the Country. This allows for a knowledge-based platform that provides quantitative measures of the resilience of the systems and a rigorous description of the needs of the infrastructure for maintenance efforts, with the possibility of comparing different strategies and rationalize and optimize maintenance and future construction investments.

A core philosophy in how my group develops these numerical tools is that they should be inherently capable of interacting with the physical system, by means of embedded or remote sensors. The input parameters for the multiscale numerical model are, in fact, directly obtained from live measurements streaming from embedded and remote sensors and are augmented by advanced aerial remote sensing techniques. In particular, we 66

In recent research, we have developed smart digital platforms that synergistically combine computational mechanics tools, advanced surveying and Artificial Intelligence techniques to perform the quasi-real time resilience assessment of Flood Protection Infrastructure (FPI) systems. This novel toolkit leverages advanced multiscale computational models for the simulation of the degradation of the infrastructure and are based on novel random lattice modeling approaches developed and extensively validated by the research group. The algorithmic complexity of such advanced models, however, makes it impossible to perform detailed simulations at the observation scale that is of interest in the context of FPI resilience assessment. To overcome this limitation, we define Machine Learning-based approaches trained by means of large datasets of solutions retrieved from the numerical simulations. This innovative approach allows for the new interesting capability of performing quasi-real time simulations for problems that were computationally prohibitive before.

Figure 1 - Overview of different applications of random lattice modeling for the solution of Civil Engineering problems

are interested in developing new data fusion approaches to leverage the use of UAVs for the digital reconstruction of the infrastructure by augmenting the photogrammetric results with LiDAR (laser-based) measurements. This approach can allow for a substantial increase in the information acquired through the remote sensing operations, which in turn can provide greater accuracy in the numerical models that use such data. The overarching goal for our research is the definition of new tools to deploy actionable, science-based decision-making by combining the three pillars of the research (i.e., the random lattice models, the ML-based Reduced Order Models and advanced remote sensing), ultimately enabling groundbreaking advancements in operational control and maintenance of civil infrastructure systems for safer, more resilient and sustainable communities.