Dr Alessandro Fascetti
Lecturer in Civil Engineering
Qualifications: PhD, MEng, BEng, MEngNZ
Personal Website: http://fascetti.org
My research focuses on the multiscale numerical and experimental simulation of materials for civil engineering applications. I developed and validated a Random Lattice Modeling technique that is capable of representing complex 3-Dimensional fracture modes in cementitious and polymeric composites. The input parameters for the numerical simulations are obtained by means of meso- and micro-scale experimental tests, which provide a more reliable and cost-effective alternative to traditional experimental investigations.
Other areas of interest in my research are infrastructure resilience and natural hazard mitigation. I recently developed a Dual Random Lattice Model to simulate internal erosion in earthen embankments (such as river levees and dams). The numerical models analyze the infrastructure at the cross-sectional level (local scale); the so-obtained information is scaled up by means of a Machine Learning Based Multiscale technique that allows for the simulation of the entire infrastructure.
Multiscale Modeling of Materials and Structures, Mechanics of Materials for Structure and Infrastructure Applications, Resilience Assessment of Civil Engineering Systems, Remote Sensing.
Gaetani, A., Fascetti, A., & Nisticò, N. (2019). Parametric investigation on the tensile response of GFRP elements through a discrete lattice modeling approach. Composites Part B: Engineering, 176, 107254. doi:10.1016/j.compositesb.2019.107254
Fascetti, A., & Oskay, C. (2019). Dual random lattice modeling of backward erosion piping. Computers and Geotechnics, 105, 265-276. doi:10.1016/j.compgeo.2018.08.018
Fascetti, A., & Oskay, C. (2019). Multiscale modeling of backward erosion piping in flood protection infrastructure. Computer-Aided Civil and Infrastructure Engineering, 34(12), 1071-1076. doi:10.1111/mice.12489
Fascetti, A., Bolander, J. E., & Nisticó, N. (2018). Lattice discrete particle modeling of concrete under compressive loading: Multiscale experimental approach for parameter determination. Journal of Engineering Mechanics, 144(8), 04018058. doi:10.1061/(ASCE)EM.1943-7889.0001480
Multiscale Modeling, Computational Mechanics, Machine Learning-Based Methods, Transport Problem, Infrastructure Resilience, Remote Sensing of Infrastructure Systems
Contact DetailsEmail: firstname.lastname@example.org
Phone: +64 7 838 4520