Ricerc@Sapienza

Recent seismic events are a unique opportunity to monitor and collect details of direct repair costs and the downtimes associated with massive reconstruction processes. This paper focuses on the actual repair costs of five RC buildings damaged by the 2009 L’Aquila earthquake. The repair costs for structural and nonstructural components that experienced different types of earthquake damage are discussed and then used as a benchmark for the predictions.

Data-driven modeling of dynamical systems gathers attention in several applications; in conjunction with model predictive control, novel different identification techniques that merge machine learning and optimization are presented and compared with the purpose of reducing seismic response of frame structures and minimize control effort. Performance of neural network-, random forest- and regression tree-based identification algorithms in producing reliable models exploiting historical data coming from a real structure is shown.