Anagrafe della ricerca

In Italy, more than 6 million buildings are characterized by unacceptable seismic risk. A possible solution is Demolition andre-Construction (D&C), but it is associated with environmental issues (waste, exploiting raw materials, CO2 emissions). BuildingsSeismic Retrofitting (BSR) is an alternative to D&C and represents a primary policy objective for governments aiming at minimizing earthquake losses while improving the civil security for society. Unfortunately, besides lowering seismic risk, the implementation of BSR at a territorial scale can have negative environmental repercussions. Several approaches were investigated for BSR by adopting more or less environmentally friendly techniques and materials. Traditionally, BSR design is performed without any consideration for the environmental harm and the territorial scale application. Green and sustainable materials are especially promising in this regard and can be adopted for several BSR applications. Some recent pioneering studies attempted to qualify and quantify the environmental impact of BSR mainly by adopting Life CycleAssessment. However, although BSR techniques were largely investigated, the development of an efficient computational frameworkfor the environmental impact minimization of seismic retrofitting of structures is not available yet. Such knowledge can be useful for the optimization of BSR by taking also into account environmental criteria, and it is the premise for the consequent actions forenhancing societal resilience and disaster risk management while reducing the environmental impact of the construction sector. In fact, if not adequately regulated, BSR can have a detrimental impact on the environment. The development of such a framework demands cutting-edge applications of Artificial Intelligence (AI) for the definition and optimization of meta-models based on deep learning. Following the above considerations, AI-ENVISERS aims at reaching sustainable seismic risk reduction. The main challenge is to provide clear tools for the environmental impact minimization of the seismic retrofitting of buildings through the aid of computational intelligence techniques. In this context, AI-ENVISERS proposes the development of an efficient computational framework for the environmental impact minimization of seismic retrofitting of structures at both single-building and territorial scales based on computational intelligence techniques. Specifically, the research will focus on these tasks: 1.development of a complete database containing mechanical and environmental variables for BSR mainly focusing on sustainable approaches; 2.development of a novel computational framework based on AI for the multi-objective optimization of risk and environmental impact; 3.application of the framework at a territorial scale; 4.definition of guidelines and “good practices” for sustainable seismic risk reduction.