Marco Mancini (1), Maria chiara caciolli (1,6), iolanda Gaudiosi (1), GiorGio andrea alleanza (2), Giuseppe cavuoto (3), Monia coltella (1), Giuseppe cosentino (4), vincenzo di Fiore (3), anna d'onoFrio (2), Francesco GarGiulo (2), Giuliano Milana (5), andrea pietrosante (1), pier paolo poMpa (2), Francesco silvestri (2) & Maurizio vassallo (5)

Abstract. In 2012, the Emilia-Romagna region (Italy) was struck by a seismic crisis characterized by two main shocks (ML 5.9 and 5.8) which triggered relevant liquefaction events. Terre del Reno is one of the municipalities that experienced the most extensive liquefaction effects due to its complex geostratigraphic and geomorphological setting. This area is indeed located in a floodplain characterized by lenticular fluvial channel bodies associated with crevasse and levee clay–sand alternations, related to the paleo-Reno River. Therefore, it was chosen as a case study for the PERL project, which aims to define a new integrated methodology to assess the liquefaction susceptibility in complex stratigraphic conditions through a multi-level approach. To this aim, about 1800 geotechnical, geophysical, and hydrogeological investigations from previous studies and new realization surveys were collected and stored in the PERL dataset. This dataset is here publicly disclosed, and some possible applications are reported to highlight its potential.

Abstract. In 2012, Italy was struck by a seismic crisis characterized by two main shocks (ML 5.9 and 5.8) and relevant liquefaction events. Terre del Reno (Emilia-Romagna Region) is one of the municipalities that experienced the most extensive liquefaction effects due to its complex geo-stratigraphic and geo-morphological setting. Thus, it was chosen as case study for the PERL project, devoted to defining a new integrated methodology to assess the liquefaction susceptibility in complex stratigraphic conditions through a multi-level approach. About 1800 geotechnical, geophysical and hydrogeological investigations were collected and stored in a publicly available dataset named PERL that is here presented.

No abstract

<p>The huge impact caused by liquefaction during past earthquakes stimulates the interest of researchers in investigating the factors ruling the susceptibility of subsoil and the triggering conditions. The concern of stakeholders raises the need for risk assessment methods applicable at the large scale. A crucial aspect for liquefaction risk assessment consists in the subsoil characterization, with the  stratigraphic classification into homogeneous soil layers and the identification of the susceptible volumes, with the aim of constructing 2D and 3D geo-mechanical models. In the current practice, the CPT-based soil behavior type (SBT) and the soil behavior type index (Ic), are widely used to identify soil boundaries discontinuities (Robertson, 2016). Sometimes, the interpretation of subsoil profile is not immediate and unique, due to the lack of evident boundary changes. In these cases, the need is felt for sound, widely applicable tools that provide univocal identification of subsoil strata. Statistical procedure, developed over the years, provides a less subjective interpretation of the subsoil and, in conjunction with artificial intelligence, can lead to improve the current methodology obtaining an objective and extensive site characterization. This work exposes a data-driven analysis for the subsoil stratigraphic recognition combining geostatistical tools and AI genetic algorithms. The presented procedure is calibrated and validated on the case study of Terre del Reno (Italy), severely struck by liquefaction during the 2012 Mw 6.1 earthquake and characterized by complex geo-stratigraphic conditions. The selected area, homogeneously covered by about 1700 geognostic surveys, is investigated within the "PERL" research project, carried out by the Emilia Romagna Region (RER), CNR-IGAG and UniCas-DiCeM, aiming to provide a reliable procedure for liquefaction risk assessment and a seismic microzonation. From the RER geodatabase, 102 pairs of complementary CPT and boreholes were extracted to calibrate the method, defined as the couples of surveys located at a relative distance less than 30m, considered for this purpose as spatially correlated. Starting from the information available from the boreholes, a geologic-sedimentologic study has been carried out to define the main stratigraphic units. In parallel, CPT profiles are processed with a statistical method based on the spatial variability analysis of the measured parameters, identifying statistically homogeneous layers and associating to each of them the correspondent stratigraphic unit reported in the complementary borehole. At this stage, an artificial intelligence algorithm has been calibrated merging the outcomes derived from couples of CPTs and boreholes. Subsequently, the procedure has been applied to the remaining CPTs, combining the geological and geotechnical knowledge of the subsoil in an efficient and automatic way to enable a large-scale reconstruction of the subsoil stratigraphy.</p>