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Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by the local stratigraphic and/or topographic setting and buried morphologies (e.g., irregular sub-interface between soft and stiff soils) that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near-real-time prediction of ground motion variation over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16 000 accelerometric data points and about 46 000 geological and geophysical data points was retrieved from Italian and European databases. The intensity measures of interest were estimated based on nine input proxies. The adopted machine learning regression model (i.e., Gaussian process regression) allows for improving both the precision and the accuracy in the estimation of the intensity measures with respect to the available near-real-time prediction methods (i.e., ground motion prediction equation and ShakeMaps). In addition, maps with a 50 m × 50 m resolution were generated, providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed subsoil models.

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.

Transport networks play a critical role for living communities, as they facilitate the exchange of people and goods and foster economic growth. Improving their resilience against seismic hazards, among which liquefaction is by far one of the most significant and complex, is consistent with most of the Sustainable Development Goals pinpointed by the United Nations’ Agenda. In this paper, an original methodological framework, combining innovative Geo-statistical approaches to analyze soil properties, prediction models for soil liquefaction, and calibrated transport demand models providing the social and economic cost associated with seismic-induced road damages and closures within a renewed Geographical Information Systems (GIS) workspace, is proposed. In particular, based on traditional risk assessment evaluation, an innovative approach to evaluate the exposure in terms of economic loss due to lack of accessibility is presented. The methodology is applied to a district area in northern Italy that underwent a recent seismic event that caused several soil liquefaction phenomena. Results provided by a sensitivity analysis on a stochastic (return period) basis are derived: as the seismic intensity increases, the total social costs increase, but the trend of the rates due to traffic delays and the loss of accessibility are irregular. Although further simulation scenarios need to be undertaken, the proposed methodology seems to provide an effective planning tool to evaluate preventive strategies aimed at improving the resilience of transport networks against liquefaction risk.

The huge impact caused by liquefaction during past earthquakes has raised the need for predictive formulas applicable at the large scale to forecast the movement in the foundation caused by the seismic liquefaction phenomenon in sands. A method herein developed with this aim to quantify angular distortion of framed low-rise buildings based on simple characterization of the seismic input, subsoil and structure. The analysis moves from past literature criteria introduced to quantify the vulnerability of buildings under static conditions and extends their applicability to liquefaction assessment integrating recent literature predictive formulas, parametric two-dimensional numerical analyses and artificial neural networks. Numerical calculation, performed for variable stratigraphic and mechanical characteristics of the subsoil, ground motion and equivalent flexural stiffness of the building, quantifies the role of each factor on the absolute settlement and angular distortion. Then the dependency on the different factors of the angular distortion is inferred with an artificial neural network (ANN), grouping parameters to limit the number of input variables and express results with charts that make prediction more accessible.

In the present study, a strategy to assess liquefaction risk of road infrastructures has been proposed, as combination of liquefaction hazard, infrastructures vulnerability and exposure of transportation network. The proposed methodology includes a capacity analysis of the road network performed on both pre- and post-liquefaction scenarios to evaluate the social cost in terms of delay cost suffered by the transportation system. The approach has been applied to the municipality of Terre del Reno (Italy), that in 2012 suffered a severe seismic sequence that induced extensive liquefaction evidences over the territory. A multi-layer database, on a Geographical Information Systems (GIS) platform, has been created, with the aim to overlap information about subsoil, earthquake intensity, groundwater depth and road network configuration. The Vulnerability of road has been evaluated by the settlements of embankment on liquefied soils and, according to the damage level occurred, a loss of functionality has been assigned. Finally, performing a transportation analysis, the effects on the traffic conditions have been evaluated in terms of Total Delay Cost, suffer by the road users. Preliminary results showed a redistribution of the traffic flows caused by the service loss of crucial road sections due to the liquefaction evidences on the transportation network and the related Total Delay Cost has been quantified.

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.

Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by local stratigraphic and/or topographic setting and buried morphologies, that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near real time prediction of damage pattern over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16'000 accelometric data and about 46'000 geological and geophysical data were retrieved from Italian and European databases. The intensity measures of interest were estimated based on 9 input proxies. The adopted machine learning regression model (i.e., Gaussian Process Regression) allows to improve both the precision and the accuracy in the estimation of the intensity measures with respect to the available near real time predictions methods (i.e., Ground Motion Prediction Equation and shaking maps). In addition, maps with a 50 × 50 m resolution were generated providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed sub-soil models. The variability at short distances (hundreds of meters) was demonstrated to be responsible for 30–40 % of the total variability of the predicted IM maps, making it desirable that seismic hazard maps also consider short-scale effects.

Seismic amplifications are dictated by the depth of the bedrock and the stratigraphy and dynamic properties of the soil deposits. Quantifying these properties, together with their uncertainty, is a necessary task to perform a reliable assessment of the seismic risk at an urban scale. In this paper, a multidisciplinary analysis is presented, where information of different nature is combined. Borehole logs, geophysical, geological and geotechnical surveys are interpreted with the aid of analytical, numerical and geostatistical techniques to characterise the complex shape of the bedrock and the linear dynamic properties of the sedimentary deposits filling the Cassino basin, a Quaternary intermontane basin located in central Italy. The regional and local seismic hazard is firstly identified with geological surveys that reveal an active seismogenic fault capable of producing earthquakes with estimated magnitudes up to 6.7–6.8. Boreholes reaching depths variable up to a maximum of 180 m and microtremor measurements, revealing the sharp impedance contrast at the transition between the sedimentary/arenaceous bedrock and the soft Quaternary infilling, are combined to identify the depth of the bedrock and the linear dynamic properties of soil deposits. These are one of the key factors governing the propagation to the ground level of seismic waves, and their assessment represents the first step for the seismic hazard characterisation of the plain.

The survey of the damage occurred on land, buildings and infrastructures extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil properties enables to identify the factors that determine the spatial distribution of the phenomenon. With this goal, a database was created in a Geographic Information platform merging records of local seismicity, subsoil layering evaluated by cone penetration tests and groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna- Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a portion of the village in the form of sand ejecta, lateral spreading and various damages on buildings and infrastructures. The location of damage allows to test possible relations with the factors characterizing susceptibility, triggering and severity of liquefaction. The relation among the different variables has been herein sought by training a specifically implemented Artificial Neural Network. A relation has thus been inferred between damage and thickness of the liquefiable layer and of the upper crust, seismic input and soil characteristics.

Ground improvement has become one of the most effective tools of geotechnical engineering, being adopted for an always larger variety of civil engineering applications. To reduce the role of subjective choices of operators, the use of different techniques tends to be codified by specific guidelines. In the European Union there is an ongoing effort to standardize execution and design within codes continuously reviewed by designated committees. A widespread and systematic standardisation on the ground improvement as a mean to mitigate the effects of liquefaction on buildings and infrastructures is missing. The paper presents and overview of traditional and new ground improvement technologies suitable for this application. The methods are firstly classified by considering their effects on the ground (e.g. densification, stabilization, drainage, desaturation, etc.). Design principles are then outlined for new or pre-existing buildings and infrastructures, considering the ongoing review process of the design Eurocodes.

A comprehensive study of the factors influencing magnitude and distribution of ground settlements observed during the second half of the twentieth century in the area of Bologna (Italy) is presented to derive a unified framework useful for interpreting the observed phenomena and for predicting future scenarios. Information collected over a surface of more than two hundred square kilometres includes previous geological studies, hydro-geological, geotechnical, and topographical investigations carried out with various purposes. The geological features of the whole region have been initially reviewed to figure out the local geological and hydro-geological setup. Then the stratigraphic sequence has been obtained by integrating the results of an extensive campaign carried out over the whole region for water exploitation. The mechanical characterization of the soil has been based on geotechnical tests performed in the area for the construction of new transportation infrastructures. Groundwater levels, periodically recorded on a distributed net of wells, have been interpreted with a two dimensional seepage model capable of back calculating the modification of the groundwater regime induced by water withdrawal. The spatial and temporal distributions of settlements have been derived by combining sequential topographical monitoring campaigns covering a period of about sixty years with satellite records. To simultaneously analyse all information and provide an interpretation of the observed phenomena, all data have been collected in a geographical information system interpolating the measured data with a geostatistical method. In such a way the role of the different factors has been captured, finding a logical correlation between land subsidence, subsoil composition and groundwater withdrawal, and a strategy has been traced which can be exported to the analysis of other similar situations.

A detailed and comprehensive assessment of risk is the basis to protect communities and assist decision-making towards the sustainable management of territories. For liquefaction this process implies to simultaneously investigate seismic hazard, susceptibility of the subsoil, vulnerability of structures, economic and social relevance of critical infrastructures and ultimately build a comprehensive multi-level model that considers the interaction among all aspects. Developing a methodology to achieve this goal is the scope of Liquefact, a EU H2020 project. A case study pervasively affected by liquefaction damages is here studied as a real scale scenario to identify the main factors of uncertainty. The available data concerning seismic motion, subsoil and building characteristics, damage and economic loss are used to develop a methodology aimed at quantifying and reducing uncertainties in the spatial distribution of risk.