Search

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.

This study introduces a generalisation of the classical one-dimensional liquefaction severity indexes to extend their predictive capability for the occurrence of lateral spreading. After a critical overview of the most used indexes, the rationale for extension to bi-dimensional conditions determined by non-horizontal geomorphology is presented, together with the rule to achieve generalisation. The efficacy of the new index is demonstrated with a performance-based study on two cases, the earthquakes of 20 May 2012 (Mw 6·1) at Terre del Reno (Emilia-Romagna, Italy) and 11 February 2011 (Mw = 6·2) at Christchurch (New Zealand). Stratigraphic attributes including thickness, depth, composition and relative density of the liquefiable layers, obtained over the whole territories from rich datasets of cone penetration tests, are coupled with topographic information derived from the digital elevation model to provide the input for the analysis. Consistency assessment and spatial interpolation of data are carried out with geostatistical tools implemented in a geographic information system platform. Validation against a post-earthquake damage survey, quantified with a binary classification method, shows the paramount role of the bi-dimensional conditions.

Seismic liquefaction assessment at different geographical scales provides hazard maps at increasing levels of resolution and reliability. According to the considered level, the areas prone to liquefaction are identified based on specific predisposing and triggering factors, including geological and geotechnical subsoil properties and local seismicity. The robustness of the definition of these areas is strongly correlated to the availability and spatial distribution of this information. Moreover, the data type and quality considerably influence the method of analysis and the degree of uncertainty of the results. This work proposes a methodological approach to define maps in terms of liquefaction susceptibility at two different geographical scales (i.e., regional and sub-regional scale). The Calabria region in Southern Italy was studied for the scope, characterised by a relatively high level of seismicity and liquefaction occurrences recorded during the numerous historical earthquakes. At the regional scale, the level of zonation represents a preliminary assessment of areas potentially susceptible to liquefaction. The considered predisposing factors and geospatial variables were implemented from approximately 5520 available surveys, using geostatistical tools to filter inconsistent data and quantify uncertainties. A quantitative assessment of the liquefaction predisposition was also proposed at a sub-regional scale in the identified areas prone to liquefaction. A new proposal of a Territorial Liquefaction Predisposing Index was carried out by quantifying the uncertainty due to the limited availability of data with geostatistical methods. This approach could provide helpful information for land management and emergency planning to characterise the territory for liquefaction instability and optimise resources allocation to reduce the level of uncertainty at higher levels of analysis.

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.

Terre del Reno is a municipality in the Emilia-Romagna Region (Italy) that experienced relevant liquefaction events during the 2012 seismic crisis, which was characterised by two main shocks (ML 5.9 and 5.8). Such events are mainly related to the complex geo-stratigraphic setting of the area. In this background, the present work is devoted to achieving two main objectives: i) define a new integrated methodology to assess liquefaction susceptibility in complex stratigraphic conditions through a multi-level approach; ii) perform a level 3 seismic microzonation study of Terre del Reno. To this purpose, more than one thousand geophysical and geotechnical measurements available from three different databases and some hundreds of new collected investigations were stored in a dedicated geodatabase. Data and metadata, that were spatially and statistically manipulated to guarantee their harmonization, standardization, and uniqueness, were explored to reconstruct a model for the Terre del Reno subsoil. Specifically, a geological model of the studied area (~ several hundreds of meters) was first reconstructed as well as the seismic bedrock geometry (the latter defines as the layer characterized by the stiffness requirement: Vs > 800 m/s). This model was obtained by integrating deep bore-hole data available from previous studies and geophysical and geotechnical investigations. Furthermore, a high-resolution geological reconstruction of the upper 30 m has also been performed through sedimentological and paleo morphological analysis to characterize the sedimentary units affected by liquefaction. This analysis may be used to compare both well-known and innovative geotechnical indicators for liquefaction susceptibility assessment. Thus, a set of acceleration time histories, that are spectrum-compatibles with the spectrum of reference input motion at outcropping bedrock of the site, were used as input in 1D and 2D site effect numerical modelling. The obtained results were synthetized and represented in a level 3 seismic microzonation study with the aim of providing operational indicators devoted to urban planning and for challenging problem related to liquefaction.

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.

The Seismic Microzonation studies (SMs), promoted all over the Italian territory by the Department of Civil Protection, provide fundamental knowledge of the subsoil response in seismic conditions at the urban scale. Amplification phenomena related to lithostratigraphic and morphological characteristics, instabilities and permanent deformations activated by the earthquake, are highlighted in hazard maps produced at increasing reliability levels (level 1 to 3 of SM). In particular, zones prone to liquefaction instability are firstly identified following the predisposing factors, such as geological and geotechnical characteristics and seismicity. The robustness of the definition of these areas is strongly correlated to the availability and the spatial distribution of surveys. Moreover, the typology and quality of the investigations considerably influence the method of analysis and the degree of uncertainty of the results.This work aims to establish an updated procedure of the actual SM guidelines and integrates recent research activities at different levels of SMs, to improve the hazard maps accuracy in terms of liquefaction susceptibility. For the scope, the case of the Calabria region in the south of Italy, well known for the high level of seismicity, was studied. At a regional scale, the base-level analysis was implemented for a preliminary assessment of the Attention Zones (AZ), potentially susceptible to liquefaction. The predisposing factors were implemented at a large scale, taking advantage of geostatistical tools to quantify uncertainties and filter inconsistent data. The regional-scale analysis allowed to highlight areas prone to liquefaction and effectively addressed the subsequent level of analysis. At a local scale, the quantitative evaluation of the liquefaction potential was assessed using simplified methods, integrating data from different survey types (CPT, SPT, Down-Hole, Cross-Hole, MASW) available in SM database. The definition of Susceptibility Zones (SZ) was provided considering additional indexes, combining the results obtained from different surveys typologies and quantifying the uncertainty due to the limited data availability with geostatistical methods. The analyses at the regional and municipality scale were matched with seismic liquefaction evidence, well documented in past seismic events. This multi-scale process optimises resource allocation to reduce the level of uncertainty for subsequent levels of analysis, providing useful information for land management and emergency planning.

In May and June 2012, Emilia region (Italy) was struck by a seismic crisis characterized by more than 2000 earthquakes with two main shocks (20 May and 29 May events with ML 5.9 and 5.8, respectively) and several earthquake-induced effects. Relevant liquefaction events were observed all over the area showing a maximum intensity at San Carlo and Mirabello, two main hamlets in the Terre del Reno Municipality. In this work, a methodology is proposed for assessing liquefaction susceptibility in wide areas characterized by complex geo-stratigraphic conditions through a multi-level approach based on simplified models. To this aim, extensive geological studies and more than one thousand geophysical and geotechnical surveys available from previous studies have been collected in a dedicated geographical information system. The database is structured to guarantee data and metadata harmonization and standardization, useful for the realization of an integrated and interoperable system progressively supplemented with new information. Preliminary 2D and 3D high resolution geological and geotechnical models are elaborated to reconstruct the complex subsoil setting of Terre del Reno area. This study forms the base for the 2D numerical modelling carried out with a finite difference code (FLAC) to identify the mechanism of pore pressure increase and of liquefaction triggering. The rationale behind this study concerns the definition of a simplified approach based on synthetic indicators. Specifically, starting from parametric analyses, the role of different variables on the triggering process is evaluated together with the definition of set of thresholds able to model the occurrence of liquefaction effects. The spatial variability of the soil properties, layering and mechanical characteristics is considered with a geo-statistical approach. A comparison between the liquefaction effects observed in 2012 and the results obtained from calculations is performed for demonstrating the reliability of the proposed approach in extensively simulating a liquefaction occurrence.

Road sectioning plays a crucial role in Road Asset Management Systems and nowadays high-speed laser-based devices are able to quickly collect a huge amount of data on pavement surface characteristics. However, collected data cannot be directly employed in road maintenance planning but synthetic values have to be derived and this implies a high computational effort in identifying effective synthetic indices and road homogeneous sections. To this purpose, the Geostatistical tools, in terms of Variogram scheme have been applied for characterizing road surface. “Range” and “Sill” values, deriving from the Variogram application, have been proposed as pavement surface characteristics synthetic indices (namely the macrotexture) to identify different road surfaces. Once that Variogram scheme has been applied, a dynamic sectioning procedure can be employed to detect homogeneous road pavement sections and compared with more traditional descriptors. Preliminary results obtained by an experimental smart road, seem to highlight that the Variogram variables can be promising in both road texture characterization and homogeneous section identification.

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 damage on supply and drainage water networks is a serious cause of economic disruption for urban systems affected by earthquakes. Among various concerns, the ruptures of sewer pipes and manholes generated by liquefaction determine a particularly severe sanitary hazard and require extensive, costly and time-consuming repairs. Quantitative risk assessment carried out with the characterisation and geographical mapping of seismic hazard, subsoil susceptibility, physical and functional vulnerability of the exposed elements, enables to estimate losses, identify weaknesses, inspire strategies to mitigate the impact of earthquakes and improve resilience. The present study deals with the physical vulnerability of sewer pipelines. Empirical fragility functions are derived from the evidences of liquefaction induced in Urayasu (Japan) by the 2011 Tohoku-Oki earthquake (Mw9.0). The spatial distribution of seismic signals, subsoil characteristics, pipes and surveyed damages are reconstructed in a GIS platform. An articulated methodology is developed to correlate variables and compensate their limited spatial correspondence, exploiting the complete coverage of the area with terrestrial settlements measured by LiDAR and their strong correlation with damage. Finally, ruptures of pipes are probabilistically quantified adopting a common liquefaction severity indicator as engineering demand parameter and measuring the efficiency of relations with statistical tests.

The quantitative assessment of natural risks offers a rational strategy to protect communities, undertake cost effective mitigation and plan the organic and sustainable development of urban systems. For cascade events such as earthquake-induced liquefaction, assessment implies to characterize and reconstruct the areal distribution of seismic hazard, subsoil susceptibility, physical vulnerability, economic and social relevance of structures and to combine all factors in a unitary predictive model. Considering that aleatory variability and epistemic uncertainty affect the characteristic variables and their mutual correlation, it is also necessary to quantify their influence on the prediction. Within this framework, a vulnerability model is proposed to comprehensively assess the physical damage of buildings in an urban system. A chain method is formulated combining calculation schemes recently introduced in the literature with ad hoc numerical analyses. The effectiveness of the method is tested comparing prediction with the effects observed in the city of Christchurch during the 22nd February 2011 earthquake. The unprecedented documentation available after this earthquake enables to validate different components of the model and disclose the importance of possible disregarded factors. A geostatistical methodology is proposed throughout the paper to process data, quantify and govern the different uncertainty factors.

This paper analyses with a sophisticated numerical model the long-term phenomenology induced by Earth Pressure Balance tunnelling in compressible, low permeability soils. The complex soil-structure interaction determined by the EPB technology is analysed with a three-dimensional model that closely reproduces the sequence of face pressurisation, excavation, lining installation and tail grout injection and simulates the hydro-mechanical soil coupled response with a non-linear, irreversible, anisotropic hypoplastic model. After validation on a documented case study taken from literature, the model has been applied to a real example, MRTA Project in Bangkok. The study reveals that short and long-term settlements induced by tunnelling cannot be decoupled, being both governed by the fluid-soil-lining interaction and resulting from the time dependent stress transfer among the different elements. In this scenario over pressurising face and tail void reduces the immediate soil relaxation at the expenses of increasing the long-term settlements. Local or global defects have then been hypothesized on the lining waterproofness considering different initial groundwater conditions, soil permeability models and sets of EPB operative parameters. While concentrated lining defects play a negligible role, extensive imperfection noticeably increases settlements, volume loss and bending moments on the lining, with the same function of the relative soil-lining permeability.

In questo documento, sono descritte le analisi dei dati territoriali effettuate da gennaio 2018 ad ottobre 2020, a scala comunale e a scala di Contesto Territoriale per la Regione Campania, per il progetto PON Governance 2014-2020, Rischio sismico e vulcanico, attività CAM_F6.1: "Produzione di statistiche strutturate, fino al dettaglio comunale, per una efficace valutazione delle politiche di mitigazione dei rischi naturali e di gestione dell'emergenza e della qualità della vita dei cittadini, per la Regione Campania". I contesti individuati non sono ancora stati approvati e recepiti dalla Regione Campania, per tale motivo non ci sono ancora riferimenti normativi al riconoscimento ufficiale dei contesti. Nella Parte Prima del documento, dal titolo: "Variabili e caratteristiche di base a scala di Contesto territoriale e comunale" è stata effettuata una prima elaborazione di statistiche dei Contesti Territoriali riguardo la popolazione, e la sua distribuzione per ciascun Contesto Territoriale. Sono quindi state elaborate cartografie relative alla pericolosità sismica e cartografie relative alla pericolosità vulcanica: Elemento caratteristico della Regione Campania è naturalmente la presenza del Vesuvio e delle aree vulcaniche attive come i campi Flegrei. Nella Parte Seconda del documento, dal titolo: "Indicatori sul sistema di gestione dell'emergenza" si è analizzata la presenza di studi di Microzonazione Sismica e di analisi di Condizione Limite per l'Emergenza a scala comunale validati. Sono state inoltre individuate le strutture ospedaliere della rete emergenza-urgenza e le strutture operative dei Vigili del Fuoco, a scala regionale.

Nel presente documento è riportata la metodologia per l'individuazione dei Contesti Territoriali (CT) e dei relativi Comuni di Riferimento (CR) e la sua applicazione per la Regione Campania, che ha condotto all'individuazione di 45 Contesti Territoriali. Nello specifico, la metodologia è suddivisa in quattro fasi: la fase A ha previsto l'analisi dei Sistemi Locali del Lavoro per classi demografiche; la fase B - individuazione dei Contesti Territoriali - ha previsto dei confronti in funzione delle forme associative tra Comuni - Unioni di Comuni (FASE B1) e Centri Operativi Misti, COM (FASE B2); la fase C ha individuato i Comuni di Riferimento per ciascun Contesto Territoriale; ed infine nella fase D sono state effettuate verifiche e confronti. Nel corso del 2019 e dei primi mesi del 2020 e in seguito al confronto con il gruppo di lavoro della Regione sono state effettuate delle modifiche ai perimetri dei 45 Contesti Territoriali individuati. Tali Contesti Territoriali e relative modifiche, sono ancora in fase di concertazione con la Regione Campania, e non sono ancora stati approvati con un atto formale definitivo, per questa ragione le proposte di modifiche più recenti non sono state inserite nel seguente stato di avanzamento del report CAM_F1.1.

The rapid and uncontrolled evolution of urban and industrial settlements often turns to be unsustainable for the natural and anthropic system. A prominent example is the subsidence induced by intensive groundwater exploitation. Assessing the risk produced on urban agglomerations by the groundwater table drawdown enables to visualize the impact and to undertake policies that protects structures, infrastructures and, more generally, the socio-historical-cultural environment of the cities. This task implies to assemble hydrological, hydrogeological and geotechnical factors into comprehensive models that lead to compute free field deformation. Interfacing this effect with the mechanical characteristics of buildings or infrastructures allows to quantify damage severity. The present paper reports a study carried out for the city of Bologna, one of the most emblematic examples in Italy, whose old town and suburbs have undergone an extensive and continuous subsidence from the early seventies. An integrated risk assessment methodology is implemented defining the paramount factors, intensity measures, engineering demand parameters, vulnerability and exposure and building an interpretative model based on observation.

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.

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.

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.

Geographic information systems (GIS) are very powerful tools capable of performing spatial analyses over large amounts of the territory representing large amounts of data. They consist of hardware, software, and human activities that facilitate collection, analysis, and storage of data, in different forms, related to the features of the territory. In the present chapter, the potential of the GIS tool is explored in a study of the effects of subsidence that occurred during the previous decades in the city of Bologna, where a valuable historic and cultural heritage made up of an intricate system of old masonry buildings, churches and monuments, is exposed and at risk. Data from previous geological, hydrogeological and geotechnical studies was collected and georeferenced. Then, topographical investigations and periodic records of the groundwater level in the network wells were superposed to establish a relation between causes and effects. The analysis reveals the spatial and temporal distribution of settlements in the city center and in the surrounding area. The evolution of the subsidence phenomenon has been possible, and the use of GIS has revealed the role of different factors and their mutual correlation. By combining all information, it can be seen that the area is severely affected by the distribution of total and differential settlements and that a particularly critical situation emerges in the city center. Additionally, the causes of the phenomenon could be established with great precision, enabling the identification of appropriate countermeasures.

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.