The analysis and management of risks associated with natural hazards have evolved into a complex, interdisciplinary field that requires the integration of engineering, social sciences, and governance frameworks. Within this arena, geotechnical engineers are very well positioned to actively contribute to developing and implementing effective risk governance processes that couple rigorous quantitative methods with societal, institutional, and communication dimensions. This paper, after outlining the historical evolution of risk terminology, presents a summary on how risk and reliability concepts developed in the geotechnical engineering community, followed by the examination of key domains where geotechnical engineers play a central role in disaster risk reduction, including landslide risk, seismic risk, groundwater-induced subsidence, levee and flood risk, and NaTech (Natural hazard-triggered Technological) risks. Beyond the need for thorough technical analyses, the paper underscores the importance of socio-cultural theories of risk perception, multi-hazard and cascading approaches, and disaster risk governance initiatives.

This article reports on the process and outcomes of the EduHackathon “Assessing the usefulness of Large Language Models in geotechnical education” held in Florence, Italy, as a session of the third Workshop on the Future of Machine Learning in Geotechnics (3FOMLIG) on October 16, 2025. Recognizing the increasing implementation of Large Language Models (LLMs) and Generative AI (Gen AI) for domain-specific geotechnical workflows, the event aimed to explore their potential utility in geotechnical engineering education. The EduHackathon was the culmination of a collaborative effort by a core group of geotechnical educators who presented and discussed with conference attendees a series of studies conducted, prior to the meeting, to evaluate LLM and Gen AI tools from both student and instructor perspectives. The article details aims and main outcomes of these studies, as well as the main conclusions of the discussions carried out in person, which were also driven by the results of a survey previously undertaken by 3FOMLIG participants. The findings suggest that while LLMs offer significant potential for enhancing interactive and reflective learning, they should be viewed as flexible pedagogical tools rather than autonomous instructors. It can be emphasized that the educational value of these tools depends heavily on careful integration with sound teaching principles and human supervision to mitigate potential risks, such as cognitive debt, inaccuracies, and the loss of critical information. Ultimately, the article proposes to keep exploring the potential benefits and the main limitations associated with the use of these tools within educational modules in soil mechanics and geotechnical engineering, where LLMs may serve as collaborative interlocutors to both geotechnical educators and students, in a sort of epistemic co-production with human expertise.

Road networks are typically the main component of transportation systems, acting as critical lifelines for mobility, emergency response, and access to essential services. In mountainous and hilly regions, these infrastructures are frequently threatened by slow-moving landslides, which can lead to progressive ground deformation and long-term serviceability loss. Despite the need for continuous operability, local authorities often face significant challenges in risk management due to budget limitations and lack of complete and accurate information.This study proposes a framework aimed at identifying risk-mitigation priorities for road networks exposed to slow-moving landslides at municipal scale by integrating the assessment of landslide exposure with the analysis of criticality along the road network. The exposure assessment follows a matrix-based approach combining three indicators: a statistical susceptibility index, displacement measurements derived from Advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR), and damage severity derived from virtual surveys using Google Street View. The criticality of the road stretches is quantified based on the reduction in network efficiency under disruption scenarios.The methodology was applied to the road networks in the municipalities of Vaglio Basilicata, Brindisi Montagna and Trivigno (Basilicata region, southern Italy), which are highly affected by slow-moving landslides. The results indicate that, although approximately 31% of the network intersects slow-moving landslides, only 5% of the road stretches are classified at very high level of priority. This highlights the critical role of network topology in influencing risk-mitigation priorities. The proposed framework provides a cost-effective, flexible tool for local administrations to optimize resource allocation and prioritize site-specific investigations.

Rainfall thresholds adopted in territorial landslide early warning systems (Te-LEWS) typically rely on statistical analyses of precipitation without any physical correlation with the infiltration dynamics into the soil. On the other hand, the use of physically-based modeling that feeds procedures warning for landslides is typically limited at systems operational at local scale, as they require spatially distributed geotechnical and hydrological parameters and high computational demand for real-time operation. This paper proposes an original approach integrating geotechnical modeling and data-driven analysis to interpret empirical rainfall thresholds used in Te-LEWS and to define new physically-based rainfall thresholds for the initiation of shallow landslides. Physically-based thresholds differ from the traditional empirical ones as they account for the actual physical processes governing soil behavior. To this aim, the proposed procedure exploits relationships between rainfall characteristics, geotechnical parameters, infiltration processes, and slope stability conditions. The framework is structured into four key modules: geotechnical modeling; equalization time matrix generation; thresholds interpretation; thresholds definition. The procedure is proposed and tested in Campania region, southern Italy, although it is structured to be general and can be easily adapted to other territorial warning models employing empirical rainfall thresholds. The main innovation of this study is the development of a novel framework to integrate empirical and physically-based approaches for landslide early warning at regional scale, considering them synergic rather than alternatives.

Landslide prediction is essential for developing a landslide early warning system. Recently, hydro-meteorological thresholds combining rainfall and hydrological variables have demonstrated their effectiveness in enhancing the predictive capability of landslide occurrences. However, most territorial landslide early warning systems operational worldwide are primarily developed using only rainfall thresholds, totally neglecting the hydrological process that contributes to landslide initiation. In this study, we propose a three-step procedure aimed at developing a hydro-meteorological warning model intended for operational use employing multiple hydro-meteorological thresholds derived from a probabilistic analysis, using soil saturation and precipitation data retrieved from the ERA5-Land product. The model developed herein was tested in one of the warning zones defined by civil protection for the management of geo-hydrological risk in Campania region, Italy. Performance indicators derived adopting the “event, duration matrix, performance” (EDuMaP) method highlight that the hydro-meteorological warning model developed in this study—using real-time forecasts from the Integrated Forecasting System - High-Resolution (IFS-HRES) product—outperforms the current implemented warning model, which depends exclusively on precipitation forecasts. Specifically, the inclusion of soil saturation into the warning model leads to a significant reduction of false alarms. The results achieved herein demonstrate that hydro-meteorological thresholds can be effectively employed within landslide early warning systems for real-world applications at regional scale.

A common and largely unresolved problem of national-scale landslide early warning systems is their independent evaluation. In this work, we evaluated the performance of a recently proposed deep-learning-based system for short-term forecasting of rainfall-induced shallow landslides in Italy. For our evaluation, we used hourly rainfall measurements from the same rain gauge network used to construct the forecasting system, and different and independent information on the timing and location of 163 rainfall-induced landslides that occurred in Italy in a period non considered in the construction of the forecasting system, obtained from the FraneItalia catalogue (https://zenodo.org/records/7923683). The independent evaluation confirmed the good predictive performance of the forecasting system and revealed no geographical or temporal bias in the forecasts. The analysis also revealed that the forecasting system was more effective at predicting multiple landslides in the same general area than single landslides. This was a good result, as multiple landslides are potentially more dangerous than single failures. Analysis of the few misclassified landslide cases showed that approximately one-third of the landslides were rockfalls, and for approximately another third there was uncertainty about when or where the landslides occurred. We conclude that, despite the inevitable misclassifications inherent in any probabilistically based national-scale landslide forecasting system, the deep-learning-based system analysed is well suited for short-term operational forecasting of rainfall-induced shallow landslides in Italy.

Rainfall thresholds identifying the meteorological conditions critical for landslides triggering are widely used within operational territorial landslide early warning systems (Te-LEWS). Recent studies demonstrated that hydro-meteorological thresholds, combining soil hydrological information and rainfall, can improve the prediction of landslide occurrences at territorial scale. Soil moisture is predominantly used to characterize the soil wetness conditions that predispose slopes to failure. In this study, we develop hydro-meteorological thresholds by investigating the potential use of both antecedent (i.e., before the beginning of the rainfall event) and triggering (i.e., during the rainfall event) saturation variables derived from a reanalysis product. A procedure based on a Bayesian probabilistic analysis of rainfall severity and soil saturation indices is designed and tested in an area of Campania region, southern Italy. Different hydro-meteorological thresholds demonstrate a good predictive capability, with those considering maximum saturation at the uppermost soil layer performing best. Overall, this study proves that hydro-meteorological thresholds employing antecedent and triggering saturation variables derived by time series analysis can improve the prediction of landslide occurrences at territorial scale.

Road infrastructure plays a key role in the economic development of a society. Thus, ensuring its functionality and safety conditions over time is a crucial and, at the same time, a demanding task that central and local authorities are asked to address. In Italy, road networks often develop within complex geological contexts, where active slow-moving landslides may generate risks to traveling persons and to the roads themselves, the latter being associated with socio-economic impacts. The identification of the road sections most exposed to landslide risk is critical for reducing the population potentially exposed to risk and for minimizing the repair/replacement costs. However, studies specifically oriented to roads affected by existing slow-moving landslides are quite rare in the scientific literature. This is possibly due to different reasons: landslide inventories with reliable information on the past and current state of activity of the phenomena are often not available; assessing the temporal probability of landslides characterized by a given intensity over large areas is not straightforward; the development of large datasets of road displacements and damage through traditional techniques can be time-consuming and sometimes not affordable.This study proposes a conceptual model aimed at classifying the level of exposure to slow-moving landslide risk of stretches of roads at municipal scale. The activities have been developed in the context of the “Mitigation of natural risks to ensure safety and mobility in mountain areas of Southern Italy” (MitiGO) project. Adopting a matrix-based approach, the following data are combined: landslide inventories, thematic information, displacement measurements derived from the interferometric processing of synthetic aperture radar images (DInSAR) and damage records obtained from Google Street View. First, a statistical model based on the bivariate correlations between the independent variables (i.e., each significant spatial variable derived from the thematic maps) and the dependent variable (i.e., the slow-moving landslides inventoried in the official map) is applied for zoning the susceptibility to slow-moving landslides at the municipal scale. Then, the information is combined with the level of damage and a monitored rate of movement based on DInSAR-derived ground-displacement measurements along the road network. The output is a correlation matrix combining all the information and classifying each stretch of the road network.The proposed procedure has been applied to different access routes from a major regional road, the SS407 Basentana highway, to some urban centers of municipalities located in the Basento river basin (Basilicata region, southern Italy).The analyses carried out at a municipal scale allow the classification of the road stretches potentially exposed to slow-moving landslide risk adopting a fairly simple qualitative ranking procedure, reliable in relation to the scale of analysis, which is based on a few data that are relatively easy to retrieve and to manage. The obtained results can be used to support studies of road networks over large areas aimed at the prioritization of risk-mitigation measures, as well as at the identification of road sections requiring further geomorphological surveys and geotechnical analyses, to be conducted in more detail at a larger scale.

The municipalities of the Amalfi and Sorrento Coasts (Campania Region, southern Italy) have historically been affected by weather-induced slope instability phenomena causing casualties and heavy economic damages, often associated with transport interruptions limiting the crucial tourist activities in the area. Due to the considerable geomorphological complexity of the area, even the simple cataloguing of the events is often challenging. Furthermore, the significant differences between the dynamics affecting the slopes (rockslides, debris flows, flowslides in pyroclastic covers) result in substantial differences in the atmospheric patterns able to trigger such events: from short-duration (up to sub-hourly scale) heavy precipitation events up to long-lasting rainfalls anticipated by particularly wet periods. Despite the significant differences among the dynamics, the warning system current operational in the Region considers three reference durations for cumulative precipitation (24, 48, and 72 hours) and it is based on three alert levels simply associated with the return time of potentially triggering precipitation (2, 5 and 10 years).This study wants to fill this knowledge gap by investigating the main recent weather-induced slope instability phenomena in the area in relation to the recorded characteristics of the associated weather events. The investigation aims at multiple objectives.Comparing, over a common period, the records of different landslide catalogues available over the area - FraneItalia (https://franeitalia.wordpress.com/), ITALICA (doi.org/10.5194/essd-15-2863-2023), Franceschini et al., 2022 (doi.org/10.1007/s10346-021-01799-y); Extreme Severe Weather Database (https://eswd.eu), Italian hydrological-geological Portal (https://idrogeo.isprambiente.it) - with the goal to verify consistency and to analyse the reasons leading to any discrepancies. Verifying the performance of the currently operational warning system for a set of events assumed as reliable, as they are included in more than one catalogue, taking into account the seasonality of events and the triggering precipitation patterns. Evaluating the capabilities of atmospheric reanalysis (ERA5land 10.24381/cds.e2161bac and CERRA 10.24381/cds.a7f3cd0be) made available by the Copernicus Climate Change Service to reconstruct the precipitation patterns that triggered the events (back-analysis). The topic is of great interest due to the high temporal resolution (1 hour) and spatial resolution (9 km for ERA5 land, 5.5 km for CERRA), which could therefore adequately cover areas where current sensor networks may be lacking or temporarily non-operational. Assessing if and how the soil moisture content data returned by atmospheric reanalysis can support the back-analysis and/or the forecasting of landslide events (ERA5 is updated with a delay of only 5 days with respect of present time). Given the spatial resolution of the reanalysis, they are not expected to actually reproduce local in-situ conditions, but they rather should act as proxies to evaluate the average wetness status of the slopes and then the presence of conditions predisposing to landslide triggering. The results discussed regarding the case study of the Amalfi and Sorrento Coasts can be readily extended to other geographical and geomorphological contexts, at national and continental scale.

Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future. They can be developed using different techniques and data, each bringing intrinsic limitations and potential sources of mapping errors, hence affecting the overall accuracy and reliability of the subsequent analyses.For more than one decade, the Geotechnical Engineering Group (GEG) of the University of Salerno (Italy) has been carrying out a specific research activity aimed at collecting and organizing, within a national landslide catalogue called “FraneItalia”, information on landslides that occur in Italy from online news sources. To this aim, the news aggregator Google Alerts has been used for screening web pages and news articles published in Italian language. The FraneItalia catalogue is freely accessible at https://zenodo.org/records/7923683. A description of the main features of the catalogue and the procedures adopted to fill it out can be found at https://doi.org/10.1186/s40677-018-0105-5.FraneItalia, which is being continuously updated, to date contains data on more than 9000 landslide events that occurred in Italy during the period 2010-2024. The catalogue includes both fatal landslide events and events that did not produce physical harm to people. The main peculiarity of the catalogue is the distinction between single landslide events, SLE (i.e., records only reporting one landslide) and areal landslide events, ALE (i.e., records referring to multiple landslides triggered by the same cause in the same geographic area). The structure is organized as a database where each reported landslide event is characterized by 40 unique fields, which are grouped in 9 thematic tables: main info; spatial information; temporal information; landslide characteristics; consequences to people, structures, infrastructures, cars and other elements; and source. Not all fields are mandatory. A set of constraints has been adopted to ensure the correctness and the semantic integrity of the attributes. In addition, a set of confidence descriptors are associated to each landslide record to measure the level of accuracy of the spatial and temporal information. Indeed, the availability of accurate and up-to-date information is essential for improving the accuracy and the quality of the subsequent analyses in landslide research.Different subsets of the catalogue have been already used to carry out studies on landslide risk in Italy (https://franeitalia.wordpress.com/publications/), including: calibration and validation of models for landslides prediction at territorial scale; detection and mapping of spatio-temporal clusters of landslides; susceptibility, hazard, and risk assessment. Given the rising demand for high-quality data to be used in comprehensive analyses at regional and national scales, this dataset might be very useful for supporting decision-making in landslide risk management in Italy. Moreover, the methodology to define and populate FraneItalia is deemed to be general and can be used to develop similar initiatives in other countries.

The contribution addresses, from a conceptual point of view, the complex issue of evaluating the performance of warning systems that are operating over large areas to cope with the risk posed by extreme weather events. In the protocol, the performance of the systems is evaluated, at each step in the warning production process, considering the “warning value chain” schematization developed in the HIWeather project of the World Meteorological Organization (http://hiweather.net/Lists/130.html). In a perfect warning chain, the warning received by the end user would contain precise and accurate information that perfectly met their need, contributed by each of the many players in the chain; in real warning chains, information, and hence value, are always lost as well as gained at each link in the chain (Golding 2022, https://link.springer.com/book/10.1007/978-3-030-98989-7). The protocol is structured as a three-part evaluation process: 1) description of the system; 2) assessment of criticalities during high impact events; 3) routine assessment of daily operations. For each part, the protocol prescribes a set of must-do. The description of the warning system must be based on the schematic subdivision of the warning value chain, i.e., six main capabilities and outputs and five information exchanges elements. An important focus on the evaluation of an operational warning system must be devoted to high impact events. For such cases, the evaluation must include: essential information on the event; information on how each element of the warning value chain has been working during the event; synthetic assessment on the performance of the warning system. Finally, the routine assessment must include: identification of the system’s operational elements; identification of the areas covered by the system; identification of period for which to conduct the assessment and sources of data to be used; identification of appropriate and computable (considering the available data) performance indicators for the different elements of the warning value chain; analysis of relevant data for the chosen time period in the identified areas; evaluation of the performance of the different elements of the waring value chain; final judgment on the overall performance of the system. This study is being carried out within the Horizon Europe project “The HuT: The Human-Tech Nexus - Building a Safe Haven to cope with Climate Extremes” (https://thehut-nexus.eu/). The protocol has been developed considering two cases studies, and will be further put to test during the remaining part of the project. Through this action, detailed information from many different warning systems will be collected and used for a comparative study between warning systems operating, in different areas of the world, for different weather and climate related risks.

Exceeding the ambitions of the Sendai Framework for Disaster Risk Reduction 2015-2030, to switch the focus of DRR (Disaster Risk Reduction) solutions from top-down to people-centred approaches, requires a better understanding of the complexity and dynamics of citizen and stakeholder engagement in DRR processes. This paper focuses on how to improve co-creation processes -namely the involvement of stakeholders in the design, development, implementation, monitoring and evaluation of solutions at the local level, how to raise interest in and awareness of the topic, and on how to integrate different types of knowledge to achieve better DRR outcomes. A systematic literature review and a workshop with scientists and practitioners (n = 51) was conducted to better understand the common barriers to stakeholder engagement and develop a framework with enabling factors to overcome them. The results indicate that a first crucial but challenging step for a successful co-creation process is to increase stakeholder interest and motivation to participate, to raise awareness on the topic of risk and DRR by combining different tools, methods, as well as local and scientific knowledge. Balancing shared responsibilities and giving a sense of ownership without overburdening participants emerged as a key factor. On the opposite, lack of personal and organisational capacity, the lack of knowledge and experience, as well as issues with communication and including all social groups pose major challenges to successful co-creation processes. Developing binding policy instruments for stakeholder engagement stands as a prospective solution and further research should be focused on identifying pathways for their implementation.

Shallow rainfall-induced landslides are triggered by intense or prolonged rainfall. Warning models employed within territorial landslides early warning systems (Te-LEWS) are typically based on rainfall thresholds expressed in terms of cumulative rainfall or average intensity with respect to the duration of the rainfall event, completely neglecting antecedent conditions. However, recent studies demonstrated that introducing, directly or by means of models, the effects of antecedent soil moisture content in empirical thresholds can improve the performance of the warning models. This preliminary study focuses on the definition of a pilot monitoring site that produces rainfall and soil moisture data measured by an Internet of Things (IoT) monitoring network and by the use of analogous reanalysis products (i.e., ERA5-Land dataset). The activities are being developed in the context of the Horizon Europe project “The HuT: The Human-Tech Nexus - Building a Safe Haven to cope with Climate Extremes”. The final aim is to use IoT monitoring of rain and soil moisture, combined with reanalysis data, to improve, at municipal level, the territorial warning procedures already existing and operational at regional level. The test site has been installed within the Campus of the University of Salerno in Fisciano, Campania region (Italy) since February 2023. The pilot site has been instrumented with sensors monitoring soil moisture from different providers and with a weather station; the sensors have been installed at different depths and with different procedures. The collected data were analyzed and processed using various data analysis algorithms, with the aim of: i) establishing correlations between the local weather conditions and the hydrologic soil response; ii) make a comparison between the data collected from different providers and in different local conditions. Establishing these relationships allowed to evaluate the peculiarities and reliability of the different sensors and to identify the best configuration for future in-situ installations. More generally, this study highlights the importance of developing a monitoring network based on diffuse low-cost sensors and a proper real-time data transmission, analysis and processing, in order to provide further knowledge to system managers of territorial warning systems in the analysis of the monitoring data, and thus support for their decisions before and during extreme weather conditions.

A procedure designed to develop probabilistic thresholds for rainfall-induced landslides adopting novel rainfall variables is proposed and tested in an area of Campania region, southern Italy. The dataset used comprises 180 rainfall-induced landslides in the period 2010–2020, derived from the FraneItalia catalogue ( https://franeitalia.wordpress.com/ ), and rainfall records retrieved from a network of 22 regional rain gauges active in the area. The procedure starts by reconstructing rainfall events responsible and not responsible for landslides in the period of analysis. Subsequently, a large number of rainfall variables are derived automatically, adopting a time series feature extraction tool, and their significance in identifying landslide-triggering events is evaluated. Then, adopting a Bayesian approach, the most significant rainfall variables are used to define a set of probabilistic thresholds in one, two, and three dimensions. Finally, the most effective thresholds are identified by means of standard performance indicators. The probabilistic thresholds developed using novel rainfall variables outperform those employing conventional variables. Specifically, looking at the minimum distance from the perfect classification point (δ), thresholds employing novel variables yield a minimum δ of 0.230, while those adopting conventional variables lead to a minimum δ of 0.292. The results achieved herein demonstrate that the use of novel rainfall variables within territorial landslide warning models can represent a promising option for improving the performance of these models.

The HuT (The Human-Tech Nexus) project aims at finding effective strategies to manage the risks associated with extreme climate events by means of specific demonstrators over the European territory in which different Disaster Risk Reduction strategies are prototyped and tested. In this context, we show here two distinct innovative insurance prototypes to cope with risks associated with wildfires and landslides over two peculiar areas in Sardinia and Campania regions (Italy). While the hazard posed by the two perils show distinct characteristics and origins, in both cases an insurance product can play a crucial role in the aftermath of the events for communities and private stakeholders. Since the risk assessment is crucial both in terms of financial structure and pricing strategies of a natural hazard insurance product, prototypes are developed through a Nat Cat modeling-based hazard assessment, while the vulnerability and finance considerations are related to the specific characteristics of the area of interest. Eventually, two prototypes are fully developed: “Landslide First Rescue”, a semi-parametric product designed to cope with the immediate economic needs after a landslide events; and “Fire Safe Community”, proposed as a community-based efficient tools to restore the economic losses related to wildfires. The prototypes present specific discounts if the policy holders are willing to implement risk reduction solutions to cope with the specific natural hazard. Results prove that the final premium associated with the products would be affordable and several consultations with interested stakeholders have shown how these kinds of products could also play a role in the development of nature-based solutions over broader regions.

Numerous studies have looked at rainfall infiltration as a triggering factor for rainfall-induced landslides. In addition to rainfall characteristics, soil hydraulic properties are recognized to play a crucial role in instability mechanisms. This study proposes a methodology for assessing the soil-water characteristic curve and hydraulic conductivity function through finite-element seepage modelling of physical model tests. The approach is applied to an instrumented, small-scale slope model built with uniformly graded sand with a 30 degree inclination, exposed to homogeneous rainfall until the occurrence of failure. In a first stage, a sensitivity analysis is carried out to assess the influence of the Mualem–van Genuchten model parameters on the slope response. For this purpose, six physically based indicators are utilized to compare the numerical modelling results with the experimentally obtained data regarding the hydraulic response of the slope model. In the subsequent stage, a trial-and-error calibration stage is conducted to determine the set of parameters for which the difference between the numerically and experimentally acquired data is minimized. Ultimately, the suggested methodology facilitates the assessment of the optimal set of hydraulic parameters, to which both the sensitivity analysis and the trial-and-error calibration phase are anchored. The approach has demonstrated its effectiveness in calibrating the unsaturated hydraulic properties of the considered soil, as it properly addresses the physical mechanisms associated with rainfall infiltration in a slope.

There are few studies on shallow foundations placed close to the crest of slopes. Many of those studies are based on small scale physical models and include recommendations for the reinforcement layout (usually normalised to the width of the footing, B). Such studies have limitations, particularly regarding the unrealistically low stress levels applied. To address such limitations, this paper presents a numerical analysis of a full-scale footing close to the crest of a sandy slope reinforced with geosynthetics, using the finite element method. The parametric analysis focuses on the effect of the depth of the upper reinforcement layer (u) and number of reinforcement layers (n). The response of numerical models reinforced with one layer of geosynthetic was compared to that of the unreinforced model analysed under the same conditions. The installation of a reinforcement at 0.3B allowed a significant increase in the bearing capacity of the shallow foundation. For the same prescribed displacement, the failure mechanism of the reinforced model involved a smaller mass of soil and exhibited more localised shear strains than the unreinforced model. In contrast to what has been reported in the literature for reduced scale models, the optimal depth for one layer of reinforcement was 0.3B (and not 0.5B). This value agrees with recent studies performed in centrifuge and full-scale models. For the optimal depth of reinforcement, adding a second reinforcement layer (equally spaced from the first), led to the maximum bearing capacity improvement: 70% more of the unreinforced model and 38% more of the model with one layer of reinforcement.

Αn online questionnaire was developed to find out (i) whether geotechnical engineering instructors have available a variety of satisfactory educational material and (ii) the types of educational material they desire. The title of the questionnaire was phrased as “What Geotechnical Engineering Educational Material can we dream of?”, in order to convey that the main purpose of the survey project reported herein is to learn about these desired educational materials. In doing so, the survey also aims to assemble information on related issues, such as: existing educational materials, where do instructors search for them and how satisfied they are with available material. The questionnaire has 12 close-ended (four yes/no and eight multiple choice) and four open-ended questions. From the 94 completed questionnaires received, 63 were deemed to be conscientious attempts to answer its questions and were analyzed in detail. The most revealing findings from the close-ended questions include the following. The majority of the instructors (52%) are not adequately satisfied with the material they use. Likewise, whereas a significant percentage has searched for additional material, a little less than half of them (45%) are not satisfied with material found. Respondents need materials for their lectures, materials to engage students outside lecture time and, to a lesser extent, materials to assess students. In terms of topics of interest, case studies and laboratory-related educational materials are the most popular. The online supplement of the paper includes broad-stroke and fine-stroke descriptions of desirable educational materials that provide directions for developing them.