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Purpose: The importance of sustainability in architecture currently necessitates the integration of innovative teaching strategies on the subject into architecture programs. This study aims to introduce and examine peer learning pedagogy by peer tutoring to educate architecture students in sustainable design. Design/methodology/approach: Based on class assignments proposed in two different architecture sustainability-focused courses in the second and fourth years of the Bachelor of Science in architecture program, a total of 103 students assessed the proposed peer learning experience and its impact on their sustainability mindsets and education. Subjective surveys for evaluating the peer learning experience were designed and delivered at different stages of the course sequences. A total of 502 survey responses were obtained in the study. Findings: The qualitative and quantitative data analysis confirms that the proposed peer learning by peer tutoring increased students' knowledge, motivation and commitment to sustainable design. In addition, participants became more confident in applying sustainable design skills and their academic grades improved more than 25% compared to previous courses using traditional teaching methods. Originality/value: Traditional architecture education has long been criticized for their pedagogical methodologies based primarily on passive learning. Recently, these programs have begun to prepare students to become active learners and communicators in collaborative and multidisciplinary environments. A mixed-method approach of combining pre-/post-experience surveys and analysis of final grades was used to determine the level of success and the quantifiable behavior change delivered by students involved in this peer learning experience.

Although the principal aim of the rockfall management is to prevent rock boulders from reaching the buildings instead of the buildings resisting the boulder impacts, there usually exists a residual risk that has to be assessed, even when structural protection measurements are taken. The evaluation of the expected damage of buildings due to rockfalls using empirical data from past events is not always possible, as transferring and applying damage observations from one area to another can be unrealistic. In order to simulate potential rockfall scenarios and their damage on buildings, numerical methods can be an alternative. However due to their increased requirements in expertise and computational costs, their integration into the risk analysis is limited, and simpler tools to assess the rockfall vulnerability of buildings are needed. This paper focuses on the application of artificial intelligence AI methods for providing the expected damage of masonry walls which are subjected to rockfall impacts. First, a damage database with 672 datasets was created numerically using the particle finite element method and the finite element method. The input variables are the rock volume (VR), the rock velocity (RV), the masonry wall (t) and the masonry tensile strength fm. The output variable is a damage index (DI) equal to the percentage of the damaged wall area. Different AI algorithms were investigated and the ANN LM 4-21-1 model was selected to optimally assess the expected wall damage. The optimum model is provided here (a) as an analytical equation and (b) in the form of contour graphs, mapping the DI value. Known the VR and the RV, the DI can be directly used as an input for the vulnerability of masonry walls into the quantitative rockfall risk assessment equation.

Quantitative hazard analysis of rockfalls is a fundamental tool for sustainable risk management, even more so in places where the preservation of natural heritage and people’s safety must find the right balance. The first step consists in determining the magnitude-frequency relationship, which corresponds to the apparently simple question: how big and how often will a rockfall be detached from anywhere in the cliff? However, there is usually only scarce data on past activity from which to derive a quantitative answer. Methods are proposed to optimize the exploitation of multi-source inventories, introducing sampling extent as a main attribute for the analysis. This work explores the maximum possible synergy between data sources as different as traditional inventories of observed events and current remote sensing techniques. Both information sources may converge, providing complementary results in the magnitude-frequency relationship, taking advantage of each strength that overcomes the correspondent weakness. Results allow characterizing rockfall detachment hazardous conditions and reveal many of the underlying conditioning factors, which are analyzed in this paper. High variability of the hazard over time and space has been found, with strong dependencies on influential external factors. Therefore, it will be necessary to give the appropriate reading to the magnitude-frequency scenarios, depending on the application of risk management tools (e.g., hazard zoning, quantitative risk analysis, or actions that bring us closer to its forecast). In this sense, some criteria and proxies for hazard assessment are proposed in the paper.

An automated, fixed-location, continuous time-lapse camera system was developed to analyze the existence of rockfall precursory movements and quantify volume changes after detachments. It was implemented to monitor the basaltic formation on which Castellfollit de la Roca village is built. Due to the geometrical conditions of the area, the camera system consists of three digital cameras managed by a control unit that contains a Raspberry Pi 4 microprocessor. Images taken every day are sent to a server for processing. A workflow has been developed to work with a set of images with an irregular time interval to detect precursor movement. The first step consists of matching the images with a reference master image and filtering the vegetation to improve the process using a mask obtained by a green leaf index (GLI) index. Then, the adjusted images are used for a forward-backward correlation process carried out to detect movements. If movement is detected, a 3D model is built using structure from motion (SfM) to quantify the movements. The system has been working since September 2021. During this period, movements from 0.01 to 0.5 m and several rockfalls of a small volume have been detected.

In the last two decades, both the amount and quality of geoinformation in the geosciences field have improved substantially due to the increasingly more widespread use of techniques such as Laser Scanning (LiDAR), digital photogrammetry, unmanned aerial vehicles, geophysical reconnaissance (seismic, electrical, geomagnetic), and ground-penetrating radar (GPR), among others. Furthermore, the advances in computing, storage and visualization resources allow the acquisition of 3D terrain models (surface and underground) with unprecedented ease and versatility. However, despite these scientific and technical developments, it is still a common practice to simplify the 3D data in 2D static images, losing part of its communicative potential. The objective of this paper is to demonstrate the possibilities of extended reality (XR) for communication and sharing of 3D geoinformation in the field of geosciences. A brief review of the different variants within XR is followed by the presentation of the design and functionalities of headset-type mixed reality (MR) devices, which allow the 3D models to be investigated collaboratively by several users in the office environment. The specific focus is on the functionalities of Microsoft’s HoloLens 2 untethered holographic head mounted display (HMD), and the ADA Platform App by Clirio, which is used to manage model viewing with the HMD. We demonstrate the capabilities of MR for the visualization and dissemination of complex 3D information in geosciences in data rich and self-directed immersive environment, through selected 3D models (most of them of the Montserrat massif). Finally, we highlight the educational possibilities of MR technology. Today MR has an incipient and reduced use; we hope that it will gain popularity as the barriers of entry become lower.

The monitoring of morphologic changes in high-mountain environments is an important, but complex task. Terrestrial as well as airborne laser scanning (TLS and ALS) and digital photogrammetry (DP) using Unnamed Aerial Vehicle (UAV) can determine recent variations. In contrast, changes that occurred 20 or more years ago, need generally the application of DP and historic aerial photograph. We compared these four geomatic techniques and the resulting digital elevation models (DEM) that ranged over a 70-year time span (1946–2016). The accuracy of the different models and also the effect of different cell size were analysed. The accuracy analysis of the resulting DEMs shows important drawbacks, when historic aerial photographs are used. In these DEMs, the error in elevation can be important and a detailed analysis of morphologic changes is limited. Nowadays, the combination of DP and UAV is the technique with best cost-benefit ratio, although TLS could reveal similar precision. However, the application of TLS is restrained by the complex morphology and the presence of vegetation, which produce many shadows. Finally, the erosion rate was determined in our catchment. The resulting values range between 0.06 and 0.16 m3/m2/y, which coincides rather well with data observed in other studies.