AN HISTORICALLY-INFORMED APPROACH TO THE CONSERVATION OF VERNACULAR ARCHITECTURE: THE CASE OF THE PHLEGREAN FARMHOUSES

Abstract. Landscape is always the object of countless mutations: some of them disrupt its identifying features; others leave intact its original traits. Vernacular architecture is linked closely to the vocation of its landscapes, especially agricultural ones: this is the case of Pianura, a neighbourhood in the Phlegrean western suburban area of Naples, where the remains of vernacular architecture and its connections to agriculture are still traceable among the unstoppable process of building speculation which, since the 1960s, has torn up the rural fabric. In this uncontrolled development of the modern city, the architectural heritage of the farmhouse has shown its resilience: although parts of it appear to have been completely engulfed by the uncontrolled expansion of the city, in as many cases farmhouses have endured time, degradation, and indifference towards their historical value. In the heart of the neighbourhood, the masseria, with all its recurring features, remains the most widespread housing model, despite more recent interventions. Through the study of the history and architectural features of Masseria S. Lorenzo, this contribution aims to identify possible guidelines and strategies for the conservation of the material and immaterial values of these examples of vernacular architecture, putting them on a restoration and re-functionalisation path that is mindful of their past heritage and future potential

High Himalayan Discontinuity: a key structure driving the earlier exhumation of the Greater Himalayan Sequence in Central Himalayas

Abstract HKT-ISTP 2013 A

Earthquake Early Warning System for Structural Drift Prediction Using Machine Learning and Linear Regressors

In this work, we explored the feasibility of predicting the structural drift from the first seconds of P-wave signals for On-site Earthquake Early Warning (EEW) applications. To this purpose, we investigated the performance of both linear least square regression (LSR) and four non-linear machine learning (ML) models: Random Forest, Gradient Boosting, Support Vector Machines and K-Nearest Neighbors. Furthermore, we also explore the applicability of the models calibrated for a region to another one. The LSR and ML models are calibrated and validated using a dataset of ∼6,000 waveforms recorded within 34 Japanese structures with three different type of construction (steel, reinforced concrete, and steel-reinforced concrete), and a smaller one of data recorded at US buildings (69 buildings, 240 waveforms). As EEW information, we considered three P-wave parameters (the peak displacement, Pd, the integral of squared velocity, IV2, and displacement, ID2) using three time-windows (i.e., 1, 2, and 3 s), for a total of nine features to predict the drift ratio as structural response. The Japanese dataset is used to calibrate the LSR and ML models and to study their capability to predict the structural drift. We explored different subsets of the Japanese dataset (i.e., one building, one single type of construction, the entire dataset. We found that the variability of both ground motion and buildings response can affect the drift predictions robustness. In particular, the predictions accuracy worsens with the complexity of the dataset in terms of building and event variability. Our results show that ML techniques perform always better than LSR models, likely due to the complex connections between features and the natural non-linearity of the data. Furthermore, we show that by implementing a residuals analysis, the main sources of drift variability can be identified. Finally, the models trained on the Japanese dataset are applied the US dataset. In our application, we found that the exporting EEW models worsen the prediction variability, but also that by including correction terms as function of the magnitude can strongly mitigate such problem. In other words, our results show that the drift for US buildings can be predicted by minor tweaks to models

P-T-t evolution of the Himalayan metamorphic core in the Mugu Karnali transect (Central Himalaya): preliminary data based on calculations of pseudosections

Abstract HKT-ISTP 2013 A