The analysis of the collapse of a precast r.c. industrial building during the 29th may 2012 emilia romagna earthquake

Analysis of the collapse of a precast r.c. industrial building during the 2012 Emilia earthquake, focus on the failure mechanisms in particular on the flexure-shear interactions. Analysis performed by a time history analysis using a FEM model with the software SAP2000. Finally a reconstruction of the collapse on the basis of the numerical data coming from the strength capacity of the elements failed, using formulation for lightly reinforced columns with high shear and bending moment.

Semi-engineered earthquake-resistant structures: one-storey buildings built up with gabion-box walls

This thesis studies the static and seismic behavior of simple structures made with gabion box walls. The analysis was performed considering a one-story building with standard dimensions in plan (6m x 5m) and a lightweight timber roof. The main focus of the present investigation is to find the principals aspects of the seismic behavior of a one story building made with gabion box walls, in order to prevent a failure due to seismic actions and in this way help to reduce the seismic risk of developing countries where this natural disaster have a significant intensity. Regarding the gabion box wall, it has been performed some calculations and analysis in order to understand the static and dynamic behavior. From the static point of view, it has been performed a verification of the normal stress computing the normal stress that arrives at the base of the gabion wall and the corresponding capacity of the ground. Moreover, regarding the seismic analysis, it has been studied the in-plane and out-of-plane behavior. The most critical aspect was discovered to be the out-of-plane behavior, for which have been developed models considering the “rigid- no tension model” for masonry, finding a kinematically admissible multiplier that will create a collapse mechanism for the structure. Furthermore, it has been performed a FEM and DEM models to find the maximum displacement at the center of the wall, maximum tension stresses needed for calculating the steel connectors for joining consecutive gabions and the dimensions (length of the wall and distance between orthogonal walls or buttresses) of a geometrical configuration for the standard modulus of the structure, in order to ensure an adequate safety margin for earthquakes with a PGA around 0.4-0.5g. Using the results obtained before, it has been created some rules of thumb, that have to be satisfy in order to ensure a good behavior of these structure.

Nonlinear modelling of non-structural masonry walls under a low-intensity earthquake motion: calibration through a parametric study

This thesis aims to understand the behavior of a low-rise unreinforced masonry building (URM), the typical residential house in the Netherlands, when subjected to low-intensity earthquakes. In fact, in the last decades, the Groningen region was hit by several shallow earthquakes caused by the extraction of natural gas. In particular, the focus is addressed to the internal non-structural walls and to their interaction with the structural parts of the building. A simple and cost-efficient 2D FEM model is developed, focused on the interfaces representing mortar layers that are present between the non-structural walls and the rest of the structure. As a reference for geometries and materials, it has been taken into consideration a prototype that was built in full-scale at the EUCENTRE laboratory of Pavia (Italy). Firstly, a quasi-static analysis is performed by gradually applying a prescribed displacement on the roof floor of the structure. Sensitivity analyses are conducted on some key parameters characterizing mortar. This analysis allows for the calibration of their values and the evaluation of the reliability of the model. Successively, a transient analysis is performed to effectively subject the model to a seismic action and hence also evaluate the mechanical response of the building over time. Moreover, it was possible to compare the results of this analysis with the displacements recorded in the experimental tests by creating a model representing the entire considered structure. As a result, some conditions for the model calibration are defined. The reliability of the model is then confirmed by both the reasonable results obtained from the sensitivity analysis and the compatibility of the values obtained for the top displacement of the roof floor of the experimental test, and the same value acquired from the structural model.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.