Modern monitoring techniques contribute to accurately describing the structural health conditions of historical buildings and to optimizing the plan of maintenance as well as the restoring intervention. Particularly, dynamic testing gives knowledge about global structural behavior and can be used to calibrate numerical models and to predict the response to dynamic and earthquake loading. In some circumstances, vibration-based monitoring can also help in evaluating safety conditions. The present article proposes a discussion about the methodological multidisciplinary approach to modal testing when applied to architectural heritage buildings and structures, along with the description of selected case studies. These examples were chosen to cover the various issues connected to test design and interpretation.

After the seismic events of the 20th and 29th of May 2012 in Emilia (Italy), most of the monumental and historic buildings of the area were severely damaged. In a few structures, partial collapse mechanisms were observed (e.g. façade tilting, out-of-plane overturning of panels…). This paper presents the case-study of the bell tower of the Santa Maria Maggiore cathedral, located in Mirandola (Italy). The dynamic response of the structure was evaluated through operational modal analysis using ambient vibrations, a consolidated non-destructive procedure that estimates the dynamic parameters of the bell-tower. The dynamic tests were carried out in pre-intervention and post-intervention conditions in order to understand the sensitivity of dynamic measurements to safety interventions. Furthermore, a comparative study is made with similar cases of undamaged masonry towers up to the 6th mode. Finally, an investigation on the state of connections and of the building itself is carried out via FE model updating.

Sensitivity-based approaches to model updating have become widely used because of their capability to calibrate themodel by taking into account the influence of updating parameters associated to different structural elements. Global sensitivity analysis (SA) allows model updating to be carried out even in the case of elevated uncertainty about the material characteristics. Architectural heritage structures deserve specific attention on account of their intrinsic geometrical complexity and heterogeneity. In this article, the concept of global SA is applied for the first time to complex monumental structures, and a comparative view is offered on more classical local sensitivity approaches. Different finite element (FE) calibration techniques—via global and local SA—were applied to the intriguing case of the church of S. Maria del Suffragio in L'Aquila (Italy), severely damaged by the 2009 earthquake. The FE updating was based on experimental data acquired by a dynamic monitoring system. Finally, calibration strategies were assessed through time history analyses by comparing the responses to the recorded seismic even

When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensors (e.g., camera, LIDAR) is capable of mutually offering useful complementary information to enhance the robustness of 3D detectors. In this paper, a deep neural network architecture, named RoIFusion, is proposed to efficiently fuse the multi-modality features for 3D object detection by leveraging the advantages of LIDAR and camera sensors. In order to achieve this task, instead of densely combining the point-wise feature of the point cloud with the related pixel features, our fusion method novelly aggregates a small set of 3D Region of Interests (RoIs) in the point clouds with the corresponding 2D RoIs in the images, which are beneficial for reducing the computation cost and avoiding the viewpoint misalignment during the feature aggregation from different sensors. Finally, Extensive experiments are performed on the KITTI 3D object detection challenging benchmark to show the effectiveness of our fusion method and demonstrate that our deep fusion approach achieves state-of-the-art performance.

Optical measurements from high‐speed, high‐definition video recordings can be used to define the full‐field dynamics of a structure. By comparing the dynamic responses resulting from both damaged and undamaged elements, structural health monitoring can be carried out, similarly as with mounted transducers. Unlike the physical sensors, which provide point‐wise measurements and a limited number of output channels, high‐quality video recording allows very spatially dense information. Moreover, video acquisition is a noncontact technique. This guarantees that any anomaly in the dynamic behaviour can be more easily correlated to damage and not to added mass or stiffness due to the installed sensors.However, in real‐life scenarios, the vibrations due to environmental input are often so small that they are indistinguishable from measurement noise if conventional image‐based techniques are applied. In order to improve the signal‐to‐noise ratio in low‐amplitude measurements, phase‐based motion magnification has been recently proposed.This study intends to show that model‐based structural health monitoring can be performed on modal data and time histories processed with phase‐based motion magnification, whereas unamplified vibrations would be too small for being successfully exploited. All the experiments were performed on a multidamaged box beam with different damage sizes and angles.