Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

A study of existing Ontologies in the IoT-domain

Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of Thing

The XII century towers, a benchmark of the Rome countryside almost cancelled. The safeguard plan by low cost uav and terrestrial DSM photogrammetry surveying and 3D Web GIS applications

“Giving a bird-fly look at the Rome countryside, throughout the Middle Age central period, it would show as if the multiple city towers has been widely spread around the territory” on a radial range of maximum thirty kilometers far from the Capitol Hill center (Carocci and Vendittelli, 2004). This is the consequence of the phenomenon identified with the “Incasalamento” neologism, described in depth in the following paper, intended as the general process of expansion of the urban society interests outside the downtown limits, started from the half of the XII and developed through all the XIII century, slowing down and ending in the following years. From the XIX century till today the architectural finds of this reality have raised the interest of many national and international scientists, which aimed to study and catalog them all to create a complete framework that, cause of its extension, didn’t allow yet attempting any element by element detailed analysis. From the described situation has started our plan of intervention, we will apply integrated survey methods and technologies of terrestrial and UAV near stereo-photogrammetry, by the use of low cost drones, more than action cameras and reflex on extensible rods, integrated and referenced with GPS and topographic survey. In the final project we intend to produce some 3D scaled and textured surface models of any artifact (almost two hundreds were firstly observed still standing), to singularly study the dimensions and structure, to analyze the building materials and details and to formulate an hypothesis about any function, based even on the position along the territory. These models, successively georeferenced, will be imported into a 2D and 3D WebGIS and organized in layers made visible on basemaps of reference, as much as on historical maps

A Proposal for Semantic Map Representation and Evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset