Structural Health Monitoring (SHM) concerns the continuous monitoring of civil and industrial buildings to increase human safety and to reduce maintenance costs. The SHM system furnishes information about the alterations in a single part or in the whole structure caused by materials aging, action of the environment, or accidental events. Typically, SHM systems are devoted to monitoring: humidity, temperature, accelerations, tensile stress, compressive stress, and building materials degradation. The methods used are non-invasive and require the deployment of sensors in checkpoints well defined by the experts. The information from the sensors is merged with the mathematical models to determine the structure safety.

Internet of Things (IoT) paradigm extends the connectivity capability to everyday life object through Internet The availability of billions of connected devices spread all over and each of them able to acquire and elaborate data open new scenarios in the development of new data acquisition systems and in the design of methods for the automatic management of such big data, allowing new digital services. In the last few years a new field of application of the IoT is the Structural Health Monitoring (SHM) and the systems developed in this framework have been experimented with good results. Such systems are mainly devoted to monitoring, for example, multiple parts of a civil/industrial structure, reducing the risks for people due to sudden collapse, The aim of this work is to give an overview of the different types of Structural Health Monitoring (SHM) used to identify, detect and characterize the degradation and damage of all types of engineering structures, highlighting their benefits, and their improvement obtained thanks the application of IoT paradigm.

Structural health monitoring (SHM) is responsible for identifying techniques and for prototyping systems performing a state diagnosis of structures. Its aim is to prevent sudden civil infrastructure failure as a result of several invisible sources of damage. Since structural damage is often caused by ground phenomena involving circumscribed geographical areas, it is useful to extend SHM systems to allow for the exchange of information among nearby buildings and then to increase the timeliness of the alerts. To this end, in this article, an SHM based on the IoT paradigm is proposed (SHM-IoT). SHM-IoT carries out both localised monitoring on a single building, and it uses information collected by several sensors correlated in time, aiming to identify potentially dangerous damage. It also performs a wider monitoring on a group of buildings in order to alert a larger number of people. SHM-IoT also sends a remote notification, which is finalised in order to alert the authorities and rescuers about the status of each monitored building. In this context, synchronisation problems arise because the information collected by each sensor of the SHM-IoT needs to be correlated in time in order to be used for damage evaluation and alert generation. In this paper, the hardware and software architectures of the proposed SHM-IoT are presented together with the synchronisation requirements and the methods of satisfying them. Experiments are undertaken to validate the SHM-IoT in real scenarios.

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.