This paper presents a study about the integration of Piezoelectric Energy Harvesting Systems (PE-EHSs) into building envelopes for powering Fiber Bragg Grating (FBG) sensors, enabling efficient and low-consumption monitoring with the objective of leveraging structural health monitoring (SHM). The research includes preliminary tests conducted in a real environment to validate the PE-EHS when fully integrated into a ventilated façade, capturing mechanical vibrations generated mainly by wind loads. Based on these activities, the final configuration of PE-EHSs is defined to provide a complete system for façade monitoring. This integrated system includes the piezoelectric generator (PEG), supercapacitor (SC), Power Conditioner Circuit (PCC), Fiber Optic Sensing (FOS) interrogator, and the IoT gateway transmitting measurement data within an Internet of Things (IoT) monitoring platform. This configuration is tailored to address the challenges related to the structural integrity of building envelopes. Results demonstrate a potential for a stand-alone solution in the façade sector but raise issues for certain limitations, requiring further investigation. In particular, the study emphasizes constraints related to the energy production of PE-EHSs for façade integration. It highlights the necessity to carefully consider these limitations within the broader context of their applicability, providing insights for the informed deployment of piezoelectric energy harvesting technology in building envelope monitoring.

In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson’s correlation coefficient > 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE < 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment.

This paper details the comprehensive activities conducted in a laboratory setting to assess the structural health monitoring (SHM) of prefabricated building envelopes. Integrating sensors into building components like curtain wall facades poses challenges but offers opportunities for monitoring structural health, requiring compliance with regulatory standards. The research investigates the possibility of defining a kit of conventional and multi-parameter sensors integrated within the building envelope to monitor its behavior during the performance test conducted. The kit of sensors also includes Fiber Optic Sensors for effectively monitoring building envelope behavior and structural integrity. In this context, the European project InComEss (H2020-GA862597) aims to define a stand-alone solution for SHM using Piezoelectric Energy Harvesting Systems (PE-EHS) for façade monitoring through FBG/FOS system. After analyzing the main façade structural stress, a series of FBGs, accelerometers, and force washers were integrated within a 1:1 scale façade prototype and tested in a laboratory following the test sequence parameters required by the curtain wall standard EN 13830. The data collected were analyzed with the aim of monitoring the façade behavior before and after the tests. The results show that the façade’s performance passed the assessing test criteria without reporting any damages. In addition, the outcomes demonstrated the effectiveness of the defined kit of multi-parameter sensors for the building envelope’s SHM.

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials.

Ensuring the durability of civil buildings is critical for preventing long-term structural failure. Traditional damage assessment methods, based on visual inspections, are labour-intensive and subjective. The adoption of digital platforms emerges as a solution for complete monitoring of a building during its life cycle. This study, carried out as part of the European BIM2TWIN project, focuses on the monitoring of concrete surface quality using vision techniques and deep learning algorithms. Four neural network models are employed for cracks, honeycombing, pitting, and exposed bars, collectively analysing the images to identify and quantify defects. Project managers can assess the criticality of defects based on accurate pixel counts and geometric features, converting measurements from pixels to millimetres. Data is then stored in the digital platform, providing a historical record for future reference and decision-making by project managers.

This paper details the comprehensive activities conducted in a laboratory setting to assess the Structural Health Monitoring (SHM) of prefabricated building envelopes. Aimed at enhancing the structural evaluation of buildings, the focus of this research lies in employing nondestructive sensors for SHM with the objective to advance in the understanding and assessment of facade conditions. The paper outlines the methodologies employed, starting with a modal analysis to define the structural vibration's modal frequency. Subsequently, tests were defined and executed to validate building envelope compliance in case of stress typical of high-rise building. Throughout these tests, continuous monitoring of the façade was conducted using both conventional (accelerometers, extensimeter washers) and non-conventional sensors (Fiber Optic Sensing) The gathered data underwent thorough processing and analysis to provide an overall perspective on the structure's behavior during the stress test. The investigation aimed to identify whether specific physical phenomena correlate with distinct signals and to evaluate if continuous façade monitoring over the building's lifespan aids in preventing potential structural damage. Moreover, two modal analyses were conducted to compare final data and frequencies with earlier data, enabling the detection of any structural alterations. The results from compliance testing and structural analysis yielded noteworthy insights contributing to the evolving field of structural health monitoring, offering valuable findings that advance our ability to proactively manage and maintain the integrity of building structures.