Up to now, primary resources have been the main choice of raw material selection for production. Now, global market tendencies have brought significant attention to secondary resources as the price has been raised for primary materials, and there is a shortage of their delivery. This could bring an additional effort to increase the recycling level of construction and demolition waste, including expanded polystyrene (EPS). Efforts have been made to develop new efficient building materials with a high content of recycled EPS. In this paper, composite insulation material made of gypsum hemihydrate and recycled EPS beads by casting and compression methods were evaluated, and properties were compared. Thermal and sound insulation properties were characterized. Density from 48 to 793 kg/m³ was obtained and the thermal conductivity coefficient from 0.039 to 0.246 W/(m·K) was measured. Compression strength was from 18 kPa to 2.5 MPa. Composites produced with the compression method have a sound absorption coefficient α > 0.9 in the range from 600 to 700 Hz, while the samples produced by casting showed poor sound absorption with wide deviation. Compression methods had an advantage over the casting method as more homogenous and lightweight materials were produced with improved insulation properties.

Health monitoring of critical structures, that form parts of serial operating objects, is a pressing task. The Operational Modal Analysis (OMA) techniques could be the optimal solution. An inexpensive measurement system, such as the OMA, uses a lot of sensors for structural response assessment. The health monitoring of serial structures has to also consider possible deviations between samples. A solution providing the OMA application includes the compact measurement system based on piezoelectric film sensors and modal passport (MP) techniques. For validation of the proposed approach, a series of five similar composite cylinders, with a network of piezoelectric film sensors, was used. Applying modal tests on the specimens, and using OMA with MP methods, the set of typical modal parameters was determined and analyzed. The results of the study confirmed the feasibility of the sensor network and its applicability for structural health monitoring of serial samples using OMA methods. The proven effectiveness of OMA/MP techniques, combined with a sensor network, provides a prototype of intelligent sensor technology, which can be used for health monitoring of structures, including those that are part of an operating facility.

Health monitoring of structures operating in ambient environments is performed through operational modal analysis, where the identified modal parameters, such as resonant frequencies, damping ratios and operation deflection shapes, characterize the state of structural integrity. The current study shows that, first, time-frequency methods, such as continuous wavelet transform, can be used to identify these parameters and may even provide a large amount of such data, increasing the reliability of structural health monitoring systems. Second, the identified resonant frequencies and damping ratios are used as features in a damage-detection scheme, utilizing the kernel density estimate (KDE) of an underlying probability distribution of features. The Euclidean distance between the centroids of the KDEs, at reference and in various other cases of structural integrity, is used as an indicator of deviation from reference. Validation of the algorithm was carried out in a vast experimental campaign on glass fibre-reinforced polymer samples with a cylindrical shell structure subjected to varying degrees of damage. The proposed damage indicator, when compared with the well-known Mahalanobis distance metric, yielded comparable damage detection accuracy, while at the same time being not only simpler to calculate but also able to capture the severity of damage.