Donato Abruzzese

Traditional Chinese sticky rice–lime mortar, a key material for restoring historic masonry buildings, suffers significant degradation under combined salt erosion and freeze–thaw cycling. This study experimentally investigated the coupled effects of chloride, sulfate, and freeze–thaw action on sticky rice–lime mortar under simulated service conditions. Specimens prepared using traditional methods were subjected to freeze–thaw cycling in pure water, 5% Na₂SO₄ solution, 5% NaCl solution, and 5% NaCl + 5% Na₂SO₄ solution. Their mechanical properties, phase compositions, and pore structures were characterized through compressive, dynamic elastic modulus, X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP) tests. After six freeze–thaw cycles, the relative dynamic elastic modulus (0.72, 0.58, 0.57, 0.55), mass loss (1.7%, 3.69%, 4.82%, 5.60%), and compressive strength loss (30.05%, 43.90%, 47.56%, 52.43%) progressively worsened from pure water to Na₂SO₄ to NaCl to compound salt conditions, indicating that under the same concentration, the deterioration induced by sodium chloride freeze–thaw is more severe than that caused by sodium sulfate, while the compound salt freeze–thaw condition leads to the most severe deterioration. Under compound salt freeze–thaw, the deterioration mechanisms include expansion due to gypsum formation, salt crystallization, ice formation, and the dissolution of cementitious phases driven by CaCl₂ attack. Furthermore, clear correlations are observed among the mass loss rate, compressive strength loss rate, and relative dynamic elastic modulus, as well as between the peak strain and secant modulus. These findings provide valuable insights for improving the durability of historic restoration mortars.

Timber structural performance is significantly influenced by natural knots, which serve as critical indicators in ancient architectural heritage preservation and modern sustainable building design. However, existing studies lack a comprehensive quantitative analysis of how the randomness of timber knot parameters relates to load-bearing capacity degradation. This study introduces a multiscale evaluation framework that integrates physical testing, probabilistic modeling, and data-driven techniques. Firstly, static tests on full-scale timber beams with artificially introduced knots reveal the failure mechanisms and load capacity reduction associated with knots in the tension zone. Subsequently, a three-dimensional Monte Carlo simulation, modeling random distributions of knot position and size, demonstrates that the midspan region is most sensitive to knot effects, with load capacity loss being more pronounced on the tension side than on the compression side. Finally, a predictive model based on a fully connected neural network is developed; feature analysis indicates that the longitudinal position of knots exerts a stronger nonlinear influence on load capacity than radial depth or diameter. The results establish a mapping between knot characteristics, stress field distortion, and ultimate load capacity, providing a theoretical basis for safety evaluation of historic timber structures and the design of defect-tolerant timber beams in modern engineering.

An accurate identification of structural modes is considered crucial for operational modal analysis, and stochastic subspace methods have been extensively employed for this purpose. In stochastic subspace identification, the number of block rows ([ ]) and the model order ([ ]) are two important parameters that affect the quality of the stabilization diagram. An index is proposed for optimizing these parameters based on the Covariance-driven Stochastic Subspace Identification (SSI-Cov). The stabilization diagrams obtained with the optimized parameters are characterized by fewer spurious modes. The influences of different parameter settings on the identification are then studied by automatically examining the stabilization diagram using hierarchical clustering. The variations in the estimated modal frequencies, damping ratios, and mode shapes are minimized with the optimized parameters. A computational scheme for the optimization of parameters in SSI-Cov is presented, with a reduction in subjective intervention. The SSI-Cov with optimized parameter settings does not require an excessively large model order for estimation, which also results in improved computational efficiency.

The ongoing revolution in the plastic sector is the use of renewable and compostable materials obtained from biomass. In this paper the influence of natural reinforcing agents on the properties of a milk-based compostable bioplastic is investigated.

The permanent monitoring of structural behavior of monuments is a crucial target in the framework of cultural heritage preservation. The paper deals with the monitoring and static risk assessment using the technique of dynamic parameter measurement in a broad wireless sensors network (WSN) system. The historical buildings exposed to the traffic vibration, which can induce stresses inside the material, as well as small seismic actions, are vulnerable. Then a permanent monitoring of the micro-shocks/vibrations is suggested to be applied to the most vulnerable monuments. The use of a WSN requires to provide a robust infrastructure to support the data collecting and decision making before, during, and after a crisis event. When a crisis occurs, through the use of such information collection methods an appropriate plan is dynamically instantiated with the specific details of the crisis.