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The goal of this research was to examine factors affecting the feasibility of manufacturing particleboards at significantly lower density, while reducing the formaldehyde emissions. A further goal was to not significantly affect other important physical and mechanical properties of the boards, including swelling in thickness, surface absorption, bending strength, modulus of elasticity, internal bond, and surface soundness. By varying the raw material recipe (ratio between hardwood and softwood chips), it was found that increasing the amount of hardwood chips led to a significant decrease of the formaldehyde emissions, but also to a significant increase of the thickness swelling and surface absorption. The simple density reduction of particleboards was not a viable alternative because all properties were seriously affected. Therefore, the tests on particleboards with reduced density were repeated, but this time an isocyanate-based additive was added into the recipe at 0.25% and 0.4%. A noticeable improvement of all analyzed properties was achieved. Keywords: Particleboards with lower density and reduced formaldehyde emission; Influence on bending strength; Modulus of elasticity; Internal bondContact information: a:

Structural dynamic modeling is a key element in the analysis of building behavior for different environmental factors. Having this in mind, the authors propose a simple nonlinear model for studying the behavior of buildings in the case of earthquakes. Structural analysis is a key component of seismic design and evaluation. It began more than 100 years ago when seismic regulations adopted static analyzes with lateral loads of about 10% of the weight of the structure. Due to the dynamics and non-linear response of the structures, advanced analytical procedures were implemented over time. The authors’ approach is the following: having a nonlinear dynamic model (in this case, a multi-segment inverted pendulum on a cart with mass-spring-damper rotational joints) and at least two datasets of a building, the parameters of the building’s model are estimated using optimization algorithms: Particle Swarm Optimization (PSO) and Differential Evolution (DE). Not having much expertise on structural modeling, the present paper is focused on two aspects: the proposed model’s performance and the optimization algorithms performance. Results show that among these algorithms, the DE algorithm outperformed its counterpart in most situations. As for the model, the results show us that it performs well in prediction scenarios.

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