Search

This document is a special issue of the journal Materials, which explores and analyzes eco- and biomaterials through experimental research and computational methods. The collection covers various topics, including mechanical properties, strength estimation, numerical and analytical techniques, and the behavior and applications of these materials. Specific studies are highlighted, including the deflection behavior of steel support plates, seismic performance of stiffened corrugated steel plate shear walls, aluminum-timber composite bolted connections, connection types between thin-walled beams and sandwich panels, and stress-fractional plasticity models. These studies contribute to the understanding and application of eco- and biomaterials in construction, offering insights into their mechanical behavior and potential for sustainable building practices. The document also emphasizes the importance of integrating computational approaches with experimental research to effectively innovate and apply sustainable materials. [Extracted from the article]

The impact of perforation patterns on the compressive strength of cardboard packaging is a critical concern in the packaging industry, where optimizing material usage without compromising structural integrity is essential. This study aims to investigate how different perforation designs affect the load-bearing capacity of cardboard boxes. Utilizing finite element method (FEM) simulations, we assessed the compressive strength of packaging made of various types of corrugated cardboards, including E, B, C, EB, and BC flutes with different heights. Mechanical testing was conducted to obtain accurate material properties for the simulations. Packaging dimensions were varied to generalize the findings across different sizes. Results showed that perforation patterns significantly influenced the compressive strength, with reductions ranging from 14% to 43%, compared to non-perforated packaging. Notably, perforations on multiple walls resulted in the highest strength reductions. The study concludes that while perforations are necessary for functionality and aesthetics, their design must be carefully considered to minimize negative impacts on structural integrity. These findings provide valuable insights for designing more efficient and sustainable packaging solutions in the industry. [ABSTRACT FROM AUTHOR]

Artificial intelligence is increasingly used in various branches of engineering. In this article, artificial neural networks are used to predict the crush resistance of corrugated packaging. Among the analysed packages were boxes with ventilation openings, packages with perforations and typical flap boxes, which make the proposed estimation method very universal. Typical shallow feedforward networks were used, which are perfect for regression problems, mainly when the set of input and output parameters is small, so no complicated architecture or advanced learning techniques are required. The input parameters of the neural networks are selected so as to take into account not only the material used for the production of the packaging but also the dimensions of the box and the impact of ventilation holes and perforations on the load capacity of individual walls of the packaging. In order to maximize the effectiveness of neural network training process, the group of input parameters was changed so as to eliminate those to which the sensitivity of the model was the lowest. This allowed the selection of the optimal configuration of training pairs for which the estimation error was on the acceptable level. Finally, models of neural networks were selected, for which the training and testing error did not exceed 10%. The demonstrated effectiveness allows us to conclude that the proposed set of universal input parameters is suitable for efficient training of a single neural network model capable of predicting the compressive strength of various types of corrugated packaging. [ABSTRACT FROM AUTHOR]

The urgent requirement to reduce greenhouse gas emissions during Portland cement production and to enhance the durability of concrete in destructive environments are essential reasons to seek other alternative materials like alkaline activated binders. In this study, the feasibility of producing durable alkali-activated slag-based concrete under deteriorative environmental conditions was studied using 0, 10, 20, 30, and 40% of metakaolin (MK) and 0, 5, and 10% of silica fume (SF) instead of ground granulated blast furnace slag (GGBFS) and optimizing contents through the response surface method (RSM).To evaluate the performance of studied alkali-activated slag-based concrete in an aggressive environment, the permeability and the reduction in compressive strength of alkali-activated slag-based concrete under sulfuric acid attack have been investigated. In addition, the mass change of specimens after exposure was measured. The results indicate that replacing 40% of the slag with metakaolin and 10% with silica fume in alkali-activated concrete has reduced 9% and 34.9% of the compressive strength at 28 days, respectively. Also, increasing MK replacement up to 40% increased the water absorption to 27.8%, but 10% SF replacement reduced it to 17.7%. In addition, the alkali-activated slag-based concrete mass changes after 90 days of acid exposure were 2.3%, while the replacement of slag by 40% of MK and 10% of SF reduced this value to 1.14%. However, it improved the durability performance of alkali-activated concrete against sulfuric acid attacks. [ABSTRACT FROM AUTHOR]

Corrugated board is an environmentally friendly, commonly used packing material. Its basic structure consists of two liners and a flute between them. The mechanical properties and strength of the corrugated board depend not only on the constituent papers but also its geometry, which can be distorted, however, due to various factors related to its manufacturing process or use. The greatest distortion occurs in the corrugated layer, which, due to crushing, significantly deteriorates the functional properties of cardboard. In this work, two algorithms for the automatic classification of corrugated board types based on images of deformed corrugated boards using artificial intelligence methods are presented. A prototype of a corrugated board sample image acquisition device was designed and manufactured. It allowed for the collection of an extensive database of images with corrugated board cross-sections of various types. Based on this database, two approaches for processing and classifying them were developed. The first method is based on the identification of the geometric parameters of the corrugated board cross-section using a genetic algorithm. After this stage, a simple feedforward neural network was applied to classify the corrugated board type correctly. In the second approach, the use of a convolutional neural network for corrugated board cross-section classification was proposed. The results obtained using both methods were compared, and the influence of various imperfections in the corrugated board cross-section was examined. [ABSTRACT FROM AUTHOR]