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This study aims to present a novel methodology for the evaluation of tribological properties of new nanocomposites with the A356 alloy matrix reinforced with aluminium oxide (Al₂O₃) nanoparticles.

Metal matrix nanocomposites (MMnCs) with varying amounts and sizes of Al₂O₃ particles were produced using a compocasting process. The influence of four factors, with different levels, on the wear rate, was analysed with the help of the design of experiments (DoE). A regression model was developed by using the response surface methodology (RSM) to establish a relationship between the observed factors and the wear rate. An artificial neural network was also applied to predict the value of wear rate. Adequacy of models was compared with experimental values. The extreme values of wear rate were determined with a genetic algorithm and particle swarm optimization using the RSM model.

The combination of optimization methods determined the values of the factors which provide the highest wear resistance, namely, reinforcement content of 0.44 wt.% Al₂O₃, sliding speed of 1 m/s, normal load of 100 N and particle size of 100 nm. Used methods proved as effective tools for modelling and predicting of the behaviour of aluminium matrix nanocomposites.

The specific combinations of the optimization methods has not been applied up to now in the investigation of MMnCs. In addition, using of small content of ceramic nanoparticles as reinforcement has been poorly investigated. It can be stated that the presented approach for testing and prediction of the wear rate of nanocomposites is a very good base for their future research.

In recent years, 3D printing technologies have been widely used in the construction industry. 3D printing in construction is very attractive because of its capability of process automation and the possibility of saving labor, waste materials, construction time and hazardous procedures for humans. Significant researches were conducted to identify the performance of the materials, while some researches focused on the development of novel techniques and methods, such as building information modeling. This paper aims to provide a detailed overview of the state-of-the-art of currently used 3D printing technologies in the construction areas and global acceptance in its applications.

The working principle of additive manufacturing in construction engineering (CE) is presented in terms of structural design, materials used and theoretical background of the leading technologies that are used to construct buildings and structures as well as their distinctive features.

The trends of 3D printing processes in CE are very promising, as well as the development of novel materials, will gain further momentum. The findings also indicate that the digital twin (DT) in construction technology would bring the industry a step forward toward achieving the goal of Industry 5.0.

This review highlights the prospects of digital manufacturing and the DT in construction engineering. It also indicates the future research direction of 3D printing in various constriction sectors.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure.

Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page.

Three-dimensional printing (3DP) is an established process to print structural parts of metals, ceramic and polymers. Further, multi-material 3DP has the potentials to be a milestone in rapid manufacturing (RM), customized design and structural applications. Being compatible as functionally graded materials in a single structural form, multi-material-based 3D printed parts can be applied in structural applications to get the benefit of modified properties.

The fused deposition modelling (FDM) is one of the established low cost 3DP techniques which can be used for printing functional/ non-functional prototypes in civil engineering applications.

The present study is focused on multi-material printing of primary recycled acrylonitrile butadiene styrene (ABS), polylactic acid (PLA) and high impact polystyrene (HIPS) in composite form. Thermal (glass transition temperature and heat capacity) and mechanical properties (break load, break strength, break elongation, percentage elongation at break and Young’s modulus) have been analysed to observe the behaviour of multi-material composites prepared by 3DP. This study also highlights the process parameters optimization of FDM supported with photomicrographs.

The present study is focused on multi-material printing of primary recycled ABS, PLA and HIPS in composite form.

The purpose of this paper is to prepare stabile emulsions with 0–15% of colloidal silica and high monomer/water ratio and to investigate the influence of silica addition and surface modification on the polyacrylate properties.

Improving the properties of the composite can be achieved by optimizing the compatibility between the phases of the composite system with improving the interactions at the matrix/filler interface. Therefore, the silica surface was modified with nonionic emulsifier octylphenol ethoxylate, cationic initiator 2,2'-azobis-(amidinopropane dihydrochloride) and 3-methacryloxypropyltrimethoxysilane and polyacrylate/silica nanocomposites were prepared via in situ emulsion polymerization. Particle size distribution, rheological properties of the emulsions and morphology, thermal properties and mechanical properties of the film prepared from the emulsions were investigated.

Polyacrylate/silica systems with unmodified silica, silica modified with nonionic emulsifier and cationic initiator have micrometer, while pure PA matrix and systems with silica modified with silane have nanometer particle sizes. Addition and surface modification of the filler increased emulsion viscosity. Agglomeration of silica particles in composites was reduced with silica surface modification. Silica filler improves thermal stability and tensile strength of polyacrylate.

This paper provides broad spectrum of information depending on filler surface modification and latex preparation via in situ emulsion polymerization and properties with high amount of filler and monomer/water ratio with the aim that prepared latex is suitable for film formation and final application.

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.