Prominence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed and price, as well as carbon footprint. Artificial neural networks represent a new and efficient tool in achieving optimal concrete mixtures according to its intended function. This paper addresses concrete mix design and the application of artificial neural networks (ANNs) for self-sensing concrete. The authors review concrete mix design methods and the development of ANNs for prediction of properties for various types of concrete. Furthermore, the authors present developments and applications of ANNs for prediction of compressive strength and flexural strength of carbon nanotubes/carbon nanofibers (CNT/CNF) reinforced concrete using experimental results for the learning process. The goal is to bring the ANN approach closer to a variety of concrete researchers and possibly propose the implementation of ANNs in the civil engineering practice.

Carbon nanotube/concrete composite possesses piezoresistivity i.e. self-sensing capability of concrete structures even in large scale. By incorporating smart materials in the structural health monitoring systems the issue of incompatibility between monitored structure and the sensor is surpassed since the concrete element fulfills both functions. Machine learning is an attractive tool to reduce model complexity, so artificial neural networks have been successfully used for a variety of applications including structural analysis and materials science. The idea of using smart materials can become more attractive by building a neural network able to predict properties of the specific nanomodified concrete, making it more cost-friendly and open for unexperienced engineers. This paper reviews previous research work which is exploring the properties of CNTs and their influence on concrete, and the use of artificial neural networks in concrete technology and structural health monitoring. Mix design of CNT/concrete composite materials combined with the application of precisely trained artificial neural networks represents a new direction in the evolution of structural health monitoring of concrete structures.

The interfacial transition zone (ITZ) is the weakest phase in concrete, characterised by higher porosity and being prone to microcrack formation. Additionally, the ITZ is created when dispersed fibre reinforcement is present. Although fibres improve flexural strength, they can negatively impact other properties. This research investigates the ITZ of fibre-reinforced concrete where macro-basalt fibres (BFs) and oil shale ash (OSA), as an SCM, were used with the aim of modifying the properties of concrete, enhancing the ITZ, and reducing its carbon footprint. Six different concrete mixes with OSA doses between 10% and 30% and a constant BF dose of 8.0 kg per 1 m³ of concrete were prepared and tested. The ITZ was analysed with SEM images and verified through its mechanical properties. The results showed that the presence of OSA improved bonding and densified the microstructure of the paste, especially in the ITZ, resulting in a nearly constant flexural strength at up to a 20% replacement and only a 6.7% decrease in compressive strength while reducing the global warming potential by 19.24 kg CO₂ equivalent in the mix with 10% OSA replacement. Higher replacement ratios had a negative impact on the mechanical properties, as the OSA had not reacted entirely and served partly as an inert filler.

The thermal conductivity of steel is high compared to other materials such as concrete or timber. Therefore, fire protection measures are applied to prolong the duration between the onset of fire exposure and the final loss of load-bearing function of a steel structure. The most common passive fire protection measure is the application of intumescent coating (IC), a thin film that expands at elevated temperatures and forms an insulating char layer of lower thermal conductivity. This paper focuses on structural steel beams with IPE open-section profiles protected by a water-based IC and subjected to static and standard fire loading. ANSYS 16.0 is used to simulate heat transfer, with thermal conductivity function described by standard multivariate linear regression analysis, followed by mechanical analysis considering degradation of material mechanical properties at elevated temperatures. Simulations are conducted for all IPE profile sizes, with varying initial degrees of utilisation, beam lengths, and coating thicknesses. Results indicated fire resistance times ranging from 24 to 53.5 min, demonstrating a relatively good level of fire resistance even with the minimal IC thickness. Furthermore, artificial neural networks were developed to predict the fire resistance time of steel members with IC using varying numbers of hidden neurons and subset ratios. The model achieved a predictability level of 99.9% upon evaluation.

External bonding of fiber reinforced composites is currently the most popular method of strengthening building structures. Debonding performance is critical to the effectiveness of such strengthening. Many models of bond prediction can be found in the literature. Most of them were developed based on laboratory research, therefore, their accuracy with less popular strengthening systems is limited. This manuscript presents the possibility of using a model based on neural networks to analyze and predict the debonding strength of steel-reinforced polymer (SRP) and steel-reinforced grout (SRG) composites to concrete. The model is built on the basis of laboratory testing of 328 samples obtained from the literature. The results are compared with a dozen of the most popular analytical methods for predicting the load capacity. The prediction accuracy in the neural network model is by far the best. The total correlation coefficient reaches a value of 0.913 while, for the best analytical method (Swiss standard SIA 166 model), it is 0.756. The sensitivity analysis confirmed the importance of the modulus of elasticity and the concrete strength for debonding. It is also interesting that the width of the element proved to be very important, which is probably related to the low variability of this parameter in the laboratory tests.