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 article presents laboratory tests on the impact of the mixing water content used in the preparation of fresh mortar on the flexural and compressive strength of one of the dry-mix mortars produced by a leading European producer and dedicated to bricklaying with clinker elements. The development of these parameters in relation to curing time was also analyzed. The mortar samples were prepared from a factory-made mortar mix using 4.0 L (the value recommended by the mortar manufacturer), 4.5 L, and 5 L of water per 25 kg bag of ready-made, pre-mixed dry mortar mix. All samples were tested in five series after 5, 9, 14, 21, and 28 days of sample curing. The results of these tests showed that the use of 6 and 18% more mixing water than recommended by the manufacturer (4.5 and 5 L per bag) adversely affected the basic mechanical parameters of the tested mortar. Moreover, it was found that the highest compressive strength values were obtained after 21 days of curing and not after 28 days as usual. It was also found that hardening time and higher than recommended water content adversely affected the bending strength of the mortar.

It is believed that the use of mortars based on air lime in the construction and renovation of brick buildings has a number of advantages, especially those closely related to the durability and strength of the structure. However, there is still a noticeable difference in the mechanical properties of these materials. This research investigated the mechanical characteristics of a mixed cement–lime mortar with the two most popular proportions of an air lime, cement, and sand mix: 1:1:6 and 1:2:9 (by volume). Mechanical tests were performed on standard and non-standard samples to assess compressive strength, tensile strength, flexural strength, and fracture energy. The obtained results indicate the possibility of using these mixtures in modern masonry construction, as well as in the aspect of sustainable development. Additionally, lime mortar with a higher lime content can be used in non-load-bearing walls and in renovation and repair works.